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View this post on the web at https://www.growth-memo.com/p/does-topical-authority-matter-in Some brands get away with broad topical focus. Forbes, for example, ranks well for everything from “bitcoin price” to “best pillows for neck pain.” But most brands are more visible in search when focusing their content strategy [ https://substack.com/redirect/9e679e49-1d0a-4bb1-8de2-37ccd9dd8d3f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] on core topics. Topical Authority [ https://substack.com/redirect/9dcbb8ab-14de-470a-89a3-61c7c3acdf39?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] is one of the most used concepts in SEO to justify content creation. It’s gone so far that swinging too far from the base can lead to algorithmic demotions (Hubspot and Clickup, to name a few significant ones). But does it translate to AI Search? Or are LLMs such a new playing field that brands can really swing big? Imagine a payroll software company planning its next 20 articles. It can chase broad, high-volume finance topics, or build out the payroll questions it wants to be known for: W-2 deadlines, contractor classification, payroll-tax errors, overtime rules, and state registration. The topical-authority bet is to choose the second path, even when ChatGPT can answer some of those questions without sending a click. The goal is not traffic from every glossary page. It is to become a brand that appears consistently when people ask related questions, including the eventual “Which payroll software should I use?” question. Topical authority matters in AI search, and the reason is durability. This study shows AI answers develop topic owners: Once a brand earns an outsized share of mentions in a category, it usually keeps it. Build your strategy with the definitive study into AI visibility AI visibility isn’t uniform. Performance diverges sharply across platforms and industries, meaning a strategy built for one environment can fail to perform in another. Semrush’s AI Visibility Index tracks 126 million prompts across 22 industries and ChatGPT, Google AI Mode, Gemini, and Google AI Overviews. It’s our biggest ever piece of research into AI visibility, complete with playbooks, expert guidance, and deep-dives into the winning brands. Build a strategy that wins across where your audiences search. Start with the AI Visibility Index. Download the complete report [ https://substack.com/redirect/5ced670e-5ef0-4290-9aec-567ac813a110?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Method and limitations To find out, the Semrush team gave me access to a big dataset based on the Semrush AI Visibility Toolkit [ https://substack.com/redirect/bb2649aa-a565-48f5-b7be-fecc684b89c0?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Scope: 1,094 US categories, 5 prompts per category, and monthly snapshots from January through June 2026 in ChatGPT, USA only. The study covers +220,000 domains and +50,000 brands, +600,000 citations and +220,000 URLs This study tracks which brands ChatGPT mentions most often across 5 prompts per category. It does not measure sentiment, recommendation quality, user trust, or purchase impact. The study has no topic-level organic rankings, organic visibility, or SEO share-of-voice data. Semrush’s export records whether a brand appears in an answer, not the surrounding text or sentiment. A mention can be positive, neutral, or negative. Definitions: Owner: The brand with the highest share of brand mentions, named in at least 4 of the category’s 5 prompts, and at least 5 percentage points ahead of the runner-up. Emerging leader: The most-mentioned brand appears in at least 3 prompts but does not meet the clear-leader threshold. Unsettled category: No leading brand appears in at least 3 prompts. Important note: The definition for unsettled category makes sense based on the threshold we’ve seen in this specific study, but don’t treat it as a universal truth at this time. While almost half of cited pages in the study were hard to classify (a mix of cryptic URLs like “examplesite.gov/0982349” that don’t have common/standard page types), we found that the most cited page types are product or service landing pages, followed by classic editorial content. Homepages were only cited 4% of the time. 1. Most categories have a leader, but some have an owner Category owners dominate the category, of course. We defined them as +80% Share of Voice (available brand mentions in AI answers) and at least a 5 percentage point lead to the 2nd place. Most categories that we analyzed in ChatGPT have not yet settled on an owner. In June 2026, only 15.2% of categories had a clear owner, while 53.7% were open fields with several contenders for the top. Over half of the +1,000 categories we analyzed have a leader that can be dethroned by a challenger. Plenty of opportunity, which I personally don’t think is true in classic Google Search [ https://substack.com/redirect/07c109ab-52e7-45bb-9a70-727d8fcbc6d6?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. When we look at AI (search) volume, which describes how many people might prompt in a given topic*, we find that the biggest categories by volume do not have an owner. *Yes, I’m aware that AI (search) volume is not perfect, but when we abstract search keywords and prompts into topics and look at aggregate search volume, we can at least guess how big a topic in AI Search is. In Semrush’s US sample for our study, the biggest topics are the least likely to have an owner. We ranked all 1,094 categories by AI-search volume and split them into 2 groups of 547. The top group holds 98% of all volume-based AI-search demand in the sample. It also has the lowest owner rate: 11.3%, versus 19.0% in the bottom group. Stack those up and 89.3% of estimated AI-search demand sits in categories with no clear owner. Demand concentrates, but in this dataset, ownership does not. When we split the data out by quartiles or deciles, we see the same trend of top-heavy categories being more open-field opportunities than smaller ones. A category with no owner is an open budget conversation waiting to happen. The AI SEO Budget Reallocation Planner [ https://substack.com/redirect/bb1f9833-ad0c-4dca-9fd1-b047636e876b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] generates 5 growth-priority scenarios and a screenshot-ready visual in under 7 minutes, so the categories you choose to attack come with a number attached. Find it in the Premium Subscribers Resource Library. [ https://substack.com/redirect/983cd659-ac50-4e0f-aef1-2dcd641c0277?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 2. Owners win on brand mentions, not citations This study counts whether a brand appears in an answer, not how prominently it appears or whether it is recommended. A category owner is therefore the brand named across the largest share of its tested prompts. Previous Growth Memo user-behavior studies have repeatedly shown that users pay little attention to citations and most to brand mentions. In shortlists, users demonstrate strong preference for the top results for purchase decisions (75% of users pick the top brand). In other words, brand mentions are AEO gold. I applied this lens to this data as well. From How Consumers Navigate High-Stakes Purchases in AI Mode [ https://substack.com/redirect/85c6331f-f902-4a9d-90a6-caecc2ed5cf5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]: In classic search, 56% of participants built their own shortlist from multiple sources. In AI Mode, only 8 out of 147 codeable tasks produced a genuinely self-built shortlist. The user’s comparison process didn’t just shrink when using AI Mode. For most participants, it didn’t happen at all. 64% of AI Mode participants clicked nothing at all during their task. They read the AI’s text, sometimes scrolled through inline product snippets, and declared their finalists. The no-click rate varied by category. Just like in classic search, the top answer carries outsized weight. 74% of participants chose the item ranked first in the AI’s response as their top pick. The mean rank of the final choice was 1.35. Only 10% chose something ranked third or lower. While citations influence the AI answer to a prompt, the relationship between citations and brand mentions is slightly negative (-0.229). In the study, the most cited domain was rarely (20.8%) the most mentioned. But the most mentioned brand was often cited (69.9%) at least once. All industries exhibit clear owners, except for legal. In retail, we see the highest owner rate with 61.6% (meaning 61 out of 99 categories we analyzed have a clear owner). 3. Characteristics of category owners The available data lets us identify traits associated with ownership, but not prove how ownership was created. When compared to the the 2nd place in a category, we do see that owners have: Higher brand search volume (55.7%) Higher organic traffic (48.4%), and Higher authority (52.5%, as measured by Semrush’s Authority Score) The branded-demand result means that, in 577 of 1,035 owner-runner-up pairs, the owner had higher branded search volume. It does not mean branded demand explains 55.7% of ownership. These are useful clues, but not a recipe. The better working hypothesis is: Brand strength may help a domain enter the answer set. Category ownership is more likely determined by unmeasured topic-specific factors, including source relevance, cited content quality, reputation, and how well a domain fits the prompt. 4. Can a challenger win a category? Category owners are defined by their position. They’re hard to dethrone. It’s not impossible to dethrone them, but the odds depend heavily on how settled the category is. A clear owner retained first place in 90.4% of next-month (or month over month, MoM) comparisons. Emerging and unsettled leaders change much more often. Leaders switched in 1,950 of 5,470 MoM comparisons. But those changes were concentrated in categories where the initial lead was narrow: Switching leaders had a median 1.3-point lead, versus 2.9 points for leaders that retained first place. 3 examples where a clear leader was replaced by the previous month’s runner-up: As you can see in the examples, you can trend up but reverse course (example 1), be flat and overtaken by a competitor (example 2) or grow and still be overtaken by a competitor that grows faster (example 3). Your trend line says little about your safety. You need a big enough margin (at least 5%). We cannot say why these switches occurred from this dataset alone. They could reflect changes in sources, answer generation, prompt interpretation, or real changes in each brand’s underlying relevance. What the data does show is the condition for disruption: Narrow-lead categories are contestable; wide, sustained ownership is much harder to displace. 5. How to compete for category ownership Use topical ownership to decide where to focus, not to declare that more content will make ChatGPT mention you: Watchlist. List 10 to 20 commercially important categories for your brand. For each, track at least 5 representative prompts: a definition, comparison, alternatives, use case, and buying question. Read more about how to make your prompt tracking more accurate [ https://substack.com/redirect/2dc27ce0-a304-45d0-8895-325a2b7fddda?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] here. Classify. A clear owner is the most-mentioned brand with a strong majority of prompts and a clear lead over the runner-up. Everything else is either an emerging-leader category or an unsettled category. Invest. Prioritize commercially valuable categories with no clear owner, or a small gap between the leader and runner-up. Defend categories you already lead. Be more selective about attacking a category where an established brand leads across nearly every prompt. Gaps. Close gaps across the prompts where competitors appear and you do not. That can mean clearer product positioning, comparison pages, documentation, expert guidance, proof points, and credible third-party coverage. The study does not show which of these creates ownership. It gives you a disciplined place to test them. Measure. Track brand mentions as the ownership metric, citations as a source-authority metric, and referrals, self-reported pipeline, or revenue as the business metric. A monthly change in the most-mentioned brand is a reason to investigate, not proof that a competitive win created demand. Closing prompt gaps starts with knowing which pages AI systems already rely on. The AI Citation Audit [ https://substack.com/redirect/81387083-68cf-4c33-81f3-8714bd4afbcf?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] saves a junior analyst about 2 hours of manual checking per cycle and tells you which cited pages are carrying your category. Premium is only $150/year for now. See the full library [ https://substack.com/redirect/983cd659-ac50-4e0f-aef1-2dcd641c0277?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Topic focus is worth the “content constraint” because ownership compounds over time. A category you win in is a category you tend to keep, and the brands that spread their content creation across every topic that’s semi-adjacent to their core offering rarely reach the mention share that makes a lead stick. The Semrush data shows that, currently, the window is open… and it will not stay open. 89.3% of AI-search demand sits in categories with no owner (as defined by this study), which means the cost of picking the wrong 5 categories today is lower than it will be in 12 months. Pick the categories you want to be the answer to. Defend the ones you already lead. Skip the ones where an established brand appears in every prompt. Broad coverage still has a job in marketing, but it does not build the position that makes ChatGPT name you. Premium: Topic category defense playbook Clear topic category owners held first place month over month in 90.4% of the observed comparisons. That makes ownership durable, but not permanent. This playbook explains how to protect your category lead before a close challenger turns into the new default answer. Important: In this study, ownership means brand-mention leadership, not citation volume. A category owner appears in at least 4 of 5 representative prompts and leads the runner-up by at least 5 percentage points. Citations matter because they help shape AI answers. Brand mentions matter because they are what users see and act on. What this playbook covers Protecting a lead in topic categories your brand already owns Finding weak prompts before competitors take them Strengthening the pages and third-party sources that support AI answers Building external proof that makes your brand harder to displace Setting an operating cadence for competitive response This is not a claim that any one tactic creates topical ownership. The study identifies the conditions worth defending. Teams still need to test which actions change mentions in their category... 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View this post on the web at https://www.growth-memo.com/p/where-ai-agents-get-stuck-on-your The next mass frontier of AI is agentic: Google introduced agentic tasks in Search The web gets more visits from bots than humans [ https://substack.com/redirect/6e133369-c25c-4df5-afd8-1a2119429a1f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] now Salesforce found 20% of sales coming from agents marks a signpost 60% of companies use agents live in production, and 3 out of 4 companies [ https://substack.com/redirect/d43b85d2-b590-41e6-87ec-945561ca6399?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] invest in AI agents To figure out how ready B2B sites are for agentic visitors, I teamed up with David Kaufman, founder of Siteline [ https://substack.com/redirect/99f12cda-c9ff-4dd2-b0a3-97bc8a92c499?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], and I analyzed how agents scan websites and where they get stuck. The answer: most sites are agent-ready, but there is one critical breaking point. Agents don’t read websites like humans. They receive a task, search the web, fetch pages, extract facts, and cite the sources they used. A page can persuade a human and still fail an agent if the facts are hard to find (opacity), hard to fetch (machine-readability), or hard to cite (access friction). AI agents turn websites from showrooms into barcodes. Methodology How we looked at agent behavior: The agent had to find the official site itself. We did not provide starting links, eg to homepages. We gave agents 3 buyer-related tasks for 100 B2B products: find pricing/features, integrations, and security/compliance. We ran each task 5 times to measure the impact of the probabilistic nature of LLMs. We weren’t comparing whether or not the information existed somewhere on the web; instead, we measured whether the agent could reliably answer from the vendor’s own site. Everyone’s bracing for Google Zero. Cyrus Shepard studied 400+ sites and found the ones still growing share five specific traits. Zero-click search is real. The “SEO is dead” takes are not the full story. Cyrus mapped what’s actually driving organic growth right now, plus the 17 content types most likely to survive Google Zero. We’re hosting him for a practical session on what every content team is trying to decide: what to keep investing in, what to rethink, and where AI actually helps. You’ll walk away with: The five traits behind sites still growing in Google Which content formats still earn their keep, and which are getting harder to justify How to turn proprietary assets you already own into content Where AI fits now: what to stop producing faster, what to build instead Built for SEO and content leaders, content directors, and B2B growth teams. July 23, 3pm ET. Live on Zoom, recording sent to everyone who registers. Save your spot [ https://substack.com/redirect/53f8937a-7161-43ef-ad12-7977857c1b40?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Featuring Cyrus Shepard 1/ Pricing breaks first-party sites The moment a prospect looks at pricing, they stop browsing and start comparing. #High buyer intent, bottom of the funnel. That makes pricing the hardest and most important test of whether a vendor site can serve agents directly. Pricing also sits in a triangle of 3 “wants” that good pricing pages need to satisfy: Companies want to control pricing disclosure. Buyers want fast comparison. Agents need clear, fetchable, citable facts. When AI agents try to retrieve pricing, they get stuck much more than for security or integrations. Pricing/features: 79% first-party answer rate, 84% first-party citation share Integrations: 93% and 99% Security: 92% and 99% Pricing/features produced 77% of all third-party citations If you wonder whether that’s because some B2B companies don’t publicly show pricing, you’re only half right. 2/ Hidden pricing is only part of it Hiding prices forces agents to look elsewhere, but published prices do not fully solve the problem. Among pricing prompt runs where the vendor did not disclose a real price, 45% cited at least one third-party source. The other 55% stayed on first-party citations, usually by saying the vendor required contact sales or did not publish a concrete price. Even when the vendor showed a numeric public price, agents still cited at least one third-party source in 18% of runs, suggesting price can be on the page but still be hard for the agent to extract, trust, or cite cleanly. You can try to hide your pricing, but you better make sure no one else knows and writes about it. Once it’s “out there”, it’s too late. If you have complex pricing methodology, the best way is to explain it clearly and make it accessible to agents. Some pricing pages are visible to humans but not reliable enough for agents to parse and cite. You can’t always trust your eyes. 3/ Agents fail for 3 reasons Agents fail to retrieve pricing from a brand for 3 reasons: opacity, machine-readability, and access friction. Pricing opacity simply means the brand doesn’t publicly disclose the price, or it’s vaguely packaged. Opacity explains elevated fallback, meaning agents have to rely on 3rd parties for information. Machine-readability describes the situation when prices exist, but agents still do not confidently extract them. Machine-readability explains fallback despite disclosed pricing. Machine-readability fails when the price is hard to extract because of page structure, JavaScript, calculators, toggles, screenshots, PDFs, or ambiguous tables. Access friction is what most people expect to be the problem with agents. The agent hits fetch failures, rate limits, blocking, or unreachable pages, making agent runs more costly. Access errors were not the main reason agents left first-party sources, but when they happened, they were severe. They appeared in only 7% of all runs. In pricing runs, access errors pushed third-party fallback to 77%, compared to 17% without access errors. The impact of errors on agent run cost (tokens, web searches, fetches, retrieves, time) is significant when comparing the 90th with the 10th percentile in our study: Cost: 4.4x Token: 4.7x Time: 2.0x Brands don’t pay that bill directly, but it is a useful proxy for friction. The harder your site is to retrieve, the more work an agent has to do before it can answer from your page. If your pricing page is blocked, slow, hard to fetch, or hard to parse, the agent has two choices: spend more work on your site… or get the answer somewhere else. 4/ The fallback web is messy Fallback is when agents have to rely on 3rd party sources as opposed to 1st party, as a result of the 3 failure modes. This is the biggest risk because 3rd party information is spotty, and it’s not in your control. Agents do not fall back to one clean source category. They reconstruct pricing from a mixed web of explainers, directories, app stores, partner pages, and low-trust aggregators. Key stats from the 580 pricing third-party citations: 52% were editorial (blogs, media articles, comparison guides, explainers, and other article-style pages) 46% fell into the directory category (review, procurement, and software-listing sites such as G2, Capterra, Vendr, Tekpon, and similar domains) 2% from broader ecosystem pages (app stores, marketplaces, partner pages, and integration directories tied to another platform) The examples show the risk of missing pricing transparency and agent stumbling blocks on your site. Example journey: 5/ How to make your site agent-proof An agent-proof pricing page is how you keep the agent quoting you instead of a directory like Vendr. The fixes map to the three failure modes. Disclose the fact (opacity) Publish real prices in text for every self-serve tier. If a tier is genuinely custom, say what drives the number instead of “contact sales.” Keep plan names, prices, limits, and features on one canonical pricing URL, and point every other mention back to it. Mark legacy plans clearly so third-party content can’t keep stale tiers alive. Make the fact extractable (machine-readability) Put prices in crawlable HTML. Many agent fetches never run JavaScript, so a price rendered client-side is invisible. In testing, prices in server HTML got read in under a second; a JavaScript-only price got missed. Add schema.org Product and Offer markup with price and priceCurrency. This single lever moved a page from 73 to 93 in the readiness test. Explain usage-based pricing in text, not a calculator-only widget. Let the agent in (access friction) Allow AI crawlers in robots.txt (GPTBot, ClaudeBot, PerplexityBot, Google-Extended). Check you aren’t allowing Googlebot while blocking them. Don’t block server-side AI fetches on pricing pages. Access errors hit only 7% of runs, but they push fallback from 17% to 77% when they do. Keep the price early in the DOM and the page light. A 1 MB pricing page taxes every agent and pushes cheap runs to route around you. Fix opacity and machine-readability first; they drive most of the fallback. Then run the query yourself, “Find all pricing and features for [product].”,” and measure it with the skill below. Premium: Check the agent-readiness of a page A well-known B2B SaaS pricing page scored 73 out of 100 the first time the skill ran, and 92.5 the second. Same page, same hour. The 20-point jump came entirely from how the page got fetched. The skill simulates an agent answering a buyer question from one page: Fetch it, pull the fact, measure the cost across five weighted dimensions. 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View this post on the web at https://www.growth-memo.com/p/growth-intelligence-brief-21 Welcome to another Growth Intelligence Brief, where organic growth leaders discover what matters and guidance on how to stay ahead of the competition in under 5 minutes. Data window: SEO visibility and AI prompt counts June 1 to June 29, 2026 (28 days, matched windows). Exec Summary The 5 biggest companies by AI mentions all gained SEO visibility and lost AI Overview mentions in the same 4 weeks to June 29. For 2 quarters, SEO trended down and AI up. No mas. The 5 biggest companies by AI mentions all gained SEO visibility and lost AI Overview mentions in the same 4 weeks to June 29. Their AI Mode mentions went the other way and grew. LinkedIn gained 43.3% in visibility over the 4 weeks to June 29 (289.4 to 414.7), while AI mentions held flat at -2.0%. The gain is all organic, and it still leaves LinkedIn 9.1% below its April 2024 peak of 456.3. Barnes & Noble lost 55.4% of its visibility in 4 weeks (24.76 to 11.04), the steepest drop of any tracked company with a real base. AI mentions across the index have been flat for 8 weeks near 6.0 million per week. That flat number hides a rotation between AI surfaces that reverses how the divergence reads. Home improvement bucked the flatline with 25% to 50% AI-mention growth in 4 weeks, while the same cluster’s organic visibility stayed flat. Summer project demand is showing up inside AI shopping answers now, not in 2027. Let’s dig in. SEO landscape check Below is a quick read on the current state of search, built from the SSI (Search Signals Index): roughly 2,600 companies tracked across 26 verticals on 2 dimensions, SEO visibility and AI mentions. The biggest platforms got their visibility back. Reddit, Facebook, Instagram, LinkedIn, and YouTube each gained between 11% and 43% in SEO visibility over the 4 weeks to June 29, and and each lost AI Overview mentions over the same period. GIB 20 [ https://substack.com/redirect/e0d85cca-a5c8-4aad-99f7-d5403771582b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] flagged Reddit declining on both dimensions for the first time. Half of that fall reversed inside 3 weeks. Google’s May core update finished rolling out on June 2, and the volatility ran through mid-June (Search Engine Land [ https://substack.com/redirect/43a915bb-dad2-41e9-9636-6dd982692781?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]), reshuffling organic shelf space back toward the largest platforms while AI Overviews moved the other way. The platforms clawed back their visibility Social Networks gained 21.1% in visibility as a vertical over the 4 weeks to June 29, the third-strongest move in the index. And quite coincidental with Google’s announcement of organic social visibility tracking in Google Search Console [ https://substack.com/redirect/2d9ea64a-777c-42fa-a6d7-736b96055bcb?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. The recovery concentrates on the names that got hit in GIB 17 [ https://substack.com/redirect/64de9faa-71eb-4d62-a7ab-32d789d1a74b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and GIB 20 [ https://substack.com/redirect/e0d85cca-a5c8-4aad-99f7-d5403771582b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Reddit is the clearest case. Its visibility bottomed at 2,297.6 on June 8, climbed to 3,041.2 by June 22, then settled at 2,765.7, up 18.1% on the 4-week window. GIB 20 measured Reddit down 11.7% through June 8. The decline reversed inside 3 weeks. The rest of the vertical moved in lockstep. Facebook gained 20.1% in visibility Instagram gained 21.4% LinkedIn gained 43.3% X gained 38.6% All of this occurred over the 4 weeks, ending June 29. The jump clustered in the June 15 to June 22 window for every one of them, which is the fingerprint of an algorithm change rather than organic demand. But their AI Overview mentions went the opposite way: Reddit lost 12.8% Facebook dropped 12.1% TikTok lost 12.1% Instagram decreased 9.7% The platforms Google just rewarded in organic are the same ones AI Overviews cited less often in June. That split is the story of the month. SEO visibility and AI citation used to drift together for the mega-platforms. In June, they came apart. Movers & Shakers Top 5 Movers (4-week SEO) Yellow Pages (Local Search) +66.3%. Vis 14.52 to 24.13, an all-time index high. 2nd consecutive issue on this list after +137.2% in GIB 20 [ https://substack.com/redirect/e0d85cca-a5c8-4aad-99f7-d5403771582b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]‘s window. AI mentions stayed flat at +0.8%, so Google routing is doing all the work. Trustpilot (Ecommerce) +54.3%. Vis 63.51 to 97.96, also an all-time index high, adding 34.5 raw visibility points. GIB 20 asked whether the local routing favors Yelp specifically or review aggregators broadly. Trustpilot answers that: broadly. Ace Hardware (Ecommerce) +43.9%. Vis 9.66 to 13.90, with AI mentions up 45.8% over the same window. Both dimensions point the same way, and it sits inside the home-improvement cluster below. LinkedIn (Social Networks) +43.3%. Vis 289.43 to 414.74, adding 125.3 raw visibility points. Full breakdown in the Winner callout below. Bed Bath & Beyond (Ecommerce) +41.6%. Vis 13.44 to 19.03, with AI mentions up 23.3% in the same window. Ecommerce as a vertical gained 4.3% in 4 weeks, so this is domain-specific. Top 5 Shakers (4-week SEO) Barnes & Noble (Ecommerce) -55.4%. Vis 24.76 to 11.04. 2nd consecutive issue on this list after -32.0% in GIB 20 [ https://substack.com/redirect/e0d85cca-a5c8-4aad-99f7-d5403771582b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]‘s window. Full breakdown in the Loser callout below. Alibaba (Ecommerce) -40.6%. Vis 7.05 to 4.18. AI mentions stayed flat at +0.9%, so nothing is absorbing the loss. Shutterfly (Images) -34.4%. Vis 6.90 to 4.53. A full round trip: GIB 20 ran Shutterfly as its #2 mover at +88.9%, and it now sits back below the 5.98 where the April slide began. Angi (Local Search) -29.5%. Vis 4.84 to 3.41 while AI mentions grew 78.9%, the sharpest split in the index. Angi has moved against its own vertical for 2 straight issues, and Local Search gained 22.8% in 4 weeks. VRBO (Travel) -25.8%. Vis 6.92 to 5.13, with Airbnb down 23.9% over the same 4 weeks. The vacation rental squeeze GIB 20 flagged has not let up. Winner: LinkedIn LinkedIn gained 43.3% in visibility over the 4 weeks to June 29, from 289.4 to 414.7. It climbed every week after June 8: 280.7, then 335.7, then 398.4, then 414.7. AI mentions held roughly flat at -2.0% over the same window, so the gain is all organic. LinkedIn still sits 9.1% below its April 2024 peak of 456.3, which makes this a recovery rather than a breakout. Its user-generated pages are exactly the large-platform real estate the June reshuffle favored. Loser: Barnes & Noble Barnes & Noble lost 55.4% of its visibility over the 4 weeks to June 29, from 24.76 to 11.04, the steepest drop of any tracked company with a real base. Mid-tier retail took the other side of the platform trade: As Google handed shelf space to the largest sites, names like Barnes & Noble, Kohl’s (-20.1% in 4 weeks), and JCPenney (-15.2% in 4 weeks) gave it up. Macy’s, the surprise winner from GIB 16 [ https://substack.com/redirect/50796da4-6191-4326-91c7-5afa77d3f54d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], slid another 11.9% in June after the bulk of its round trip landed in GIB 20 [ https://substack.com/redirect/e0d85cca-a5c8-4aad-99f7-d5403771582b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. The flat AI line hides a surface rotation AI mentions across the index have been flat for 8 weeks. The summed total for AI Overviews and AI Mode has held in a band around 6.0 million per week since late April, with June 29 at 5.96 million. The explosive growth that defined GIB 15 [ https://substack.com/redirect/e107aa0f-fcec-433c-a083-bff344247dd0?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] through GIB 17 [ https://substack.com/redirect/64de9faa-71eb-4d62-a7ab-32d789d1a74b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] stopped at the aggregate level. That flat line is 3 trends stacked on top of each other. Over the 4 weeks to June 29: AI Overviews fell 3.2%, from 5.47 million to 5.29 million AI Mode grew 22.5%, from 547,193 to 670,163 ChatGPT mentions fell 28.4%, from 1.20 million to 860,743 AI Overviews carries roughly 8x the volume of AI Mode, so the sum barely moves while the mix underneath rotates. The platform decline is mostly AI Overviews. Total AI Overview mentions fell only 3.2% while the 5 biggest platforms fell 10% to 17%, so the platforms lost share within the surface. Commercial sources took it: Yelp’s AI Overview mentions grew 14.9%, Home Depot’s 21.5%, and Ace Hardware’s 35.1% over the same 4 weeks. AI Mode coverage held steady near 342 tracked companies all month, so the AI Mode growth reflects Google citing more sources rather than the index tracking more companies. The signature divergence trade went quiet with it. Screening for companies with at least 2,000 AI mentions at baseline, AI mentions up more than 10%, and visibility down more than 5% over the 4 weeks to June 29 returns 5 names: Zapier Office Depot GetYourGuide Nordstrom Hilton All sit on small bases. Angi runs a sharper split than any of them and falls just under that mention floor. Keep in mind what flat means here: AI engines still mention these 2,600 companies about 6 million times a week. The growth curve bent, but the level held. Home improvement is where AI demand is still rising One cluster bucked the plateau. Home-improvement and big-box retail posted 25% to 50% AI-mention growth over the 4 weeks to June 29: Wayfair +48.1% Ace Hardware +45.8% Lowe’s +32.8% Home Depot +30.9% Best Buy +28.8% Michaels +29.2% Ecommerce was the second-fastest vertical for AI mentions at +10.9% over the same window. Organic search never moved. The same cluster’s combined visibility fell 0.7% over the identical 4 weeks, and June has been flat for these companies every year the index has tracked them: 0.0% in 2023, -2.8% in 2024, and +0.9% in 2025. Seasonal project demand is real, and it only shows up in AI surfaces. The caveat sits in the data window: AI mention tracking began in February 2026, so June 2027 is the first chance to confirm the pattern repeats. If it does, AI surfaces reprice seasonal commercial intent faster than the organic index does, and AI shopping answers become a seasonal channel a growth team can plan against. Double Click: Google runs two rankers... Unsubscribe https://substack.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.ci9dFOzwAir7IJofBQXXVLWMozETHSMCQ8XYB5esoao?

View this post on the web at https://www.growth-memo.com/p/why-most-original-data-never-gets Part 1 tackled those all-important third-party citation signals [ https://substack.com/redirect/0576ce72-8af7-4b24-bf1d-aba45f7638f7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], while Part 2 made the case for publishing original data [ https://substack.com/redirect/96a66dbc-ed13-426d-8d9a-281c786f0472?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]: It is the strongest single predictor of page originality, and the bar to earning visibility/authority via this play is low. This memo has more ammo to back up your use of proprietary data in content creation. Publishing the number is necessary. But it’s not always what gets cited. We pulled Gauge’s citation data to find out what AI actually rewards when it comes to publishing first-party data, and the answer is narrower and more useful than “original data wins.” (Although original data does, in fact, win.) AI rewards one format almost to the exclusion of everything else: The benchmark that answers “which is best.” In this memo: First-party research is rare in AI citations, but it earns 3.3x more 75 of 90 primary-research citations came from a single content format Owning data is not the asset. A benchmark is. Build your strategy with the definitive study into AI visibility AI visibility isn’t uniform. Performance diverges sharply across platforms and industries, meaning a strategy built for one environment can fail to perform in another. Semrush’s AI Visibility Index tracks 126 million prompts across 22 industries and ChatGPT, Google AI Mode, Gemini, and Google AI Overviews. It’s our biggest ever piece of research into AI visibility, complete with playbooks, expert guidance, and deep dives into the winning brands. Build a strategy that wins across where your audiences search. Start with the AI Visibility Index. Download the complete report [ https://substack.com/redirect/c1e9f9d0-9e47-4f01-95a3-f97791bca766?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] First-party research is scarce and punches above its weight We worked from Gauge’s cited-URL set: 301 live pages that AI systems cited (316 unique prompts across 7 verticals), carrying 1,075 citations between them. After a full URL audit, only 8 of those 301 pages qualified as primary research, meaning the original source of the data and methodology are on the page… rather than a writeup of someone else’s numbers. 8 pages out of 301 is 2.7% of the set. Those same 8 pages earned 90 of 1,075 citations, or 8.4% of citation volume. First-party research shows up rarely, then over-indexes 3x on citation share when it does. The cleaner way to see it is density. Primary research averaged 11.3 citations per page. Everything else averaged 3.4. A primary-research page was 3.3x as citation-dense as a non-primary one. Primary research compounds citations. This is the same shape as the information gain finding discussed in Part 2 [ https://substack.com/redirect/96a66dbc-ed13-426d-8d9a-281c786f0472?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], viewed from the AI side instead of the classic 10 blue links side. There, original data correlated with page originality more than any other trait. Here, original data correlates with citation density. Both point the same direction: The number only you can produce is the lever. Original research wins when the question has a benchmark Here’s where the “original data wins” filter gets sharper. The 90 primary-research citations are not spread across the 8 pages evenly, and they are not spread across topics evenly. 75 of the 90 came from one cluster: cloud data warehouse benchmarks. Fivetran’s warehouse benchmark [ https://substack.com/redirect/65489da6-a2f7-4a59-9c37-83920717d866?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] alone took 44 citations, just under half of every primary-research citation in the set. (More on that below.) Reality: Strip the benchmark cluster out and first-party research barely registers in the citation set. The win is not “we published original data.” The win is “we published a benchmark that answers a buying comparison,” and almost nobody builds one. (“Benchmark” meaning you measure a set of named things against each other on a specific yardstick, and publish the results as numbers.) Original research is most effective when it is packaged in a way that directly answers commercial comparison queries. This is what Google is after with non-commodity content: new and helpful information that is hard to get. Primary-research citations clustered where the prompt asked AI to compare options on measurable specs: speed, cost, latency, yield, or performance. That explains the warehouse benchmark spike. The “HR Tech / Compensation” label is noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use. Crypto / Solana shows the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need first-hand ecosystem data, not generic explainers. The pattern disappears in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning, none of those topics produced a cited primary-research page. A closer look at the content that held 44 of the citations Fivetran’s warehouse benchmark [ https://substack.com/redirect/65489da6-a2f7-4a59-9c37-83920717d866?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] took 44 of this data set’s citations on its own, and Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations in the set. Why? It’s a piece of content from 2022, but when you examine it, it’s easy to see why LLMs prefer it. 1/ It answers a measurable comparison head-on. Named warehouses, BigQuery, Redshift, Snowflake, and Databricks, ranked on speed and cost. It’s entity rich and not afraid to name all the major players. 2/ It runs on real first-party data. Fivetran tested against actual customer usage rather than synthetic assumptions, and called out that choice directly. 3/ It shows the method, step by step. Trust signals. Separate sections walk through what data they queried, what queries they ran, and how they configured and tuned each warehouse. A reader (or a model) can see exactly how the numbers were produced. 4/ The structure is built to be lifted. Descriptive headings (”Results,” “How much did performance improve?,” “Why are our results different from previous benchmarks?”) let AI map a question to the one passage that answers it. 5/ It links to its raw data and sources. The page footnotes its references, including the C-Store paper, and points to the underlying data, so every claim is verifiable. Not many brands put this much work into a data-backed content piece, let alone offer the full data set for transparency. 6/ It shows its seams. Dated correction notes from December 2022, named qualitative limits, and an honest “performance floor” caveat make the quantitative claims more credible… not less. They also note corrections. 7/ The URL never moved. A 2022 page is still collecting citations in 2026 because it stayed put at one canonical address. The data behind a page like this is easier to pull and analyze than it has ever been. What is not easy is everything around it: the clean method, the linked sources, the corrections, the navigable structure, the willingness to name what the numbers do not prove. That’s craft, and that’s the moat here. This first-party-data focused piece isn’t a sloppy press release with half-assed pulled data. It took a lot of work, and it’s holding authority 4 years out.The takeaway: AI does not reward “original data” by default. It rewards first-party research when the page gives it a clean answer to a measurable comparison that’s built to signal depth of expertise and trust. The open opportunity here is to publish a retrievable dataset for a buyer question where AI currently has no clean benchmark source. This maps onto the unanswered-questions finding [ https://substack.com/redirect/10d82e7a-f3a2-4a36-857a-e7ce073de6e6?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] from Part 2: the open door exists, and in these verticals nobody has walked through it with a real dataset. Original data needs a citation-ready package Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the question. That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists. The citation does not. The pages that won in this dataset had both: original numbers and a clean citation shape. Stable URL. Clear method. Named comparison. Results that answered a buyer question directly. Who wins: Brands sitting on proprietary product, usage, or pricing data who package it into a comparison a buyer can act on, one that informs LLM outputs for recommendations. Who loses: Brands publishing original numbers buried in narrative, on slow or unstable pages, with no comparison frame for AI to lift. A citation-ready research page has four parts: 1/ Lead with the comparison result. The headline finding (“X is fastest, Y is cheapest at scale”) goes in the first 30% of the page. Result, then method, then nuance. 2/ Box the methodology. Sample, time window, what was measured, how. Attribution confidence is part of what makes a number citable. Make your methodology clear on the page. 3/ Explicitly frame it as a comparison if it is one. AI reaches for benchmarks on “which is best” prompts. A table that compares named options on named specs is the shape it lifts. 4/ Keep the URL stable. One canonical page, kept live, not migrated or renamed every redesign. The citation you earn this quarter only compounds if the page is still there next quarter. Of 365 cited URLs in this data set, 64 were dead, redirected, or otherwise broken, taking 203 citations down with them. This is the work behind a citable benchmark, and it is more involved than it looks. HockeyStack documented its own version in a playbook on launching research reports [ https://substack.com/redirect/fa07b485-c6eb-4730-b4f5-5cacde09696c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]: they published 18 original reports built entirely on anonymized first-party customer data, the kind no competitor could replicate. Their process names every step the Fivetran page demonstrates: list the data points you need, get a teammate to pull them with SQL, define and document the method so the numbers hold up to scrutiny, then structure the report around a real ICP question. They call methodology non-negotiable for a reason, noting that without it someone will always dispute your data. With AI analysis, the data is the easy part now. Building the content into something that is citable, demonstrates EEAT, and is still earning visibility 4 years out for commercial queries is where the hard work lies. What sites are already trusted for your topic? When a benchmark you did not publish is taking the citations in your category, the Citation Source Mapper [ https://substack.com/redirect/05c7abf0-2ab6-4832-bee3-71af0e1433db?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] maps that trusted set into a ranked, pitchable target list. It is in the premium library [ https://substack.com/redirect/9f2b73aa-9109-4cc3-a5ae-c2e9498d67c6?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Premium: Benchmark draft builder - Claude skill Turn the data your product already produces into a benchmark AI cites as the canonical source. The Benchmark Builder skill fits into your existing Claude workflows and interviews you one question at a time: what data you own, which buying question it answers, which comparison is worth publishing. It pushes back on incomplete comparisons, helping you refine with data points you have already on hand... Unsubscribe https://substack.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.8NxgLmsBYpVMc4W_RWTjqSjEN9OtCwuRx7kMOndSNr0?

View this post on the web at https://www.growth-memo.com/p/why-proprietary-data-is-your-most Publish original numbers. It’s the single most reliable lever for making a page more original, and the most defensible numbers are a byproduct of the business itself… not data you assembled to feed a content calendar. The old play was paying a PR or research firm for a survey loosely tied to your product, like a car insurance FinTech buying road-trip research to land in Yahoo. That’s outdated. Almost every product now generates data worth publishing, and pulling it has never been easier. You don’t need a research team. The bar to clear the field is lower than you think. In this memo: Original data is the strongest single predictor of page originality Owning the number doesn’t guarantee the citation. Structure and trusted-source presence decide who gets it. Built your content for extraction for better chances of earning the citation Coming up next week: We’re finalizing a new analysis on using first-party data to establish brand authority and visibility—keep an eye out for it. It drives the findings of this memo even further. Meet the AI visibility leaders in retail, plus new tactics AI search is reshaping customer journeys and how shoppers discover and choose products. But many retail marketers don’t yet have a clear benchmark of AI visibility, who’s winning and why. Semrush’s AI Visibility Index changes that. Our new study is built on a US prompt database of 126 million prompts, mapping exactly which retailers are dominating AI recommendations across ChatGPT, Google AI Mode, Gemini, and AI Overviews. As well as where the opportunities lie. Download free study [ https://substack.com/redirect/3f12b34f-38da-4293-9654-d8f98e1a8f64?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] First-party data: The strongest correlation of originality On-Page.ai’s recent information gain study [ https://substack.com/redirect/8ecf7d1c-9026-46fe-8cc2-db53e9c5931b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] scored 150 top-3 Google pages across 50 keywords and 10 verticals on how much each adds beyond the rest of its ranking cohort, grading the contribution from 0 to 100 by meaning rather than wording. The median page scored 52, and original data correlated with that score more than any other page-level trait, including length. Pages with at most 1 unique figure averaged an information gain score of 40.2, while pages with 15 or more averaged 62.1, and the score climbed steadily at every step in between Good news, the bar to beat is low. The study found that it might not take much original evidence to outclass top-visible pages in classic Google search: top organic results typically have only 4 unique data points on average. Publish a page that includes more than 4 real original claims, figures, or answers, and that’s one more lever to pull for increasingly competitive organic visibility. This analysis also found that in almost every search, there are plenty of adjacent unanswered questions that are being left on the table. On-Page ran their analysis with synthetic reader questions, a sample of plausible questions generated for the study, that were a closely related set to the search topic of each query, and results showed an open door for new pages to answer them and stand out. (Remind you of anything? Query fan-out [ https://substack.com/redirect/0de204f9-8b4c-47c2-8656-4434d0192a92?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], perhaps?) We had a similar finding in an analysis of ChatGPT citations: “A single evergreen page covering 10+ query intents is worth more in AI citation reach than 10 single-intent pages. The ROI of comprehensive content is front-loaded: one well-built page compounds citation reach over time. The long tail exists, but the top 5% of pages capture a disproportionate share of ongoing citation activity.” - The science of how AI picks its sources [ https://substack.com/redirect/81aa4a0f-5e27-464b-8afa-5798da119199?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] In fact, your brand’s high-intent prompts should be monitored across a journey for this purpose. Turn them into journeys that follow the buyer across the 5 stages from Reasoning Lift: [ https://substack.com/redirect/53bb4dff-cd12-4929-bf42-799cb612db47?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Problem, Exploration, Comparison, Validation, Selection. (Read more about more accurate AI prompt tracking [ https://substack.com/redirect/9698a46e-7b4d-47a5-bbae-6a0ac858d654?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] here.) Answer those questions on the page with the knowledge and expertise that only your brand can to stay competitive against this finding in the analysis. The main takeaway from the findings: Most pages are middling on originality, genuinely original pages are a minority, and scoring high enough to stand out is achievable… without being an extraordinary feat or lift. The gap in the findings? This study focuses on classic search visibility and rankings (the SEO concept of information gain was birthed out of a Google patent language, after all). It doesn’t take AI citations or mentions into consideration, and there’s no mention of including AI Mode or AI Overviews in the analysis. Caveat: Being the primary source alone may not win the citation This is the part most proprietary data-publishing advice skips. Everyone says publish original research. Few test whether AI rewards the brand that originated the number or the page that presents it most readably. More data analysis is coming next week, but what we do know from analyses we’ve completed at Growth Memo this last year: The entity types that predict ChatGPT citations the most are DATE and NUMBER (from The science of what AI actually rewards [ https://substack.com/redirect/7079bc3c-7f97-4f44-8904-e9c72d695f53?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]). High-cited pages are dense with specific entities: a particular methodology, a precise statistic, a named comparison. Even if your proprietary findings are picked up by another source that is cited instead, i those external third-party authority signals are likely to build. Entity-richness and balanced sentiment matter (from The science of how AI pays attention [ https://substack.com/redirect/49846f61-67b9-45ae-93e6-ec445ce47d42?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]). Generic advice is risky and vague, but specific entities are grounded and verifiable. Proprietary data produces, verifies, or validates and creates entity-rich content. (Think: why a feature works to save % of dollars, how many hours saved by clients vs. competitors they worked with before, etc.). Add balanced sentiment into the analysis and explanation of your data, and you’ve got yourself a 2-for-1 tactic. If the hypothesis that first-party data is crucial in the era of AI search holds, publishing content based on proprietary data is necessary… but it is not sufficient. LLM extraction structure (along with the sites that AI search engines trust for the topic) decides who gets the citation, even when your brand owns the data. Unfortunately, an aggregator who repackages your benchmark into a cleaner answer-ready page can collect the citation your research earned. (Truth sucks.) Who wins: Brands sitting on proprietary product, usage, or pricing data who also structure it for extraction and don’t ignore other organic brand authority building plays. (Learn more in How to build an AI SEO strategy that outlasts tactics [ https://substack.com/redirect/1362dde0-a870-4bb6-96d5-47f8ee1794ea?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ].) Who loses: Brands producing opinion content any tool can replicate, ignore other crucial ways of building off-site authority, along with primary sources who bury their own numbers in narrative instead of surfacing them. Whether some verticals reward data content more than others, we do not know yet. The science series [ https://substack.com/redirect/7079bc3c-7f97-4f44-8904-e9c72d695f53?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] found citation signals vary sharply by vertical, so a uniform payoff would be the surprise, but we will not assert a pattern without data. Head’s up: Reading this data is one thing. Running the training session that changes how your writers write is another. Premium subscribers [ https://substack.com/redirect/3ca0b1a8-aac3-4b1f-b9e0-d6b70900e48a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] get the How AI Pays Attention training deck [ https://substack.com/redirect/0aa1182d-a98e-4ae9-8c6b-639dea474081?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] plus the AEO writing checklist. The slides and the exercises are already built. How to structure data for extraction Owning the data gets you into the fight for visibility. But how you structure your content could be what wins it. We analyzed 18,012 verified ChatGPT citations [ https://substack.com/redirect/49846f61-67b9-45ae-93e6-ec445ce47d42?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and found a ski-ramp distribution: 44.2% of all citations come from the first 30% of a page. The middle 30-70% earns 31.1%, and content buried deep in a long post is roughly 2.5x less likely to be cited. The follow-up analysis across 7 verticals [ https://substack.com/redirect/25757bde-0ec4-476a-ab88-192df92f91f4?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] sharpened the target: The 10-20% band of a page is where AI reads hardest in every vertical, while the first 10% is typically navigation and intro filler that AI skips. The bottom 10% of any page earns 2.4-4.4% of citations regardless of vertical. Applied to a data study, the structure of your content writes itself: 1/ Lead with the headline statistic. Your strongest number goes in the first 30% of the page, ideally right after the title block where the 10-20% band begins. Number → comparison → implication, in the first screen. 2/ Define the metric immediately. One sentence on what the number measures and the population it covers. An undefined statistic is harder to lift with confidence. 3/ Box the methodology. Sample size, time window, collection method, in a short labeled block. Attribution confidence is part of what makes a number citable. 4/ Front-load every secondary finding. Findings ranked by strength, strongest first. The 20-paragraph narrative buildup is a human-retention pattern that costs you machine citations. 5/ Skip the suspense close. AI reads like a busy editor, not a patient student. The payoff-at-the-end structure that worked for ultimate guides actively works against extraction. What sites are trusted in your brand’s topics? When an aggregator collects the citation your research earned, it’s because they’re already in AI’s trusted-source set for the topic and you’re not. The Citation Source Mapper [ https://substack.com/redirect/5079709e-c96b-4642-865f-fa9f11be45dc?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] maps that set into a ranked, pitchable target list. It’s in the premium library [ https://substack.com/redirect/3ca0b1a8-aac3-4b1f-b9e0-d6b70900e48a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Premium: 6 first-party data plays by category You don’t need a research budget to publish original data. Every business produces data it never publishes. Below are 6 plays, each tied to a type of data your business already produces, so you can match the play to the byproduct you already own. Unsubscribe https://substack.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.FbQZd1tIcC7PY1cEbony9_OClfZzyb2KSkffp6BXEXY?

View this post on the web at https://www.growth-memo.com/p/stop-trying-to-replace-people-with Sign up for Growth Memo Premium for exclusive tools and frameworks. Heads up: This memo is a slight deviation from the usual topics I regularly write about. But it’s an important one. Also, new toy: I recently met Bryant Chou, former Webflow CTO in NYC, and he showed me how quickly Ploy.ai [ https://substack.com/redirect/7558b20c-96c7-467c-bfa6-229446b53708?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] can help ICP-focused landing pages for advertising, SEO/AEO, ABM, etc. Pretty cool. Check it out! The biggest positioning mistake in AI marketing is selling your product as a replacement for people. It wins attention now, but costs you trust later. In this memo: Why “substitution positioning” feels good short term and decays your brand long term The data showing AI isn’t actually replacing people How to better position AI Getting retrieved by AI search is not the same as getting cited. [ https://substack.com/redirect/1924a8ba-0aec-4444-9fb3-7dec04b5e9c6?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Strong rankings and clean pages still lose when AI systems decide which sources to reference. The gap is everything that happens between the prompt, the fan-out, and the final citation. On June 25, AirOps Head of Research Oshen Davidson breaks down where citation wins actually come from, alongside Melanie Dell’Olio (AI Solutions) and Josh Spilker (Head of Search Marketing). You’ll walk away knowing: Why many retrieved pages never make the final answer Which page elements get content parsed and reused: headings, lists, tables, stats, schema How to find pages being surfaced but not selected, then prioritize the right fixes What to measure beyond rankings: citations, mentions, repeat visibility, refresh timing Tactical, no fluff, built for teams who want AI visibility that moves pipeline. Save your spot → [ https://substack.com/redirect/1924a8ba-0aec-4444-9fb3-7dec04b5e9c6?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] From Research to Revenue: 5 AEO Actions to Take Now. June 25, 3:00pm EST. The cardinal sin of positioning in the AI era is replacement. I call it “substitution positioning.” Short term, it’s yummy. Long term, it plants cavities in your perception. In January 2026, Anthropic CEO Dario Amodei predicted software engineering jobs to go away in the following 6-12 months. Demand for software engineers is reaching new heights today. “I think we might be 6 to 12 months away from when the model is doing most, maybe all of what SWEs do end to end.” — Dario Amodei (source [ https://substack.com/redirect/d06babfe-fe9a-4d7c-86f0-0460cdf6f9ec?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]) In September 2025, OpenAI CEO Sam Altman predicted customer support jobs will go away. Almost immediately after he said that, customer service hiring started outpacing the broader job market. “I’m confident that a lot of current customer support that happens over a phone or computer, those people will lose their jobs, and that’ll be better done by an AI.” — Sam Altman (source [ https://substack.com/redirect/4ce39a2b-85fc-4049-b53c-1232d1eed6b5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]) AI companies use fear as a marketing tool. Specifically, the fear of being replaced. They got me, too. I was deeply worried about my future when Opus 4.5 got traction. However, as long as I see that even Anthropic is hiring copywriters and SEOs, I sleep much better. Times were more uncertain before AI came on the scene (I’ll spare you the TED talk about unprecedented times). Fear sells. Tapping into our primal fight-or-flight response is a sure way to get attention. And layoffs act as a double agent in this environment: They make companies that underperform or overhired look good (“Look, we don’t need as many people anymore because we’re so innovative!”) and feed the substitution positioning narrative. But the facts show that recent layoffs are closer to AI washing than AI spring cleaning: New York state added an option for companies to indicate when they lay staff off due to AI (technological innovation or automation). In March of this year, over 160 companies filed mass layoffs of ~28,300 workers. Not even one [ https://substack.com/redirect/9de5810d-a805-48a5-a402-c13dcd99b95c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] chose AI as the reason. That list includes Amazon and Goldman Sachs. Researchers at Yale looked at the CPS [ https://substack.com/redirect/81923566-1146-480c-b30e-3ee97485d798?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], a government survey that tracks employment across the US, over the last 33 months and found no evidence of job displacement from AI [ https://substack.com/redirect/739ac454-40b8-48c2-8ba6-1f02b5180871?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. The way AI impact works looks very similar to how computers and the internet changed work. Premium is $150/year. That’s less than one walked-back AI announcement. The Growth Memo resource library [ https://substack.com/redirect/1b4f88d0-4588-421b-8f29-5ef4fecf6fec?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] includes the positioning reframe asset in this memo… and the rest of the audits, workflows, and decks built for the AI visibility era. Stop trying to make “replacement” happen. It’s not going to happen. — Regina George from Mean Girls in an alternate multiverse AI is just not there… yet. Synthetic intelligence in its current form is heavily paradoxical: AI can do some things better than humans and others not even at the level of a chimpanzee. This phenomenon is called the Jagged Frontier. It’s based on a paper where BCG and Harvard researchers [ https://substack.com/redirect/453c1666-72f3-45b9-8069-85cc9ee68602?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]conducted a randomized controlled trial of 758 knowledge workers and found that you get the most (12.2% more tasks, 25.1% faster) out of AI if you know what it’s good and bad at. That’s why some people are lukewarm on AI and others see it as new salvation. “Currently, measures of exposure, automation, and augmentation show no sign of being related to changes in employment or unemployment.” — From “Evaluating the Impact of AI on the Labor Market: Current State of Affairs, [ https://substack.com/redirect/739ac454-40b8-48c2-8ba6-1f02b5180871?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]” Gimbel et al. Microsoft came to the same conclusion. In its 2026 Work Trend Index Annual Report [ https://substack.com/redirect/9ecd974a-e1e8-45e8-af0f-261dc8665ed3?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], the Windows mother found that ~16% of the 20,000 AI users surveyed are “Frontier Professionals” or the most advanced AI users in the research set who use agents for multi-step workflow systems. 80% of those Frontier Professionals said they produce work they couldn’t have a year ago, compared to 58% across the board. They are not just using AI more, but also know which mode a task calls for. They have the judgment to navigate the Jagged Frontier. Despite some of the gains you can see from AI, we’re far away from replacing people. Do you truly trust any AI workflow enough to leave it on full automation without any maintenance or quality assurance? Look at a company that tried: Klarna publicly bragged its AI did the work of 700 agents and cut headcount ~40%, Then they reversed and rehired humans [ https://substack.com/redirect/48e8bece-096a-498e-afbe-1428e5dd16cc?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] after the CEO admitted cost-cutting produced “lower quality” and that customers want a human option. As a result, choosing substitution positioning means you gain short-term attention but sacrifice long-term credibility. Anthropic calls for a public pause on AI development on June 4 and launches Fable 5 on June 9. OpenAI did the same: Altman told the U.S. Senate in May 2023 that AI needs licensing and a global watchdog [ https://substack.com/redirect/849de4fd-94d5-4909-8286-6abf756b13e7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], then opposed binding rules and lobbied against California’s SB-1047 and mandatory federal pre-launch approval. The words don’t match the actions. Substitute positioning could work if customers (1) actually wanted the replacement and (2) if the technology was there. Neither are true. FYI: The Adapting for AI-Based Search deck [ https://substack.com/redirect/3cdb6f10-76a0-40f1-9954-0562967c5e08?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] gives directors a validated, data-backed story to present to executives who keep asking what changed. Find it in the Premium Subscribers Resource Library [ https://substack.com/redirect/1b4f88d0-4588-421b-8f29-5ef4fecf6fec?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Antagonism Cost reduction is a much stronger AI-use argument than productivity growth because it hits the P&L immediately. Productivity gains show up later. They build slowly inside a company and even slower across the economy, the same lag we saw with computers and the internet. However, because replacement positioning goes way beyond just cost-cutting (it’s people-cutting), you also directly antagonize the people you sell to: Duolingo’s AI-first memo drew a “torrent of pushback” on social media/TikTok. CEO von Ahn said [ https://substack.com/redirect/ef751322-91da-4d83-9aff-0ea0da246304?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] he “did not expect the amount of blowback” and walked it back, reframing AI as a “tool to accelerate,” not replace contractors ~31% of workers have refused to use AI tools at work [ https://substack.com/redirect/df92f5c7-b79d-4db6-bee6-963368c1fd99?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], often citing job-loss fear Pew: 50% of US adults are more concerned than excited about AI [ https://substack.com/redirect/d19e182a-83f2-4666-8c52-796ed3d04246?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] in daily life; only 10% more excited than concerned, up from 37% concerned in 2021 71% of Americans are afraid of being replaced [ https://substack.com/redirect/0e1479f0-d8aa-49f4-adca-18054308d300?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] by AI A survey of 1,294 knowledge workers shows those who sense the goal is replacement produce more low-quality “workslop” [ https://substack.com/redirect/f69e4f6a-ec3e-4da2-b31e-3159faa51f5a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Surveys show that leadership is commonly more excited about AI [ https://substack.com/redirect/47b55e6d-ba91-4535-b7d3-bcbc595de243?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] than the employees who use it. In April, UBER execs bragged about burning their annual token budget in just 4 months. Today, that’s a massive liability. Token cost is growing despite being heavily subsidized. But soon, it will reach a price point that makes junior employees start to look interesting again. Entry-level jobs might soon be cheaper than tokens accomplishing the same output, and young people don’t have the same baggage and reflexes that experienced experts have. What is the replacement for “replacement”? Instead of trying to replace people, the solution is to position your AI use as enhancement. The opposite of fear-based marketing is aspiration and empowerment. AI goes both ways: You can reduce the number of people or grow output with the same number of people. And the data shows that productivity gains have a higher ROI than substitution. In a paper by the National Bureau of Economic Research [ https://substack.com/redirect/32d1f975-67ac-441d-83eb-152758ab98a5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], Researchers asked 750 executives about the impact of AI on productivity and labor markets. While larger firms have a higher interest in replacing labor cost (people), the highest ROI comes from growing productivity. The moral of the story? Doing more with what you have works better than firing copywriter Alex because only Alex can judge good copy. Premium: A Claude skill to assess positioning in your content Building products has gotten easier, but distribution has not. When supply explodes, the scarce thing is being the product that actually gets chosen, and positioning (right alongside product quality) is exactly how you get chosen. Premium subscribers get the Claude skill that performs a quality check on your content’s positioning for any AI-based tooling within your product, offering quality reframes for your team that avoid substitution positioning. Sign up fo Growth Memo premium to get the skill... Unsubscribe https://substack.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.2H2SP-fokT-3t8HlBNjQv_JnUHI4P3U37BlB1vNWUy8?

View this post on the web at https://www.growth-memo.com/p/topics-matter-for-third-party-authority The common advice is to build authority off your own site through digital PR, mentions, and high-authority links. Grow your third-party mentions, grow your AI visibility, right? Right instinct. But the source set that an AI relies upon differs for every topic, so your off-property authority-building efforts must also be topic-driven. This week: Why AI trusts a different set of sources for every topic, and what that changes about where you build it Why scattered off-property authority wastes budget, and how to aim it How to find the exact sources AI cites for your topic and earn your way in Premium subscribers also get a citation mapper skill for Claude or Codex that helps you build a pitchable list of citations. Discover how people are really searching, discovering, and engaging online AI continues to reshape how users discover, engage, and click across search environments. But most teams are still making decisions without a clear picture of what’s actually shifting. The Datos State of Search Q1 2026 report, created in collaboration with Rand Fishkin, maps real search behavior across millions of users in the US, EU, and UK. AI adoption, regional contrasts, e-commerce discovery, and intent signals are all explored in detail using real clickstream data. Get your free report [ https://substack.com/redirect/4fa52ee7-3431-41d0-b090-afbb5054ac5d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] AI builds a different trusted-source set for every topic AI search engines rebuild which sources to trust around the subject of the question. Ask about invoicing and it leans on one set. Ask about starting a business, and it leans on a mostly different one. This data is a sample set from an anonymous pool of clients, but it illustrates the gap is large. AI citations have a source-type pattern that follows the specific topic. In invoicing questions, competitor domains hold 33.5% of what AI cites. In starting-a-business questions, that same source type holds 7%. Same model, two topics, and the kind of source it reaches for nearly flips. This small sample analysis confirms something I wrote about in Operationalizing your topic-first SEO strategy [ https://substack.com/redirect/f9bd2b41-935b-4f48-b9b4-dcf9063b3ec5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]: You should be employing a topic-based backlink strategy, too. You don’t just want backlinks - you want links that have authority in your target topics and/or with your audience. Meanwhile, Video and Social surfaces run on separate mechanics and deserve their own separate play in your visibility mix, cited at ~6.5% across this sample set. YouTube stays an exception [ https://substack.com/redirect/b7d76cda-c938-4498-a88a-4be7b10dd3a5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] across LLMs, and UGC platforms like Reddit behave differently again. But this memo sets the Video / Social / UGC slice aside on purpose and covers the publisher, research, and expert sources that behave like earned media. So where you build authority depends on the topic you want to win. Lift a PR plan from a neighboring topic or vertical and you’re likely to aim at the wrong sources. Premium is $150/year. That’s less expensive than your next AI SEO mistake. The Growth Memo Premium Resource Library [ https://substack.com/redirect/e6165b72-f8e0-4798-8f73-5f326f155023?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] has 30+ (and growing) checklists, light tools, AI workflows, and stakeholder slide decks to build your confidence in your marketing tactics for the AI visibility era. AI trusts entities it already recognizes AI does not form a fresh opinion about your brand on every query. It reuses the trust already attached to the sources it pulls from, and it favors documents and entities it already associates with authority on that topic. (Which is why, yes, building topical authority [ https://substack.com/redirect/0e81bd30-214c-47ec-8721-3b20fe8db281?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] still matters.) Your owned blog/site is one input; it’s a crucial input, but it’s likely one of the weakest. The publications, analysts, experts, competitors, and communities that mention you carry significant weight. That gap is why 2 brands with identical on-page optimization can get cited at different rates: off-property reputation the model already trusts. Add to that, a named author with a byline appears to beat the same content published under a brand. We don’t have a clean dataset on this across platforms yet and it’s based on one-off qualitative findings via clients and industry chatter, so treat this as a working belief, not a measured finding. LinkedIn’s own analysis of AI visibility factors [ https://substack.com/redirect/5ba1f077-4fee-407c-bf3a-fec679928c6f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] reported that authorship and timestamps tracked with better performance: fresh, expert-authored, clearly time-stamped content earned the fastest visibility and citation gains in their testing. Our early testing showed meaningful lift in visibility and citations across the topics we focused on, with owned content delivering the fastest and most scalable gains so far…. Publishing authoritative, fresh content improves visibility. LLMs favor content that signals credibility and relevance, authored by real experts, clearly time-stamped, and written in a conversational, insight-driven style on platforms like LinkedIn. Source: How LinkedIn Is Adapting to AI-Led Discovery [ https://substack.com/redirect/d3b7be82-1078-4306-af05-b61ab0e8cfef?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] The mechanism is reasonable: A human author with a track record (someone who has written on the topic across other sites, has an active social presence, holds a license, or sits on your executive team) gives the model an entity to attach authority to. A faceless brand post gives it less to anchor on. Authority pays out in steps, so depth in your topic beats spread More quality mentions should mean more citations. Authority does not pay evenly though. It pays in steps. Our analysis of 1,000 domains with Semrush [ https://substack.com/redirect/989969ac-748d-488b-8590-c3470f70552d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] showed this for backlinks: Authority Score is the strongest backlink predictor of AI mentions in the study at 0.65 Pearson, ahead of raw link count, and the curve bends rather than climbing straight. Why it matters: A “little more” third-party authority in a crowded middle tier probably won’t change how often AI cites you. The thing that changes citation is getting into the top tier of authoritative sources for your topic. Read alongside the topic finding, the move is clear. Depth in your topic’s top-tier sources beats spread. 3 placements in a top-decile source move you more than a dozen scattered across low-authority sites. That’s where you invest. One question stays open: whether citation rises smoothly with mentions or trips past a tipping point, a volume of in-topic mentions after which a brand gets cited consistently instead of occasionally. We don’t know yet. More data and time will tell. Whatever the tipping-point answer turns out to be, authority-building through (1) third parties in your topic via (2) authoritative sites in that topic is not an open-ended ask to your executives. It’s a target you can budget for and measure against. Premium is built for operators who need to defend decisions. Brand Tax Calculator, Brand SEO Scorecard, AI SEO Budget Reallocation Planner. All in one library. [ https://substack.com/redirect/e6165b72-f8e0-4798-8f73-5f326f155023?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] How to build authority in the sources AI cites for your topic Not every third-party signal carries equal weight. A research publication picked up across high-authority industry blogs likely moves citation more than a few executive podcasts a year, while YouTube stays an exception [ https://substack.com/redirect/b7d76cda-c938-4498-a88a-4be7b10dd3a5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] across LLMs. (UGC will be tackled in a later post as part of this building authority series.) Test these moves, in order. 1/ Pick 2 to 3 willing SMEs. They don’t need an existing audience, and they don’t need to be a founder or on the executive team. They need: Credibility in your topic Deep understanding of your brand and product, and Willingness to publish. Give them a process (and permission) to develop sharp content with a strong branded point of view geared toward specific personas. How-to guides and roundups together account for 62.3% of cited source rows (see chart below), so build your SME’s work in those shapes. 2/ Map the set AI already cites, then target the entity, not the logo. Run your highest-intent prompts through an audience research tool like SparkToro [ https://substack.com/redirect/f278624f-e084-4de6-b01e-02cd1d0ae41f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and record which domains, social accounts, YouTube channels, and named authors show up. Do the same across AI search engines, and note overlap or inconsistencies in who gets cited and when. Then chase the people, not only the publications. Get your SME quoted by the same journalist, booked on the same webinar series, or co-authoring with the author the model and your audience already trust. Co-occurrence with a trusted entity pulls you into the candidate set faster than a standalone post. Create better AI visibility reporting. Single-run, monthly, blended prompt tracking misreports reality. Learn how to upgrade your prompt tracking [ https://substack.com/redirect/70cad617-ec1b-4c25-9069-05c5f039b3ac?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. 3/ Concentrate on the authority tier. Depth beats spread: 3 placements in a top-decile source move you more than a dozen scattered across low-authority sites. Rank your target set by authority and spend there first. Use tools like Qwoted [ https://substack.com/redirect/7e7942e0-3d11-400d-8c40-5f1d53260593?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and HARO [ https://substack.com/redirect/d86c6ab3-90cb-4024-9012-dbce2b8fe3b4?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] to discover opportunities. 4/ Mine nofollow on purpose. Don’t skip these. The Semrush link study [ https://substack.com/redirect/989969ac-748d-488b-8590-c3470f70552d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] found nofollow pulls almost the same weight as follow for AI mentions (0.509 Spearman vs 0.504). They are easier to earn and the models still count them. Build a list of the nofollow-heavy sources your category links to and pitch them deliberately. 5/ Ship embeddable data under your expert’s name. Publish original charts and infographics bylined to your SME and let other sites embed them with attribution. One chart can earn citations across dozens of pages you never pitched. The format matters: AI leans hard on answer-ready pages. 6/ Use LinkedIn as one fast lane. According to their own testing, named-author posts get indexed and surfaced quickly, and first-hand reports in our space describe brands entering AI answers within weeks of consistent publishing under a person rather than a page (sometimes days, depending on a built-in LinkedIn audience). Possible play: Partner with LinkedIn experts and influencers in your area of authority. Premium: Citation Source Mapper Tool - Build your targeted off-site authority Below is what that mapping looks like in practice: The top cited domains change by topic, which means the outreach target list should change by topic too... 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View this post on the web at https://www.growth-memo.com/p/growth-intelligence-brief-20 Welcome to another Growth Intelligence Brief, where organic growth leaders discover what matters and guidance on how to stay ahead of the competition in under 5 minutes. Data window: SEO visibility and AI prompt counts May 11 to June 8, 2026 (28 days, matched windows). Year-over-year comparisons run against June 7, 2025. Exec Summary Yelp’s all-time record, Macy’s round trip, and Reddit’s first decline show the same mechanism: Google reallocates shelf space within categories whose demand hasn’t changed. Yelp posted the highest visibility reading in index history (646.5 on June 1), carrying Local Search to +30.5% in 4 weeks, the strongest vertical move in the index. Macy’s lost 53.8% of its visibility in 4 weeks, a full round trip of the March gains we flagged in GIB 16 [ https://substack.com/redirect/224012b8-1db1-43cf-a6cb-1191729ebb30?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Its AI mentions grew 36.6% over the same window. Reddit is declining on both dimensions for the first time since AI tracking began in February: visibility -11.7% and AI mentions -10.9% in 4 weeks. X gained 40.9% over the same window. SaaS AI mentions grew 34.5% (like-for-like) while visibility slid another 3.9% in 4 weeks. The divergence GIB 19 [ https://substack.com/redirect/489c9a97-90cd-4058-b030-f1685c47982b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] flagged kept widening: the visibility decline slowed, but AI mention growth doubled. Airbnb (-44.0%) and VRBO (-40.3%) slid into booking season while delivery went the other way: GrubHub +45.1%, DoorDash +43.4%, all in 4 weeks. A refresher of what the SSI is: Movers & Shakers Top 5 Movers (4-week SEO) Yellow Pages (Local Search) +137.2%. Vis 6.54 to 15.51. The smallest baseline on this list, and a directory written off a decade ago riding the same local routing shift as Yelp. Shutterfly (Images) +88.9%. Vis 3.83 to 7.23. A full reversal of the slide we flagged in GIB 19 [ https://substack.com/redirect/489c9a97-90cd-4058-b030-f1685c47982b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (-32.5% in that issue’s window). It now sits above the 5.98 where that slide began in April. Serious Eats (Publisher) +55.9%. Vis 44.81 to 69.86. The biggest 4-week gainer with a baseline above 40. Publisher as a vertical was flat at -1.3% in 4 weeks, so this is domain-specific. X (Social Networks) +40.9%. Vis 210.93 to 297.27, with 5 consecutive weekly gains since May 4. The other side of the Reddit story. Full breakdown in the Double Click. Yelp (Local Search) +29.7%. Vis 495.71 to 642.78. An all-time index high and 147 raw visibility points added, more than any other domain this month. Full breakdown in the Winner callout below. Top 5 Shakers (4-week SEO) Macy’s (Ecommerce) -53.8%. Vis 110.20 to 50.93. Full breakdown in the Loser callout below. Twitch (Entertainment) -48.2%. Vis 6.82 to 3.54. Entertainment as a vertical gained 7.1% in 4 weeks, so this is Twitch-specific. Airbnb (Travel) -44.0%. Vis 8.69 to 4.87. 2nd consecutive issue on this list after -24.9% in GIB 19 [ https://substack.com/redirect/489c9a97-90cd-4058-b030-f1685c47982b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]‘s window. The vacation rental story continues in the Bigger Picture below. VRBO (Travel) -40.3%. Vis 11.88 to 7.09. Moving in lockstep with Airbnb, which points to a category squeeze. Barnes & Noble (Ecommerce) -32.0%. Vis 27.87 to 18.96. Part of a broad Ecommerce slide: the vertical fell 10.0% in 4 weeks and 24.0% YoY. Winner: Yelp (+29.7%) Yelp hit 646.5 visibility on June 1, the highest reading since SSI tracking began in November 2022, and held 642.8 through June 8. The 4-week gain accounts for nearly the entire Local Search move: 147 of the vertical’s 155-point lift. Yelp’s AI mentions grew 39.1% over the same window (13,479 to 18,743), so both dimensions point the same way. Local Search leads the index in 4-week growth at +30.5%, and its 168.5% YoY gain trails only the far smaller Events vertical. The catch: Angi moved against the vertical, down 18.5% in visibility, while its AI mentions grew 31.1% in 4 weeks. Google is concentrating local real estate on the biggest review aggregator and starving the rest. Worth watching whether the routing favors Yelp specifically or review aggregators broadly. Yellow Pages at +137.2% in 4 weeks suggests broadly. Loser: Macy’s (-53.8%) Macy’s gave back the gift. Visibility jumped from 48.8 to 102.0 in the single week ending March 16, the cliff that made Macy’s the surprise winner of GIB 16 [ https://substack.com/redirect/224012b8-1db1-43cf-a6cb-1191729ebb30?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and GIB 17 [ https://substack.com/redirect/8a2314c7-e618-4dae-897c-d2937adc898b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Now the round trip: 110.2 on May 11, then 4 straight down weeks to 50.9 on June 8, putting Macy’s back near its February baseline. The decline outpaces the market around it. As a vertical, Ecommerce fell 10.0% in the same 4 weeks, so Macy’s dropped at 5x the vertical rate. But its AI mentions grew 36.6% over the same window, to 20,613, among the largest retail AI footprints in the index. Algorithm-gifted visibility is a rental. Double Click: The Reddit trade unwinds... 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View this post on the web at https://www.growth-memo.com/p/how-to-make-prompt-tracking-much Upgrade to Premium for the full archive, research, frameworks and templates! By now, you understand that LLMs are probabilistic systems and that AI answers are highly variable. That fact has convinced a lot of people that prompt tracking is extra noise. But discounting prompt tracking as nonsense is the wrong conclusion. Even though prompt tracking is much less deterministic than keyword tracking, we can significantly increase the accuracy of tracking AI mentions and citations. Repeated runs, fixed sampling rules, and confidence intervals turn variance from a reason to quit into a number you can defend. By the end of this Memo, you’ll know how to build that system. This memo assumes that you’re already: 1/ Operating under the philosophy of persona-based prompt design, argued for in Synthetic Personas for Better Prompt Tracking [ https://substack.com/redirect/ed8c58a1-6de7-4def-bb3c-ad4f5c097d4b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. 2/ Bought into doing AI SEO / AEO and need a measurement system that actually tracks your progress vs. noise. Check out How Much Can We Influence AI Responses [ https://substack.com/redirect/285b261e-7dd3-49c8-9b76-7504efd0ad09?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] to learn more. Google didn’t update Search at I/O 2026. It replaced it. AI Mode is expanding. The Search box just got its biggest AI upgrade in decades. Search agents now crawl the web and answer for users in the background, before anyone reaches your page. If you’re still grading yourself on rankings and traffic, you’re scoring a game that no longer exists. Chris Long, co-founder of Nectiv, breaks down what changed, what it means for B2B visibility, and how to build the workflows that get you cited in AI search. You’ll walk away knowing: Why the SGE to AI Overviews to AI Mode shift is bigger than it looks The I/O 2026 updates that belong on your roadmap now How to measure visibility past rankings: citations, prompt coverage, mentions What makes content surface in fan-out and decision-stage queries The workflows to audit, refresh, and publish content built for AI search Date: June 17 · 3pm ET · Zoom · Recording sent after Spots are limited. Claim yours [ https://substack.com/redirect/a7306dd6-d13e-4655-ba17-9b3b83f020ea?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] before Search moves again. The prompt-tracking backlash is only half-right Prompt tracking critics are not wrong. 5 people running the same prompt get 5 different answers. Within-LLM variance from sampling alone hits 10-34% on identical prompts [ https://substack.com/redirect/497330b8-913e-481a-8a83-21c01ecb27d2?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Reporting a point estimate from one run is astrology. Together with AirOps, I looked at 815,000 prompt-page pairs [ https://substack.com/redirect/620130a0-04aa-4898-a2f4-80ba09a688cf?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and found that after running the same prompt 3x in ChatGPT, only 2.2% of citations remain. Every prompt is n = 1. Given that the average prompt is 5x longer than classic search keywords, the chance that 2 people around the world use the same exact prompt is close to 0. We currently don’t have any insight into what users prompt, and we might never get that data (although both Bing [ https://substack.com/redirect/3dc61331-4413-4c02-ab07-4527571c31b4?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and Google [ https://substack.com/redirect/79f68d02-476b-40ce-b6bf-0148778c248b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] are keeping us satiated, for now, by offering some AI-visibility data). But “probabilistic = unmeasurable” is lazy thinking. Weather is probabilistic. Credit Scores are probabilistic. We still forecast and track them. Keyword tracking was never as clean as we’d like to remember Classic keyword tracking was more deterministic, but not as much as you think: For local searches, results were personalized by location and device. Google re-scores results daily, so every rank tracker reports a position range, not a fixed number. The industry standardized the sampling, fixed location, clean profile, daily crawl, etc., until the noise disappeared. Prompt tracking needs the same move, applied to a harder problem. An added challenge: Keyword tracking was focused on Google, but now we have tons of engines. As the market consolidates, tracking simplifies. I’d argue there’s no escaping this either as Google transitions from classic search to AI search. More searches than ever show AI Overviews (AIOs), all while AIOs and AI Mode increasingly merge. At I/O 2026, Search head Liz Reid said users increasingly ask “longer, more natural-language questions,” and Sundar Pichai described Search as “less about individual queries” and “more like an ongoing conversation” (blog.google[KI1] [KI2] /innovation-and-ai/sundar-pichai-io-2026, May 2026). Where common prompt tracking breaks Over the last 2 years, prompt tracking tools multiplied while the methodology behind them stalled. Where’s the innovation? The common prompt-tracking approach looks something like this: Define 25–50 prompts (brand/category/problem split) Run each prompt once per platform Track daily Score for citation, mention, sentiment, position Here are the problems I see with that approach: Variance: Only 2.3% of citations remain after 3 prompt runs [The Consensus Gap [ https://substack.com/redirect/d69bdfb9-90f3-4bfb-9278-bae774b0636a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]]. One run is a coin flip with the answer hidden. Reasoning: High vs. low reasoning opens an 18 percentage point citation-rate gap and changes how the model searches, with high reasoning firing 4.6x more fan-out queries [Reasoning Lift [ https://substack.com/redirect/50d60cb2-c23e-4ac2-b6bb-72c9278ad921?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]]. An aggregate score blends 2 different engines into one misleading number. Personalization: Most prompt-tracking is not persona-specific [ https://substack.com/redirect/ed8c58a1-6de7-4def-bb3c-ad4f5c097d4b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], so it reports generic answers that no one sees. Monthly cadence: SISTRIX tracked [ https://substack.com/redirect/b35ff0ce-51c0-4c6e-9207-a2dabeccb88e?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 82,619 prompts over 17 weeks and found Google AI Mode replaces 56% of its cited sources every week, while ChatGPT replaces 74%. At that drift, monthly tracking is like checking your bank account once a quarter. Cross-platform aggregation: Blending your ChatGPT + Perplexity + Gemini visibility into one “AI visibility score” is like averaging your Google rank with your Bing rank. Conversations: A single Turn 1 query tells you whether you get mentioned. It says nothing about whether you survive Turn 2 onward, when the user asks about alternatives, pricing, integrations, or risk. AI is a conversational interface, so the journey is the unit of measurement, and a one-shot prompt misses most of it. Context: Pure mention counting with no context treats every appearance as a win. Get named first for “what are the worst CRMs to avoid?” and a mention tracker still records a victory. So, while we can’t remove AI answer variance, we can run prompts multiple times and measure what parts, brand mentions, and citations of the AI answer remain. Mirroring follow-up prompts is hard because we don’t know exactly what people will ask, but we can use AI to estimate likely follow-ups, enrich them with real conversation transcripts, and track the follow-ups LLMs suggest inside their own answers. We can also record the attributes a brand gets mentioned with, not only whether it shows up. What good prompt tracking looks like in practice Worked example: B2B SaaS, CRM category. Prompt set: 40 seed prompts, weighted toward problem prompts where purchase intent lives (12 brand, 12 category, 16 problem). Platforms: ChatGPT, Perplexity, Gemini, Google AI Overviews. Tracked separately. Run config: 5 reps per prompt per platform, every week. Personas: the 28 category and problem prompts are customized for 3 key personas (CFO, IT, marketing). Metrics: mention rate (± CI), citation rate (± CI), average position when mentioned (1-5), sentiment, and the attributes attached to each mention. Level it up by adding the journey layer. A flat list of 40 prompts only measures Turn 1. To measure conversations, build the high-intent prompts into journeys that follow the buyer across the 5 stages from Reasoning Lift: [ https://substack.com/redirect/50d60cb2-c23e-4ac2-b6bb-72c9278ad921?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Problem, Exploration, Comparison, Validation, Selection. Each seed prompt for Turn 1 becomes the “seed prompt,” and each stage adds a natural follow-up prompt on subsequent turns. For a buyer evaluating CRMs, one journey runs: Problem: “How do I know if my sales team needs a CRM?” Exploration: “What types of CRM software exist for B2B SaaS?” Comparison: “HubSpot vs. Salesforce vs. Pipedrive for a 50-person sales team” Validation: “Is HubSpot worth the price for mid-market B2B?” Selection: “How do I get started with HubSpot Sales Hub?” Run the full sequence as 1 conversation rather than 5 isolated prompts, and score every turn. The payoff is persistence: in Reasoning Lift, a brand cited at the Problem stage carried all the way to Selection in 4 journeys under high reasoning and in zero under minimal. Persistence is the metric a one-shot tracker can never see. Scope it so the run volume stays sane. Track all 40 seed prompts at Turn 1 for breadth, and build the 16 problem prompts into full 5-stage journeys for depth. Insight example: HubSpot is mentioned in 78% ± 6pp of fiproblem prompts on ChatGPT vs. 34% ± 9pp on Perplexity. Perplexity pulls from comparison posts (G2, Capterra); ChatGPT pulls from HubSpot’s own blog plus integration and compliance docs. Action: invest in integration guides and API docs to win ChatGPT. Invest in G2 review velocity and comparison content to win Perplexity. The next generation of tracking looks like polling Prompt tracking won’t become keyword tracking. AI answers are too variable, too personalized, and too dependent on source selection. But that doesn’t make them unmeasurable. The next iteration of prompt tracking will look less like rank tracking and more like polling: repeated runs, clear sampling rules, confidence intervals, segmented panels, and raw-answer audits. Until existing tools get there, you can build a simple version yourself. Premium: How to upgrade your prompt tracking You now know why single-run, monthly, blended prompt tracking misreports reality. Here is the build to fix it. Growth Memo is a reader-supported publication. To receive exclusive insights and tools, consider becoming a free or paid subscriber... 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View this post on the web at https://www.growth-memo.com/p/traditional-intents-can-no-longer Support Growth Memo research with a premium membership and get more insights and tools. Intent still tells you what to write. But when an AIO lands on the SERP, users no longer behave the same way as classic search. In this memo: AIO compresses 5 distinct search intents into one reading pattern What winning the “second impression” looks like for product, category, and blog pages The 1-slide explanation that reassures stakeholders the content team’s last 3 years were not wasted (premium) A Claude skill that audits your meta descriptions against the competitors sharing your SERP (premium) Scale up your brand visibility, everywhere search happens Semrush for Enterprise empowers businesses to own every layer of brand visibility. By unifying SEO, AI search, and technical site health into one platform, teams gain comprehensive performance tracking and complete optimization capabilities. Swap busywork for impactful strategy with advanced automations that are backed by the market’s leading search database. It’s how winning brands ensure they show up wherever their customers search. Explore Semrush for Enterprise [ https://substack.com/redirect/30afa0aa-1f89-44da-aafa-a9e1a2a18b9b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] The new mental model of search intent Last week, I shared how Eric Van Buskirk of Clickstream Solutions [ https://substack.com/redirect/b81e57c5-d9a9-4f9c-8a2a-8534425387f7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and I analyzed anonymized clickstream data from approximately 846K U.S.-based Google search sessions. The most significant finding? The time-on-page for a user on the SERP is no longer dependent on search intent when an AIO is present. The AIO compresses search intent behavior to look similar across intent types. Old mental model of search intent: Navigational searches are “fast;” informational ones are “slow.” Time-on-SERP follows intent, and SERPs without an AIO clearly show this pattern (similar to classic search pre-AI outputs), demonstrated by 12% of navigational searches vs. 32% of local searchers still being on the SERP after 21 seconds. New mental model of search intent: There is barely a difference in how long users spend time on SERPs between user intents when there is an AIO present. 42-48.5% of users are still on the SERP after 21 seconds across all the 5 major intents. From The same user behaves differently in AIOs vs. AI Mode [ https://substack.com/redirect/b4ce8fc8-bdd7-4296-9c12-51e91b7df3a2?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (bold added): At 21 seconds into a session without an AIO, only 12% of navigational searchers are still on the page. 32% of local searchers are. In classic search, time-on-page has always followed intent: navigational users leave fast because they know where they’re going, local users stay because the SERP is dense with maps and listings, informational users fall somewhere in between. With an AIO present, the spread compresses to barely 6 points. All five intent types (informational, local, navigational, transactional, video) cluster between 41.9% and 48.5% time-on-page at 21 seconds. Notice how much longer average SERP sessions are - almost 4x! So, we can conclude that AI Overviews don’t just compress user intent but also prolong the time users spend with search results. The reason? Additional context. Direct answers from the AIOs provide more information and take longer to read. The intent behind the initial query matters less. This is the gap between links and answers in the new AIO-filled SERP. In the past, giving users a list of (ten blue) links meant the user was responsible for verifying accuracy and finding the information after the click through. Therefore, Google gets user feedback from their behavior. But when Google (or other LLMs) gives the answer directly, that onus is on the answer engine. Bing’s blog Evolving the role of the index [ https://substack.com/redirect/ad44396e-efbc-47e9-8f4f-848eb54a806b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] brings this to a point: Grounding an AI–generated answer introduces a fundamentally different constraint: The system is no longer just pointing to information, it is using it. The goal shifts from “fetch the best documents” to “fetch the best information to synthesize into a reliable, verifiable answer.” Lastly, it also means there is utility in tracking branded prompts more diligently and making sure LLMs return the desired information about a brand. Just like companies bid on their brand as a defense mechanism, they should monitor branded prompts, not just product- or painpoint-related ones. Heads up: The Adapting for AI-Mode Based Search deck [ https://substack.com/redirect/3c87cb0e-e730-4365-bb35-afc6d8b78744?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] gives directors a validated, data-backed story to present to executives who keep asking what changed. Find it in the Premium Subscribers Resource Library [ https://substack.com/redirect/41901e35-036f-4b63-ae59-7e725a468bbd?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Why this matters For 20 years, what you searched told Google and SEOs how you’d behave. Type a brand name (navigational search), and you’re in and out in seconds. Search “best CRM for startups” (comparison search), and you settle into a set of comparison pages. Intent sorted everyone. The AIO erased that tell. By dropping a block of answer text at the top of the page, it pulls every searcher into a reading session, no matter why they came. The brand-name searcher reads the AIO. The product researcher reads another. Both slow down, both stay, both behave alike on the SERP page. That flattening is the intent compression. Most Google users never chose this, because most Google users are not AI early adopters. They meet AI through Google’s search results as Google forces guides them into AIOs and AI Mode at the top of the results. AI is changing its search behavior passively and without searchers’ explicit consent. Many Google users might not even realize they’re using AI, and that’s one reason we’re seeing growing installs [ https://substack.com/redirect/8429ca92-a6d5-49a3-8423-2b7257ec0b52?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] of DuckDuckGo. Google reports more than 1.5 billion people use AI Overviews, so this is not an edge case. It’s how the web is searching now. More than 1.5 billion users around the world use AI Overviews for help with their questions. Source: Google [ https://substack.com/redirect/34bef41e-60f2-4c13-8630-ae3b97a6ef58?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] What winning the second impression looks like The AI Overviews vs. AI Mode behavior analysis [ https://substack.com/redirect/b4ce8fc8-bdd7-4296-9c12-51e91b7df3a2?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] revealed the significance and optimization opportunity of the second look. The second impression is what searchers see on the back-scroll, after they’ve already passed your listing once. It’s like a double-take made by a grocery shopper in the cereal aisle who has dozens of options: The shopper scans every box in view, then circles back to reread the one that caught their eye. Metadata is the trigger for selecting search results. Rich snippets catch attention early on, but they might not be enough to convert users to a click, especially if new search behavior has shifted to include a thorough reading of the SERP and a second scroll up. What can earn the click is what shows up next to your listing on that second pass, and it has to be relevant and trustworthy. Different page types require different relevance and trust indicators. Product detail pages (PDP) Users compare star ratings, review count, price, and stock status. 3 things to control outside the meta: Product schema with aggregateRating, review, offers, and availability. Miss any one of these, and a competitor’s listing renders fuller than yours. Review count is a comparison field. 47 reviews next to a competitor’s 2,300 loses on the second pass even if your description is sharper. Review velocity is a competitive moat. Multiple images in the schema array so Google has options for different SERP layouts. Category detail pages (CDP) Category pages compete with the AIO’s own list. If the AIO already enumerated 5 options, your category page has to look like the place where the user goes to actually choose between them. 3 things to control outside the meta: ItemList schema on the category page so Google can render product carousels in the SERP. A carousel takes more vertical space than a single listing and dominates the back-scroll. Filter and sort UI visible in the SERP preview. Google sometimes surfaces sitelinks for category facets (”by price” or “by brand”). Internal linking to those facets makes them eligible. Page count and depth. A category page with 12 products competes badly against one with 240. The second impression carries an implicit “Is this comprehensive?” check. Blog content The AIO has already given the user the answer. To earn a possible “validation click” [ https://substack.com/redirect/eb50569c-6238-4f4a-b2b4-365c1798c923?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], or at least a thorough second impression for your brand, what the user is looking for is credibility on who said it and when. 2 things to control outside the meta: Visible datePublished or dateModified in the SERP. A 2024 article next to a 2026 article likely loses, whether the user considers the description or not. Article schema with a named author field that links to a sameAs URL (LinkedIn, author bio page). This makes the author an entity Google can resolve, which matters for E-E-A-T scoring even if no visible card renders. The AI SEO Change Management Plan [ https://substack.com/redirect/60a6d786-39db-4fa8-af6f-ea55df65e73e?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] alone saves directors 10+ planning hours for retraining your team against current best practices, but Premium is only $150/year. See the full library. [ https://substack.com/redirect/41901e35-036f-4b63-ae59-7e725a468bbd?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] What intent compression means for operators The last 3 years of intent-based content work produced the right pages for the right queries. What’s shifting is one prediction layer on top of that strategy: how long users stay, where they look, when they click. That layer is now AIO-driven, not intent-driven. User search intent still drives what your brand needs to write, but it is no longer a good estimate of how users will behave on a SERP page. Therefore, more optimization work moves to the SERP, focusing on how your listing reads against the AIO and the results around it, separate from the intent logic that decides what the page content should be to answer the query. Your core optimization efforts don’t change. A content team built around intent clusters keeps its cluster map intact. What gets updated is the optimization pattern per page (meta descriptions, title tags, the second-impression framing from finding 7 shared last week with Premium subscribers [ https://substack.com/redirect/6d7c3a14-c546-4636-8f3e-c006a4b1178f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]), not the underlying taxonomy or content strategy. (Premium-only) Claude skill for identifying 3+ competitor words at scale Last week, I instructed Premium Growth Memo readers on how to optimize for more competitive metadata within the SERP. Today, I’ve included a Claude skill that’s ready to do that for you. Also included: A short helpful explainer to educate your stakeholders on the user-behavior changes we found across the AIO-based SERP. Unsubscribe https://substack.com/redirect/2/eyJlIjoiaHR0cHM6Ly93d3cuZ3Jvd3RoLW1lbW8uY29tL2FjdGlvbi9kaXNhYmxlX2VtYWlsP3Rva2VuPWV5SjFjMlZ5WDJsa0lqbzBNalkxTVRVd05EQXNJbkJ2YzNSZmFXUWlPakl3TURBNU5ETXdPQ3dpYVdGMElqb3hOemd3TXpBNU1qZzFMQ0psZUhBaU9qRTRNVEU0TkRVeU9EVXNJbWx6Y3lJNkluQjFZaTB4TkRVek5EVXlJaXdpYzNWaUlqb2laR2x6WVdKc1pWOWxiV0ZwYkNKOS40S1NyZ1dZNmNIWEhxXzZITEJvUk5vTFRJdmxtN1psYWIyQVRNQU5LVGVZIiwicCI6MjAwMDk0MzA4LCJzIjoxNDUzNDUyLCJmIjp0cnVlLCJ1Ijo0MjY1MTUwNDAsImlhdCI6MTc4MDMwOTI4NSwiZXhwIjoyMDk1ODg1Mjg1LCJpc3MiOiJwdWItMCIsInN1YiI6ImxpbmstcmVkaXJlY3QifQ.yOCLwvEguOYMxxyRP4za9HV6c4Pjmc0gC4-9SIumOw8?

View this post on the web at https://www.growth-memo.com/p/growth-intelligence-brief-19 A note from Kevin: Welcome to a polished version of the Intelligence Brief! This new version prioritizes the data from the SSI (SEO Site Index) higher. Free subscribers get the overview, premium subscribers [ https://substack.com/redirect/5bdca1be-dabb-460d-b1b5-941ccc88d8fc?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] get the data deep dive, bigger picture and related industry news. As a reminder: the SSI tracks thousands of domains across organic ranks and AI Visibility in over 20 verticals, from e-commerce to SaaS. Exec Summary Three things moved this week. The SaaS slide we celebrated as “finally turning” in GIB 18 [ https://substack.com/redirect/a6784173-b6c6-47f2-8138-f8bc220d1c00?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] reversed inside 30 days. The Jobs vertical is having its strongest month since I started tracking. Local Search posted +44% on the back of a single domain. Each of these tells a different story about where Google is routing traffic right now. SaaS visibility dropped -7.8% in 30 days, erasing last issue’s rebound. AWS (-22.7%), Oracle (-17.0%), Stripe (-16.8%), Atlassian (-15.6%), and GitHub (-14.2%) all fell in lockstep while their AI mentions grew 25 to 90%. The Jobs vertical posted +21.2%, with Glassdoor (+64.2%) and ZipRecruiter (+53.5%) doing nearly all the work. Local Search rose +44.0%, almost entirely on Yelp, which gained more raw visibility points (151) than any other site in the Index this month. SaaS AI prompt counts grew +16.9% over the matching window. The divergence economy has resumed at a wider gap than any prior reading. Trustpilot (+39.5%) and Glassdoor (+64.2%) extend the GIB 18 thesis: review aggregators continue to absorb traffic that used to go to original sources. Movers & Shakers Top 5 Movers (30-day SEO) Glassdoor (Jobs) +64.2%. Vis 14.31 to 23.49. The single biggest 30-day gainer with a meaningful baseline. Full breakdown in the Winner callout below. ZipRecruiter (Jobs) +53.5%. Vis 40.09 to 61.56. Running the same play as Glassdoor in parallel, which is what makes the Jobs story a routing story, not a single-domain win. Nextdoor (Social Networks) +43.2%. Vis 20.14 to 28.83. Third consecutive month of double-digit gains. Local community signal keeps compounding while Threads and TikTok lose ground. Yelp (Local Search) +42.7%. Vis 354.60 to 505.91. Single-handedly responsible for the entire vertical’s +44.0% lift. Yelp added 151 raw visibility points, more than any other site in the Index. Trustpilot (Ecommerce) +39.5%. Vis 39.79 to 55.53. Third review aggregator in three issues to make the top risers, following G2 and Capterra in GIB 18. Top 5 Shakers (30-day SEO) Shutterfly (Images) -32.5%. Vis 5.98 to 4.04. The slide flagged in GIB 18 continues. AI mentions are up, but the SEO loss is steeper than the AI gain. Costco (Ecommerce) -24.9%. Vis 57.32 to 43.05. Surprise loser this month. Worth watching whether this is a one-month dip or the start of something structural. Airbnb (Travel) -24.9%. Vis 7.23 to 5.43. The Travel vertical itself is only -1.5%, so this is Airbnb-specific, not category-wide. Substack (Content Platform) -22.0%. Vis 42.56 to 33.22. Notable one for the readers of this brief. AWS (SaaS) -22.7%. Vis 10.40 to 8.04. Biggest SaaS faller of the month. Full breakdown in the Loser callout below. Winner: Glassdoor (+64.2%) The single biggest 30-day SEO gainer for any company with a meaningful baseline. Glassdoor went from 14.31 visibility to 23.49, with ZipRecruiter (+53.5%) running the same play in parallel. The Jobs vertical as a whole posted +21.2%, and those two aggregators account for most of the lift. Smaller job boards and individual employer pages barely moved. The likely driver is Google routing more “[company] reviews” and “[role] salary” queries back to the big aggregators after a brief period of generating those answers directly inside AIO. This looks like the same pattern we tracked with G2 and Capterra absorbing SaaS comparison queries in GIB 18 [ https://substack.com/redirect/a6784173-b6c6-47f2-8138-f8bc220d1c00?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], only now applied to employer research. Worth watching whether Indeed (+13.4% this month, much smaller move) catches up by next issue, or whether Glassdoor specifically is getting favored by the routing change. Loser: AWS (-22.7%) The biggest SaaS bellwether had its worst month in the Index history. AWS visibility dropped from 10.40 to 8.04 in 30 days, while its AI mentions for the same window grew only modestly. The product isn’t the story. The story is what AWS shares with Oracle (-17.0%), Stripe (-16.8%), Atlassian (-15.6%), Square (-17.9%), and GitHub (-14.2%): every major SaaS brand is bleeding visibility at similar rates this month. What makes the pattern interesting is the diversity of products. Cloud infrastructure, payments, dev tools, project management, all moving in the same direction within the same window. Double Click: The SaaS slide resumed at a wider gap... 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View this post on the web at https://www.growth-memo.com/p/users-behave-differently-in-ai-overviews Support Growth Memo research with a premium membership and get more insights and tools. The average Netflix user spends 18 minutes browsing the home screen before picking what to watch. They scroll past tiles, hover for trailers, scroll back to a show they almost picked, then circle back to the row they started in. The browse IS the experience. This week: 4 behavioral shifts that show up when an AI Overview is on the page, measured across 846,000 real Google sessions Why brand-name searches no longer give you the shortcut they used to 1 finding that should change how you write title tags and meta descriptions this quarter Eric Van Buskirk of Clickstream Solutions [ https://substack.com/redirect/afd8fcef-f9f8-4297-bca5-f4d0b3c18001?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] analyzed anonymized clickstream data provided by Surfer SEO [ https://substack.com/redirect/c36ea0ee-a99c-4017-91f1-cf4668999ce0?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], drawing findings from approximately 846,000 U.S.-based Google search sessions collected in February and March 2026. It’s the fifth user-behavior study on Google’s AI features in the last 12 months. The 70-user UX study from May 2025 [ https://substack.com/redirect/84a7a7f3-d2ad-41a7-a2bb-0365244a0bcf?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] used think-aloud and screen recording. The 250-session AI Mode study from October 2025 [ https://substack.com/redirect/4b68cf3c-4bc1-451a-9051-92c89f75b0e7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] captured how users behave inside AI Mode itself. This one trades qualitative depth for the scale to find behavioral patterns the smaller studies couldn’t see. For context: Prior public Google SERP mouse-tracking studies measured dozens of people. The largest had a few thousand tasks. This study analyzed queries from a panel of tens of thousands of Google Search users. The most significant pattern shown: Users behave in opposite ways in AI Overviews and AI Mode. AI Mode is autoplay. AI Overviews is the Netflix browse. This article covers 4 findings from the new study (full methodology here [ https://substack.com/redirect/39e64622-cff7-4e43-8504-b31c3f443e89?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]) and what they mean for how you write title tags and meta descriptions in 2026. Complete brand visibility: Whether you’re a global brand or nationwide nonprofit Good Guys SEO secured buy-in and built an end-to-end search strategy for one of the US’s most recognized nonprofits using Semrush for Enterprise. The result: +37% AI share of voice for homelessness services, with visibility connected directly to campaign traffic and donations. Complete SEO and AI search capabilities in one platform. The outcomes speak for themselves, whatever your industry or business model. Read the full story [ https://substack.com/redirect/ab5ebc02-3853-423a-b3c4-4454576cd225?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 1/ Same user, opposite behaviors In AI Mode, users often accept the answer while continuing to evaluate the SERP in AI Overviews. The April 2026 study of 185 high-stakes purchases [ https://substack.com/redirect/a7fd12a5-3a2b-4223-b4a1-bb71aa97ed4f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] found that in 88% of AI Mode tasks, users took the AI’s shortlist as-is, 74% picked the item ranked first, and 64% clicked nothing at all. AI Mode often behaves like a closed loop: The user reads the answer, picks from inside the answer, and moves on. New cursor tracking data on AI Overviews shows a different behavior pattern. Cursor positions spread across the equivalent of 83% of a viewport, compared with 66% when no AI Overview appeared. Users kept their cursors still 44% of the time, compared with 29% without an AIO. And in the median session, reverse-direction scrolling accounted for nearly half of scroll movement. Taken together, this suggests that AI Overviews turn the SERP into more of a comparison environment. The user reads, pauses, weighs, returns to earlier content, and reconsiders before clicking. The implication for strategy: AI Mode optimization and AI Overview optimization are not the same job. Showing up in an AI Mode shortlist is a visibility problem at the model layer. Showing up in an AIO is a comparison problem on the SERP itself. 2/ Half of all scrolling now goes backward Among users who reverse direction in an AI Overview SERP, the median user spends 47.5% of total scrolling going back up the page. Without an AIO present, that figure is 27%. The cursor data shows scrolling isn’t a one-way trip. On AIO SERPs, scrolling is roughly a 50/50 split between going down and going back up. That’s the behavior of someone re-reading, not someone scanning. The 70-user study from May 2025 found this pattern qualitatively. 38% of AIO sessions in that study [ https://substack.com/redirect/9eea8e92-46fa-46d3-bcf6-6e59102c41f9?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] showed reassurance-seeking clicks where users opened a second link “just to be sure.” The new clickstream data shows that the validation work has now moved onto the SERP itself. Users used to leave Google to validate a result. Now they validate by reversing back over results they already saw. This is exactly the Netflix browse pattern. You hover on a tile. You scroll past it. Something pulls you back, so you scroll up to read the description again. The decision happens during the reversal, not the first pass. For e-commerce and high-consideration decision categories, this is the most consequential finding in the study. Your listing in an AIO SERP isn’t getting one impression anymore. It’s getting 2 or 3, and the second impression is when comparison happens. 3/ Search type no longer predicts behavior For 2 decades, search intent has been the foundational segmentation framework in SEO. You could predict how long a user would stay on a Google SERP by knowing what kind of search they did. The cursor data shows that’s no longer true when an AI Overview is on the page. At 21 seconds into a session without an AIO, only 12% of navigational searchers are still on the page. 32% of local searchers are. In classic search, time-on-page has always followed intent: navigational users leave fast because they know where they’re going, local users stay because the SERP is dense with maps and listings, informational users fall somewhere in between. That 20-point spread is what every SEO mental model is built around. With an AIO present, the spread compresses to barely 6 points. All five intent types (informational, local, navigational, transactional, video) cluster between 41.9% and 48.5% time-on-page at 21 seconds. Intent stops predicting how long users will stay in the search results. This is the most novel finding in the study. Nothing in prior research has shown AIO collapses time-on-page differences between intent types into a single band. The 70-user UX study segmented by query risk, the 250-session AI Mode study by task type. Both showed intent matters for engagement. The new clickstream data says intent stops predicting how long users stay on the SERP once an AIO appears. The scoping matters. This is a time-on-page finding, not a “behavior” finding. Scroll depth still varies by intent under AIO, and it actually reshuffles: local jumps from third deepest to first, video falls from first to third. That part of the story is in the next section. The practical implication is uncomfortable. Most SEO playbooks recommend different optimization patterns by intent. Local pages get one treatment, transactional pages another, informational pages a third. However, our time-on-page data suggests that when an AIO is on the SERP, users stay for similar amounts of time regardless of why they searched. The main takeaway here is that intent-based segmentation still matters for what content you write, but it matters less for predicting how long users will stick around on the SERP itself. 4/ Brand searches lost their shortcut Cursor activity for navigational queries (someone typing a brand name into Google) increased 40% when an AIO is on the page. Even users who came knowing where they wanted to go are sweeping the page first. Without an AIO, navigational searchers were the most focused group in the study. They scored 19.7 on the cursor scatter measure (essentially, how much people bounce around the page), the lowest of any intent type. Only 12% were still active at 21 seconds. They came to Google with a destination, found it, and left. With an AIO present, that profile changes completely. Cursor scatter for navigational searchers jumps to 27.5. 45.8% are still active at 21 seconds. The brand-name shortcut, the fastest path through Google search for the past 20 years, no longer works the way it used to. The 70-user study from May 2025 found that brand and authority is the first gate users apply when reading a SERP. They check who’s cited before they check what’s said. The new clickstream data shows that the gate now fires even when there’s nothing to gate against. A user who typed “Lenovo” is still doing the authority check on the AIO content first. This is the same Netflix pattern again. You open a show you’ve already decided to watch, but you pause first to read the synopsis and check the rating. The brand recall got you to the page. The browse decides whether you click through. The implication for branded search: Brand recall is no longer enough on its own. Even users who searched for you specifically are now evaluating what’s around you on the SERP before they click through. Premium: Where AIOs reshape the SERP the most, plus the 3 rewrites that can earn the click The above data shows how AIOs converted the SERP to a browse environment. The next 3 sections cover the following: Cross-study reconciliation (why the 70-user study said one thing about AIO scroll depth and the new clickstream data says another, and how both are right) Sectors where the reading-mode shift hits hardest and the one where it doesn’t hit at all, and Specific rewrites to make in your title tags and meta descriptions to win in this new environment. Please sign up for Growth Memo Premium if you want to access the content below... 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View this post on the web at https://www.growth-memo.com/p/reasoning-lift-what-happens-to-ai This Memo was sent to 26,659 subscribers. Welcome to +100 new readers! Upgrade to Premium [ https://substack.com/redirect/bfcc0aaf-3da7-4565-a354-db9d7a4bf7bf?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates Support Growth Memo research with a premium membership and get access to exclusive information and tools. AI offers a conversational experience. We use LLMs through chatbots. But no one has yet looked at how citations and mentions evolve in a conversation. I analyzed data from the Semrush AI Visibility Toolkit [ https://substack.com/redirect/52032710-d028-4ff4-8bed-23713ab2281d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] to review 20 buyer journeys across 4 different verticals to compare high vs. low reasoning for ChatGPT5.2. In this analysis: Why high reasoning cites a nearly different web (only 25.6% domain overlap with minimal) and which source types gain or lose ground Why TOFU content has a payoff again: Grands cited at the Problem stage are more likely to persist all the way to Selection under high reasoning, and never under minimal How to split your prompt tracking by reasoning mode so your AI visibility reporting reflects 2 different systems, not an averaged one Premium subscribers also get bonus data from the full analysis and the Buyer Journey Prompt Map: a fillable worksheet that turns these findings into a stage-by-stage audit of where your brand persists across an AI buyer journey, and where it goes missing. Discover how people are really searching, discovering, and engaging online AI continues to reshape how users discover, engage, and click across search environments. But most teams are still making decisions without a clear picture of what’s actually shifting. The Datos State of Search Q1 2026 report, created in collaboration with Rand Fishkin, maps real search behavior across millions of users in the US, EU, and UK. AI adoption, regional contrasts, e-commerce discovery, and intent signals are all explored in detail using real clickstream data. Get your free report [ https://substack.com/redirect/15785a14-e06d-4ba2-b143-dbdd65b22ec4?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Methodology Data comes from the Semrush AI Visibility Toolkit [ https://substack.com/redirect/52032710-d028-4ff4-8bed-23713ab2281d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], which captures the prompts, citations, and fan-out queries ChatGPT generates per response. We ran 100 prompts twice through GPT-5.2, once with minimal reasoning and once with high reasoning, for 200 total responses. Prompts span 20 buyer journeys across 4 categories (B2B SaaS, Finance, Consumer Tech, Health/Lifestyle), with 5 stages per journey: Problem, Exploration, Comparison, Validation, Selection. Citation rate is the share of prompts where the response cited at least one external source. Avg citation counts sources per cited response. Fan-out queries are the sub-queries the model fires internally to research the prompt before answering, surfaced via the Semrush API. GPT 5.2’s high reasoning cites and searches more Turn high reasoning on, and the citation rate jumps from 50% to 68% (+18 percentage points), the average sources per response nearly doubles (2.6 to 4.5), and fan-out queries go up 4.6x. High reasoning also pulls from 173 unique domains across the test set vs. 127 for minimal; 99 of those domains never appear under minimal reasoning. This is grounding at its finest. When the model thinks harder, it relies more on web search. Reasoning plays a major role in brand visibility, though we don’t know how many users activate reasoning vs not. Query intent is a cleaner proxy than user demographics. Free-tier users have reasoning access too, just rate-limited, and ChatGPT auto-routes hard prompts to Thinking mode without the user clicking anything. So the question isn’t who can afford reasoning. It’s which prompts trigger reasoning automatically. Multi-criteria comparisons, evaluation frameworks, regulatory and compliance questions, and complex shopping builds are the prompts most likely to fire reasoning regardless of plan. Map your audience by query type, not by paywall status. High reasoning fires more fan-out queries deeper in the funnel Users move through problem-solving and purchase decisions in stages, often within the same conversation. The gap between minimal and high reasoning isn’t constant. It scales with where the user sits in the journey. What the 5 stages look like in practice. Take a buyer evaluating CRM software: Problem: “How do I know if my sales team needs a CRM?” Exploration: “What types of CRM software exist for B2B SaaS?” Comparison: “HubSpot vs. Salesforce vs. Pipedrive for a 50-person sales team” Validation: “Is HubSpot worth the price for mid-market B2B?” Selection: “How do I get started with HubSpot Sales Hub?” The 3 patterns hold across all 20 journeys: Citation rate climbs through the funnel under both modes, but high reasoning closes the early-stage gap most aggressively: +35pp at Problem, only +5pp at Validation. The model treats early-funnel questions as research tasks when high reasoning is on, whereas it answers-from-memory when it’s off. Fan-out queries peak at Comparison. High reasoning fires 24 sub-queries per response there vs. 5.5 for minimal. Selection runs 15.4 vs. 2.6. Average citations per response peaks at Comparison (9.8 high, 5.8 minimal) and narrows at Selection (4.7 high, 2.6 minimal). The model resembles an hourglass across funnel stages. At the aggregate level, minimal reasoning fires 245 search queries across 100 prompts. High reasoning fires 1,130. When the model operates with high reasoning, it runs a mini investigation per prompt, and most of the investigation happens at the Comparison and Selection stages. What does a fan-out actually look like? A B2B SaaS prompt under high reasoning comparing Salesforce, HubSpot, and Pipedrive for a 50-person sales team breaks into separate queries about API rate limits per vendor, SOC 2 / ISO 27001 compliance, SAML/SSO/SCIM support, webhook architecture, OAuth flow, developer documentation, enterprise pricing tiers, and change-data-capture support. Each becomes its own retrieval. The brand that wins the answer is the one whose documentation surfaces clean for each sub-query, not the one that ranks for the parent prompt. The Selection stage has the widest per-response query variance: 0 to 40 fan-out queries on the same five-stage cohort. The driver is prompt specificity. Bounded prompts (like “should I finance through the dealer at 0% APR or use a bank?” or “draft an RFP to 3 SEO agencies”) run zero queries because the answer’s structure is given. Open-ended product builds (”shopping list for a $3,000 home gym” or “which travel card ecosystem fits our grocery spending?”) run 28 to 40 queries. The Selection stage isn’t bounded by one type of question, and the model’s research effort tracks how many degrees of freedom the prompt leaves on the table. For marketers: Early-funnel visibility is a reasoning-mode story. If your buyers use ChatGPT with reasoning on, problem-stage and exploration-stage content is in play. If they don’t, you’re effectively invisible until Comparison. Reasoning affects how brands appear in a conversation An LLM session is a conversation, not a single query. The question that it opens up: Does a brand cited at the start of the journey carry through to the end? If yes, early-funnel visibility compounds. If not, every stage is a fresh fight. When a brand gets cited in the Problem stage (step 1), does it survive to the Selection stage (step 5)? When using minimal reasoning: No. Zero journeys show this kind of persistence. In high reasoning: Yes. Brand continuity is maintained in 4 journeys across all 5 stages. Within a single response, high reasoning also anchors harder on individual sources. 51 of 100 high-reasoning responses cite the same domain more than once in the same answer, vs. 26 of 100 for minimal. High reasoning quotes a source repeatedly when it commits to it. Brand mentions tell a softer version of the same story. If you loosen the test from the cited domain to the brand name in the answer text, persistence shows up in 3 high-reasoning journeys (HubSpot across CRM Selection, American Express across Business Credit Cards, Sony and Canon across Mirrorless Camera) and 2 minimal-reasoning journeys (HubSpot, Mercury). Consumer Tech shows up here even though it doesn’t show up in the citation persistence table. Brands like Sony and Canon are mentioned through the conversation without the model linking out to them, which is its own form of category dominance and worth tracking separately. High reasoning builds a consistent mental model of the solution space throughout a session. The headline finding: TOFU prompts have value. If a brand shows up at the Problem stage, it tends to carry through to Selection. Top-of-funnel content isn’t just brand awareness for AI visibility. It’s a leading indicator of where the model lands at decision time. Two more implications: All 4 persistent journeys are in Finance, which suggests persistence rides on the same authoritative-source content (regulatory pages, official brand sites) that drives the +28pp Finance lift overall. For marketers running an account-based or category-creation play, reasoning-mode visibility is the prize. It’s the only mode where early-funnel content compounds into selection-stage citations. Reasoning mode is a separate search engine The brand that wins under minimal reasoning is not the brand that wins under high reasoning: 3 in 4 cited domains are different. The mix of source types is different. The stages where citations appear are different. I’m excited about 2 findings in particular from this analysis: 1/ The first is measurement. We need to track low vs. high reasoning in our prompt trackers. It’s best to avoid an aggregate view because the mechanisms are truly different. Bad news: This adds more effort and cost to prompt tracking. Good news: We can make prompt tracking a lot more accurate. 2/ The second is funnel stages. In the latest AI Mode user behavior study [ https://substack.com/redirect/e3d9fa99-92e7-4681-9b63-7c2edda7f09c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], I found that users react strongly to shortlists, demonstrating a similar behavior seen with Google’s classic search results where the top result matters most. That result made it seem to me that focusing on BOFU prompts that return shortlists is the game. However, now we know there is value in TOFU prompts because of persistence: Brands that appear early in the buyer journey can persist all the way through. The best way to find that out for yourself is to map buyer journeys and track your persistence. For Premium subscribers: winner & loser categories in high reasoning, buyer prompt journey worksheet and how Reddit performs Most AI visibility strategies treat ChatGPT as a single system. The data says otherwise. Reasoning mode and minimal mode pull from different corners of the web. Different source types. Different domains. Different winners. If your content strategy is built on the AEO / GEO / AI SEO you saw last year, it’s likely underperforming. Below, premium subscribers get extra findings from this analysis, including which source types are losing ground, which ones are quietly taking over, and the categories where the gap is widest. You’ll also get the Buyer Journey Prompt Map, a fillable worksheet for running an audit on your own brand across all 5 stages... 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View this post on the web at https://www.growth-memo.com/p/growth-intelligence-brief-18 Welcome to another Growth Intelligence Brief [ https://substack.com/redirect/765764b8-341e-44e3-aa03-6fb11b05aaef?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], where organic growth leaders discover what matters, get insights into the bigger picture and guidance on how to stay ahead of the competition. As a free subscriber, you’re getting the first big story. Premium subscribers get the whole brief. Today’s Growth Intelligence Brief went out to 660 marketing leaders. This week, we’re looking at: Microsoft openly redefining what the search index is for in the age of AI answers Ramp’s 32-day A/B test that killed the schema-first playbook for AI agents Amazon’s LLM-built knowledge graph that quietly added hundreds of millions in revenue I’ll also connect the dots on what this all means for you. Microsoft just redefined what the search index is for [ https://substack.com/redirect/f4f3f6ce-9818-4048-a170-736848d36a28?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Here’s what happened: Microsoft’s Bing team published a post on May 6 making the most explicit case I’ve seen from a major platform on how AI search inverts the role of the index. The core argument: When an AI system grounds an answer, the index is no longer pointing to information, it’s using it. The unit of value shifts from documents to “groundable information,” discrete facts with clear provenance that a model can responsibly cite. The post lays out 3 specific shifts. The goal moves from “fetch the best documents” to “fetch the best information to synthesize into a reliable, verifiable answer.” Factual fidelity becomes the key quality metric: Does the indexed representation of a page accurately preserve the meaning of the original content? Freshness failure carries a categorically different cost when the index is grounding an answer instead of surfacing options. Why this news matters: This is the first time a major search platform has publicly written down what the new optimization target is. Most of the AI search discussion still treats the problem as “how do I rank in AI Overviews,” which is the wrong frame. Microsoft is saying the right frame is “how do my facts get retrieved into a synthesized answer.” That changes what counts as a win. A grounding function is accountable for the quality of evidence it provides, not the order of links it returns. The metric is whether the model picks up your specific facts, attributes them correctly, and surfaces your provenance to the user when the user wants to verify. My take on this: This is the doc I’ve been waiting for. Volume sits with Google’s surfaces, but the conceptual leadership is coming from Microsoft. Bing is the one writing down the playbook, partly because they have less to lose and partly because they got there first with Copilot integration. The piece I’d add to Microsoft’s perspective: Grounding has a discoverability problem. A model can only ground against content it has seen, parsed correctly, and stored as retrievable evidence. That’s a 3-step funnel, and most SEO teams are still optimizing for step 0, which is “rank in a SERP.” The new funnel? Crawl, parse, retrieve. If there’s a break in any of those 3 steps, you don’t get cited. Here’s what to do: Audit your content for 3 things this month: 1/ Are your key facts written as distinct, attributable claims rather than buried inside narrative paragraphs? 2/ Do your pages carry author bylines, publication dates, and visible source links that a model can use to assign provenance? 3/ Can a non-Google crawler (try a tool like Firecrawl or run a Cloudflare bot log audit) actually retrieve the parts of your page that matter, or are they hidden behind JavaScript that AI crawlers won’t execute? The fastest tactical win: Rewrite your top 20 product or category pages so the load-bearing facts (price, feature, comparison, capability) appear in clean text inside the first 600 words. That’s the part the grounding layer will read. Markdown beats schema for AI agents [ https://substack.com/redirect/b4279f5d-9294-4893-87de-60c556144d98?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Subscribe to premium and learn about Ramp’s test, Amazon’s new knowledge graph and why Snapchat is growing at an unprecedented pace... Unsubscribe https://substack.com/redirect/2/eyJlIjoiaHR0cHM6Ly93d3cuZ3Jvd3RoLW1lbW8uY29tL2FjdGlvbi9kaXNhYmxlX2VtYWlsP3Rva2VuPWV5SjFjMlZ5WDJsa0lqbzBNalkxTVRVd05EQXNJbkJ2YzNSZmFXUWlPakU1TnpnME9ERXpOeXdpYVdGMElqb3hOemM0T0RRM056VTBMQ0psZUhBaU9qRTRNVEF6T0RNM05UUXNJbWx6Y3lJNkluQjFZaTB4TkRVek5EVXlJaXdpYzNWaUlqb2laR2x6WVdKc1pWOWxiV0ZwYkNKOS56UVdSRU8zenFPM2c4Y0hyUHJJRFdJM21kTDBjdFhSelpZZFgtRFdiSXNzIiwicCI6MTk3ODQ4MTM3LCJzIjoxNDUzNDUyLCJmIjp0cnVlLCJ1Ijo0MjY1MTUwNDAsImlhdCI6MTc3ODg0Nzc1NCwiZXhwIjoyMDk0NDIzNzU0LCJpc3MiOiJwdWItMCIsInN1YiI6ImxpbmstcmVkaXJlY3QifQ.sDwZPTwkHtpgvlS-H4_006_Z8jL2wAN_-u1eswTN5s4?

View this post on the web at https://www.growth-memo.com/p/the-consensus-gap This Memo was sent to 26,564 subscribers. Welcome to +115 new readers! Upgrade to Premium [ https://substack.com/redirect/ed2c344c-dcbb-4f36-b1cb-74a19f9437a7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates Support Growth Memo research with a premium membership and get access to exclusive information and tools. Most teams talk about “AI visibility” like it’s one thing. New data on 3.7 million citations across ChatGPT, Perplexity, and Google AI Overviews suggests it isn’t. And the gap between the 3 engines is wider (and more strategically important) than your dashboard likely admits. Today’s memo breaks down: Why a blended AEO score hides the only finding that matters Which page types and domains actually travel across engines The shift from measuring AI presence to measuring portability Premium subscribers also get the YouTube exception data, the universal-winners domain list, and 3-step operator guidance for separating engine-level visibility from market-level claims. I get pitched a lot of AI search tools. Most tell you where you rank. Almost none tell you what to do about it. AirOps just launched the first serious answer to that gap I’ve come across, and I think it resets the category. Not a dashboard. Not a writing assistant. A growth platform built for marketers operating in AI search: see where you’re cited, ship the work that closes the gaps, and measure what actually moves. On May 21, the team is going live with a walkthrough of what they shipped and how to use it. Live demo with the team building it: Amr Shafik (VP Product) Eoin Clancy (VP Growth) Melanie Dell’Olio (Manager, AI Solutions). May 21, 3pm ET. Live on Zoom. Recording goes out after. Live room gets the unfiltered version. Register now [ https://substack.com/redirect/e0c58b39-598a-4d63-8e08-f38908a4e3bd?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] One of the biggest differences between AEO and SEO is that AEO plays on more platforms. Omnia [ https://substack.com/redirect/0ecd63a8-aa06-4c2a-9599-9232aaa49fd1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] data shows across multiple samples that only 2.35% to 2.45% of cited URLs appeared in ChatGPT, Perplexity, and Google AI Overviews for the same prompt. 91% of citations appeared in only one engine. Conclusion: AI visibility is not a single leaderboard. Instead, it’s 3 different distribution systems that sometimes overlap and usually do not. Only 2% of URLs get cited by all 3 engines Most people would guess that if a URL gets cited by one major AI engine, it has a reasonable shot at appearing in the others. But the 20,000 prompt sample shows only 2.37% of cited URLs show up across all 3 engines for the same prompt. Meanwhile, 91.07% show up in only one. Those 2 numbers belong next to each other because they explain each other. The remaining ~7% overlap in pairs, which means engines are drawing from largely disjoint pools rather than ranking the same pool differently. For AEO / SEO teams, that means a single composite visibility score is the wrong unit of measurement. Averaged AEO scores hide this. A brand can look strong in aggregate and be invisible in 2 of 3 engines. Teams chasing one blended AI visibility number are compressing 3 ranking systems into one metric and calling it strategy. The 2% holds across every cut The ~2% overlap rate and ~91% exclusive rate stays almost perfectly flat across 4 samples. That consistency matters more than the exact decimal point. The consensus gap is not an artifact of one query set or one time window. It looks structural. In Q3 2025, universal overlap was 2.2%. In Q4 2025 and Q1 2026, it rose to 2.7%. Engine-exclusive citations fell from 90.1% to about 88%. So yes, a small amount of convergence. But even after that shift, fragmentation still dominates. Commercial prompts don’t converge either The intent split is one of the quietest but most useful parts of the dataset. You could argue that commercial queries should produce more consensus. When someone searches for the best CRM, best running shoes, or best project management software, the pool of acceptable sources feels narrower than it does for broad informational prompts. Surprisingly, the data does not support a big difference. Commercial prompts show 2.4% universal overlap. Informational prompts show 2.0%. Even when the query should narrow the answer set, the engines still choose different sources most of the time. That pushes against a common instinct in SEO and content strategy. Teams often assume high-intent queries are where shared authority will show up. The opposite looks closer to true. Even in commercial territory, each engine’s own retrieval logic, what sources it trusts, what formats it prefers, is doing most of the work. Guides beat homepages by 2x The page type breakdown below shows guides and tutorials have the highest cross-engine overlap at 2.3%, followed by blogs at 1.8%, category pages at 1.6%, product pages at 1.2%, and homepages at 1.1%. Two lessons: First, explanatory content travels better than brand or transactional assets. If you want the best shot at showing up across engines, the strongest candidate is not the homepage and not the product page. It is the page that helps, explains, compares, or teaches, but keep in mind that these are also content formats that AIs can answer directly well. Second, even the best page types perform badly in absolute terms. Guides are not winning across engines in any meaningful sense. The right read on this is not “publish more guides and you will win everywhere.” It’s simpler than that: Helpful content travels better than brand content. Visibility is not the same as portability One of the easiest mistakes in this space is to confuse citation frequency with citation portability. Wikipedia is the cleanest example. It appears 16,073 times in the dataset, but only 1.3% of those appearances are universal across engines. Reddit appears 14,267 times, but only 0.1% are universal. Reuters shows up 1,202 times and still lands at 0.0% universal overlap. That is why an important metric is portability. A domain can show up all over one engine and barely travel, which means a brand looking dominant in an aggregate dashboard may be one platform’s habit away from invisibility. Presence tells you whether you are visible. Portability tells you whether that visibility is resilient. What this means for operators The practical implication is simple: Stop treating AI visibility as one thing. Examine the comprehensive visibility of your domain by measuring: 1/ Presence, the % of your tracked prompts where your domain appears in any engine. Presence tells you whether you’re visible. 2/ Portability, the % of your cited URLs that appear in all 3 engines. Portability tells you whether that visibility is resilient. 3/ Concentration, the % of your citations that come from a single engine. Concentration tells you which engine your current dashboard is secretly built on. If overlap between engines is this low, a single AEO strategy is too abstract to be useful. When we approach AI visibility from a holistic perspective, it forces sharper questions: Which engine matters most for us? Which of our assets travel across engines, and which only work in one? Are we measuring presence when we should be measuring portability? This also changes how brand teams should think about diagnostics. A weak homepage across engines may not be a homepage problem. It is a symptom of something broader: Engines favor utility over brand centrality. In that world, visibility comes less from being the official source and more from being the useful source. The strategic question is no longer How do we rank in AI? We should instead be asking ourselves How do we build assets that survive different engine preferences? That is a narrower question. It is also a better one. Methodology There are a few caveats to this analysis: The dataset is skewed toward Omnia’s [ https://substack.com/redirect/0ecd63a8-aa06-4c2a-9599-9232aaa49fd1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] customer base. The intent and page-type cuts rely on regex classification, which is useful for directional analysis but not perfect taxonomy work. Those caveats do not weaken the main finding much. The biggest signal is not precision at the edges. It is consistency at the center. No matter how the cuts change, the same pattern resurfaces: very little overlap, very high engine-specificity, and only modest differences by time, intent, or page type. Dataset size and time window The analysis draws on 4 prompt samples. Three cohorts of 5,000 prompts each, tracked from 1 January 2025, 1 July 2025, and 1 January 2026. A separate 20,000-prompt random sample underpins the headline 2.37% and 91.07% figures. The time-view cut spans Q3 2025 through Q1 2026 (to date) and covers 3.7 million URL citations in total. Commercial / Informational / Other intent splits are drawn from roughly 2.6 million URLs across the combined sample. Page-type splits span 4.1 million URL appearances. How prompts were selected The 20,000 prompts are drawn as a random sample from Omnia’s [ https://substack.com/redirect/0ecd63a8-aa06-4c2a-9599-9232aaa49fd1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] live prompt monitoring pool. The pool reflects what real marketing teams chose to track, weighted toward Omnia’s customer geography (Spain-heavy, plus UK, Nordics, and other EU markets). Each prompt runs in its country’s primary language, so Spanish is overrepresented versus a US-only dataset. Industry mix is fintech/insurtech, travel, SaaS, B2B services. Treat findings as directional for European AI search. Engine coverage The study covers 3 engines: ChatGPT, Perplexity, and Google AI Overviews. Each fires the same prompt concurrently within the same minute, twice a day, with country localization and each engine queried in its default web-enabled, unauthenticated state. Perplexity tracking runs on sonar, while ChatGPT and Google AI Overviews use each vendor’s default production model for logged-out web browsing (which neither OpenAI nor Google pins publicly to a specific version). Classification methodology Intent and page type are assigned by regex. Intent buckets are Commercial, Informational, and Other. Page-type buckets are Guide / tutorial, Article / blog, Category page, Product page, Homepage, Wikipedia, and Other. The rules are keyword- and URL-pattern-based, which makes them fast enough for a multi-million-URL dataset but coarse at the edges. Edge cases fall into Other, which is why Other carries a high share in both the intent and page-type tables. Treat the regex cuts as directional, not authoritative. Premium: The YouTube exception and the few universal domain winners Domain-level data sharpens the page-type finding. Most social and UGC platforms are invisible across engines. As mentioned above, Reddit is at 0.1% universal overlap. But the UGC platform pattern holds: LinkedIn is at 0.1%. TikTok is at 0.1%. Facebook is at 0.0%. Quora is at 0.0%. Then there is YouTube. And a small set of outliers that illustrate even some “portable” winners are only partially so. Support Growth Memo research with a premium membership and get access to exclusive information and tools... Unsubscribe https://substack.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.pAhNm5J0QVfXXCxUD9PKt9z34uPUR-vgq_-IbjdtiPQ?

View this post on the web at https://www.growth-memo.com/p/ai-changed-my-work-and-yours-too This Memo was sent to 26,431 subscribers. Welcome to +97 new readers! Subscribe to get the free memo weekly or upgrade to Premium [ https://substack.com/redirect/f0bbbf0c-482f-486c-858c-ed1c406a99ee?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. Come join me at AirOps Next [ https://substack.com/redirect/bfe076ff-9c13-4d37-ad2e-5e6f3e465598?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] in NYC on May 13th. We’re joined by the greats from Airbnb, Ramp, Anthropic, etc. How LTM Interactive achieved +400% project ROI with Semrush for Enterprise Global businesses operate under relentless pressure. But LTM Interactive successfully juggled more clients, growing expectations, and tighter margins by consolidating their search operations into Semrush for Enterprise. After cutting fragmented tools and turning wasted manual work into high-value client delivery, their teams improved search project ROI +400%. Read LTM’s full story [ https://substack.com/redirect/52bf2261-edc0-4b1c-901c-b05274a21d47?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Am I still an advisor? Or a builder? I’m having an existential moment. My work has forever changed in a way I’m still trying to understand. 6 months ago, agentic vibe coding crossed a threshold. Since then, I have used AI to raise my impact by a magnitude. I designed landing pages end-to-end for a major travel brand that made it into production. I automated topic prioritization, SEO testing, and SEO reporting for my clients with full-blown apps. I built an array of useful applications for myself, from automating the SSI (SEO Site Index found in the bimonthly Growth Intelligence Briefs [ https://substack.com/redirect/de34bc5c-9248-4303-a6a8-6c446f1b9928?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]) to Openclaw agents that help me with research and charts. The work I shipped improved, while it also became harder to define. But when the cost of building collapses due to AI, judgment is the only thing that doesn’t compress. Meanwhile, most operators are still hiring, budgeting, and measuring as if execution is the constraint. I’m not alone: AI companies are reaching $100M ARR faster than ever, in large part because they’re AI native. Their whole product development philosophy is fundamentally different. Heck, Anthropic went from $9 to $30 billion USD in 6 months and is now worth about as much as Starbucks, Mastercard, or McDonald’s. And I have my feelings about Matt Schumer’s essay “Something Big Is Happening,” [ https://substack.com/redirect/e866e7ec-979f-4877-bc92-d6c066fbed56?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] but with reportedly 80 million views [ https://substack.com/redirect/e6154a60-e2a8-4d96-babf-0615c2ca6113?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], it clearly hit a nerve. So, I want to take a beat from publishing research this week and take measure of how agentic coding [ https://substack.com/redirect/a1a68a13-7126-4189-9ba4-a77cf779b142?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] changes software, distribution, and people. The effect on software In 2024, I made a bold prediction [ https://substack.com/redirect/587a57ba-53e7-4130-8e8a-1c1d57c7b3a0?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] that AI agents would hit 100 million users in 2025. I was off by about a year. Agents didn’t hit 100 million users in 2025, but they did hit production in 2026, and the gains are measurable: METR found [ https://substack.com/redirect/15846a4c-cf11-4835-921e-7890cc2a0660?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 1.5 to 13x (!) time savings when technical staff used Claude Code. A 40% reduction in cost and 60% reduction in time from agentic AI is not unrealistic [ https://substack.com/redirect/60ab19be-26e6-4a66-bd1f-ee8189e45aee?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Bain & Co estimates [ https://substack.com/redirect/f70ed40d-dec8-4346-959f-726c71fe8a3f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] a 30-50% gain in productivity from deploying AI agents and automation. What happens to software when non-engineers can ship code? After the iShares software ETF (IGV) cratered [ https://substack.com/redirect/1ff581db-8d34-44cf-b3d4-97a3ca7e7530?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 24% in Q1 2026 (steepest quarterly drop since Q4 2008), you could sense a panic in the air that AI would make software companies redundant. But software is more than code. Enterprise software has strong guards against AI redundancy. Anyone who has ever purchased a CRM or migrated to another vendor knows how hard this is and how much is involved. Enterprise software is more than code. It’s code plus integration, security, uptime, sales and support… all wrapped up in procurement cycles, IT review, and legal sign-off. AI can chip away at any one of those pieces. For example, an agent can handle an integration, run a security audit, even book a demo. But no agent shows up to get sued when a mission-critical system goes down at 3 am. Accountability is the part that doesn’t unbundle. Enterprise companies don’t replace this stack; they build their own agents and AI workflows on top of it. Self-serve software is a different beast. Anyone can now spin up a simple task tracker in a Kanban format. I personally rather pay a few dollars a month and spare myself the hassle of bug fixing, but it’s possible and quick. Self-serve products need to move upmarket. The playbook shows up in Notion’s, Figma’s, and Canva’s moves [ https://substack.com/redirect/e252d6c9-8bc7-4ed0-a7c0-3058f92ca50e?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] into enterprise. In this shift, 2 archetypes stand out: Data providers System of records 1/ Data providers provide value by making data that the market could not otherwise access. These companies lose leverage from their user interfaces but gain it from their data. For example, let’s say a data provider gives you app store rankings. The user interface for that company is slowly turning into friction as more people can code their own dashboards. But their data becomes much more interesting. The durable levers for APIs / MCPs in this world are data completeness, uniqueness, stability, and cost. The logical move is to shift to a headless experience for early adopters and keep the user interface for legacy users. 2/ Systems of record (SOR) are the canonical place where a company’s own data lives. Salesforce, Workday, or Coupa are the bane of existence for many people, but they’re billion-dollar companies because they’re extremely hard to replace. The moat is the tangle of permissions, audit trails, integrations, compliance posture, and decades of workflow conventions built around that data. An agent can generate a CRM in an afternoon; replacing Salesforce at a Fortune 500 is a multi-year change-management project. These companies have already started and will continue to use AI more to provide better user experiences. But their levers are depth of integrations, compliance and audit posture, switching cost, and the quality of their agents. The winners in the SOR space are the ones whose agents make the existing system of record more useful, not the ones trying to replace it. The effect on distribution Distribution is more important than product, or so the saying goes, but getting it in 2026 is hard. Platforms are closing (by reducing clickouts and keeping users inside), and they’re taking opportunities away to convert or build direct relationships with visitors outside of the platform. AI Overviews and AI Mode make more than 50% of clicks redundant and keep users on the Google platform. AI chatbots send a tiny fraction of traffic out. Social is flooded, word of mouth is uncontrollable, and paid gets more expensive. From The Brand Tax [ https://substack.com/redirect/f7c39f13-485a-4d56-928c-8a6a60b803ce?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]: Cost per visit climbed 9.4% in 2025 alone, adding to a 30% cumulative increase over 3 years. Conversion rates fell 5.1%. How do you get distribution in this AI-first world? 2 levers compound: Velocity Product 1/ Velocity means you execute faster (and better) than your competitors. When all distribution channels decline and no alternatives open up, the only way to grow is to leverage them better. Play the game better than the competition. Fast shipping speed becomes table stakes, and ideas + compute become the differentiators. PwC found [ https://substack.com/redirect/0dd441ef-9a7e-4774-88bc-50bfe84af061?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] AI speeds content production up by 3-10x. In plain words, we need to automate more. But not at the cost of trust [ https://substack.com/redirect/e4f49a11-6029-4461-9391-c2b00e245685?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. When you lose trust, you lose the game. 2/ Product is the marketing now, with 2 distinct effects: AI sees through marketing gloss. Agents can read ingredient lists, parse reviews, compare specs. “We’re the best X in the world” doesn’t survive an agent that actually checks. But strong products get chosen consistently. Free product is the new top of funnel. Standalone tools that solve a real problem are easier to build than ever, and they acquire better than ads. Ramp Sheets [ https://substack.com/redirect/e4e28135-6f1c-4848-8876-6519b3f75bc1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] routes users toward Ramp’s core product without a marketing budget. When product is the marketing, the emphasis shifts to product growth: onboarding, engagement, retention. The fastest growing [ https://substack.com/redirect/9d582bba-0c83-42b0-955c-95606474a5b7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] products these days all have product-led growth motions. So, marketing and product development melt together. The effect on people AI capability is racing ahead, but human cognition… isn’t. Until we reach AGI (God knows when; I hope not any time soon), human cognition is what limits AI productivity. We can only ship as much as we can review. AI tools can take in more input than ever before, while our own human attention span is declining: AI’s context windows grew 3,906x [ https://substack.com/redirect/635761f2-9a5b-4f20-9cab-a4573fd06d6d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (!) over the last 10 years, from 512 to 2 million tokens, while human attention has shrunk [ https://substack.com/redirect/da5ac30d-ad2b-4dac-b227-06aecf355412?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. We’re outsourcing thinking faster than we’re learning to check it. Two cost curves are racing each other: the Cost to Automate (exponential decay) versus the Cost to Verify (biologically bottlenecked). In “Some Simple Economics of AI,” Catalini et al. argue [ https://substack.com/redirect/e56087e3-e8f9-43e6-aa29-f81ee1544de9?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] that tasks with a verifiable output will be automated the fastest. Work that requires a human to check it compounds slower, so we’ll automate work that’s easy to measure faster. I feel it whenever I’m running four terminal windows at once: the focus drain is as high as the throughput. At scale, what holds us back is how much we can proofread and direct. When anyone can build anything, the ways we’re limited change: Skill and tools matter less. But judgment, ideas, and time decide whether you run in the right direction or in circles. It’s very easy to get distracted with AI because the cost to build is now so low. Judgment is the part that doesn’t compress. I can ask Claude Cowork for a contract review, but I have to know what it missed. Claude will happily write me a Q4 plan, but it’s only as good as my read on which market to attack and what my competitors are about to do. Over the last 6 months, I implemented more agentic and automated systems than I’ve done hands-on work. My clients now have access to unique software they can’t get anywhere else that solves their unique problems. 3 things are now compressing toward zero: the cost to build software, the cost to produce content, the cost to spin up a tool. But another cost is trending far away from zero: The cost to know whether any of it is right. I’m not directly “doing” the work anymore in a traditional sense. I’m now building the thing that does the work, then checking it. The work that matters now is the part I can’t hand to an agent… knowing what to build, what to kill, and what the agent missed. And I’m here to figure out what that means - with you. Premium: My 4 top rules for AI automation At Shopify, we built the initial concept of a “keyword universe” when I was there between 2020 and 2022. I wrote about it publicly for the first time in February 2024. In June 2025, I republished the post with a Claude Artifact that helps you build your own keyword universe [ https://substack.com/redirect/77e371ca-efad-45c6-b6df-c78b72dcd6d0?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. In March of this year, I built a full web app in Next.js for my client. I could probably charge a decent subscription fee if I were to publish it. That keyword universe arc shaped how I work with agents today. The same patterns power an SEO testing system I built for another client: 4 rules carry across both. Upgrade to premium to keep reading... Unsubscribe https://substack.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.KC8SQ6tomVk1EwLVR-8j-P-k27k4v2uvvwBP1PPNJpw?

View this post on the web at https://www.growth-memo.com/p/the-ai-skills-salary-premium This Memo was sent to 26,313 subscribers. Welcome to +85 new readers! Subscribe to get the free memo weekly or upgrade to Premium [ https://substack.com/redirect/be31d35a-db74-4ee5-988f-d3fc91857a61?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. Friendly shoutout from me this week to Aleyda Solis’ new venture Flinchling [ https://substack.com/redirect/7fd9fcc4-ad7a-4026-9b02-ea2a43e1216d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], which scans the news cycle and tells you exactly which stories you should react to, and why. I normally write about strategy and search behavior, not labor markets. But the SEO job market is the clearest leading indicator I’ve seen of how companies are actually valuing AI skills, so I followed the data off the usual map. 946 SEO job postings show companies are willing to pay a premium for AI skills. But the signal is buried in descriptions, and the salary premium only truly activates at mid-level and above. SEO jobs that mention AI in the title pay $113,625 at the median compared to $89,438 for jobs that don’t. That 27% gap is live in the market right now; it’s not a projection. In this memo, I’m covering: Where the 25–27% AI pay premium actually shows up in SEO postings Why screening jobs by title filter misses 4 out of 5 of the roles paying more How to position your resume (or your job description if you’re a hiring manager) so the right opportunities land on your side of the table This week, premium subscribers get a resume audit tool that translates existing SEO work into the AI-skill bullets these job descriptions are actually looking for. Don’t miss this one… because every week you send a resume without AI evidence in the top third is a week you’re pricing yourself against an older market that doesn’t exist anymore. The AI SEO Resume Evaluator guides you to a fix. About this data: 946 full-time SEO roles from SalaryGuide.com [ https://substack.com/redirect/67df7679-dbfa-414f-a912-a7a81fbbb3c1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] were included in this analysis, posted December 2025 through March 2026, deduped at company + job title. Salary midpoints from the 41.8% of roles that disclosed pay. “AI mention” means the title or description contains “AI,” “LLM,” “AEO,” “GEO,” “Answer Engine Optimization,” or “Generative Engine Optimization.” Only 22.5% of teams have fully integrated SEO and AI search workflows. Is yours one of them? Most brands are already losing in AI search without knowing it. And most marketing leaders can’t pinpoint where. Semrush published two new playbooks to solve this, giving marketers the foundation to understand the landscape, secure internal buy-in, and start building toward execution. Brands that treat SEO and AI visibility as separate workstreams are building on a fault line. The ones pulling ahead are making visibility an enterprise operating model. Get the playbooks [ https://substack.com/redirect/cb3443ac-644f-4be6-a33a-c330f50df94b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Companies pay 27% more salary for AI skills AI in the job title commands the bigger salary premium, but the description signal covers far more ground. Only 146 jobs carry AI in the title. 563 include it in the description. The description bucket captures 4x more roles and still delivers a 25% median salary lift over non-AI descriptions ($100,000 vs. $80,000). The dollar deltas are $24,187 for the title bucket and $20,000 for the description bucket. Compounded across salary negotiations over a career, neither is marginal. The AI requirement is hidden in the job description Only 15.5% of SEO postings include AI in the title. 59.5% require it somewhere in the description. Employers are building AI into the role without putting it in the headline. At senior levels, the pattern becomes near-universal: 78.3% of director/executive descriptions mention AI. 67.4% of manager descriptions do. Even at mid level, 1 in 2 job postings include it. A hangup here? Filtering job searches by AI in the title misses 80% of AI-required roles. The requirement sits in the body text, not the headline. The AI skill premium grows with seniority At entry level positions, AI skills in the description carry a slight negative premium (-2.3%). Employers don’t pay new grads more for knowing AI. The signal flips at mid level (+14.3%), then compounds sharply at the management layer. A director with AI in the description earns $35,250 more at the median than one without. Senior roles may earn more, but the premium is due AI judgment (instead of tool skills). The market pricing is applied accordingly. Junior candidates may need AI on their resume to get the interview, but getting paid more for AI skills happens at mid level and above. If you’re a Manager or Director, the AI SEO Resume Evaluator tool (included below) lets you put your current work into the verbs and scope hiring managers at that level are writing. Upgrade to premium for access [ https://substack.com/redirect/be31d35a-db74-4ee5-988f-d3fc91857a61?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. 9+ years in, AI skills are assumed Experience requirements tell the same story with a steeper slope: For junior 0-1 year roles, 40.9% mention AI in the description. For roles requiring 9+ years of experience, that number is 92%. At 9+ years, AI isn’t listed as a differentiator. Instead, it’s embedded in the role definition. The 8% of senior postings that don’t mention it are the outliers. The market has decided, but the titles haven’t caught up Even if the salary premium compresses later, pricing your skills against job description-level signals is still the right move today. 1/ If you’re a job candidate: screen descriptions, not titles. The title filter misses 80% of the AI-required roles and the 25–27% premium that rides with them. Put AI evidence in the top 1/3 of your resume, or it won’t register for the postings that pay more. 2/ If you’re a hiring manager: Your pay bands are already 2-tier, whether you’ve formalized it or not. Roles requiring AI pay more at the median, and most of yours don’t say so upfront. Close that gap now. 3/ Mid-career and up: This is where the premium actually compounds. If you’re 4+ years in and AI doesn’t appear in the first 1/3 of your resume, you’re pricing yourself against an outdated market. Quote from Josh Peacok, founder of Search for Hire [ https://substack.com/redirect/faa8bbc6-e212-4c40-8855-4dd82622dbf0?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]: Having been on hundreds of discovery calls with companies hiring SEOs and having built out hundreds of search teams at Search for Hire, the pattern is undeniable: SEO talent is being priced on two axes now - fundamentals and AI capability. The candidates commanding a premium aren’t the ones who can use ChatGPT, they’re the ones who can build scalable systems with it. But AI without precision judgement can take you a long way in the wrong direction, fast. The real unicorns combine that build capability with deep technical skill, strategic thinking and the ability to sit in front of a client. That combination barely exists and when it does, it doesn’t stay on the market long. Premium: Translate your SEO work into the bullets the market pays a premium for Put this data analysis to work on your resume. Get an evaluation of your current tasks and work, with recommendations on how to shift your resume copy to showcase your AI SEO skills. Unsubscribe https://substack.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.MEd-LNvFIoCAu_kRCnbKy_D65VaiKQ2dgqxM879y19M?

View this post on the web at https://www.growth-memo.com/p/the-ghost-citation-problem This Memo was sent to 26,187 subscribers. Welcome to +95 new readers! Subscribe to get the free memo weekly or upgrade to Premium [ https://substack.com/redirect/85c035fb-8ab4-4848-af61-8021bf0f7626?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. When an AI answers a question using your content, it usually cites you with a source link. What it doesn’t do, 62% of the time, is say your name. The link is there. The brand mention is not. This is what I like to call a ghost citation: the AI using your content doesn’t mention you in the answer. This week, I’m sharing: Why being cited and being mentioned are 2 different outcomes that require different strategies Which LLMs name brands vs. which treat them as anonymous source material The query format and content type that produce 30x more brand mentions Premium subscribers learn which query formats produce 30x more brand mentions and which content type is feeding AI engines anonymously. Upgrade today [ https://substack.com/redirect/85c035fb-8ab4-4848-af61-8021bf0f7626?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. A note from Kevin: I’m a big fan of HubSpot’s Marketing Against the Grain. I had Kieran, one of the co-hosts, on my Tech Bound podcast back in 2023. Now, they launched a newsletter with smart experiments, fresh perspectives, and practical lessons on what’s working right now. So, I thought I would give a friendly shoutout: Check it out [ https://substack.com/redirect/e88bb11f-f1c7-4706-872b-d69543b8fb00?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Hertz drove +50% more bookings from AI search. Here’s the blueprint. Leading brands are using Semrush for Enterprise to control their brand visibility and convert it into customers. In a few months, Hertz Iceland secured executive buy-in, delivered a complete seasonal strategy, and grew bookings from AI search significantly. Their team has a brand visibility blueprint backed by the market’s most complete digital visibility data. And now they’re scaling it year-round. See how they did it [ https://substack.com/redirect/65f63b7a-9b15-4eef-962b-8f0e29b332eb?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] This analysis draws on 3,981 domains across 115 prompts, 14 countries, and 4 AI search engines (ChatGPT, Google AI Overviews, Gemini, AI Mode), using data from the Semrush AI Toolkit. Every appearance is tagged as ‘cited’ (source link present) and/or ‘mentioned’ (brand name appears in the answer text). The gap between those 2 states is the ghost citation problem. 1. 62% of your brand’s LLM citations are functionally invisible. Most brands assume being cited means being seen. The data says otherwise. 74.9% of domains were cited, and 38.3% mentioned. 61.7% of citations are ghost citations: the domain gets a source link but zero name recognition in the answer text. Only 13.2% of appearances convert into both a citation and a mention. Not a single domain was cited, but not mentioned at all or vice versa. 2. Every LLM shows a different behavior The 4 AI engines treat citations and mentions in fundamentally different ways: Gemini names brands in 83.7% of appearances, but only generates a citation link 21.4% of the time. It operates more like a conversationalist drawing on brand knowledge. ChatGPT is the opposite: It cites 87.0% of the time but mentions brands in only 20.7% of answers, functioning more like an academic paper with footnotes. Google AI Overviews (AIOs) sit in the middle but lean toward citation. Google’s AI Mode offers about 17% more brand mentions than ChatGPT in its outputs, but also functions closer to an academic paper than its Gemini sibling. For brands, this means Gemini visibility and ChatGPT visibility are not the same thing. (This data set showed clear evidence that there wasn’t much overlap with ChatGPT citations/mentions and Gemini citation/mentions for the same prompts.) Optimizing for one does not help with the other. There is no single “AI visibility metric.” There are at least 4 different behavioral systems running in parallel. 3. Strong brands get named in the text A clear pattern emerges among domains appearing 3 or more times: Content aggregators and academic sources are cited repeatedly but almost never mentioned. Medium.com was cited 16 times for the same prompts across 3 different engines and named zero times. Wikipedia.org was cited 27 times and mentioned in only 2 answers, both times for the same conversational query (“what is the most dangerous creature in the world?”). Wired.com, sciencedirect.com, harvard.edu: same pattern. Consumer brands with strong public identity get mentioned in the output at near 100%. The AI doesn’t feel the need to cite. Instead, it mentions consumer brands outright. It knows the data about the brands came from somewhere but doesn’t feel the need to explicitly say so to users. For publishers whose value proposition is information authority, this is a structural problem. * Mention rate above 100% means the brand is named in the answer text even when not cited as a source link - the engine references the brand by name without linking to it. For values in this data set over 100%, think about being cited 10x and mentioned 10x as = 100%. If a brand is mentioned 12x and cited 10x, that’s 120%. 4. LLMs disagree on the same brand 22% of the time 454 prompt+domain combinations were tested across multiple engines. In 22% of those outputs (100 total), LLMs disagreed on whether to mention the brand: Instagram.com was mentioned by ChatGPT and Gemini but only cited (not named) by Google. Facebook.com was mentioned by Gemini in 3 out of 3 appearances. Google AI cited Facebook 9 out of 9 times, but named it in only 1. The same brand, the same query, but different engines and different outcomes. This matters for measurement: A brand can appear “visible” in one engine’s data while being completely anonymous in another. Aggregate AI visibility metrics mask this divergence. 5. In-text brand mention rates vary by geography Controlling for the LLM, country-level differences in mention rates are meaningful: India and Sweden show the highest mention rates (50%), suggesting more conversational or brand-forward query patterns in those markets. Italy, Brazil, and the Netherlands show the lowest mention rates (18–22%), with very high citation rates (82–94%). The UK and Canada are mid-range but above the global average. * Note: the dataset uses localized prompts confirmed by Semrush, so language is not a confound. Being cited and being named are not the same, and require a different approach From this analysis, 4 takeaways stood out to me the most for brands and their content strategies: 1. Being cited means an AI is drawing on your content. Being mentioned means it is naming you. We don’t yet know enough about the implications of mentions and citations, but we can say for sure that there’s a system that decides when you’re cited vs. mentioned. 2. Your strategy must be LLM-specific. A Gemini-first strategy is different from a ChatGPT-first strategy. Any AI visibility report that aggregates across LLMs is misleading. 3. Comparative content gets brands named. Informational content feeds the machine anonymously. If the goal is brand mentions, not just citations, focus your content strategy toward evaluation, comparison, and recommendation. 4. Prompt format matters. Brands should map not just which topics they want to appear in, but specifically which phrasing patterns produce mentions vs. ghost citations. Short conversational queries and long structured queries behave like different products. Methodology Data source: Semrush AI Toolkit: 3,981 domain appearances across 115 prompts, 14 countries, and 4 AI search engines (ChatGPT, Google AI Overviews, Gemini, Google). Every row in the dataset represents a domain that appeared in an AI answer. Each appearance is tagged as “cited” (the domain appears as a source link) and/or “mentioned” (the brand name appears in the answer text). The gap between those 2 states is what this analysis calls a ghost citation: the AI used your content but did not say your name. For premium subscribers: What creates a 30x difference in brand mentions The ghost citation problem has a solution. In fact, 2 findings in this analysis point directly to it: one about how you phrase queries, one about what type of content you produce. Together, they explain a 30x difference in mention rates on the same topic. Premium subscribers get both findings, plus the content strategy implications. Upgrade to read the full analysis The first 5 findings describe the problem. These 2 explain what drives it, and what to do differently... Unsubscribe https://substack.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.oTTpL1QnyrfKgeH9JgXXCWZr21GqdubQ5pmSGoXb9Ao?

View this post on the web at https://www.growth-memo.com/p/growth-intelligence-brief-17 Welcome to another Growth Intelligence Brief [ https://substack.com/redirect/9c9303f1-674f-487e-b0da-769052f9b28c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], where organic growth leaders discover what matters - getting insights into the bigger picture and guidance on how to stay ahead of the competition. As a free subscriber, you’re getting the first big story. Premium subscribers get the whole brief. Today’s Growth Intelligence Brief went out to 639 (+9) marketing leaders. This week, we’re looking at: New research shows that 65-85% of what people ask ChatGPT is invisible to traditional keyword tools Why AI Overviews are wrong far more often than Google’s scale can afford Shopify’s bet that the next product discovery layer will be built for AI agents, not humans I’ll also connect the dots on what this all means for you. Most of what people ask ChatGPT can’t be tracked in traditional keyword tools [ https://substack.com/redirect/01e43f30-894a-4f9f-91fa-b476c26cbf59?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Here’s what happened: Semrush published a clickstream analysis of 17 months of ChatGPT usage data. The headline finding: between 65% and 85% of ChatGPT prompts have no matching keyword in Semrush’s database. They’re queries that simply don’t exist in traditional search. Meanwhile, ChatGPT’s outbound referral traffic grew 206% year over year (Jan ‘25 vs Jan ‘26), and the share of that referral traffic going back to Google climbed from 14% to over 21%. Why this news matters: This data puts hard numbers to a problem: The majority of AI-driven discovery is invisible to existing measurement tools. If 65-85% of prompts don’t match any keyword, then optimizing for keywords only captures a fraction of the surface where your brand could show up. The referral traffic growth also flips the narrative that ChatGPT is purely cannibalizing Google. It’s creating a new discovery loop where users bounce between AI and search. My take on this: This is the strongest evidence yet that AI search and traditional search are becoming complementary surfaces, not substitutes. The keyword-invisible prompts are where the opportunity lives: long, conversational, context-rich queries that reward brands with deep, authoritative content. The fact that ChatGPT sends a growing share of its referral traffic back to Google suggests users treat ChatGPT as a starting point for research, not a final destination. Sites need to show up in both places. Here’s what to do: Stop treating your keyword list as the complete map of how people find you. Start monitoring your AI citations (tools like Ahrefs and Semrush now offer this). Look at your analytics for referral traffic from chat.openai.com and chatgpt.com. Find real user questions in sales conversations or customer research. If these are both growing, that’s signal that your content is being cited in prompts you’ll never see in a keyword report. Build content for the questions your customers actually ask, not just the queries they type into Google. AI Overviews are wrong a lot more than Google wants you to think... [ https://substack.com/redirect/01e43f30-894a-4f9f-91fa-b476c26cbf59?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Unsubscribe https://substack.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.1rxyUMm_YzXDHz1J74MXLABPEieasdJMj59UVIQiv0w?

Quick one today: no essay, just a favor. I’m partnering with the team at Minuttia on something I think will be genuinely useful: the first industry-wide survey on how SEO and growth teams are actually approaching AI Search right now. The results will be published as a free report with charts, data breakdowns, and actionable commentary. It takes 3 minutes. Multiple choice only, no open-ended questions. What you get for completing it: For 10 respondents drawn at random: Minuttia is covering a 1-year Growth Memo annual subscription on your behalf. Already a paid subscriber? They’ll extend your subscription by a year instead. Those same 10 respondents also receive Minuttia’s AEO Report plus a 10–15 minute video walkthrough of the key findings. For everyone who completes the survey: an invite-only live session with George from Minuttia and me, where we break down the results and get into what it means and what to do next. The more people respond, the more useful the data. I’d appreciate you taking 3 minutes to share where things actually stand in your world. Thanks for being part of this, Kevin

View this post on the web at https://www.growth-memo.com/p/shorter-focused-content-wins-in-chatgpt This Memo was sent to 26,074 subscribers. Welcome to +105 new readers! Subscribe to get the free memo weekly or upgrade to Premium [ https://substack.com/redirect/56378fbe-b2fd-4866-a5b7-2a011fed1d67?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. For years, SEOs have operated on a simple assumption: The more ground your content covers, the more likely it is to surface in AI-generated answers [ https://substack.com/redirect/7d2be59c-6f49-435e-9975-127893fa153d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. In fact, every “best practice” in classic SEO content pushes you toward more: more subtopics, more sections, more words. Build the “ultimate guide.” An analysis of 815,000 query-page pairs across 16,851 queries and 353,799 pages says otherwise: Fanout coverage is nearly irrelevant to citation rates 2 signals actually predict whether ChatGPT cites your page 6 concrete changes to your existing content library help Premium subscribers get 3 additional findings: whether the density-vs-query match tradeoff shifts by vertical, heading patterns from 6.8 million H2-H4s, and what the data says about “best X” searches. ChatGPT judges a page by its cover. Pages with headlines that directly answer the question get cited 41% of the time. Pages with loosely related headlines drop to 29%. I partnered with AirOps on a study of 16,851 ChatGPT queries and 353,799 pages across 10 industries. Several more findings should change how you approach AI visibility: Retrieval rank is the #1 signal: A page at position 1 has a 58% chance of being cited. By position 10, that drops to 14%. Do comprehensive guides still win? Not exactly. Pages covering 26-50% of ChatGPT’s fanout sub-queries get cited more than pages covering 100%. Domain authority predicts nothing: Always-cited pages have lower DA than never-cited pages. Content quality is what counts. The full report covers 20+ signals, with controlled comparisons across each. Get a head start on how you can win visibility. Read the full report [ https://substack.com/redirect/cac82769-2c00-43e4-9b8d-64cc2d029770?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 1/ The study AirOps ran 16,851 queries through ChatGPT 3 times each through the UI, capturing every fanout sub-query, every URL searched, every citation made, and every page scraped. Oshen Davidson built the pipeline. I analyzed the data. Each query generates an average of 2 fanout queries [ https://substack.com/redirect/9fbacff7-a94b-4a21-87bf-445352d434fc?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. ChatGPT retrieves roughly 10 URLs per sub-search, reads through them, then selects which ones to cite [ https://substack.com/redirect/921881cd-588a-40a5-8e74-6f83b8e8e1cf?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. We scored how well each page’s H2-H4 subheadings matched those fanout queries using cosine similarity on bge-base-en-v1.5 embeddings. That score is what we call fanout coverage: the share of subtopics a page addresses at a 0.80 similarity threshold. (The 0.80 similarity threshold cutoff was used to decide whether a subheading counts as a match to a fanout query. Think of it as a relevance bar.) The question: Do pages with higher fanout coverage get cited more? You’ll find even more information in the co-written AirOps report [ https://substack.com/redirect/1203bb0a-2249-49b7-9b19-c901ce46ff80?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. 2/ Density barely moves the needle Across 815,484 rows, the relationship between fanout coverage and citation is weak. Covering 100% of subtopics adds 4.6 percentage points over covering none. That gap shrinks further when you control for query match (how well the page’s best heading matches the original query). Among pages with strong query match (>= 0.80 cosine similarity): Moderate coverage (26-50%) outperforms exhaustive coverage. Pages that cover everything score lower than pages that cover a quarter of the subtopics. The “ultimate guide” strategy produces worse results than a focused article that covers 2-3 related angles well. 3/ What actually predicts citation These 2 signals dominate: retrieval rank and query match. 1/ Retrieval rank is the strongest predictor by a wide margin. A page at position 0 in ChatGPT’s web search results (the first URL returned by its search tool) has a 58% citation rate [ https://substack.com/redirect/e5203edb-42ec-4c14-a03b-8890b076cd67?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. By position 10, that drops to 14%. We ran each prompt 3 times consecutively for this analysis, and pages cited in all 3 runs have a median retrieval rank of 2.5. Pages never cited: median rank 13. 2/ Query match (cosine similarity between the query and the page’s best heading) is the strongest content signal. Pages with a 0.90+ heading match have a 41% citation rate compared to the 30% rate for pages below 0.50. Even among top-ranked pages (position 0-2), higher query match adds 19 percentage points. Fanout coverage, word count, heading count, domain authority: all secondary. Some are flat. Some are inversely correlated. 4/ The Wikipedia exception One site type breaks the pattern. Wikipedia has the worst retrieval rank in the dataset (median 24) and the lowest query match score (0.576). It still achieves the highest citation rate: 59%. Wikipedia pages average 4,383 words, 31 lists, and 6.6 tables. They are encyclopedic in the literal sense. ChatGPT cites Wikipedia from deep in the search results where every other site type gets ignored. This is density working as a signal, but at a scale no publisher can replicate. Wikipedia’s content is exhaustive, richly structured, and cross-linked across millions of topics. A 3,000-word corporate blog post with 15 subheadings is not the same thing. 5/ The bimodal reality 58% of pages retrieved by ChatGPT in this dataset are never cited. 25% are always cited when they appear. Only 17% fall in between. The always-cited and never-cited groups look nearly identical on most content metrics: similar word counts (~2,200), similar heading counts (~20), similar readability scores (~12 FK grade), similar domain authority (~54). The on-page signals we can measure do not separate winners from losers. What separates them is retrieval rank. Always-cited pages rank near the top when they surface. Never-cited pages rank in the bottom half. The retrieval system, whatever signals it uses internally, is the gatekeeper. Everything else is a tiebreaker. 6/ What this means for your content Conventional SEO content writing wisdom says cover more subtopics, add more sections, build density. The data says the conventional approach produces “mixed” pages, the 17% in the middle that get cited sometimes and ignored other times. Mixed pages have the highest word counts, the most headings, and the highest domain authority in the dataset. They are the “ultimate guides.” They are also the least reliable performers in ChatGPT. The pages that win consistently are focused. They: Match the query directly in their headings, Tend to be shorter (the citation sweet spot is 500-2,000 words), and have enough structure (7-20 subheadings) to organize the content without diluting it. Build the page that is the best answer to one question. Not the page that adequately answers twenty. Premium subscribers receive 3 more findings: If this density-vs-query match depends on the vertical Common successful patterns for writing headings for AI citation (with 6.8 million H2-H4 headings analyzed), and What you need to know about “Best x” type searches. Unsubscribe https://substack.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.7XC7_iO0beDFeseaOP0g6GDLMPDIySa4auIEPGOVnWs?

View this post on the web at https://www.growth-memo.com/p/how-consumers-navigate-high-stakes AI Mode is compressing the stage where buyers compare, reject, and discover brands on their own. Our new usability study of 185 documented purchase tasks shows that 74% of AI Mode final shortlists came directly from the AI’s output - no external check, no triangulation, no second opinion. This analysis will cover: How the comparison search phase has collapsed What this means for brands competing in categories with high competitor AI Mode saturation The 3 levers that determine whether your brand shows up Premium subscribers receive exclusive screen recordings of real user purchase tasks in AI Mode, providing valuable, detailed insight into user behavior and illustrating the three core findings. These clips can be used to illustrate to stakeholders the importance of securing brand visibility in the AI Mode outputs. The market’s most complete brand visibility data Semrush for Enterprise is built for brands who refuse to be second. It unifies the industry’s largest keyword, backlink, and clickstream datasets into a single connected system. Think 27B keywords, 43T backlinks, 213M prompts, and 500TB of web activity data at your fingertips. Act on shifts first and prove impact with insights from intent to purchase. This is what visibility leadership looks like. Explore Semrush for Enterprise [ https://substack.com/redirect/a8d0d832-9cb3-4e72-b2ad-837e0c2e1283?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Why we conducted the study AI transforms Search from a list of results to a list of recommendations (shortlist). Until now, we have no idea how users treat AI shortlists. Do they take it at face value or thoroughly validate it? That’s why I partnered with Citation Labs and Clickstream Solutions [ https://substack.com/redirect/84636adc-124c-4930-88c3-d510a15f67b1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] to record real users and their interactions when facing high-stakes purchases. This usability study of 48 participants completing 185 major-purchase tasks reveals that AI Mode operates as a recommendation environment, not a comparison one. In traditional search, people click through results, comparing across sources to assemble a candidate set. In AI Mode, they accept the AI’s candidates and move on. 74% of AI Mode shortlists came directly from the AI’s output with no external check. In traditional search, more than half of users built their own shortlist from scratch. The study covers four categories (televisions, laptops, washer/dryer sets, and car insurance). Participants completed tasks using both AI Mode and traditional search in a within-subjects A/B design, producing 149 AI Mode task observations and 36 search observations. The behavioral patterns are consistent enough across categories and participants to carry weight. (Full study design is at the end.) From Garret French [ https://substack.com/redirect/9cc62088-0d53-4727-a126-34bf2ec26523?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], founder of Citation Labs: “In AI Mode, buyers often use a shortlist synthesis to shortcut the cognitive effort of Standard Searching and comparing. This raises the value of onsite decision assets and third-party sources that provide AI with clear trade-offs, specific evidence, and sufficient contextual structure to describe a brand’s offering with confidence.” From Eric Van Buskirk [ https://substack.com/redirect/0ff62208-0e34-4d94-9c92-407898ce3610?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] The absence of narrowness frustration is the most intellectually significant finding. 15% in AI Mode vs 11% in Search, with no meaningful statistical difference. That’s the finding that rules out the obvious alternative explanation: that users accepted the AI’s shortlist because they felt trapped. They didn’t push back. They weren’t frustrated. They were satisfied. That makes the acceptance harder to dismiss. Here’s what happened. 1. 88% of users took the AI’s shortlist outright Across the laptop and insurance tasks, where participants used both search surfaces (classic search and AI Mode), the gap in constructing a product shortlist was stark. Definitions: AI Adopted: The participant took the AI’s recommended candidates as their shortlist with no changes or external verification. User Built: The participant ignored the AI’s (or Search’s) suggestions and assembled their own candidate list from independent sources. AI Verified: The participant started with the AI’s candidates but checked them against an outside source (a retailer site, a review, a manufacturer page) before finalizing. Hybrid: The participant combined AI-suggested candidates with at least one candidate they found independently. In classic search, 56% of participants built their own shortlist from multiple sources. In AI Mode, only 8 out of 147 codeable tasks produced a genuinely self-built shortlist. The user’s comparison process didn’t just shrink when using AI Mode. For most participants, it didn’t happen at all. 64% of AI Mode participants clicked nothing at all during their task. They read the AI’s text, sometimes scrolled through inline product snippets, and declared their finalists. The no-click rate varied by category: Insurance participants delegated most heavily. Washer/dryer participants clicked the most, likely because appliance decisions involve specific physical constraints (capacity, stacking compatibility, dimensions) that the AI summary didn’t always resolve. The 36% who did interact with individual results within AI Mode broke into 2 groups: About 15% of the AI Adopted group (17 of 117 participants) verified inside AI Mode: They opened inline product cards or merchant pop-ups to check a price or spec, then returned to the AI’s list. Others used follow-up prompts as verification tools, asking the AI for prices or narrowing by constraints. A separate 23% of all AI Mode tasks involved at least one visit to an external website, mostly retailers (Best Buy appeared in 10 of 34 tasks with external visits) and manufacturer sites. The destination pattern matters: Users left AI Mode to confirm a candidate they’d already accepted from the AI’s list, not to find new ones. Of the 117 participants who adopted the AI’s shortlist directly, roughly 85% showed no internal verification behavior at all. Participants who built their own lists took an average of 89 seconds longer and consulted more than twice as many sources. “Given that the first paragraph says Lenovo or Apple... going with that,” said one user about laptops when searching via AI Mode. Position one in the AI response was the entire decision. Another AI Mode user remarked: “I liked it more than anything else I’ve ever used for product searching. It made it a lot quicker to find the options.” They experienced speed as a valuable feature, not a shortcut. In classic search, the pattern reversed. Nearly 89% of participants clicked something. One insurance participant clicked out to Progressive and GEICO independently, read both landing pages, consulted an Experian article, and then arrived at a shortlist. A laptop participant applied hardware filters and flagged a review score discrepancy: “It shows 4.6 out of 5 stars for the reviews, but when you actually click the link: not reviewed yet.” Active skepticism of aggregated data was a behavior absent from AI Mode transcripts. 2. The AI’s top pick becomes the user’s top pick 74% of the time Just like in classic search, the top answer carries outsized weight. 74% of participants chose the item ranked first in the AI’s response as their top pick. The mean rank of the final choice was 1.35. Only 10% chose something ranked third or lower. Position one in the AI’s output carries an outsized advantage because of where it sits: inside a curated section that typically contains 2 to 5 items, after the AI has already done the filtering. The first item is the AI’s top pick. When people engage with AI mode, we know they read almost all of the output: The first AI Mode study [ https://substack.com/redirect/ed027d04-d351-4a5f-bcde-a1a7f217afef?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] found users spend 50 to 80 seconds reading AI Mode output, more than double the dwell time on AI Overviews. Users are reading carefully. They just read within a set the AI already narrowed. However, 26% of participants in this study overrode rank order. The driver: brand recognition. They spotted a brand lower on the list and preferred it regardless of where the AI placed it. TV and laptop categories saw this most, where participants arrived with existing preferences for Samsung, LG, Apple, or Lenovo. But overriding rank did not mean rejecting the AI’s output: 81% of rank-override participants still chose from the AI’s candidate set. 3. The AI’s words become the trust signal “Travelers and USAA actually tell me how much, whereas State Farm and GEICO give percentages. Just knowing the exact amount makes me want to pick Travelers or USAA right off the bat.” That quote captures a core pattern in AI Mode trust. The AI’s formatting shaped the decision: Dollar amounts versus percentage discounts determined which brands made the shortlist. AI framing (37%), meaning how AI talks about the product, and brand recognition (34%) were the top 2 trust drivers in AI Mode. They run nearly even: Brand recognition led when participants arrived with brand preferences AI’s wording filled the gaps where participants didn’t already have preferences In classic search, the dominant trust mechanism was multi-source convergence: Participants built confidence by checking whether multiple independent sources agreed about a product. Essentially, users triangulated. One checked Progressive, then GEICO, then an Experian article. Another compared aggregated star ratings against reviews on the actual site. They were building a case from separate inputs. That behavior was almost absent in AI Mode (5%). Instead, AI framing (how the AI worded its description of a product) and brand recognition were the top 2 trust drivers. The split between these 2 signals tracked closely with product category: For televisions and laptops, where most participants arrived with existing brand preferences, brand recognition dominated. For insurance and washer/dryer, where participants had less prior knowledge, AI framing dominated. When you lack a prior view, the AI’s description becomes the trust signal. In AI Mode, the synthesis is the corroboration. Participants treated the AI’s summary as if the cross-checking had already been done for them. The first study [ https://substack.com/redirect/ed027d04-d351-4a5f-bcde-a1a7f217afef?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] showed a related pattern from the supply side: AI Mode matches site type to intent, surfacing brands for transactional queries and review sites for comparisons. This study shows the demand side of the same behavior: When the AI surfaces a brand the user already knows, brand recognition drives the decision; when it doesn’t, the AI’s own framing fills that role. The site-type matching and the trust mechanism reinforce each other. 4. If you’re not in the list, you don’t exist Purchase outcomesBrand outcomes in AI Mode concentrated heavily. For laptops, three brands captured 93% of all AI Mode final choices. In classic search, the distribution was broader: HP EliteBook variants appeared three times, ASUS once, and other brands got consideration they never received in AI Mode. Two distinct problems emerged: Brands that never appeared in the AI’s output were never considered. Participants didn’t see them, so they couldn’t evaluate them. The AI decided who made the list, not the buyer. Brands that did appear but lacked recognition faced a different problem: They weren’t seriously considered. Erie Insurance showed up in AI Mode results, but multiple participants eliminated it on name recognition alone. The brand was present but hadn’t built enough awareness to survive the moment of selection. One participant dropped a brand because it lacked a hyperlink in the AI output, reading that formatting gap as a credibility signal: “There’s not even a link there.” Another participant said when using AI Mode: “I’m already eager to believe these are good recommendations because it mentions LG and Samsung, two brands I consider very reliable.” The AI didn’t say those brands were better. The participant inferred it from familiarity. Participants didn’t feel constrained by the narrower set. Narrowness frustration appeared in 15% of AI Mode tasks and 11% of classic search tasks, statistically indistinguishable. The option set shrank, but the feeling of having enough options didn’t change. The most skeptical AI Mode participant in the comparison set, who complained the AI kept pointing to “teen drivers, teen drivers, teen drivers,” still chose GEICO and Travelers: the consensus AI result. 5. Users leave to buy, not to research 23% of AI Mode tasks involved an external site visit, but keep in mind these prompts reflect high-stakes situations. In standard search, that figure was 67%. The volume difference matters less than the intent difference: AI Mode participants who left went to retailer sites and manufacturer pages to verify a price or spec for a candidate they’d already selected. Standard Search participants left to discover candidates: Reddit for peer opinions, editorial review sites for expert takes, insurance aggregators for comparison. In the first AI Overviews study [ https://substack.com/redirect/74bc0bde-24a4-4e27-8500-2925ca7309a4?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], we found that high risk leads users to verify AI claims more and reference against answers from other users on UGC platforms (like Reddit). In this study, Reddit appeared in 19% of standard search tasks and only twice across all 149 AI Mode sessions. The peer-opinion layer that shapes a large share of traditional Search barely exists in AI Mode behavior. There’s irony in that pattern. Google leans heavily on Reddit content to train its models. However, the source that users rely on most in standard search is the one they almost never visit when the AI synthesizes those same sources for them. The first study found the same pattern at a different scale. Across 250 sessions, clicks were “reserved for transactions:” Shopping prompts drove the highest exit share, while comparison prompts drove the lowest. The exit destinations were retailers and brand sites, not editorial or peer-opinion sources. Six months and a different task set later, the pattern holds: When users leave AI Mode, they leave to buy. 6. 3 levers: Visibility, framing, and pricing data 3 things that excite me most about the study: First, we can apply the mental model of rankings (higher = better) to AI Mode as well. Most users choose the first product. Now, we can apply this to prompt tracking by focusing more on prompts that lead to shortlists and use our position as goalpost. Second, trust trumps rank. We know this since the first user behavior studies I published, but this study reinforces the importance of building trust with users before they search. It’s the ultimate cheat code. Third, we now know buyers trust AI’s recommendations. Obviously, there’s a high risk here if the AI is wrong, but seeing how quickly buyers take the AI’s recommendation also shows us how fast consumers adopt AI. It truly is the future of Search. Keep in mind: 1/ Visibility at the model layer is the new threshold. If AI Mode doesn’t surface your brand, you have a visibility problem at the model layer. Query your own category the way a buyer would (i.e., “best car insurance for a family with a teen driver,” “best washer dryer set under $2,000”) and document which brands appear, in what order, and with what framing. Do this across multiple prompt variations. Do it regularly, because AI responses shift over time. 2/ How the AI describes you matters as much as whether it appears. Brands cited with concrete attributes (specific model, specific price, named use case) held stronger positions than brands described generically. The content on your site that the AI draws from not only affects whether you show up, but also how confidently and specifically you show up. A brand with structured pricing data, clear product specs, and explicit use cases gives the AI better material to work with. 3/ For categories with context-dependent pricing, AI Mode creates a false-confidence problem. 63% of insurance participants were rated overconfident about pricing. They accepted AI-quoted rate estimates without checking whether the figures applied to their actual state, driving record, or current insurer. They made elimination decisions based on numbers that may not have applied to them. Where shopping panels showed explicit retailer-confirmed prices (washer/dryer), 85% of participants understood pricing clearly. Where they didn’t (insurance, laptops), confusion and overconfidence filled the gap. Structured pricing data through Merchant Center feeds and schema markup is the most direct lever for brands selling physical products. For services, the lever is editorial: Make sure your landing pages and FAQ content frame pricing as conditional (”your rate depends on X, Y, Z”) so the AI has that framing to draw from. Study design Citation Labs [ https://substack.com/redirect/e2b2d214-d1df-4a79-984f-1b514ad9aa07?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and Clickstream Solutions [ https://substack.com/redirect/781d6fd3-93c0-4431-8620-499cc599b1d5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] ran this as a remote, unmoderated usability study with 48 U.S.-based participants recruited through Prolific. Each participant completed up to 4 major-purchase shortlisting tasks across televisions, laptops, washer/dryer sets, and car insurance. The comparison between AI Mode and traditional standard search used a within-subjects A/B design: participants used both surfaces, not one or the other. Significance calculations were normalized for the exact number of participants in each group (149 AI Mode task observations, 36 standard search task observations). This matters because the groups are unequal in size, and raw percentage comparisons between them would overstate confidence without that correction. Sessions were screen-recorded with think-aloud audio. Trained analysts annotated each recording for behavioral markers (click-through, shortlist origin, trust signals, external site visits) and qualitative markers (stated reasoning, brand mentions, frustration signals). The 185 task-level observations provide a larger analytical base than the 48-participant headcount suggests, but confidence intervals remain wider than a large-scale survey. Findings are directional, not population-level estimates. Notes on terminology used throughout this report: Shortlist: The final set of brands a user would consider buying from AI Adopted: The participant took the AI’s recommended candidates as their shortlist with no changes or external verification. User Built: The participant ignored the AI’s (or Search’s) suggestions and assembled their own candidate list from independent sources. In Search, when there was no AIO present, they had no option for relying on AI suggestions. AI Verified: The participant started with the AI’s candidates but checked them against an outside source (a retailer site, a review, a manufacturer page, further prompting, or interaction with a panel outside the main AI text block ) before finalizing. Hybrid: The participant combined AI-suggested candidates with at least one candidate they found independently. AI framing: The specific words and structure the AI used to describe a product, such as labels like “best for affordability” or explicit price comparisons. Brand recognition: The user chose or eliminated a brand based on prior familiarity, not the AI’s description or any external research. AI trust (general): The user accepted the AI’s output as credible without citing a specific reason, such as a particular label or description. Source trust: The user trusted a recommendation because of where it came from, such as a retailer, manufacturer, or named publication surfaced in results. Multi-source convergence: The user built confidence by checking whether multiple independent sources agreed on the same recommendation. Rank override rate: The share of users who chose a brand other than the AI’s top-ranked option, regardless of whether they stayed within the AI’s candidate list. Premium: See real user behavior from the study—and take action The challenge? Getting out of our own headspace and into how real people (not SEOs and growth marketers) use these new search surfaces. The data above tells you what happened across 185 tasks, but premium subscribers get access to screen recordings below. Each clip is a real participant thinking aloud as they build a shortlist in AI Mode. We grouped them by the finding they illustrate: 1/ “The AI said it, so I’ll go with it” 2/ “I know that brand.” 3/ “I didn’t question the numbers.” Each of these recordings gives valuable, detailed insight into how users are experiencing brands in the AI Mode outputs, but you can also use them to illustrate to stakeholders the importance of securing brand visibility in the outputs themselves, not just LLM citations. Unsubscribe https://substack.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.US0U6Ojl0GySThWxg1wxb21eX7FaEkHZ-aS7ElvSO5k?

View this post on the web at https://www.growth-memo.com/p/growth-intelligence-brief-16 Welcome to another Growth Intelligence Brief [ https://substack.com/redirect/f83126ce-4fc7-40c0-835e-04e40f37ac3a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], where organic growth leaders discover what matters - getting insights into the bigger picture and guidance on how to stay ahead of the competition. As a free subscriber, you’re getting the first big story. Premium subscribers get the whole brief. Today’s Growth Intelligence Brief went out to 637 (+18) marketing leaders. This week, we’re looking at: New research reveals how much of ChatGPT’s citation surface sits outside standard keyword tracking What Bing’s latest AI Performance update means for the teams trying to close that gap Why Google Personal Intelligence is creating a measurement layer that third parties may never reach I’ll also connect the dots on what this all means for you. 95% of the queries driving AI citations don’t show up in your keyword tools [ https://substack.com/redirect/c7a952d2-698c-4f6f-8eeb-bd0d0f49dfe7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Here’s what happened: AirOps analyzed 548,534 retrieved pages across 15,000 original prompts: The headline finding is that 85% of pages ChatGPT retrieves are never cited in the final answer. The more operationally significant finding is about fan-out: ChatGPT generates 2 or more follow-up searches on 89.6% of queries... and 95% of those fan-out queries had zero monthly search volume by traditional metrics. Zero volume search queries matter. That gap has direct consequences, though. 32.9% of cited pages appeared only in SERPs for a fan-out query, not the original prompt. Brands tracking primary keywords only are missing nearly a third of the citation surface entirely. AirOps found that Google rankings still carry over. Pages ranking #1 in Google were cited 3.5x more often than pages outside the top 20, and 55.8% of all cited pages ranked in the top 20 for at least one query. Pages with 50% or greater title-to-query overlap had a 20.1% citation rate versus 9.3% for pages with less than 10% overlap - a 2.2x lift from alignment alone. Much of their findings track with the analysis I did with Gauge on The science of how AI pays attention [ https://substack.com/redirect/13c2fd57-ad86-4636-90e5-e75119df3117?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Why this news matters: Most AEO / AI SEO strategies are built around the queries teams already track or have tracked for years. And this data says that approach accounts for roughly 2/3 of citation opportunities at best, and misses the follow-up searches that ChatGPT generates while building an answer. Fan-out queries behave differently by intent type. Commercial queries decompose into sub-queries covering pricing, comparisons, alternatives, and features - meaning a brand optimizing only for the head term is absent from the supporting research ChatGPT does before it writes its answer. My take on this: The data shows how important fan-out queries are for visibility in ChatGPT. Where it gets interesting is that about ⅓ of queries would not get caught by classic keyword research because they have zero search volume. For us, this means we need to factor fan-out queries into our research process and then systematically target those keywords. Thinking one step further: how many of those queries really don’t have any searches (always hard to tell without access to GSC, and even then it can be sampled). And how would Google treat domains that have content that has no search demand from humans but from bots? Here’s what to do: Build content for zero volume searches that are crucial to your brand authority Audit your content against the fan-out pattern, not just head terms. For your top 10-20 commercial keywords, manually prompt ChatGPT and document the follow-up searches it generates. Those sub-queries (pricing, comparisons, alternatives, “[brand] vs [competitor]”) are your actual citation surface. If you don’t have content for them, you’re invisible for ~1/3 of citations. Prioritize title-to-query alignment as a discrete optimization lever. AirOps found a 2.2x citation lift from title overlap alone. Review your top pages and tighten title tags to match the phrasing AI models use in their sub-queries, not just what humans type into Google. Don’t abandon traditional SEO to chase AI citations. Pages ranking #1 in Google still get cited 3.5x more than pages outside the top 20. The playbook isn’t “pivot to AI optimization”; it’s “keep winning in Google AND cover the fan-out surface you’ve been ignoring.” Bing just gave brands a map of which queries drive citations to which pages. Google hasn’t... [ https://substack.com/redirect/9016603f-5e74-41d2-ae70-e99a08bdc108?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Unsubscribe https://substack.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.kMcEpnDZvKoEIv-_zku0Qv0vhKDcB3nRxcd_WvMQuBE?

View this post on the web at https://www.growth-memo.com/p/the-science-of-what-ai-actually-rewards This Memo was sent to 25,823 subscribers. Welcome to +224 new readers! Subscribe to get the free memo weekly or upgrade to Premium [ https://substack.com/redirect/3faa047c-a39f-4f35-8a80-b7db6615ded7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. In The science of how AI pays attention [ https://substack.com/redirect/eaabefa5-05c1-489c-906d-46f75a47dbcd?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], I analyzed 1.2 million ChatGPT responses to understand exactly how AI reads a page. In The science of how AI picks its sources [ https://substack.com/redirect/1c237d9a-14f0-4934-9aa2-903420103670?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], I analyzed 98,000 citation rows to understand which pages make it into the reading pool at all. This is Part 3. Where Part 1 told you where on a page AI looks, and Part 2 told you which pages AI routinely considers, this one tells you what AI actually rewards inside the content it reads. The data clarifies: Most AI SEO writing advice doesn’t hold at scale. There is no universal “write like this to get cited” formula - the signals that lift one industry’s citation rates can actively hurt another.. The entity types that predict citation are not the ones being targeted. DATE and NUMBER are universal positives. PRICE suppresses citation in 5 of 6 verticals and KG-verified entities are a negative signal. The one writing signal that holds across all 7 verticals: Declarative language in your intro, +14% aggregate lift. Heading structure is binary. Commit to the right number for your vertical or use none. 3-4 headings is worse than zero in every vertical. Corporate content dominates. Reddit doesn’t. AI citation behavior does not mirror what happened to organic search in 2023-2024. Premium subscribers get a SEO content audit tool that scores any page against all 3 parts of this series and generates a custom rewrite prompt. LinkedIn is an AI citation machine. Take advantage of it. LinkedIn ranks #2 for AI citations across ChatGPT Search, Perplexity, and Google AI Mode, appearing in 11% of AI responses. With semantic similarity scores of 0.57–0.60, AI models are closely mirroring your LinkedIn content in their answers. Are you taking advantage? Enterprise AI Optimization lets you track and steer how your brand shows up in AI search, across your markets and domains. See what Semrush sees and stay ahead. Read the full research [ https://substack.com/redirect/68403493-127b-48b4-a61e-f61d1c4bda50?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 1/ Specific writing signals influence citation, while others harm it. While The science of how AI pays attention [ https://substack.com/redirect/eaabefa5-05c1-489c-906d-46f75a47dbcd?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] covers parts of the page and types of writing that influences ChatGPT visibility, I wanted to understand which writing-level signals - word count, structure, language style - predict higher AI citation rates across verticals. Approach I compared high-cited pages (3+ unique prompt citations) vs low-cited across 7 writing metrics: word count, definitive language, hedging, list items, named entity density, and intro-specific signals. I analyzed the first 1000 words for list item count, named entity density, intro definitive language token density, and intro number count. Results: Across all verticals, definitive phrasing and including relevant entities matter. But most signals are flat. What the industry patterns showed: When splitting the data up by vertical, we suddenly see preferences: Total word count was strongest in CRM/SaaS (1.59x). Finance was an anomaly with word count: Shorter pages win (0.86x word count). Definitive phrases in the first 1K characters was positive for most verticals. Education is a signal void. Writing style explains almost nothing about citation likelihood there. Top takeaways: 1/ There is no universal ‘write like this to get cited’ formula. For example, the signals that lift CRM/SaaS citation rates actively hurt Finance. Instead, match content format to vertical norms. 2/ The one universal rule: open with a direct declarative statement. Not a question, not context-setting, not preamble. The form is “[X] is [Y]” or “[X] does [Z].” This is the only writing instruction that holds regardless of vertical, content type, or length. 3/ LLMs “penalize” hedging in your intro. “This may help teams understand” performs worse than “Teams that do X see Y.” Remove qualifiers from your opening paragraph before any other optimization. 2/ The entity types that predict citation are not the ones being targeted. Most AEO advice focuses on named entities as a category: pack in more known brand names, tool names, numbers. The cross-vertical entity type analysis below tells a more specific (and more useful) story. Approach Ran Google’s Natural Language API on the first 1,000 characters (about 200-250 words) of each unique URL. Computed lift per entity type: % of high-cited pages with that type / % of low-cited pages. Analyzed 5,000 pages across 7 verticals. * A quick note on terminology: Google NLP classifies software products, apps, and SaaS tools as CONSUMER_GOOD, a legacy label from when the API was built for physical retail. Throughout this analysis, CONSUMER_GOOD means software/product entities. Results: DATE and NUMBER are the most universal positive signals. Interestingly, PRICE is the strongest universal negative. What the industry patterns showed: DATE is the most universal positive signal, with the exception of Finance (0.65x). NUMBER is the second most universal. Specific counts, metrics, and statistics in the intro consistently predict higher citation rates. Finance (0.98x) and Product Analytics (1.10x) mark the floor and ceiling of that range. PRICE is the strongest universal negative. Pages that open with pricing signal commercial intent. Finance is the sole exception at 1.16x, likely because price here means fee percentages and rate comparisons, which are the actual reference data financial queries are looking for. CONSUMER_GOOD (software/product entities) is mixed. In Healthcare, product entities signal established brands and tools. In Crypto, naming specific protocols and products is core to answering technical queries. PHONE_NUMBER is a positive signal in Healthcare (1.41x) and Education (1.40x). In both cases, it is almost certainly a proxy for established brands/institutions/providers with real physical presence, not a literal signal to add phone numbers to your pages. The Knowledge Graph inversion deserves its own note here: The data showed that high-cited pages average 1.42 KG-verified entities vs. 1.75 for low-cited pages (lift: 0.81x). Pages built around well-known, KG-verified entities (major brands, institutions, famous people) tend toward generic coverage, which isn’t preferred by ChatGPT. High-cited pages are dense with specific, niche entities: a particular methodology, a precise statistic, a named comparison. Many of those niche entities have no KG entries at all. That specificity is what AI reaches for. Top takeaways 1/ Add the publish date to your pages and aim to use at least one specific number in your content. That combination is the closest thing to a universal AI citation signal this dataset produced. But Finance gets there through price data and location specificity instead. 2/ Avoid opening with pricing in non-Finance verticals. Price-dominant intros correlate with lower citation rates. 3/ KG presence and brand authority do not translate to AI citation advantage. Chasing Wikipedia entries, brand panels, or KG verification is the wrong lever. Specific, niche entities (even ones without KG entries) outperform famous ones. 3/ Heading structure: Commit to one or don’t bother. We know headings matter for citations from the previous 2 analyses. Next, I wanted to understand whether heading count predicts citation rates and whether the optimal structure varies by vertical. Approach Counted total headings per page (H1+H2+H3) across all cited URLs. Grouped pages into 7 heading-count buckets: 0, 1-2, 3-4, 5-9, 10-19, 20-49, 50+. Computed high-cited rate (% of URLs that are high-cited) per bucket per vertical. Results: Including more headings in your content is not universally better. The sweet spot depends on vertical and content type. One finding holds everywhere: Strangely, 3-4 headings are worse than zero. What the industry patterns showed: CRM/SaaS is the only vertical where the 20+ heading lift is confirmed: 12.7% high-cited rate at 20-49 headings vs. a 5.9% baseline. The 50+ bucket reaches 18.2%. Long structured reference pages and comparison guides with one section per tool outperform everything else here. Healthcare inverts most sharply. The high-cited rate drops from 15.1% at zero headings to 2.5% at 20-49 headings. A page with 30 H2s on telehealth topics signals optimization intent, not clinical authority. Finance peaks at 10-19 headings (29.4% high-cited rate). Structured but not exhaustive: think rate tables, regulatory breakdowns, and advisor comparison pages with moderate heading depth. Crypto peaks at 5-9 headings (34.7% high-cited rate). Technical documentation in this vertical tends toward dense prose with moderate navigation structure. Over-structuring breaks up the technical depth. Education is flat across all heading counts, which is consistent with the writing signals finding. Heading structure explains almost nothing about citation likelihood in education content. The 3-4 heading dead zone holds across every vertical without exception. Partial structure confuses AI navigation without providing the full benefit of a committed hierarchy. Top takeaways: 1/ The 20+ heading finding from Part 1 is a CRM/SaaS finding, not a universal one. Applying it to Healthcare, Education, or Finance could actively suppress citation rates in those verticals. 2/ The principle that holds everywhere: Commit to structure or don’t use it. The middle ground costs you in every vertical. A fully-structured page with the right heading depth outperforms a half-structured page in every vertical. 3/ Use the optimal heading range for your vertical. Crypto: 5-9. Finance and Education: 10-19. CRM/SaaS: 20+ (with H3s). Healthcare: 0 or 5-9 at most. Long CRM reference pages with 50+ sections are the one case where maximum heading depth pays off. 4/ UGC doesn’t dominate The “Reddit effect” reshaped organic search between 2024 and 2025. I wanted to understand whether ChatGPT cites user-generated content (Reddit, forums, reviews) at meaningful rates or whether corporate/editorial content dominates. The common industry assumption - that AI also preferentially cites community voices - is not what we found in the data. Approach Classified these cited URLs as (1) UGC: Reddit, Quora, Stack Overflow, forum subdomains, Medium, Substack, Product Hunt, Tumblr, or (2) community/forum prefixes or corporate/editorial by domain. Computed citation share per category per vertical. Dataset: 98,217 citations across 7 verticals. Results: Corporate content accounts for 94.7% of all citations. UGC is nearly invisible. What the industry patterns showed: Finance is the most corporate-locked vertical at 0.5% UGC. YMYL (Your Money, Your Life) content appears to systematically suppress citations to community opinion. Healthcare sits at 1.8% UGC for the same structural reason. Clinical, telehealth, and HIPAA content draws almost exclusively from institutional sources. Crypto has the highest UGC penetration in the dataset at 9.2%. Community-generated content (Reddit technical threads, Medium tutorials, developer forum posts) answers a meaningful proportion of analyzed queries. In a fast-moving technical niche where official documentation consistently lags, community posts fill the gap. Product Analytics and HR Tech sit at 6.9% and 5.8% UGC. Both are verticals where Reddit comparison threads and product review communities provide genuine signal alongside corporate content. Top takeaways: 1/ The “Reddit effect” in SEO has not translated proportionally to AI citations. In most verticals, reddit.com captures 2-5% of total citations. This finding is in-line with other industry research, including this report from Profound [ https://substack.com/redirect/5f247440-f4a5-43d6-bb07-77bd8403018d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. 2/ For Finance and Healthcare: UGC has near-zero AI citation value. Invest in structured, authoritative corporate content with clear sourcing. Community engagement may matter for other reasons, but it does not contribute meaningfully to AI citation share in these verticals. 3/ For Crypto, Product Analytics, and HR Tech: Community presence has measurable citation value. Detailed Reddit comparison threads, technical Medium posts, and structured developer forum answers can supplement corporate content reach. What this means for how you strategize for LLM visibility Across all 3 parts of this study, the consistent finding is that AI citation is not primarily a writing quality problem. Part 2 showed it is a content architecture problem: Thin single-intent pages are structurally locked out regardless of how well they’re written. This piece shows the same logic applies inside the content itself. The aggregate writing signals table is the most important chart in this analysis. Not because it shows you what to do, but because it shows how much of what the AI SEO/GEO/AEO industry is telling you doesn’t survive cross-vertical scrutiny. Word count, list density, named entity counts… all flat or negative at the aggregate. The signals that work are vertical-specific and smaller than our industry’s consensus implies. The meta-lesson from this analysis is that findings are vertical (and probably topic) specific, which is no different in SEO. This part concludes the Science of AI - for now. Because the AI ecosystem is constantly changing. Methodology We analyzed ~98,000 ChatGPT citation rows pulled from approximately 1.2 million ChatGPT responses from Gauge. Gauge is extending a one-time 75% discount for Growth Memo subscribers to help them grow their AI presence. Mention Growth Memo during a live demo [ https://substack.com/redirect/f5767edb-95eb-4f54-bc1e-0f253c309e06?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] or use GROWTHMEMO at checkout to redeem. Because AI behaves differently depending on the topic, we isolated the data across 7 distinct, verified verticals to ensure the findings weren’t skewed by one specific industry. Analyzed verticals: B2B SaaS Finance Healthcare Education Crypto HR Tech Product Analytics For premium subscribers: Your content audit tool is below Premium subscribers get an easy-to-use tool to check their actual pages against all 3 parts of this series: paste a URL and get a scored audit against the: Ski ramp position data Writing signal benchmarks Entity type signals, and Heading structure norms for your vertical Plus, once the URL is audited, you’ll get a ready-to-use prompt to take into Claude to rewrite the page based on those findings... Unsubscribe https://substack.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.Nsqb4RyDXVzHu2X2adSZBwh0ffE0bu8Nq1KcHHexTcQ?

View this post on the web at https://www.growth-memo.com/p/the-science-of-how-ai-picks-its-sources This Memo was sent to 25,523 subscribers. Welcome to +148 new readers! Subscribe to get the free memo weekly or upgrade to Premium for the full archive, research, frameworks, and templates. In The science of how AI pays attention [ https://substack.com/redirect/b889fe27-5038-48dc-848f-0bca66f1fb5d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], I analyzed 1.2 million ChatGPT responses to understand exactly how AI reads a page. This is Part 2. Where Part 1 told you where on a page AI looks, this one tells you which pages AI routinely considers. The data clarifies: Why ~30 domains own 67% of citations in any topic The page structure that earns citations across 50+ distinct queries vs. the one that gets cited once Whether the ski ramp from Part 1 is actually steeper or flatter in your vertical Premium subscribers get a checklist to integrate the study results into their workflows. Among pages ranking #1 in Google, 43.2% were cited by ChatGPT. That was 3.5 times higher than the citation rate for pages ranking beyond Google’s top 20 results. ChatGPT retrieves about 6x more pages than it cites. In research across 548,534 retrieved pages and 15,000 prompts, AirOps found: 85% of pages ChatGPT retrieved were never cited. ⅓ of cited pages came from fan-out queries, and 95% of those had zero search volume. Among pages ranking #1 in Google, 43.2% were cited by ChatGPT. That was 3.5x higher than the citation rate for pages ranking beyond Google’s top 20 results. Ranking well helps, but it doesn’t guarantee citations. AirOps surfaces these fan-out queries so teams can see the full search path ChatGPT uses to build an answer and act on it. Read the full report [ https://substack.com/redirect/1cb0cba7-63be-4b0e-a0ca-28a52787880d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 1/ ~30 domains own 67% of AI citations per topic. Classic search is a winner-takes-all game [ https://substack.com/redirect/95aab7ce-f6b0-4707-a787-6bd649465f0c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. The top result gets disproportionately more clicks than the second. Is that also true for ChatGPT answers? Is the distribution of cited domains democratic or totalitarian? Approach: Compute the citation share per domain per vertical Calculate the cumulative share captured by the top 10% of domains Dataset: 21,482 ChatGPT citation rows, 670 unique domains, 2,344 unique URLs, 127 unique prompts Results: The top 10 domains take 46% of all citations in a topic. The top 30 take 67%. AI citation is slightly less concentrated than traditional organic search, but still extreme: Effectively, there are ~30 seats (domains) at the citation table for any given topic. Everything else is nearly invisible. Example: storylane.io appears as a cited source across 102 distinct prompts (unique questions asked of ChatGPT), reprise.com across 98. Even though reprise.com has more total citations (1,369 vs. storylane.io’s 968), storylane.io shows up in answers to a broader range of different questions. We confirmed these findings in product-comparison verticals (SaaS tools, financial advisors). However, you’ll see below that the pattern is weaker in healthcare and open web topics, where no single domain dominates. Notably, the education sector receives the most AI citations of any vertical we studied. What the industry patterns showed: The findings above are from product comparison verticals (SaaS, financial advisors), but the pattern is weaker in healthcare and open web topics, where no single domain dominates, and stronger in the education sector. Education is winner-take-most: the top 10% of domains capture 59.5% of all citations. If you are not already in the top 5-10 domains in education, achieving citation breadth is exceptionally hard [ https://substack.com/redirect/58eb16d4-e9c6-48cf-8be1-ff0ccb0d888c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. tefl.org alone answers 102 unique prompts and holds 18.75% of all Education citations. The next three domains (internationalteflacademy.com 7.83%, gooverseas.com 5.87%, reddit.com 5.22%) leave the top 3 controlling about 32% of all citations. Crypto is the second most concentrated at 43.0% for the top 10%. A small set of technical documentation and comparison sites (alchemy.com, quicknode.com, chainstack.com) dominate Solana RPC and infrastructure queries. The technical nature of Solana queries means few credible sources exist; once a domain earns trust in this niche, it captures a large share. Finance sits at 29.4% for top-10%. Concentration is query-type specific: Financial advisor locator pages (forfiduciary.com at 139 unique prompts, smartasset.com at 168 unique prompts) dominate city-level advisor queries. But the long tail of financial product queries keeps total concentration moderate. Healthcare is the least concentrated at 13.0% for the top 10%. No single domain dominates. New entrants have a realistic path to citation reach. The citation surface is spread across hundreds of domains, each covering a small slice of telehealth, HIPAA compliance, and healthcare app queries. CRM/SaaS and HR Tech are similarly diffuse (16.1% and 14.4% top-10%). These are multi-product software categories where dozens of comparison sites, review platforms, and vendor pages split citations. monday.com leads CRM with only 2.88% of all citations (37 unique prompts). A genuinely open competitive field Top takeaways: 1/ Breadth of topic coverage matters more than domain authority. A single well-structured comparison page (learn.g2.com: 65 unique prompts, 495 citations) can still outperform the entire domain portfolio of a well-known brand. The goal is not to rank for one query, but to answer a cluster. 2/ Concentration reflects category maturity. Fragmentation is an opportunity. Education and Crypto have narrow, well-defined query spaces where a few authoritative sources have locked in trust. Healthcare and CRM are broad, fragmented categories where no single domain dominates. That fragmentation is your opening. 3/ Citation reach (the number of distinct prompts a domain answers) is a more useful strategic metric than raw citation count. In low-concentration verticals like Healthcare and CRM, a focused 30-50 page strategy [ https://substack.com/redirect/6c5d3ddb-f558-4b14-ab44-3dfc6d1ee998?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] can realistically compete for a seat at the table. In high-concentration verticals like Education and Crypto, the path is narrower: become the definitive resource on a specific sub-topic or accept that you’re fighting for scraps. 2/ The citation advantage starts at 10,000 words. In classic Search, word count and page length are somewhat indicative of ranks, as long as the quality is high. I wondered, again, if that is also true for showing up in ChatGPT answers? Approach: Measure raw text length of every cited page Group length into 7 buckets For each bucket, calculate average citations per page Results: More words do indeed correlate with more citations, but there’s a ceiling. The 5K-to-10K jump is the largest single step - nearly 2x. Pages above 20,000 characters average 10.18 citations each vs. 2.39 for pages under 500 characters. What the industry patterns showed: The length effect is vertical-specific: Finance inverts it entirely. High-cited Finance pages average 1,783 words vs. 2,084 for low-cited pages - a 0.86x lift. Authoritative compact sources, rate tables, and regulatory summaries outperform comprehensive guides there. The 10,000-character rule holds for SaaS and editorial content. Finance peaks at 5K-10K words (10.9 citations/page), then drops sharply at 10K-20K (4.92 citations/page). Finance also shows the steepest absolute gain: Pages under 500 words earn only 3.84 citations/page while 5K-10K pages earn 10.9, which is a 2.8x multiplier from length optimization alone. Very long Finance pages may dilute the citation-triggering content with redundant detail. Education shows the clearest length-wins-everything pattern. Citations per page climb steadily from 1.85 (under 500 words) to 6.05 (20K+ words) with no drop-off. Crypto and Product Analytics behave similarly to Education. Length consistently pays off, plateauing around the 10K-20K tier (5.34 and 4.01, respectively). Both are technical verticals where comprehensiveness signals authority. SaaS shows the weakest length effect: Citations per page ranges from 1.06 (1K-2K words) to 2.77 (20K+ words). Even the longest CRM pages only get 2.77 citations per page on average. In this vertical, length alone does not determine citations. Format, structure, and domain authority appear more important. Healthcare shows a moderate length effect (1.74 to 3.92 citations/page). But with one anomaly: 5K-10K words (2.80) underperforms vs. 2K-5K words (3.36). Very long Healthcare pages may include too much clinical detail that dilutes citation-triggering content. Top takeaways: 1/ Universal finding: Very short pages (under 1K words) underperform in every vertical. The underperformance of thin content is consistent, but the reward for long content is vertical-specific. 2/ Target your length based on industry, content type, and query intent, not a universal word count. For Finance verticals: Aim for 5K-10K words. Education, Crypto, and Product Analytics: Go as long as possible. CRM/SaaS: Prioritize structure over word count. 58% of cited URLs are cited once. When we look at the citations within a topic, we often see many pages on a domain getting cited. So, how many citations can a single page get? Approach: Count the number of unique prompts for each page Classify number of citations into: 1, 2-5, 6-10, 11+ Inspect the top URLs per vertical for structural patterns Results: On average, 67% of cited URLs appear in only one prompt. Think of it like a footprint game. Raw citation count tells you how popular a page is. Citation breadth tells you how strategically valuable it is. An evergreen page in AI citation is not one that gets cited a lot; it is one that keeps appearing across diverse queries. The top 4.8% of URLs (cited 10+) are all category-level comparisons or guides answering “what is it,” “who uses it,” “how to choose,” and “pricing” in a single URL. What the industry patterns showed: The citation pool isn’t a meritocracy of the best answer, but the degree varies sharply. CRM/SaaS has the highest one-hit rate at 84.7%. Finance produces the highest-reach evergreen pages: forfiduciary.com covers 119 unique prompts. Crypto generates the most concentrated evergreen pages at 55.4% in the technical tier: chainstack.com/best-solana-rpc-providers-in-2026 (63 prompts), alchemy.com/overviews/solana-rpc (62 prompts), and rpcfast.com/blog/rpc-node-providers (61 prompts). All three are comparison pages covering the Solana RPC provider landscape from slightly different angles. Education evergreen pages follow a different logic: tefl.org, internationalteflacademy.com, and gooverseas.com get cited broadly because they answer TEFL-adjacent queries (cost, location, certification type) from a single resource. One URL serves many query angles. Top takeaways: 1/ Evergreen pages share consistent structural patterns: Category-level guide format (best X for 2025/2026), broad topic coverage within a single page (what is X, how to choose X, top X vendors, pricing), and explicit year anchoring in URL or title. Pages that answer a class of questions earn citation breadth. 2/ The top 5 evergreen pages in every vertical are either comparison roundups, authoritative guides, or directory/listing pages. No thin single-topic page reaches the 11+ prompt tier in any vertical. 3/ A single evergreen page covering 10+ query intents is worth more in AI citation reach than 10 single-intent pages. The ROI of comprehensive content is front-loaded: one well-built page compounds citation reach over time. The long tail exists, but the top 5% of pages capture a disproportionate share of ongoing citation activity. 4/ The ski ramp is steeper in some verticals. The science of how AI pays attention [ https://substack.com/redirect/b889fe27-5038-48dc-848f-0bca66f1fb5d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] showed that ChatGPT cites 44.2% from the top 30% of any page. Does that trend hold across different verticals? Approach: Re-run the same positional analysis across 7 verticals with 42,460 matched citations. Results: The trend is real but varies by topic. One number holds everywhere: The bottom 10% of any page earns 2.4-4.4% of citations, roughly a quarter of what the peak band earns. The conclusion section is nearly invisible to AI, regardless of vertical. What the industry patterns showed: The true peak decile across all verticals is not the very opening. The 10-20% band is where AI reads hardest in every vertical. The first 10% is typically navigation, headlines, and intro fluff that AI skips. Finance is the extreme case. 43.7% of citations land in the first 30% of the page. Finance pages front-load rate data, percentages, and key figures. AI grabs them and rarely reads past the halfway point. Healthcare and HR Tech have the flattest ramps. Useful content is distributed more evenly across those pages. Education peaks at the 30-40% decile rather than 10-20%, because educational content tends to bury the key answer slightly deeper after the intro. Top takeaways: 1/ Put your most citable claims and data in the first 30% of the page - no matter what industry you’re in. Summaries and conclusions rarely get cited. 2/ For Finance brands: Front-load your thesis and statistics as much as possible. What this means for how you build LLM visibility The domains that own citation share didn’t get there by writing better sentences. They built pages that hold true topical authority [ https://substack.com/redirect/d2545207-3fb6-4f08-999e-79c7e697cddb?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], addressing multiple queries in one place, and then repeated that authority across enough sub-topics to hold multiple seats at the table. Getting cited across 30, 60, or 100 distinct prompts [ https://substack.com/redirect/585156b9-ddc6-4b18-b0d5-b4497ca0aed6?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] requires a targeted content architecture [ https://substack.com/redirect/b25a5da2-a99a-4877-a152-bdb2dfaa6ecb?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]: pages built around query clusters and owning entire topics rather than individual keywords. Teams that keep the traditional “one keyword, one page” model will be structurally locked out of AI citation, even if their individual pages are beautifully written. But as the data shows, there is no universal playbook. The tactics that work for a broad CRM platform could actively harm a Finance brand. Methodology: We analyzed ~98,000 ChatGPT citation rows pulled from approximately 1.2 million ChatGPT responses from Gauge [ https://substack.com/redirect/f9d51425-80ca-4bba-a6d9-dbdea93c7894?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Gauge is extending a one-time 75% discount for Growth Memo subscribers to help them grow their AI presence. Mention Growth Memo during a live demo [ https://substack.com/redirect/66c952a0-910a-4e89-ad17-72495e66e41f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] or use GROWTHMEMO at checkout to redeem. Because AI behaves differently depending on the topic, we isolated the data across 7 distinct, verified verticals to ensure the findings weren’t skewed by one specific industry. Analyzed verticals: B2B SaaS Finance Healthcare Education Crypto HR Tech Product Analytics To reverse-engineer the citation selection, I ran the data through several layers of analysis: Structural parsing: I measured the raw character length of every cited page and mapped heading hierarchies (H1s, H2s, H3s) to see how information architecture impacts visibility. Positional mapping: I used Jaccard sliding-window similarity to pinpoint exactly where on the page the AI extracted its answers from, down to the specific decile. Entity & Sentiment extraction: I ran the opening text of unique cited URLs through the Google Natural Language API to classify named entities (dates, prices, products) and used TextBlob to score sentiment, comparing the performance of corporate content against user-generated content (UGC). For premium subscribers: Your citation audit is below. Most teams will see the 30-domain concentration figure, recognize they’re not in it, and move on without changing how they build content. Single-intent pages will keep shipping. Topic coverage gaps will stay unaddressed. Premium subscribers get the tool to help change that: A citation-readiness checklist that scores your current content setup against the 4 signals from this analysis - domain footprint, page length by vertical, evergreen URL structure, and positional front-loading. ... Unsubscribe https://substack.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.T52YhXN4ruOePccqMHPfH79tRWSiRkv6gC5kPWcDFgE?

View this post on the web at https://www.growth-memo.com/p/the-brand-tax-how-google-profits This Memo was sent to 25,371 subscribers. Welcome to +199 new readers! Get the free memo weekly. Upgrade to Premium [ https://substack.com/redirect/422c6c75-0479-407f-92a4-42d00e5d93a8?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. Branded search inflates your return on ad spend (ROAS) by taking credit for demand you already own, and every input in the paid acquisition model is getting worse simultaneously. This week, you’ll cover: 1/ The math on how branded search distorts performance reporting across the industry 2/ Why AI-driven discovery will expose this distortion faster than any audit 3/ A concrete framework for separating real acquisition from expensive demand capture. Premium subscribers [ https://substack.com/redirect/422c6c75-0479-407f-92a4-42d00e5d93a8?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] also get the Brand Tax Calculator, an interactive tool that separates your real acquisition return from branded search inflation using your own spend data, along with 3 simple tests to run before your next budget review. Be first everywhere customers search with Semrush for Enterprise. Billions of data points across demand, authority, search, and customer journeys. Content optimization built for maxing out your SEO and AI search performance. And the infrastructure to scale it all. The most comprehensive brand visibility platform gets more powerful. Explore Semrush for Enterprise [ https://substack.com/redirect/1f0e7cb8-c08b-45f3-ac6a-4cd4819ab0d3?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] The economics of performance marketing are deteriorating, but the metric most teams use to justify their budget is hiding the problem. Contentsquare’s 2026 analysis of 99 billion sessions [ https://substack.com/redirect/34be8ab2-7fbb-45a7-99bc-67df732b10d9?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] shows every paid acquisition input degrading simultaneously. Yet, while ad costs rose 30% and conversion rates fell, Google’s Q4 search revenue still grew 17% [ https://substack.com/redirect/b048ad60-d0c2-457e-a40c-9f2ad04e9e1f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. The data points to three hidden traps in how we measure performance. More importantly, it highlights why the financial case for AI SEO gets stronger with every dollar wasted on paid clicks that bounce 1/ Last year, ad costs rose 30%. Conversion rates fell 5%. The visitors who convert best are the ones who already know you... and the visitors you pay the most to acquire are the ones most likely to leave. Contentsquare measured the full acquisition funnel across 9 industries, and the picture is consistent: More money in, less value out. (I touched on this study briefly for premium subscribers in this Growth Intelligence Brief [ https://substack.com/redirect/49bd7755-1ee3-4710-88b1-46cab14c6685?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]). Cost per visit climbed 9.4% in 2025 alone, adding to a 30% cumulative increase over 3 years. Conversion rates fell 5.1%. But their analysis showed paid search bounces at 59% and paid social bounces at 65%, while organic visits have a bounce rate of about 42%. Channel-level conversion rates are brutal: 2% for paid search, 1.6% for display, 0.4% for paid social, and 1.8% for organic search. Those bounce rates mean more than half of every paid search dollar produces a visitor who leaves without seeing a second page. Paid social is worse. Every input in the acquisition model is degrading… all at the same time. Gallant Chen [ https://substack.com/redirect/e76256c5-e436-4d3c-917d-e8ee23a86d7a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], growth advisor to companies like Shopify, DocuSign, New Relic and others: My client’s results were similar. Typically, sometime in 1st half of 2025, most of my clients saw a decrease in overall paid search traffic (brand and non-brand) combined with corresponding increases in CPCs (e.g., 20% drop in Paid Search clicks, but 20% increase in CPCs). Basically, this was Google rolling out AI Overviews and, in doing so, ensuring they retained steady state revenue. AI Overviews decreased clicks. But the advertisers that still got clicks ended up paying more per click. So net, net, Google did not have to sacrifice revenue to go all in on AI Overviews. I predict Google’s AI Overviews and AI Mode will continue to accelerate this. Google shows AI-generated answers on roughly 16% of search results in Q4 of 2025, according to Semrush data [ https://substack.com/redirect/493bf215-93bb-40a0-b124-a2e28da15ccb?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], and that number is climbing. Shrinking click inventory does not necessarily shrink demand for ads, of course - but it does concentrate bidding onto fewer clicks, which drives cost per click higher. One Contentsquare finding sharpens the problem: Repeat visitors - the 13% who return [ https://substack.com/redirect/763f2b9b-4f01-4469-9431-6e5967ea714a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] within 30 days - account for the majority of conversions on many sites. AI-referred traffic, still just 0.2% of total visits when you look at the whole picture, bounces less and converts closer to organic rates. 2/ That means you’re likely taxing your own demand. If every acquisition input is getting worse, why do most dashboards still show paid search as the top-performing channel? Because branded search is doing the heavy lifting, and branded search is not acquisition… it’s demand capture. Dreamdata’s analysis of B2B Google Ads accounts [ https://substack.com/redirect/bb32ca7d-7bdd-497b-a9d3-8bd81e0c3c1e?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] found that 18% of search ad budget - an estimated $47 billion - goes to branded keywords. Branded campaigns returned 1,299% ROAS versus 68% for non-branded. That gap looks like a success story until you test whether the ad caused the sale. In 2024, Rand Fishkin explained the attribution mechanism [ https://substack.com/redirect/f2926400-1378-453b-b24a-29cc5609f02b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] that makes this invisible: When people hear about a brand through social, podcasts, or word of mouth, they go to Google and search the brand name. Google gets attribution credit for the conversion. CFOs look at analytics and see that the best traffic comes from Google, which reinforces the investment in Google Ads. The more a company invests in brand-building elsewhere, the better branded search numbers look, which makes Google look like the best channel… which leads to more Google spend. Google’s collecting the toll on conversions it had nothing to do with, and if you’re not careful about measurement, this can distort what’s actually going on. In catching up with Rex Gelb [ https://substack.com/redirect/029fd0ad-cd86-4bc4-8d70-758409e33ec1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], Founder & CEO at Summit Chase [ https://substack.com/redirect/fb2c3699-861b-42bf-8fa6-563cfc5e6e4a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and Head of Paid Media at Cursor, he mentioned: Branded search is one of the most misunderstood metrics in performance marketing. High ROAS on brand campaigns usually reflects demand that your marketing efforts already created elsewhere. That doesn’t mean branded search is useless - it often protects conversion paths and captures high-intent traffic. The real mistake is reporting blended ROAS without separating brand and non-brand. Once you split them, the economics of acquisition become much clearer. Gallant Chen seconds that notion: My preferred approach is for teams to think about Brand Paid Search as an “opex” item akin to other G&A elements that, unfortunately, you must invest in to run your business. Brand Paid Search does not drive incremental revenue. Focus on NonBrand, which does drive incremental revenue. 3/ Branded spend defends 70% of search - and ignores the rest. The brand tax would be easier to justify if Google were the only place people search… but we know it’s not. Branded keyword defense does nothing on Amazon, YouTube, Reddit, or any AI surface. SparkToro and Datos published new research [ https://substack.com/redirect/7c2cd024-7fee-4b7a-add2-adf9d0adee6d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] this month analyzing desktop search behavior across 41 domains: Roughly 80% of searches happen on traditional search engines (Google was found responsible for 73.7% of all desktop searches). Commerce sites account for 10% (like Amazon and eBay), social 5.5% (TikTok, Youtube), AI tools 3% (ChatGPT, Claude). Brands are paying to defend their name on a platform that represents 70% of search, all while it’s actively shrinking (albeit slowly)… and user discovery is shifting to surfaces where the brand tax does not apply: The one I’m most excited about is invisible — it’s the 34 sites outside the top 7 growing their share of search — one of the only areas of web behavior we’ve investigated in the last decade(?!) where the biggest sites aren’t getting more dominant with time. Fingers crossed this trend continues. A brand that spends 90% of its paid budget on Google is optimizing for one platform in a search economy that now spans 41 and counting - 34 smaller sites outside the top 7 are the fastest-growing segment of search. That’s risky. The math does not hold when you account for where people actually look for products, answers, and recommendations. 4/ Increased ad costs and high bounce rates make the case for AI SEO. If influence is more valuable than traffic - and it is, although harder to measure - brands should build presence on the platforms where their audience already spends time rather than (over)paying to pull them through a branded click. Contentsquare’s 2026 retention data supports this: Repeat visitors who return within 30 days convert at multiples higher than first-touch paid visitors. AI-referred visitors, arriving with clearer intent from upstream AI conversations, bounce less and convert closer to organic rates. The pattern is consistent: Brand familiarity built before the click can produce better economics than paid acquisition at the click. And this is one of the biggest financial cases for AI SEO, even when the ROI of LLM visibility is hard to quantify today. If more than half of every paid search dollar produces a bounce - and it’s likely AI Overviews will push that number higher - then investing in brand visibility and trust inside AI answers makes financial sense for many brands. The comparison is not “AI SEO versus proven ROI.” The comparison is “AI SEO versus a high bounce rate that is getting worse.” A channel that builds brand recognition upstream and balances your dependency on paid demand capture does not need to prove attribution the same way a direct-response campaign does. It needs to prove that branded search spend went down while total revenue held. And that’s a test you can run. 5/ Premium: Brand Tax Calculator and 3 tests to run before your next budget review Your dashboard reports a 5.9x return. Your true acquisition ROAS might be 7x, or it might be 1.2x. The difference is the brand tax: the capital you spend capturing demand you already own. The Brand Tax Calculator lets you plug in your paid search spend, branded keyword share, and reported ROAS to see the gap in dollar terms, including how much revenue your paid campaigns are cannibalizing from organic. Unsubscribe https://substack.com/redirect/2/eyJlIjoiaHR0cHM6Ly93d3cuZ3Jvd3RoLW1lbW8uY29tL2FjdGlvbi9kaXNhYmxlX2VtYWlsP3Rva2VuPWV5SjFjMlZ5WDJsa0lqbzBNalkxTVRVd05EQXNJbkJ2YzNSZmFXUWlPakU1TVRFNU56TTJOeXdpYVdGMElqb3hOemN6TnpBME56a3pMQ0psZUhBaU9qRTRNRFV5TkRBM09UTXNJbWx6Y3lJNkluQjFZaTB4TkRVek5EVXlJaXdpYzNWaUlqb2laR2x6WVdKc1pWOWxiV0ZwYkNKOS5oMDFLbUJ5b29Ib3d1NjVnSUVQZDd6QlRPWjVLbG9ncndRSjhLVXQ4OXlBIiwicCI6MTkxMTk3MzY3LCJzIjoxNDUzNDUyLCJmIjp0cnVlLCJ1Ijo0MjY1MTUwNDAsImlhdCI6MTc3MzcwNDc5MywiZXhwIjoyMDg5MjgwNzkzLCJpc3MiOiJwdWItMCIsInN1YiI6ImxpbmstcmVkaXJlY3QifQ.THg7dsrwTs6LvT-gaXf68FkWkSfXfGfkomw2BOFqKJI?

View this post on the web at https://www.growth-memo.com/p/growth-intelligence-brief-15 Welcome to another Growth Intelligence Brief [ https://substack.com/redirect/d1b8d3e2-5bae-4fd4-88f6-7c64fed88e4a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], where organic growth leaders discover what matters - getting insights into the bigger picture and guidance on how to stay ahead of the competition. Today’s Growth Intelligence Brief went out to 620 (+38) marketing leaders. As a free subscriber, you’re getting the first big story. Premium [ https://substack.com/redirect/4e234833-bd5f-4693-b5be-acf8b22a5c12?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] subscribers get the whole brief. This week, we’re looking at what a $10 experiment with 60,000 AI-generated pages revealed about how GPTBot actually behaves, new citation data from 730,000 ChatGPT conversations, and what GPT-5.3 Instant’s web-blending update means for content that depends on being retrieved. I’ll also connect the dots on what this all means for you. GPTBot crawls everything - and cites almost nothing. [ https://substack.com/redirect/ad2fa3f3-b3cf-499b-8dac-3494f3682b85?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Here’s what happened: Metehan Yesilyurt built a 60K-page statistics website using GPT-4.1-nano for under $10, then tracked what happened next. He didn’t expect Googlebot… he expected nothing. I did not expect GPTBot to crawl a brand-new, zero-backlink domain at the scale it did. That was the real discovery. GPTBot showed up within minutes of deployment. In the first 12 hours, it made 29,000+ requests to a site with zero backlinks, zero social shares, and no Search Console submission. Googlebot made 11 requests in the same window. That’s a 470x difference in crawl intensity. The site - stateglobe.com [ https://substack.com/redirect/3b563f35-921d-49a6-807b-56569e7c0fa8?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] - was entirely AI-generated and deliberately thin. It was built as an experiment, not a real publishing effort. None of that stopped GPTBot from consuming it at roughly 1 request per second. Yesilyurt also caught a detail most site owners miss: Without server-side tracking, you can’t see any of this. Bot traffic made up ~98% of all requests, but client-side analytics tools captured none of it. He verified individual bot identities against OpenAI’s published IP ranges, ruling out user-agent spoofing. Why this news matters: Metehan’s experiment proves the barrier to getting AI crawlers onto your site is effectively zero. GPTBot will find you whether you optimize for it or not - it found a brand new domain with no authority in minutes, and then for some reason stayed for hours. His experiment results raise an important question: What’s the point of optimizing for AI crawlers? Does that access lead to anything? A site crawled 30,000 times in 12 hours still gets deindexed by Google and almost certainly won’t appear in ChatGPT answers, because ingestion vs. citation operates on entirely different logic. My take on this: High crawl volume to your site from GPTBot is not evidence of AI visibility. It’s simply evidence that OpenAI is building its index, which will include a lot of content across the web that will never surface in a citation. The experiment shows how hungry ChatGPT is for fresh and new content. While Google is very picky, probably to keep its index clean, ChatGPT can’t get enough. Important to note: ChatGPT crawled 78k times with the GPTBot user agent, but only 642 times with the ChatGPT-User user agent (as of March 8th). In other words, ChatGPT mostly crawls this site for model training. Not for showing it in answers. Since the site is data-heavy (statistics), it’s prime steak for hungry AI crawlers. Now, the downside is that content used for training gets barely cited, so the question we have to ask ourselves is how much we want to be part of the training data as opposed to beachfront property for live web retrieval. Here’s what to do: Audit your logs, not your analytics: Stop relying on client-side tools like GA4 to measure AI crawler interest. You need server-side log analysis to differentiate between GPTBot (ingestion/training) and ChatGPT-User (live web retrieval). Draw the line between training and retrieval: Decide if your proprietary data is something you want the model to train on. If you only want to be surfaced in live conversational answers, use your robots.txt to block GPTBot while keeping ChatGPT-User fully permitted. Advance past crawl metrics: A massive crawl spike from an AI bot is not a KPI. Shift your content team’s focus from “are we being crawled?” to “are we providing answers worth citing?” Is the citation economy winner-take-all?... [ https://substack.com/redirect/fd8bd37a-e648-4f93-845b-724a1f74110c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Unsubscribe https://substack.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.SPHCtVOZnY0N4ohiwt5q5CU5UsaVnTLxPNsNKf7mtR0?

View this post on the web at https://www.growth-memo.com/p/organic-rankings-vs-product-grids This Memo was sent to 25,163 subscribers. Welcome to +159 new readers! Get the free memo weekly. Upgrade to Premium [ https://substack.com/redirect/6a845080-40e3-4eb7-82bf-4bf7a0df38b5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. For a while now, SERP Features have made SEO for e-commerce distinctly different from other verticals like B2B or local. And yet, most teams still measure success against classic search results. Without third-party tools, it’s hard to get a full picture from the data that Google reports. As a result, some retailers fall significantly behind while thinking they’re ahead of the game. And so the problem remains remarkably hidden. To make it visible, I used e-commerce tracking platform Audience Key [ https://substack.com/redirect/cd5b1a5c-0b1e-4389-b23b-4ec753b66acb?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] to analyze 4,000+ keywords and almost 40,000 product grids over 9 months. (Plus, premium subscribers get my playbook for product grid optimization, including lesser-known tactics). The last satisfying mile in content ops Most content and SEO teams have figured out how to generate output with AI. The bottleneck has moved. It’s now in the space between “draft ready” and “published”: matching CMS fields, syncing statuses across tools, and fixing formatting that breaks every time content moves between systems. That gap is where teams lose hours per page and where ROI gets hard to prove, even when the work is solid. AirOps is running a live session on March 18th (2 PM EST) that gets specific about closing that gap. Justina Flamme (Product Lead, AirOps) and Josephine Cahill (Web Lead, Oyster) will walk through how to map structured content directly into your CMS, what to automate first between your AI workflows and PM tools, and a simple measurement framework for tracking minutes saved per item, error reduction, and approval cycles. If your team is producing more content than ever but publishing speed hasn’t kept up, this is the session to attend. 📅 March 18, 2026 ⏰ 2:00 PM EST 📍 Live on Zoom 👉 SAVE A SPOT [ https://substack.com/redirect/94120066-3219-4d73-8af8-dbc1cb4dd8c6?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Product grids get higher CTRs than organic results Product grids aren’t just another SERP feature to track in your dashboard. They represent Google’s final transformation from search engine to shopping marketplace - a change I’ve been documenting since E-commerce shifts [ https://substack.com/redirect/631d1391-2661-4157-80e7-e53d7a687b97?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], where I showed how Google merged web results and shopping tabs. There’s evidence that product grid CTRs are higher than classic search results. AWR data [ https://substack.com/redirect/2fce2e85-f8ed-446a-9916-2560e1544ff7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] shows product grids cut the CTR on organic results in half. The quantitative data confirms observations from Brodie Clark, who reports [ https://substack.com/redirect/258fc8bb-f248-439a-bb2e-049084e0ce6d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] an example of up to 58% CTR on product grids. I see the same with my clients. When I analyzed the state of e-commerce SERP features [ https://substack.com/redirect/b40e0310-4f81-4162-b04d-3a570c11f401?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], images and product listings were already becoming dominant. But product grids take this visual transformation to its logical conclusion: They push traditional blue links so far down the page that they become secondary navigation options, not primary discovery mechanisms. Product grids are: Filterable: Users can narrow by price, brand, condition, and features without leaving the SERP Visual-first: High-quality product images take center stage, not meta descriptions Dynamic: Content updates as Google crawls your Merchant Center feed, not when they re-index your page Commercial: They only appear for queries with shopping intent, creating a two-tier search system Product grid placements grew 82% in 9 months Google is allocating significantly more prime real estate to visual product feeds over traditional text results. In May 2025, there were 1,825 total grid placements among these brands. By February 2026, that number skyrocketed to 3,321 - an 82% increase in just 9 months. In fact, 96% of all SERPs in this dataset show product grids! 40% of SERPs show only one product grid 32% show 2 grids 22% show 3 6% show 4 or more And surprisingly, the number of more than one product grid in a single SERP declined by -4% over the last 9 months. Case study: 4 brands fighting for first place The product grid takeover is a great opportunity to show how brands can get left behind when they miss the train. The laptop query space on Google is a great example, with a case study of 4 refurbished computer hardware brands. Discount Computer Depot is the traditional SEO powerhouse. In early 2026, they held over 87% (4.6m/5.2m) standard organic rankings in the top 3 positions. Yet, their product grid presence is virtually non-existent, at just 2.4% (80/3,321). Back Market has a mere 1.7% of top 3 rankings but owns 59% of the visual product grids (see chart below). Back Market saw massive growth in grid placements, jumping from 745 in May 2025 to 1,960 in February 2026 and overtaking Newegg in late 2025. Interestingly, their data perfectly shows the inverse correlation between legacy rank and actual modern visibility (notice that product grids are on a 2nd y-axis in the chart below and have a much lower occurrence than classic search results). Other players, like PC Liquidation, have been able to grow their organic top 3 keyword rankings, but product grids are not following suit. The 2 types of search results can run completely independently of each other. Other players, like Newegg, see similar trends as Back Market: Classic organic rankings decline while product grid placements grow. The Back Market vs. Discount Computer Depot comparison reveals the new competitive landscape. Back Market didn’t win by playing the old game better. They won by recognizing the game had changed. Organic rank and grid presence are independent systems Here’s what separates product grid winners from traditional SEO winners: 1/ Feed quality over content quality: Your product descriptions still matter, but your Merchant Center feed quality matters more. Clean, complete, structured data beats beautifully written prose. Google doesn’t need to parse your HTML anymore - they want machine-readable attributes. 2/ Visual assets over backlinks: A single high-quality product image on a white background can generate more clicks than a dozen referring domains. Classic SEO authority (think high-quality backlinks) will still help you rank in traditional results, but they're invisible in product grids where image quality, price competitiveness, and merchant ratings dominate. 3/ Price competitiveness over domain authority: When users can compare prices at a glance, your 15-year-old domain and DR 70 profile mean nothing. The lowest price (with acceptable shipping terms) wins. This commoditizes traffic in a way traditional SEO never did. 4/ Merchant Center optimization over on-page SEO: Product title templates, GTIN accuracy, and feed error rates are the new meta descriptions and header tags. Many e-commerce SEOs don’t even have access to their company’s Merchant Center account. The implications extend beyond individual tactics. As I noted in How to compensate eroded traffic [ https://substack.com/redirect/72caa6b1-2b28-4751-8982-80240c8fd47f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], when you can’t win with product keywords, you need to think horizontally: different page types, new categories, and editorial content that addresses users earlier in their journey. Category pages win grids; product pages rarely qualify Even when focusing my analysis only on actively ranking URLs, Google shows a massive preference for category and listing pages (e.g., /categories/apple-refurbished.html) over individual product pages. Category Pages: 3,367 instances (~97% of the filtered data). Product Pages: 90 instances (~3% of the filtered data). While rare, Product pages (PDPs) are significantly more likely to rank for keywords that specify technical details or specific hardware models. Keywords like “17 inch desktop monitor,” “19 computer screen,” and “20in computer monitor” are among the top terms that successfully trigger specific product pages. Searches for specific builds, such as “computer dell optiplex” or “computer desktop hp i7” also lead to individual product pages rather than broad categories. There is a strong correlation between “price-intent” keywords and the density of the product grids column in the search results. Maximum grid density: Keywords such as “gaming desktop price,” “lenovo laptops prices,” and “cheap laptop” consistently trigger a maximum of 4 product grids. Visual competition: These keywords represent high-intent comparison shoppers, and Google responds by filling the SERP with visual product grids to facilitate quick price comparisons. The measurement gap makes the problem invisible For years now, e-commerce SEO has split into 2 distinct disciplines: Traditional SEO will increasingly focus on informational content, brand queries, and long-tail discovery - areas where product grids don’t dominate. This is where authority, content depth, and technical optimization still matter. Merchant Center optimization becomes its own specialization, focused on feed quality, product data accuracy, competitive pricing strategy, and visual asset production. This looks more like marketplace management than SEO. The most frustrating part of this transformation? We can’t properly measure it without expensive third-party tools. Google Search Console reports on traditional organic results. Merchant Center provides product grid analytics. But there’s no unified view. You can’t answer basic questions like: What percentage of my search visibility comes from product grids vs. traditional results? How do grid placements correlate with conversion rates? Am I losing traditional rankings because I’m gaining grid placements, or despite it? Even the comparison of product snippets and merchant listings in Search Console does not allow you to compare product grids against classic web results. Premium: Your new product grid playbook Every month your team optimizes pages without touching the Merchant Center feed, you’re investing in a surface that product grids are pushing below the fold. The playbook below covers the specific feed attributes, structured data requirements, and merchant signals that decide grid position, plus I’ll cover the Glue ranking system Google uses to score clicks inside SERP features. Unsubscribe https://substack.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.xCKKlV1wisA4Z60I1AzxONrApwQMIUnnd5wMPN9fIhw?

View this post on the web at https://www.growth-memo.com/p/how-to-build-an-ai-seo-strategy-that This Memo was sent to 24,956 subscribers. Welcome to +149 new readers! Get the free memo weekly. Upgrade to Premium [ https://substack.com/redirect/f1092c62-912f-46d7-996b-96fc05674d03?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. Most AEO “strategies” are tactic lists dressed up as long-term direction. They often break the first time a platform changes or leadership asks questions. A real AI SEO strategy starts with the business problem, builds on your brand’s unique advantages, and lets tactics come last. This week, we’re covering: How to identify your actual AI SEO challenge (it’s a business problem, not a channel problem) A 3-part strategy document structure that survives leadership scrutiny and platform shifts How to present AI SEO investment using scenario planning instead of traffic forecasts Premium subscribers also get an interactive strategy builder tool to create your AEO strategy document. Which brands are winning in AI visibility? Semrush’s AI Visibility Awards reveal the brands that are leading their industries in AI visibility. We’re recognizing those with the highest visibility overall, the fastest risers, and the fresh names who are overachieving. Get insights into what sets brands like Patagonia, Anthropic, and Nothing Technology apart from their industry competitors. Then dive into the complete AI Visibility Index for more learnings, refine your strategy, and power your climb up the rankings. Explore the winners [ https://substack.com/redirect/aeca6850-c76f-48d9-a9df-0ce480521b46?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 1. Tactics without a strategy waste quarters of work Strategy as a concept is even more misunderstood in the AI SEO era than it was in traditional SEO. Most “AEO/GEO strategies” I see are actually just tactics: optimize for long-tail queries, add structured data, create FAQ content. These might be part of your execution, but they’re not your strategy. The result? Teams chase citations [ https://substack.com/redirect/993bd065-89b1-49c6-92c1-0564bac31e53?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] in ChatGPT without understanding if that’s a solution to an actual business problem. They optimize for Perplexity when the real challenge is protecting branded search volume [ https://substack.com/redirect/2b03489c-cbcf-4d4b-99be-1bbbb4786101?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. They copy competitor tactics instead of building on their unique advantages. When you set out to build (or repair) your AI SEO strategy [ https://substack.com/redirect/83e288b5-e4e6-42a3-bedc-a37ea61037a1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], distinction matters because a tactic list can’t answer the one question strategy exists to answer: What problem are we solving? 2. Start with your brand’s unique challenge Your strategy must answer one question first: What business problem [ https://substack.com/redirect/01cad417-13d9-4658-8b45-d343d8b951ef?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] are we solving? This sounds obvious. Most teams skip it. They see “AI search is growing” and immediately jump to “we need to rank in ChatGPT” and start trying new tactics. That’s a reaction, not a clear strategy. Use the same approach I outlined in creating an SEO strategy from scratch [ https://substack.com/redirect/83e288b5-e4e6-42a3-bedc-a37ea61037a1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]: identify your actual challenge through research, then build your approach around solving it. Common AI SEO challenges I see: Brand visibility erosion. Branded queries get answered by AI without attribution, bleeding awareness over time. Pipeline protection. Qualified traffic is shifting to AI Mode, but your brand is invisible in those results. Category definition. AI models cite competitors as the category solution. Your brand doesn’t appear. Conversion influence decay. Users research in ChatGPT, arrive at your site decision-ready, or don’t arrive at all. The pre-site journey now happens inside an AI interface - and you can’t see your target audience’s detailed behaviors via analytics. These are business problems, not channel problems. Your challenge should connect directly to revenue, market share, or competitive position. If it doesn’t, you’re optimizing for a metric that can’t survive a budget review. 3. Do your research first to kill your own incorrect assumptions. You can’t build an AI SEO strategy on assumptions. What works varies by industry, query type, and user intent… and the platforms are moving and shifting fast. Your research phase should answer 4 questions: 1/ Where is your audience using AI search? Don’t assume. Survey customers, analyze referral data, review session recordings. ChatGPT usage patterns differ from Perplexity and Google AI Overview usage. Our AI Mode user behavior study [ https://substack.com/redirect/cf029725-8036-4ae7-92f4-e135b60fef90?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] showed that 250 sessions of real behavior look nothing like what most teams expect. 2/ Which queries drive the pipeline? Map the queries that connect to revenue, not just site visits from AI Mode, Gemini, or ChatGPT & Co. In zero-click environments [ https://substack.com/redirect/5f3b34aa-c7d5-474e-b94f-eb1c6003d82b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], you need to understand which visibility opportunities actually influence buying decisions. Start with pain points your sales team hears on calls. Turn those into the questions buyers type into ChatGPT or Google. Then check which of those questions generate AI answers where your brand does or doesn’t appear. That’s your revenue-connected query set. 3/ What kind of site content or external third-party mentions drive visibility in your category? Test which internal content structures (like types of blog posts and landing pages) and external-third party sites that mention your brand (like reddit and G2) earn citations in your category for revenue-connected queries. For your internal content that you have more control over, the ski-ramp data from The science of how AI pays attention [ https://substack.com/redirect/a648b165-465a-4d95-85d9-882bfd2eb95f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] shows 44% of citations pull from the first 30% of a page, which means front-loading claims, definitions, and data changes citation rates more than adding depth at the end. Run one test: rewrite the first 3 paragraphs of your top 10 pages to lead with the answer, not the context. 4/ What’s your citation baseline? Use tools like AirOps, Profound, or SearchGPT to map where you currently appear. Track competitors. Measure the gap. Compare your current performance against where you need to be. Use 5x Why analysis to identify root causes. If you’re not being cited, the problem could be content depth, authority signals, or technical accessibility. Each requires a different approach. 4. Your strategy document has 3 parts An AI SEO strategy document should include 3 components. No more. Part 1: The challenge. State the core business problem in one sentence. Example: “Our brand is invisible in AI-generated answers for category-defining queries, allowing competitors to own mindshare with buyers before they reach a search engine.” Part 2: The approach. Explain how you’ll address the challenge. This is where your unique advantages matter. Your approach should be something only your brand can do, or something you do better than competitors. Example approaches: Authority multiplication. Leverage your executive team’s expertise through strategic bylines, podcast appearances, and research publications that AI models pick up as authoritative sources. Third-party authority signals influence brand mentions and citation selection. Product-led content. Use your product data to create depth that competitors can’t replicate. Apply product-led SEO principles to AI SEO by building content assets that only your data can produce. Community signal amplification. Build visibility through customer stories, case studies, and user-generated content that demonstrates applied expertise. Personas built from real customer data [ https://substack.com/redirect/be4969ae-824d-439a-8e3b-86ecf1ca25e8?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] sharpen this work because they tell you which community signals actually match how your buyers search. Part 3: The actions. Now - and only now - list your tactics. These should flow directly from your approach: Create conversational-query content (or update existing content) that addresses hyper-specific buyer contexts Optimize technical accessibility for LLM crawlers Build systematic digital PR to drive third-party citations Develop persona-specific content that matches AI search [ https://substack.com/redirect/9f019164-496d-41c9-9d41-cb92439e507c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] patterns (using synthetic personas [ https://substack.com/redirect/190b319b-1821-4c5b-8f70-c3266f762c77?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] to scale prompt tracking) Reinforce internal linking as entity maps [ https://substack.com/redirect/c4627584-2fe1-4680-bcaa-01bdb22d7b66?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], not just crawl paths Include resource allocation: What percentage of capacity goes to each action area? Include success metrics tied to business outcomes, not just “track citations.” Read Budget for capacity, not output [ https://substack.com/redirect/ca928aa2-1eaf-4304-962f-c7fd2bc9ea03?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] to learn more about how to do this. At the end of this memo, premium subscribers get deeper guidance on how to define your brand’s challenge, what approach to take, and what actions to pair with your brand’s unique needs. 5. Scenario planning sells AI SEO to leadership Here’s where AI SEO strategy gets difficult. You’re asking for investment in a channel that’s still forming, with metrics leadership [ https://substack.com/redirect/90c7281a-7013-49a7-826d-759dbdaf478f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] doesn’t yet understand. Don’t present traffic forecasts. They’re fiction in AI search. Use scenario planning instead. Frame it like this: “If we allocate 30% of capacity to authority building and 20% to conversational content, we expect citation increases of 40-60% within 6 months, which should influence 15-20% of assisted conversions based on current attribution data.” Include stage gates. Make the investment reversible. Executives are more likely to approve experiments with clear decision points than open-ended commitments. Present 3 scenarios: conservative, moderate, and aggressive. Show what resources each requires and what outcomes they might produce. Let leadership choose. The strategy document from section 4 gives you the structure to do this. The challenge statement defines the goal. The approach defines the bet. 6. Review your strategy quarterly or it goes stale Your AI SEO strategy is not a one-time document. The platforms change, anduser behavior is shifting fast. Your own test outcomes should also change your tactics. Build quarterly strategy reviews into your plan. Each review should answer 4 questions: What changed in AI search since our last review? What did we learn from our tests? Do our tactics still serve our approach? Is our approach still solving the right challenge? Your AI SEO strategy should be a decision-making tool, not a task list. Most teams fail at AI SEO because they treat it like traditional SEO with a different name and a slight shift in tactics. Start with the business challenge. Build an approach around what only your brand can do… let your tactics flow from there. And make the whole thing reversible and adaptable, because we’re all still learning what works. Build your AI SEO strategy with the Growth Memo library Once your strategy document is set, these past Growth Memo posts cover the execution layer. Each addresses a specific capability your AI SEO approach will need. Plus, this week, premium subscribers get [INSERT HERE FINAL COPY] at the bottom of this memo. First, know your audience Personas are critical for AI search [ https://substack.com/redirect/be4969ae-824d-439a-8e3b-86ecf1ca25e8?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] covers how to turn in-house data into personas that shape briefs, prompts, and content decisions. Making SEO personas actionable across teams [ https://substack.com/redirect/c1245414-4c00-4b83-93a4-d5baab6b485d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] moves personas from a planning artifact into day-to-day workflows across content, product, and SEO teams. Synthetic personas for better prompt tracking [ https://substack.com/redirect/190b319b-1821-4c5b-8f70-c3266f762c77?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] solves the cold-start problem in prompt tracking by simulating search behavior across segments at 85% accuracy. Second, understand user behavior in AI search The first-ever UX study of Google’s AI Overviews [ https://substack.com/redirect/1d1e4d8d-81ad-4659-a9b1-4162e89643f1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] tracked 70 users across 8 tasks to map what “visibility” means when AI answers sit above organic results. What our AI Mode user behavior study reveals [ https://substack.com/redirect/cf029725-8036-4ae7-92f4-e135b60fef90?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] analyzes 250 sessions of AI Mode behavior to show how users actually interact with Google’s AI interface. Google’s AI Mode SEO impact [ https://substack.com/redirect/c02b3556-bf94-4970-9781-efb6a32facdc?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] is the second part of that study, covering what’s measurable, what’s guesswork, and what visibility means in AI Mode. Third, create content that builds long-term topical and brand authority Topic-first SEO [ https://substack.com/redirect/12c7b8eb-d62b-44e9-a2bc-a2a4ae71beb6?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] explains why keyword-first SEO creates surface-level content and cannibalization, and how topic-first thinking fixes both problems. Operationalizing your topic-first SEO strategy [ https://substack.com/redirect/db1be584-1bb9-44e9-9e1f-cf34cd84d5d9?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] is the execution blueprint for running topic-first across your team. How to measure topical authority [ https://substack.com/redirect/645bea92-a01e-4eec-b5d5-f21612100318?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] offers a method to quantify topical authority using Google leak signals and competitive benchmarks. How you can track brand authority for AI search [ https://substack.com/redirect/44d6e940-1878-48d3-8760-a3665580df33?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] covers the difference between topical and brand authority, and how to measure brand authority with real numbers. SEOzempic [ https://substack.com/redirect/87858a6c-8122-44f3-812f-7b6d8c2624f3?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] explains how less is more: less low-quality, thin pages, and more sharply targeted website content around the key topics that matter to your brand’s target audience. And understand how AI reads and cites your content - so it influences how you create it The science of how AI pays attention [ https://substack.com/redirect/a648b165-465a-4d95-85d9-882bfd2eb95f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] is an analysis of 1.2M search results showing exactly where AI pulls citations from and why content structure determines selection. Internal linking grows up [ https://substack.com/redirect/c4627584-2fe1-4680-bcaa-01bdb22d7b66?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] reframes internal linking as an entity reinforcement tool, which directly affects how AI systems understand your site’s authority. How AI really weighs your links [ https://substack.com/redirect/fd6a2a38-ec76-41cf-bfc6-1f1fe0477770?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] analyzes 35,000 datapoints on backlinks and AI visibility, with findings that should reshape your link building priorities. The science of how AI pays attention [ https://substack.com/redirect/a648b165-465a-4d95-85d9-882bfd2eb95f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] provides data-backed insights for how your content should be written and structured to increase chances of citation. For premium subscribers: Building your AI SEO strategy doc Below, you’ll get the process for completing each part along with a light AI SEO Strategy Builder tool that will guide you through building a strategy document you can share with your leadership or clients. Unsubscribe https://substack.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.fdRCeMXtlZNVGuPJvAgbO0jlaKXVZdQuhBu8jftM43k?

View this post on the web at https://www.growth-memo.com/p/ai-seo-is-a-change-management-problem This Memo was sent to 24,738 subscribers. Welcome to +119 new readers! Get the free memo weekly. Upgrade to Premium [ https://substack.com/redirect/9724f558-7728-4018-a959-df539e7029c0?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. AI-SEO transformation will fail at the alignment layer, not the tactics layer. 25 years of transformation research - spanning 10,800+ participants across industries - reveals that the gap between successful and failed initiatives isn’t technical skill. It’s organizational readiness. What you’ll get: Why AI SEO implementation challenges are people and process problems, not technical ones The specific alignment failures that kill AI-SEO initiatives before tactics ever get tested A sequenced approach that transforms you from channel executor to organizational translator Premium subscribers also get an AI SEO change management checklist - your 45-day plan in leading your brand into the next era of SEO - and a supportive stakeholder slide deck to educate your team. Feature your brand in all the right answers Discover how Semrush Enterprise helps brands like Square 10X their productivity to leave competitors behind in SEO and AI search. Our clickthrough demos let you preview: Advanced automations that scale work across thousands of pages Prompt tracking, citation analysis, and content optimization for AI search Enterprise reporting with customizable dashboards for different stakeholders Competitive intelligence powered by Semrush’s market-leading database Consolidate your stack and be first everywhere customers search, with Semrush Enterprise. Preview Semrush Enterprise [ https://substack.com/redirect/d333274f-c716-4f46-b1f8-06df790cb96f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] The underlying infrastructure of AI SEO - retrieval-augmented generation, citation selection, answer synthesis - operates on different principles than the crawl-index-rank paradigm SEO teams previously mastered. And unlike past shifts, the old playbook doesn’t bend to fit the new reality. AI SEO is different. It’s not just an algorithm update: This is a search product change and a user behavior movement. Our classic instinct is to respond with tactics: prompt optimization, entity markup increase, LLM-specific structured data, citation acquisition strategies. These aren’t wrong. But long-term, it’s likely AI SEO strategies will fail, and the reason isn’t tactical incompetence or lack of staying up-to-date and flexible. It’s internal organizational misalignment. 1. AI SEO transformation fails at the alignment layer: Here’s what it looks like Your marketing team - and your executive team - is being asked to transform their understanding of SEO during a period of unprecedented change fatigue. Those who have survived 2 decades of algorithm updates are expertly adaptable, but reeducation is required because LLMs are a new product, not just another layer of search. And this, of course, is the alignment layer fail. In AI SEO, misalignment has specific symptoms: Conflicting definitions of success: One stakeholder wants “rankings in ChatGPT.” Another wants brand mentions. A third wants citation links. A fourth wants traffic recovery. Every experiment gets judged against a different standard, and no one has agreed which matters most or how they’ll be measured. (Although our AI Overview [ https://substack.com/redirect/2414efb9-7e4a-4460-a6f5-dfb071dd3f55?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and AI Mode [ https://substack.com/redirect/81114d4d-2b62-4266-b710-dca5397996b7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] studies confirm brand mentions are more valuable than citations.) Metrics mismatch with leadership expectations: Executives ask for increased traffic in a growing zero-click environment. Classic SEO reports on influence metrics; leadership sees declining sessions and questions the investment. In our December 2025 Growth Memo reader survey, 84% of respondents said they feel their current LLM visibility measurement approach is inaccurate. Teams can’t prove value because no one has agreed on how value would be proven. Turf fragmentation: AI SEO touches SEO, content, brand, product, PR, and (at times) legal. Without explicit ownership and a baseline, agreed-upon understanding of your brand’s AI SEO approach, each team runs experiments in its silo. No one synthesizes learnings. Conflicting tactics cancel each other out. Premature tactics without a shared foundation: This looks like “Let’s test prompts” without agreeing what success means; “Let’s scale AI content to mitigate click loss” without understanding AI-assisted versus AI-generated content limits; “Let SEO handle AI” while product, PR, and legal stay uninvolved. Panic-testing instead of strategic reorientation: Teams deploy short-term tactics reactively rather than reorienting the whole ship for better long-term outcomes. This is classic change management failure: unclear mandate, fragmented ownership, mismatched incentives. No amount of tactical excellence or smart strategy pivots can fix it. Layering AI SEO tactics + tools on top without structured change management compounds fatigue and accelerates burnout. The “scrappy resilience” that has carried the industry in the past can’t be assumed to instantly apply to this new channel without a strategic transition. 2. Org transformation failure rates are stubbornly high - AI SEO isn’t an exception The playbook for dramatically improving AI SEO strategy transformation success rates during a big landscape shift already exists: It’s called change management. (Lucky us - no additional guesswork needed). A baseline understanding of organizational change management matters in the AI SEO era… because most organizational transformations fail or underperform. Your AI-SEO initiative is no different, even if changes in SEO seem contained to your marketing and product teams and stakeholders, rather than the larger organization or brand as a whole. I’d argue that AI SEO falls into the category of industry transformation that affects your brand and org. And from decades of research, failure and underperformance are the statistical norm for these big transitions - seasoned leaders know this already. No wonder they’re skeptical of your AI SEO plans. One McKinsey survey [ https://substack.com/redirect/9e806a63-da9b-42ba-8cea-80925a60dc01?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] found fewer than ⅓ of teams succeed at both improving performance and sustaining improvements during significant shifts. BCG’s forensic analysis [ https://substack.com/redirect/8195b256-28a1-4610-9fee-49a7709acd72?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] of 825 executives across 70 companies found transformation success at 30%. Multiple major consulting firms’ independent research shows that most change transformations underperform. Assuming that tactical excellence alone will carry you - without strategic reeducation and thoughtful change management as our industry shifts - is assuming you’re the exception to the rule. 3. Structured AI-SEO change management can create an 8x success multiplier The most powerful evidence for intentional change management for any initiatives - SEO, AI, or otherwise - comes from Prosci’s 12th Edition benchmarking study [ https://substack.com/redirect/3c29cff7-1e88-460e-bb43-3932af82188a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. (Prosci’s study is the largest body of change management research, covering 2K+ practitioners, 400K+ data cells, over 25 years of accumulated findings.) The correlation between the quality of managing a big shift and your project’s success is dramatic: The gap between excellent and poor represents a nearly 8x improvement. Even the jump from poor to fair quadruples success rates. BCG’s 2020 analysis [ https://substack.com/redirect/8195b256-28a1-4610-9fee-49a7709acd72?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] reinforces this from a different angle, noting 6 critical factors that increase successful transformation odds from 30% to 80%: Integrated strategy with clear goals: This is where a carefully crafted AI SEO strategy comes in, one that not only outlines growth goals, but also clear testing and what successful outcomes look like Leadership commitment from CEO through middle management: If you’re a consultant or agency, this step can’t be skipped, especially if they have an in-house team assisting in executing the strategy High-caliber talent deployment: Or I would argue, high-quality reeducation of existing talent - make sure all operators have a baseline shared understanding of what has changed about SEO, how LLM outputs work, what the brand’s goals are, and how it will be executed. Flexible, agile governance: Teams should have the ability to deal with individual challenges without losing sight of the broader goals, including removing barriers quickly. Effective monitoring: Establish core, agreed-upon KPIs to measure what winning would look like, and note what actions were taken when. Modern/updated technology: Your SEO team needs the right tools to succeed, but they also need to know how to use them effectively. Don’t skip allotting time for integration of new workflows and AI monitoring systems. Marketing teams that treat AI-SEO simply as a technical project to execute or tactics to update are leaving an 8× multiplier on the table. With that in mind, this week, premium subscribers get a done-for-you, undesigned slide deck that will [guide you in a plan to help your marketing team and stakeholders manage this huge shift.] 4. Your internal “people-process” problems are bigger than your “tools-tactics” tension The most counterintuitive change-management findings for the tool-obsessed: The tech you use to carry out organizational-level changes matters far less than the people using it. BCG’s 2024 AI implementation study [ https://substack.com/redirect/b271cf44-e3bd-4c48-97a8-b1a70639a3fe?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] found that roughly 70% of change implementation hurdles relate to people and processes. Only about 10% of challenges were purely technical. A 2024 Kyndryl survey [ https://substack.com/redirect/7a5f370b-3639-4add-9969-cac0e210807b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] found that while 95% of senior executives reported investing in AI, only 14% felt they had successfully aligned workforce strategies. Your brand’s ability to test, update tactics, learn AI workflows, implement structured data, and optimize for LLM retrieval is not the bottleneck you need to be concerned about. The real concern is whether your team - leadership, cross-functional team partners, and frontline executors/operators - is aligned on what AI SEO means, why and how you’re making changes from your classic SEO approach, what success looks like, and who owns outcomes. 5. Executive sponsorship, financial stakes, and what alignment actually looks like Active and visible executive sponsorship is the #1 contributor to change success, cited 3-to-1 more frequently than any other factor, according to 25 years of benchmarking research by Prosci [ https://substack.com/redirect/bb2062a6-3433-45b8-8074-cf09d498fb0b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Your first step as the person leading the AI SEO charge for your brand (or across your clients) is to earn executive buy-in. But the Head of SEO cannot transform a brand’s understanding and approach of AI SEO alone. [ https://substack.com/redirect/9e806a63-da9b-42ba-8cea-80925a60dc01?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]Bain’s 2024 research [ https://substack.com/redirect/84f38bad-86f4-401f-9759-f0fa27e6a2d3?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] emphasized that successful transformations “drive change from the middle of the organization out.” Keep in mind, financial benefits can compound quickly: One research analysis of 600 organizations [ https://substack.com/redirect/b7b9fb49-0354-4794-ba35-0dee07062495?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] found “change accelerators” experience greater revenue growth than companies with below-average change effectiveness. Alignment isn’t just a feeling - it’s observable. You’ll know when you get there: Stakeholders can talk through AI SEO without hyperfocusing on tools Teams agree on what to stop prioritizing (not just what to start) Cross-functional partners have explicit ownership stakes Alignment isn’t happening when: Everyone is good with “experimenting with” or “investing in” LLM visibility but no one owns outcomes Success gets retroactively defined, or Leadership asks “what happened to traffic” when you report influence metrics 6. Your new role: From channel expert to change agent AI-SEO elevates the SEO professional’s role from channel executor to something more challenging: organizational translator. Noah Greenberg outlined this pretty clearly in a recent LinkedIn post [ https://substack.com/redirect/f432c9ac-ad6b-49c7-9f59-d2c0479d0582?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] - step 0 in your AI SEO transformation is to become the expert. New responsibilities: Translating new, confusing AI-based search concepts into plain language (see this clever LinkedIn post by Lilly Ray [ https://substack.com/redirect/08c9505a-0d65-4c0e-91ac-6604f43107dc?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] as a perfect illustration) Educating stakeholders on the structural differences between classic search engines and LLM retrieval - guiding teams to explain why your CEO doesn’t see the same LLM output when they look up the brand vs. what you’re reporting Explaining the tradeoffs, not just opportunities Setting expectations executives won’t like at first but need to hear (traffic loss or slower growth than in years prior) This is uncomfortable. Less direct control. More indirect influence. Higher stakes. Your mindset - as the change agent for your clients or org - centers on three principles: Honesty over confidence. What we don’t know: the precise value of an AI mention. What we do know: your brand not appearing for related topics is a measurable miss. Progress over perfection. Alignment doesn’t require certainty. It requires shared uncertainty - agreeing on what you’re testing and how you’ll learn. Translation over broadcasting. The same strategic message needs adaptation for ICs (how their work changes), managers (how they report success), and executives (how budgets should shift). Uniform communication fails; translated communication scales. 7. The bottom line and what to do next: A practical sequence AI SEO is a product-level change forced on the industry - and on organic searchers - that requires a fundamentally different organizational response. Do this in order: Write the one-sentence AI SEO mandate for your org. If you can’t explain AI SEO in one sentence to leadership, you’re not ready to execute. Complete a high-level SWOT. Identify where your organization has existing strengths and gaps. The Brand SEO scorecard [ https://substack.com/redirect/509de7c4-eb95-4db2-aaff-817ccb8427b4?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] from The great decoupling [ https://substack.com/redirect/e941617c-7f7b-4e80-ae3d-e3487b3e17ad?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] will walk you through. Replace or supplement legacy KPIs. Add LLM visibility estimates alongside classic KPIs (rankings, sessions) to start the transition. Reporting both builds the case for the shift without abandoning the old model cold. Name cross-functional owners explicitly. Who owns brand mentions in LLM outputs - SEO, PR, or brand? Who owns citation link acquisition - SEO or content? Ambiguity is the enemy. Provide baseline education at every level. ICs need to understand how LLM retrieval differs from crawl-index-rank. Executives need to understand why slowed organic traffic or zero-click growth doesn’t mean zero impact. Kill one SEO practice without a fight. Success means everyone understands why, and you don’t receive pushback. If you can’t retire one outdated tactic without internal conflict, you haven’t achieved alignment. Only then change workflows and tactics. Tactics deployed on an unaligned organization waste resources and burn credibility. Tactics deployed on an aligned organization compound advantage. For premium subscribers: Your AI SEO change management plan Premium subscribers get 2 assets built for immediate use: A 45-day implementation checklist, and A team education deck (an unbranded, undesigned version and a Growth Memo branded version). The checklist sequences 14 core actions across 3 phases with impact ratings, effort levels, and additional guidance for consultants or agencies. The deck runs a ~45-minute session that takes your team from “SEO is keywords, rankings, and traffic” to “here’s how AI actually decides what to cite” - with blank slides for your own brand’s LLM audit data. Unsubscribe https://substack.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.jf7pxpUGesC6O3n9ogW8alxnNTvuXH2bnJ3fyriBRs4?

View this post on the web at https://www.growth-memo.com/p/growth-intelligence-brief-14 Welcome to another Growth Intelligence Brief [ https://substack.com/redirect/bc88d4e5-a750-4f65-bdb9-0fa3363f814d?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], where organic growth leaders discover what matters - getting insights into the bigger picture and guidance on how to stay ahead of the competition. Today’s Growth Intelligence Brief went out to 567 (+33) marketing leaders. As a free subscriber, you’re getting the first big story. Premium subscribers get the whole brief. This week, we’re looking at the infrastructure of the Agentic Web and the inversion of traditional internet economics. We’ll cover: Cloudflare’s “Markdown for Agents”: How the barrier to entry for agent readability just dropped to near zero. Contentsquare’s 2026 Benchmark: Why the traditional growth playbook of traffic and conversions is breaking. WebMCP: How standardizing agent actions is turning technical SEO into tool optimization. The SEO/AEO Landscape: New AI prompt data in the SSI and the impact of the recent Gemini 3 update. I’ll also connect the dots on what this all means for you. Cloudflare just made every website agent-ready by default Here’s what happened: Cloudflare launched “Markdown for Agents [ https://substack.com/redirect/339e31d2-776f-4b86-bf95-4adefd047b68?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ],” a feature that automatically converts HTML pages into clean Markdown when an AI agent requests it. Any website using Cloudflare can enable this at the network level, no code changes required. When an AI system sends a request with a text/Markdown content negotiation header, Cloudflare’s edge network converts the HTML response to Markdown on the fly. Markdown burns fewer tokens than raw HTML, which means agents can consume more of your content within their context windows and process it faster. Why this news matters: This is infrastructure-level plumbing for the agentic web, and it matters because Cloudflare powers roughly 20% of all websites. With a single toggle, millions of sites can become dramatically easier for AI agents to read, parse, and act on. Until now, making your site “agent-friendly” required deliberate technical work: building APIs, cleaning up markup, publishing structured data. Cloudflare just commoditized the first layer of that effort. The barrier to entry for agent readability just dropped to near zero, which means the sites that don’t enable it will stand out for the wrong reasons. My take on this: This is a quiet but significant shift in who controls the agent experience layer. Cloudflare is positioning itself as the translation layer between the human web and the agent web, sitting between your origin server and every bot that wants to read your content. Think about what this means strategically: your CDN provider now influences how AI agents perceive your brand. If Cloudflare’s Markdown conversion strips context, misreads your page hierarchy, or drops critical structured data during the conversion, the agent gets a degraded version of your content and you may never know it. The bigger implication is competitive. In last week’s brief [ https://substack.com/redirect/beb9e077-4323-40c1-aed0-a397964b5964?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], we talked about how the agentic web infrastructure is complete: eyes (Clarity), hands (Auto Browse), and wallet (UCP/ACP). Cloudflare just added “translation” to that stack. Sites that serve clean, token-efficient content to agents will get consumed more thoroughly. Sites that serve bloated HTML will get truncated or skipped when the agent hits its context window limit. We’re entering a world where your CDN configuration is a growth lever. I welcome the change. I’ve tested Markdown pages with clients and saw clear citation growth. Plus, Markdown is much more token-friendly (and therefore, more environmentally-friendly too). Agents are not incentivized to send traffic to websites anyway, so why not make it easier for them to parse your content? What I do expect, though, are more safety guards against cloaking. Nothing prevents you from serving a Markdown version of your page that has much more content (and potentially toxic prompt injections). Here’s what to do: Enable Markdown for Agents today. If you’re on Cloudflare, turn it on. It’s a free, zero-risk way to make your site more consumable by every major LLM and agent. Test the output. Request your own pages with the text/Markdown accept header and audit what the agent actually sees. Check whether your key content, CTAs, product information, and structured data survive the conversion intact. Don’t treat this as “done.” Cloudflare’s auto-conversion is a baseline, not a strategy. The real winners will layer purpose-built agent content (JSON-LD, dedicated API endpoints, tool schemas) on top of this foundation. If you’re not on Cloudflare, talk to your CDN or hosting provider about equivalent capabilities. This is going to become table stakes fast. The web is getting more expensive and less effective... Unsubscribe https://substack.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.cPYx2R-fVi-_0OYePjy9f0lm8uYVOrvWG6Nq4dKEWfM?

View this post on the web at https://www.growth-memo.com/p/the-science-of-how-ai-pays-attention This Memo was sent to 24,563 subscribers. Welcome to +120 new readers! Get the free memo weekly. Upgrade to Premium [ https://substack.com/redirect/823f7277-cd6a-4a0c-a850-ced2b25abf3e?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. This week, I share my findings from analyzing 1.2 million ChatGPT responses to answer the question of how to improve your chances of getting cited. Topic Compass: Find the “known unknowns” in your topic Topic Compass [ https://substack.com/redirect/d39674ea-fad5-490a-9d74-2d61bb287487?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] maps what your audience associates with any topic using semantic analysis, so you can uncover adjacent concepts and plan content that builds topical authority. Run a search > export the map into a brief > ship faster. 3 free searches/day to start. Growth Memo readers get an exclusive 10% off during launch month: Use MEMO10 in checkout (offer expires March 16th). Discover new content angles [ https://substack.com/redirect/d39674ea-fad5-490a-9d74-2d61bb287487?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] For 20 years, SEOs have written ‘ultimate guides’ designed to keep humans on the page. We write long intros. We drag insights all along through the draft and into the conclusion. We build suspense to the final CTA. The data shows that this style of writing is not ideal for AI visibility. After analyzing 1.2M verified ChatGPT citations, I found a pattern so consistent it has a P-Value of 0.0: the “ski ramp.” ChatGPT pays disproportionate attention to the top 30% of your content. Further, I found 5 clear characteristics of content that gets cited. To win in the AI era, you need to start writing like a journalist. 1/ Which sections of a text are most likely to be cited by ChatGPT? There isn’t much known about which parts of a text LLMs cite. We analyzed 18,012 citations and found a “ski ramp” distribution. 44.2% of all citations come from the first 30% of text (the intro). The AI reads like a journalist. It grabs the “Who, What, Where” from the top. If your key insight is in the intro, the chances it gets cited are high. 31.1% of citations come from the 30-70% of a text (the middle). If you bury your key product features in paragraph 12 of a 20-paragraph post, the AI is 2.5x less likely to cite it. 24.7% of citations come from the last third of an article (the conclusion). It proves the AI does wake up at the end (much like humans). It skips the actual footer (see the 90-100% drop-off), but it loves the “Summary” or “Conclusion” section right before the footer. Possible explanations for the ski ramp pattern are training and efficiency: LLMs are trained on journalism and academic papers, which follow the “BLUF” (Bottom Line Up Front) structure. The model learns that the most “weighted” information is always at the top. While modern models can read up to 1 million tokens for a single interaction (~700-800K words), they aim to establish the frame as fast as possible, then interpret everything else through that frame. 18K out of 1.2M citations gives us all the insight we need. The P-Value of this analysis is 0.0, meaning it’s statistically indisputable. I split the data into batches (randomized validation splits) to demonstrate the stability of the results. Batch 1 was slightly flatter but batches 2, 3, and 4 are almost identical. Conclusion: Because batches 2, 3, and 4 locked onto the exact same pattern, the data is stable across all 1.2M citations. While these batches confirm the macro-level stability of where ChatGPT looks across a document, they raise a new question about its granular behavior: Does this top-heavy bias persist even within a single block of text, or does the AI’s focus change when it reads more deeply? Having established that the data is statistically indisputable at scale, I wanted to “zoom in” to the paragraph level. A deep analysis of 1,000 pieces of content with a high amount of citations shows 53% of citations come from the middle of a paragraph. Only 24.5% come from the first and 22.5% from the last sentence of a paragraph. ChatGPT is not “lazy” and only reads the first sentence of every paragraph. It reads deeply. Takeaway: You don’t need to force the answer into the first sentence of every paragraph. ChatGPT seeks the sentence with the highest “information gain” (the most complete use of relevant entities and additive, expansive information), regardless of whether that sentence is first, second, or fifth in the paragraph. Combined with the ski ramp pattern, we can conclude that the highest chances for citations come from the paragraphs in the first 20% of the page. 2/ What makes ChatGPT more likely to cite chunks? We know where in content ChatGPT likes to cite from, but what are the characteristics that influence citation likelihood? The analysis shows 5 winning characteristics: Definitive language Conversational question-answer structure Entity richness Balanced sentiment Simple writing Also, at the end of this memo, premium subscribers get 2 tools to operationalize these findings: a training deck your content leads can customize and run in their next team meeting, and a checklist your writers can use on every page they touch. 1. Definitive vs. vague language Citation winners are almost 2x more likely (36.2% vs 20.2%) to contain definitive language (“is defined as,” “refers to”). The language citation doesn’t have to be a definition verbatim, but the relationships between concepts have to be clear. Possible explanations for the impact of direct, declarative writing: In a vector database, the word “is” acts as a strong bridge connecting a subject to its definition. When a user asks “What is X?”, the model searches for the strongest vector path, which is almost always a direct “X is Y” sentence structure. The model tries to answer the user immediately. It prefers a text that allows it to resolve the query in a single sentence (Zero-Shot) rather than synthesizing an answer from 5 paragraphs. Takeaway: Start your articles with a direct statement. Bad: “In this fast-paced world, automation is becoming key...” Good: “Demo automation is the process of using software to...” 2. Conversational writing Text that gets cited is 2x more likely (18% vs. 8.9%) to contain a question mark. When we talk about conversational writing, we mean the interplay between questions and answers. Start with the user’s query as a question, then answer it immediately. For example: Winner Style: “What is Programmatic SEO? It is...” Loser Style: “In this article, we will discuss the various nuances of...” 78.4% of citations with questions come from headings. The AI is treating your H2 tag as the user prompt and the paragraph immediately following it as the generated response. Example loser structure: <h2>The History of SEO</h2> (Abstract Topic) <p>It began in the early 90s...</p> Example winner structure (The 78%): <h2>When did SEO start?</h2> (Literal Query) <p>SEO started in...</p> (Direct Answer) The reason that specific example wins is because of what I call “entity echoing:” The header asks about SEO, and the very first word of the answer is SEO. 3. Entity richness Normal English text has an “entity density” (that is, contains proper nouns like brands, tools, people) of ~5-8%. Heavily cited text has an entity density of 20.6%! The 5-8% figure is a linguistic benchmark derived from standard corpora like the Brown Corpus (1 million words of representative English text) and the Penn Treebank (Wall Street Journal text). Example: Loser sentence: “There are many good tools for this task.” (0% Density) Winner sentence: “Top tools include Salesforce, HubSpot, and Pipedrive.” (30% Density) LLMs are probabilistic. Generic advice (”choose a good tool”) is risky and vague, but a specific entity (”choose Salesforce”) is grounded and verifiable. The model prioritizes sentences that contain “anchors” (entities) because they lower the perplexity (confusion) of the answer. A sentence with 3 entities carries more “bits” of information than a sentence with 0 entities. So, don’t be afraid of namedropping (yes, even your competitors). 4. Balanced sentiment In my analysis, the cited text has a balanced Subjectivity score of 0.47. The Subjectivity Score is a standard metric in Natural Language Processing (NLP) that measures the amount of personal opinion, emotion, or judgment in a piece of text. The score runs on a scale from 0.0 to 1.0: 0.0 (Pure Objectivity): The text contains only verifiable facts. No adjectives, no feelings. Example: “The iPhone 15 was released in September 2023.” 1.0 (Pure Subjectivity): The text contains only personal opinions, emotions, or intense descriptors. Example: “The iPhone 15 is an absolutely stunning masterpiece that I love.” AI doesn’t want dry Wikipedia text (0.1), nor does it want unhinged opinion (0.9). It wants the “analyst voice.” It prefers sentences that explain how a fact applies, rather than just stating the stat alone. The “winning” tone looks like this (Score ~0.5): “While the iPhone 15 features a standard A16 chip (Fact), its performance in low-light photography makes it a superior choice for content creators (Analysis/Opinion).“ 5. Business-grade writing Business-grade writing (think The Economist or HBR) gets more citations. “Winners” have a Flesch-Kincaid score of 16 (college level) compared to the “losers” with 19.1 (Academic / PhD level). Even for complex topics, complexity can hurt. A grade 19 score means sentences are long, winding, and filled with multisyllable jargon. The AI prefers simple subject-verb-object structures with short to moderately long sentences, because they are easier to extract facts from. Conclusion The “ski ramp” pattern quantifies a misalignment between narrative writing and information retrieval. The algorithm interprets the slow reveal as a lack of confidence. It prioritizes the immediate classification of entities and facts. High-visibility content functions more like a structured briefing than a story. This imposes a “clarity tax” on the writer. The winners in this dataset rely on business-grade vocabulary and high entity density, disproving the theory that AI rewards “dumbing down” content (with exceptions [ https://substack.com/redirect/eeaccfde-85c2-4ea3-9c17-6c2a87f86b2b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]). We’re not only writing robots… yet. But the gap between human preferences and machine constraints is closing. In business writing, humans scan for insights. By front-loading the conclusion, we satisfy the algorithm’s architecture and the human reader’s scarcity of time. Methodology To understand exactly where and why AI cites content, we analyzed the code. All data in this research comes from Gauge. Gauge provided roughly 3 million AI answers from ChatGPT, alongside 30 million citations. Each citation URL’s web content was scraped at the time of answer to provide direct correlation between the true web content and the answer itself. Both raw HTML and plaintext were scraped. Gauge [ https://substack.com/redirect/3ecfaad5-da23-4fec-aa2c-5753936bf4cd?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] is extending a one-time 75% discount for Growth Memo subscribers to help them grow their AI presence. Gauge will pull a custom assessment of your brand if you book a live demo [ https://substack.com/redirect/1f6e3ac5-03b7-476d-8363-710b7af42a0c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Mention Growth Memo during the call or use GROWTHMEMO at checkout to redeem. 1. The Dataset We started with a universe of 1.2 million search results and AI-generated answers. From this, we isolated 18,012 verified citations for positional analysis and 11,022 citations for “linguistic DNA” analysis. Significance: This sample size is large enough to produce a P-Value of 0.0 (p < 0.0001), meaning the patterns we found are statistically indisputable. 2. The “Harvester” Engine To find exactly which sentence the AI was quoting, we used semantic embeddings (a Neural Network approach). The Model: We used all-MiniLM-L6-v2, a sentence-transformer model that understands meaning, not just keywords. The Process: We converted every AI answer and every sentence of the source text into 384-dimensional vectors. We then matched them using cosine similarity. The Filter: We applied a strict similarity threshold (0.55) to discard weak matches or hallucinations, ensuring we only analyzed high-confidence citations. 3. The Metrics Once we found the exact match, we measured 2 things: Positional Depth: We calculated exactly where the cited text appeared in the HTML (e.g., at the 10% mark vs. the 90% mark). Linguistic DNA: We compared “winners” (cited intros) vs. “losers” (skipped intros) using Natural Language Processing (NLP) to measure: Definition Rate: Presence of definitive verbs (is, are, refers to). Entity Density: Frequency of proper nouns (brands, tools, people). Subjectivity: A sentiment score from 0.0 (Fact) to 1.0 (Opinion). For premium subscribers: Your implementation kit is below. Reading this memo won’t change your citations or writing practices. But training your writers will. Most teams will read the ski ramp data, nod, and keep writing the same way. Premium subscribers get the tools to break that pattern: a ready-for-you-slide training deck built for team meetings and a checklist that turns this research into a repeatable content audit. 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View this post on the web at https://www.growth-memo.com/p/synthetic-personas-for-better-prompt This Memo was sent to 24,402 subscribers. Welcome to +107 new readers! Get the free memo weekly. Upgrade to Premium [ https://substack.com/redirect/98c65cac-8e14-472c-a321-1c542daf4fa0?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. We all know prompt tracking is directional. The most effective way to reduce noise is to track prompts based on personas. This week, I’m covering: Why AI personalization makes traditional “track the SERP” models incomplete and how synthetic personas fill the gap The Stanford validation data showing 85% accuracy at 1/3 the cost and how Bain cut research time by 50-70% The 5-field persona card structure and how to generate 15-30 trackable prompts per segment across intent levels Premium subscribers [ https://substack.com/redirect/98c65cac-8e14-472c-a321-1c542daf4fa0?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] get a production-ready script to build synthetic personas from your support tickets, CRM data, and reviews. AI is becoming the new first page of Google AI tools like ChatGPT, Gemini, and Perplexity don’t invent authority. They repeat what credible websites already say. That’s why we launched Branded Web Mentions for AI visibility. A new service from dofollow.com [ https://substack.com/redirect/b0e094cd-43a3-46b9-aaf7-cbfe0103f865?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] that earns authoritative, contextual brand mentions on real SaaS + industry publications — so you show up more often in AI-generated recommendations and comparisons. Earned brand mentions on credible publications Context that reinforces your category + topical expertise Long-term brand recognition inside AI-generated answers and comparisons Trusted by Surfshark, Pitch, and Experian. Book a call. [ https://substack.com/redirect/b0e094cd-43a3-46b9-aaf7-cbfe0103f865?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] A big difference between classic and AI search is that the latter delivers highly personalized results. Every user gets different answers based on their context, history, and inferred intent. The average AI prompt is ~5x longer than classic search keywords (23 words vs. 4.2 words), conveying much richer intent signals that AI models use for personalization. Personalization creates a tracking problem: You can’t monitor “the” AI response anymore because each prompt is essentially unique, shaped by individual user context. Traditional persona research solves this - you map different user segments and track responses for each - but it creates new problems. It takes weeks to conduct interviews and synthesize findings. By the time you finish, the AI models have changed. Personas become stale documentation that never gets used for actual prompt tracking. Synthetic personas fill the gap by building user profiles from behavioral and profiling data: analytics, CRM records, support tickets, review sites. You can spin up hundreds of micro-segment variants and interact with them in natural language to test how they’d phrase questions. Most importantly: They are the key to more accurate prompt tracking because they simulate actual information needs and constraints. The shift: Traditional personas are descriptive (who the user is), synthetic personas are predictive (how the user behaves). One documents a segment, the other simulates it. Example: Enterprise IT buyer persona with job-to-be-done “evaluate security compliance” and constraint “need audit trail for procurement” will prompt differently than an individual user with the job “find cheapest option” and constraint “need decision in 24 hours.” First prompt: “enterprise project management tools SOC 2 compliance audit logs” Second prompt: “best free project management app” Same product category, completely different prompts. You need both personas to track both prompt patterns. Build personas with 85% accuracy for 1/3 of the price Stanford and Google DeepMind trained [ https://substack.com/redirect/52b8dc0d-754f-4c0f-80cd-3ca835ecc1fe?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] synthetic personas on 2-hour interview transcripts, then tested whether the AI personas could predict how those same real people would answer survey questions later. The method: Researchers conducted follow-up surveys with the original interview participants, asking them new questions. The synthetic personas answered the same questions. Result: 85% accuracy. The synthetic personas replicated what the actual study participants said. For context, that’s comparable to human test-retest consistency. If you ask the same person the same question 2 weeks apart, they’re about 85% consistent with themselves. The Stanford study also measured how well synthetic personas predicted social behavior patterns in controlled experiments - things like who would cooperate in trust games, who would follow social norms and would share resources fairly. The correlation between synthetic persona predictions and actual participant behavior was 98%. This means the AI personas didn’t just memorize interview answers; they captured underlying behavioral tendencies that predicted how people would act in new situations. Bain & Company ran a separate pilot [ https://substack.com/redirect/1c34e472-ad9d-4f60-bb1b-1118d5ad7d7f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] that showed comparable insight quality at 1/3 the cost and 1/2 the time of traditional research methods. Their findings: 50-70% time reduction (days instead of weeks) and 60-70% cost savings (no recruiting fees, incentives, transcription services). The catch: These results depend entirely on input data quality. The Stanford study used rich, 2-hour interview transcripts. If you train on shallow data (just pageviews or basic demographics), you get shallow personas. Garbage in, garbage out. How to build synthetic personas for better prompt tracking Building a synthetic persona has 3 parts: Feed it with data from multiple sources about your real users: call transcripts, interviews, message logs, organic search data. Fill out the Persona Card - the 5 fields that capture how someone thinks and searches. Add metadata to track the persona’s quality and when it needs updating. The mistake most teams make: trying to build personas from prompts. This is circular logic - you need personas to understand what prompts to track, but you’re using prompts to build personas. Instead, start with user information needs, then let the persona translate those needs into likely prompts. Data sources to feed synthetic personas: The goal is to understand what users are trying to accomplish and the language they naturally use: Support tickets and community forums: Exact language customers use when describing problems. Unfiltered, high-intent signal. CRM and sales call transcripts: Questions they ask, objections they raise, use cases that close deals. Shows decision-making process. Customer interviews and surveys: Direct voice-of-customer on information needs and research behavior. Review sites (G2, Trustpilot, etc.): What they wish they’d known before buying. Gap between expectation and reality. Search Console query data: Questions they ask Google. Use regex to filter for question-type queries: (?i)^(who|what|why|how|when|where|which|can|does|is|are|should|guide|tutorial|course|learn|examples?|definition|meaning|checklist|framework|template|tips?|ideas?|best|top|lists?|comparison|vs|difference|benefits|advantages|alternatives)\b.* (I like to use the last 28 days, segment by target country) Persona card structure (5 fields only - more creates maintenance debt): These 5 fields capture everything needed to simulate how someone would prompt an AI system. They’re minimal by design. You can always add more later, but starting simple keeps personas maintainable. Job-to-be-done: What’s the real-world task they’re trying to accomplish? Not “learn about X” but “decide whether to buy X” or “fix problem Y.” Constraints: What are their time pressures, risk tolerance levels, compliance requirements, budget limits, and tooling restrictions? These shape how they search and what proof they need. Success metric: How do they judge “good enough?” Executives want directional confidence. Engineers want reproducible specifics. Decision criteria: What proof, structure, and level of detail do they require before they trust information and act on it? Vocabulary: What are the terms and phrases they naturally use? Not “churn mitigation” but “keeping customers.” Not “UX optimization” but “making the site easier to use.” Specification requirements: This is the metadata that makes synthetic personas trustworthy; it prevents the “black box” problem. When someone questions a persona’s outputs, you can trace back to the evidence. These requirements form the backbone of continuous persona development. They keep track of changes, sources, and confidence in the weighting. Provenance: Which data sources, date ranges, and sample sizes were used (e.g., “Q3 2024 Support Tickets + G2 Reviews”). Confidence score per field: A High/Medium/Low rating for each of the 5 Persona Card fields, backed by evidence counts. (e.g., “Decision Criteria: HIGH confidence, based on 47 sales calls vs. Vocabulary: LOW confidence, based on 3 internal emails”). Coverage notes: Explicitly state what the data misses (e.g., “Overrepresents enterprise buyers, completely misses users who churned before contacting support”). Validation benchmarks: 3-5 reality checks against known business truths to spot hallucinations. (e.g., “If the persona claims ‘price’ is the top constraint, does that match our actual deal cycle data?”). Regeneration triggers: Pre-defined signals that it’s time to re-run the script and refresh the persona (e.g., a new competitor enters the market, or vocabulary in support tickets shifts significantly). Where synthetic personas work best Before you build synthetic personas, understand where they add value and where they fall short. High-value use cases: Prompt design for AI tracking: Simulate how different user segments would phrase questions to AI search engines (the core use case covered in this article) Early-stage concept testing: Test 20 messaging variations, narrow to top 5 before spending money on real research Micro-segment exploration: Understand behavior across dozens of different user job functions (enterprise admin vs. individual contributor vs. executive buyer) or use cases without interviewing each one Hard-to-reach segments: Test ideas with executive buyers or technical evaluators without needing their time Continuous iteration: Update personas as new support tickets, reviews, andsales calls come in Crucial limitations of synthetic personas you need to understand: Sycophancy bias: AI personas are overly positive. Real users say “I started the course but didn’t finish.” Synthetic personas say “I completed the course.” They want to please. Missing friction: They’re more rational and consistent than real people. If your training data includes support tickets describing frustrations or reviews mentioning pain points, the persona can reference these patterns when asked - it just won’t spontaneously experience new friction you haven’t seen before. Shallow prioritization: Ask what matters and they’ll list 10 factors as equally important. Real users have a clear hierarchy (price matters 10x more than UI color). Inherited bias: Training data biases flow through. If your CRM underrepresents small business buyers, your personas will too. False confidence risk: The biggest danger. Synthetic personas always have coherent answers. This makes teams overconfident and skip real validation. Operating rule: Use synthetic personas for exploration and filtering, not for final decisions. They narrow your option set. Real users make the final call. Solving the cold start problem for prompt tracking Synthetic personas are a filter tool, not a decision tool. They narrow your option set from 20 ideas to 5 finalists. Then you validate those 5 with real users before shipping. For AI prompt tracking specifically, synthetic personas solve the cold-start problem. You can’t wait to accumulate 6 months of real prompt volume before you start optimizing. Synthetic personas let you simulate prompt behavior across user segments immediately, then refine as real data comes in. Where they’ll cause you to fail is if you use them as an excuse to skip real validation. Teams love synthetic personas because they’re fast and always give answers. That’s also what makes them dangerous. Don’t skip the validation step with real customers. Premium: Synthetic persona script Premium subscribers get a production-ready script that builds synthetic personas from your actual user data, along with detailed guidance on how to generate prompts from the persona cards, as well as tracking structure for prompt monitoring. The script handles 3 things for you: data synthesis (feed it your data!), persona card generation, and prompt generation. Run the script in Google Colab. Update the core documents when your data changes, and personas stay current instead of stale documentation. Upgrade to Premium to get the script + full advisory-level guidance... 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View this post on the web at https://www.growth-memo.com/p/growth-intelligence-brief-13 Welcome to another Growth Intelligence Brief [ https://substack.com/redirect/fee85577-ac35-45aa-9501-a9b78a7df6ac?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], where organic growth leaders discover what matters - getting insights into the bigger picture and guidance on how to stay ahead of the competition. As a free subscriber, you’re getting the first big story. Premium subscribers get the whole brief. Today’s Growth Intelligence Brief went out to 534 (+28) marketing leaders. This week, we’re looking at the infrastructure of the Agentic Web coming together across major Search and Commerce updates from Google and OpenAI, and how Microsoft Clarity finally lets us track AI agent visitors. I’ll also connect the dots on what this all means for you. Microsoft Clarity [ https://substack.com/redirect/66ad9b82-2c27-4fb4-8be7-226e56809b48?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]is offering us some clarity Here’s what happened: Microsoft Clarity introduced reporting that shows AI bot traffic and activity across websites, offering more transparency into how automated agents crawl and interact with content. (And Growth Memo readers everywhere rejoiced!) The new “Bot Activity” report [ https://substack.com/redirect/66ad9b82-2c27-4fb4-8be7-226e56809b48?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] is a dashboard that tracks server-side signals to show exactly how AI agents access your site. Unlike standard analytics that track human visits, this requires a CDN or server integration to capture the “upstream” activity, meaning the scraping and crawling that happens before a user ever sees an answer. The report breaks down traffic by “Bot Operator” (e.g., OpenAI, Anthropic, Google) and, crucially, “Bot Activity” type, distinguishing between an “AI Crawler” (scraping for training data) and an “AI Assistant” (fetching a live answer for a user). Why this news matters: AI bots don’t behave like search bots; they don’t just index content, they consume it. What was once hidden in complex server logs is now visible, letting us easily track whether an AI is reading our site 10,000 times a day or ignoring it completely. This dashboard democratizes the “AI Request Share” metric, allowing us to quantify how much of our infrastructure is serving non-human agents versus actual customers without needing a data science team. It effectively separates “Training” (extractive) from “Inference” (potential visibility). My take on this: It’s okay not to get traffic. That’s the new reality we have to accept. The real breakthrough here is that now it’s much easier to know whether an AI actually used your content as the basis for those answers. Previously, “Zero Click” was a black box; we had to guess if our content was fueling the AI’s response or if we were just being ignored. Now, we have proof. If you see high AI consumption of your content, you know you are winning mindshare and influencing the answer, even if you aren’t getting the click. This metric finally validates the strategy of “feeding the bot” to maintain brand relevance in a world where the user might never leave the chat interface. Here’s what to do: Enable Server-Side Integration: You cannot get this data with just the JavaScript snippet. Connect your CDN (Cloudflare, etc.) to Clarity to see the server logs. Audit “Path Requests”: Identify what they are reading. Are they scraping your high-value proprietary data (pricing, JSON endpoints) or your brand-building content (blog, about page)? Calculate your “AI Conversion Rate”: Compare your AI Request Volume (from Clarity) to your AI Referral Traffic (from GA4). If the ratio is massively skewed, you need to rethink your content strategy for agents. From Search Engine to Personal Agent... Unsubscribe https://substack.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.1dD2ZDxzIUbMF8gyr8Ae3CQTE0_ygpaGbxPFMWW0DgM?

[https://eotrx.substackcdn.com/open?token=eyJtIjoiPDIwMjYwMjAzMjM1ODAyLjMuMTYxYmExNmFjZWRkOTdlOS5kMmM5N3RqY0BtZzIuc3Vic3RhY2suY29tPiIsInUiOjQyNjUxNTA0MCwiciI6ImJAZW1haWwuZ29tb2R1bHIuY29tIiwiZCI6Im1nMi5zdWJzdGFjay5jb20iLCJwIjpudWxsLCJ0IjpudWxsLCJhIjpudWxsLCJzIjoxNDUzNDUyLCJjIjoiZHJpcC1jYW1wYWlnbi1lbWFpbCIsImYiOnRydWUsInBvc2l0aW9uIjoidG9wIiwiaWF0IjoxNzcwMTYzMDgzLCJleHAiOjE3NzI3NTUwODMsImlzcyI6InB1Yi0wIiwic3ViIjoiZW8ifQ.NnDhdo5CPDQBSGzR4K_2ti1UWt7B09rWeDx-i2BmjJs] What would you do with 2 extra hours every week? ͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­ Most marketers aren’t drowning in work. They’re drowning in thinking about work. AI-search has changed the game. And the cognitive load of growth marketers has skyrocketed. Growth Memo Premium subscribers don’t have to guess at what works. They plug into a system that reduces uncertainty and clears the mental runway. With Premium content, I aim to give you back ~2 hours of a week - hours you’d normally spend: * Interpreting AI-search changes * Rewriting strategy slides * Trying to figure out which inputs matter * Navigating contradictory advice Here’s how: 1. Growth Intelligence Briefs: Your bi-monthly distilled updates on what current events in our industry matter, why they matters, and what to do (or not do). 2. Strategy frameworks: Think: pitch-deck templates, brainstorming and planning tools, crucial reframes, and tactical “do this next” modules. 3. Advisory-level guidance: I help you interpret the landscape and keep tabs on what’s next. 👉 Get 2 hours/week back - join Growth Memo Premium [https://email.mg2.substack.com/c/eJxs0EGO4yAQBdDTmF2sAgy2Fyxmk2tYBVQcJgYsDDPy7Vtx1FIvev31q56-w0prLqfxJew3h3HHsKYbRQwb82bwfFITI8PHEbiWMEl2ZctKiQpW8gvWHylwYE8zg_ISZ-2EnED6UYDSM1jJUeLktGTBCBAaBEgh1QSilz3X3CLX6Mj7eaS598LNY_3rugHiKvqj2aOie_UuRxaO5VHokphaGrHNPGvdj07-6cS9E_ct2ILl7NeS_9fnLVLM72In7nuhGFpkKdfwCA5ryGmp504mYnlRDWlle7OLyzG2FOq5UEK7kf882pvdvlvBGz4oOSjBirHdABeoX3PMvm3lkh7N-hwxJPOhvCWs_jp3O6i8bw5CK65gAPbPiK8AAAD__4LJioo] —Kevin © 2026 Kevin Indig 548 Market Street PMB 72296, San Francisco, CA 94104 Unsubscribe [https://email.mg2.substack.com/c/eJxskk1vqzgUhn8N7IJs871gkWlChkxNp520SbuJ_BViwDYDJoT8-qukulIXd_3qOefofQ4jVlSmnzPey27BiOqIrPRCKCJbl2cBh0mYuCKDcQxg5IPEdx_ZsRJa9MQKfiT2RwogcM8ZimMUkeTEoogIKPwgSlOQ8AhFaeoHiXBlhgCKAAI-8sMEIM_3YAQpgRFhgvM0FqnHEUtjWzMnAKpC3jDSwRLWeMwoVw7HUy8el2S2H4XbZmdru8Hxlw7KHZRP0-RVvZnseaGEMnfIQTnppIPyC3RQ_mAdlGtj5UkyYqXRDspHfd_DekmF4-fWNEI7_krMW8jQx3xAbVPUBuD6E-Ld-1SulkOh3y5s09ZFbSTd5zNBH4DsQ13ISbK_PyTdtPXn4a2jKDyR_at8qdeg3GFQ7oqhUO2ZPxUR3jEfr9YI35YBnif5dThPRW2uL6vltaybEK-q2_PTtmM-li9yO_F9YfHuFeLbK8SyGApdwk9ZRIUKL3zTKrIvz3zTXqiEkN4zXdZMtfLrv23qrder0-3r326D_5lL1dTr5_8P77S5qbJa2piH6-uCXWmC8r8a92c7Rzt3IlOkb4SVunK7kR6ZUWrU0s5HoQltBf-20Y20_U1JnsEg9IMQuX1GnQA8mvcqowwf2_6hcxgpN4pInX07uytz7R9_chxEf58ZoCiEIQiAe8nQrwAAAP__g8DuOA] Get the app [https://substackcdn.com/image/fetch/$s_!IzGP!,w_262,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack.com%2Fimg%2Femail%2Fgeneric-app-button%402x.png]https://email.mg2.substack.com/c/eJxskc1u5CAQhJ8Gbragwfb4wGGlaF7D4qfHw8b8CDe7mrdfxVFWOeTC5auiqtTeEu6lvUxosQ7epmrjngdMNh48GB3kbbpxNHJZhJyVuCl-sW3HjM0Shs3SNyqk4E-zgl4dgHAyrMsq1IwPt_rJatSoJqV5NCBgFiAUqOkmYFSjnKWzcrYeQ1gXXMcAfl3ot2dapB3Gs7uTrH8ffUk8ntuj4dXEUOvID_MkqidTvxjcGdy_qxncba2f73BSaTg0DLGhJ6bundL2tZuptw9Nsu0dKeadwXzhkgkzMfV2JQ6PUgjb4DpRyTwXio_oLcWSN3pVNP_9vHa3-ZJSz5FeG2brDgyfjWt3x5crBiP1pPQEvBnHtLhyxr2kEvrRrslnd6EkG7PZW_lLz4SpcPrxbv3E9vGnhnmSk9CC_zHwLwAA__9Ax6V-Start writing [https://substackcdn.com/image/fetch/$s_!LkrL!,w_270,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack.com%2Fimg%2Femail%2Fpublish-button%402x.png]https://email.mg2.substack.com/c/eJxskk2O3CAQhU8Du7b4M3YvWERy-gI5gMVP2U3GgIWLJH37qD3To1nMkleP7z1QeYuwlvowocb94m3abVzzBZKNGw1GBT72IwXDh4FxLdko6TmbV8hQLUKYLX6ZMs7o3fTX0GvVcz76oDRzIMarGhyXyzJqLiWNRjChmWBSyH5kopMd19xZrq2HEK4DXLsg_HXA354ollbRHc0daP1b50ui8ZiXCmcTg7UB3cwdcT-I_EHEjYjbV_fzGNfcdiJvDdN8lFY9EDm9TETop54gxJaInE7uh-hLRshI5LSUglBf8sdPETnZhmWJ2wbh8ml5lvt1Zv48WXJyRLGT260lldC2-l5NVyKngf_bFaO5YFyitxhLnvGxg0m2vgHGvNK9udmXlFqO-JghW7dBeH_73tz2uhWD4aqXqhe0mu8z6dFcKMnGbNZa_uI9QSoUv92AdkB9MpXQPe-ZYvSPEf8DAAD___kPvh4 [https://eotrx.substackcdn.com/open?token=eyJtIjoiPDIwMjYwMjAzMjM1ODAyLjMuMTYxYmExNmFjZWRkOTdlOS5kMmM5N3RqY0BtZzIuc3Vic3RhY2suY29tPiIsInUiOjQyNjUxNTA0MCwiciI6ImJAZW1haWwuZ29tb2R1bHIuY29tIiwiZCI6Im1nMi5zdWJzdGFjay5jb20iLCJwIjpudWxsLCJ0IjpudWxsLCJhIjpudWxsLCJzIjoxNDUzNDUyLCJjIjoiZHJpcC1jYW1wYWlnbi1lbWFpbCIsImYiOnRydWUsInBvc2l0aW9uIjoiYm90dG9tIiwiaWF0IjoxNzcwMTYzMDgzLCJleHAiOjE3NzI3NTUwODMsImlzcyI6InB1Yi0wIiwic3ViIjoiZW8ifQ.13Phhw6q5314XkiHcXYFmRZLTXfCdkPeuTZtJKMU7eU][https://email.mg2.substack.com/o/eJxskEGO3CAQRU_T7NqCAht7wVmsAgqatAELQ6K-fRRHI81i1k_vVek77BRr-xjf0vl0mE9MsTwpYzqYN8qLdV4ZGaE1F4vkq2Q32yMVatjJ79i_US44exkx60VSULhBcFrABjZoXGUgESwEzpIBDgsHLkHOK4dJTmIRFsWCjrzfNG2TB7fp_ss9FM8RpmvYq6N7T65mlq49NLo_Mb0NYqX2FJLDnmrZ--ckk7G9qacS2Tns7mrOo6T-2amgPcj_185hjy8reSPULNUMrBn7UPzOT7Hm6sfR7rvXsL5mTMXEVv_0V6ZcWf9xvHFR-9dUsMxi5oqz3wb-BgAA__-1cXoR]

View this post on the web at https://www.growth-memo.com/p/gsc-data-is-75-incomplete This Memo was sent to 24,218 subscribers. Welcome to +124 new readers! Get the free memo weekly. Upgrade to Premium [ https://substack.com/redirect/d5662347-5f0e-4d6d-8289-dc5becb342d5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. My findings this week show GSC data is about 75% incomplete, making single-source GSC decisions dangerously unreliable. Premium subscribers [ https://substack.com/redirect/d5662347-5f0e-4d6d-8289-dc5becb342d5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] get the scripts I used for bot share and a sample rate to apply to their own data. Show up everywhere your customers search Semrush Enterprise empowers you to dominate visibility across both search engines and AI search. It’s one platform that connects all your data, strategy and teams. Powerful technical SEO capabilities lay the foundation. Real-time tracking and optimization for both SEO and AI visibility are alongside, purpose built for global scales. Semrush Enterprise is how ambitious brands win every layer of discovery. Plus, it’s all powered by the industry’s leading search database. Discover the unfair advantages Semrush Enterprise could unlock for your brand. Explore Semrush Enterprise [ https://substack.com/redirect/12c374cc-2d9e-426f-b735-10baa5b26ab6?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 1. GSC used to be ground truth Search Console data used to be the most accurate representation of what happens in the search results. But privacy sampling, bot-inflated impressions, and AI Overview (AIO) distortion suck the reliability out of the data. Without understanding how your data is filtered and skewed, you risk drawing the wrong conclusions from GSC data. SEO data has been on a long path of becoming less reliable, starting with Google killing keyword referrer to excluding critical SERP Features from performance results. But 3 key events over the last 12 months topped it off: January 2025: Google deploys “SearchGuard,” requiring Javascript and (sophisticated) CAPTCHA for anyone looking at search results (turns out [ https://substack.com/redirect/f5ffc5e5-fc86-41be-959e-b33773673272?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], Google uses a lot of advanced signals to differentiate humans from scrapers). March 2025: Google significantly [ https://substack.com/redirect/32810285-84b1-47a7-a1ed-7425489d1c32?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] amps up the number of AI Overviews in the SERPs. We’re seeing a significant spike in impressions and drop in clicks. September 2025: Google removes [ https://substack.com/redirect/37ffbb33-bc4d-4f8b-a587-a46b52387055?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] num=100 parameter, which SERP scrapers use to parse the search results. The impression spike normalizes, clicks stay down. On one hand, Google took measures to clean up GSC data. On the other hand, the data still leaves us with more open questions than answers. 2. Privacy sampling hides 75% of queries Google filters out a significant amount of impressions (and clicks) for “privacy” reasons. One year ago, Patrick Stox analyzed a large dataset and came to the conclusion that almost 50% [ https://substack.com/redirect/8b7d9bd1-c32d-4e1b-9c61-1fc5aa0e49d9?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] are filtered out. I repeated the analysis (10 sites in B2B out of the USA) across ~4 million clicks and ~450 million impressions. Methodology: Google Search Console (GSC) provides data through two API endpoints that reveal its filtering behavior. The aggregate query (no dimensions) returns total clicks and impressions, including all data. The query-level query (with ‘query’ dimension) returns only queries meeting Google’s privacy threshold. By comparing these 2 numbers, you can calculate the filter rate. For example, if aggregate data shows 4,205 clicks but query-level data only shows 1,937 visible clicks, Google filtered 2,268 clicks (53.94%). I analyzed 10 B2B SaaS sites (~4 million clicks, ~450 million impressions), comparing 30-day, 90-day, and 12-month periods against the same analysis from 12 months prior. My conclusion: 1/ Google filters out ~75% of impressions The filter rate on impressions is incredibly high, with ¾ filtered for privacy. 12 months ago, the rate was only 2 percentage points higher. The range I observed went from 59.3% all the way up to 93.6%. 2/ Google filters out ~38% of clicks, but ~5% less than 12 months ago Click filtering is not something we talk about a lot, but it seems Google doesn’t report up to one-third of all clicks that happened. 12 months ago, Google filtered out over 40% of clicks. The range of filtering spans from 6.7% to 88.5%! The good news is that the filter rate has gone slightly down over the last 12 months, probably as a result of fewer “bot impressions.” The bad news: The core problem persists. Even with these improvements, 38% click-filtering and 75% impression-filtering remain catastrophically high. A 5% improvement doesn’t make single-source GSC decisions reliable when 3/4 of your impression data is missing. Premium subscribers get the script I used to do this analysis so they understand how much of their own data is filtered out. 3. 2025 impressions are highly inflated The last 12 months show a rollercoaster of GSC data: In March 2025, Google intensified the rollout of AIOs and showed 58% more for the sites I analyzed. In July, impressions grew by 25.3% and by another 54.6% in August. SERP scrapers somehow found a way around SearchGuard (the protection “bot” that Google uses to prevent SERP scrapers) and caused “bot impressions” to capture AIOs. In September, Google removed the num=100 parameter, which caused impressions to drop by 30.6%. Fast forward to today: Clicks decreased by 56.6% since March 2025 Impressions normalized (down -9.2%) AIOs reduced by 31.3% I cannot come to a causative number of reduced clicks from AIOs, but the correlation is strong: 0.608. We know AIOs reduce clicks [ https://substack.com/redirect/ee10ba81-c9b6-44e4-a089-f30d18d3d9ad?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (makes logical sense), but we don’t know exactly how much. To figure that out, I’d have to measure CTR for queries before and after an AIO shows up. But how do you know click decline is due to an AIO and not just poor content quality or content decay? Look for temporal correlation: Track when your clicks dropped against Google’s AIO rollout timeline (March 2025 spike). Poor content quality shows gradual decline; AIO impact is sharp and query-specific. Cross-reference with position data. If rankings hold steady while clicks drop, that signals AIO cannibalization. Check if the affected queries are informational (AIO-prone) vs. transactional (AIO-resistant). Your 0.608 correlation coefficient between AIO presence and click reduction supports this diagnostic approach. 4. Bot impressions are rising I have reason to believe that SERP scrapers are coming back. We can measure the amount of impressions likely caused by bots by filtering out GSC data by queries that contain more than 10 words and 2 impressions. The chance that such a long query (prompt) is used by a human twice is close to 0. The logic of bot impressions: Hypothesis: Humans rarely search for the exact same 5+ word query twice in a short window. Filter: Identify queries with 10+ words that have >1 impression but 0 clicks. Caveat: This method may capture some legitimate zero-click queries, but provides a directional estimate of bot activity. I compared those queries over the last 30, 90, and 180 days: Queries with +10 words and +1 impression grew by 25% over the last 180 days The range of bot impressions spans from 0.2% to 6.5% (last 30 days) Here’s what you can anticipate as a “normal” percentage of bot impressions for a typical SaaS site: Based on the 10-site B2B dataset, bot impressions range from 0.2% to 6.5% over 30 days, with queries containing 10+ words and 2+ impressions but 0 clicks. For SaaS specifically, expect 1-3% baseline for bot impressions. Sites with extensive documentation, technical guides, or programmatic SEO pages trend higher (4-6%). The 25% growth over 180 days suggests scrapers are adapting post-SearchGuard. Monitor your percentile position within this range more than the absolute number. Bot impressions do not affect your actual rankings - just your reporting by inflating impression counts. The practical impact? Misallocated resources if you optimize for inflated impression queries that humans never search for. 5. The measurement layer is broken Single-source decisions based on GSC data alone become dangerous: 3/4 of impressions are filtered Bot impressions generate up to 6.5% of data AIOs reduce clicks by over 50% User behavior is structurally changing Your opportunity is in the methodology: Teams that build robust measurement frameworks (sampling rate scripts, bot-share calculations, multi-source triangulation) have a competitive advantage. Premium: Scripts for data sampling and long query tracking The scripts below let you quantify how much of your GSC data is noise and pinpoint where GSC impressions stop being reliable. If you’re still forecasting, prioritizing, or making strategic calls off raw GSC impressions, this is how you correct the signal before it derails your decision-making... Unsubscribe https://substack.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.e7Ag--a2aguLOjhk5bfkvXI7RP8HvFI9mR4FUurFiAQ?

View this post on the web at https://www.growth-memo.com/p/how-do-you-compete-in-agentic-commerce Sent to 24,068 subscribers. Welcome to +156 new readers! Btw, Graphite published insightful research about why SEO is not dead [ https://substack.com/redirect/d6941db7-2415-4a20-b17f-9cb9cd38ea5a?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Agentic commerce transforms organic search from a source of cheap traffic into the mandatory gatekeeper of AI verification. Marketing arbitrage dies; product truth wins. This week, we’re covering: Why agentic commerce filters out marketing-first brands and rewards granular product data How ChatGPT, Copilot, and Google’s protocols reshape merchant economics and customer relationships Which feeds to optimize, which protocols to prioritize, and the implementation sequence that matters Premium subscribers [ https://substack.com/redirect/d6a2a978-cf5e-4dc4-8e7b-3d6c721b9261?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] also get my Agentic Commerce Adoption Playbook - a 70+ item implementation checklist covering Shopify settings, feed tags, and protocol-specific constraints. Want to improve ecommerce performance in ChatGPT ChatGPT uses Google Shopping results to form its product recommendations. We ran an experiment to confirm this theory and found the encoded fan-out queries that take place in the background. We also found that the top product in ChatGPT and Google Shopping overlapped 75% of the time. If you’re an ecommerce brand, your Google Shopping feed needs to be a focus. Any product pages or listings that fill these must be up to date. Check out the full findings, then start tracking and improving your product performance across AI search platforms with Semrush Enterprise AIO [ https://substack.com/redirect/da748442-2c82-47dd-a596-aebdf988d0f3?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. CTA: Read the experiment findings [ https://substack.com/redirect/6d881822-1829-4d7d-b01b-0bab4ac63d5f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Agentic commerce acts as a “great filter,” so to speak, for marketing arbitrage, transforming organic search from a source of cheap traffic into the mandatory gatekeeper of AI verification. The signal is already visible in the noise. During the 2025 holiday season, AI agents powered 20% of retail sales [ https://substack.com/redirect/f2fea223-b7c1-4c90-a596-64c1f5b651b7?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Even allowing for loose definitions, the era of agentic commerce has arrived. All major LLMs now offer direct checkout and new commerce protocols: ChatGPT has Instant Checkout with Shopify and Etsy, and ACP (Agentic Commerce Protocol) Microsoft Copilot uses ACP and offers Copilot Checkout with PayPal, Shopify, and Stripe. Google has embedded checkout in AI Mode and Gemini via its Universal Commerce Protocol (UCP). The infrastructure question is settled, but the strategic question remains: How do you compete when users don’t need to click through to websites to buy? 1. Agentic Commerce has a hole in the middle The phrasing “agentic commerce” sets the wrong expectation. Autonomous purchasing, where you give an agent a credit card and monthly allowance to buy on your behalf, is not becoming a reality in the near future. High-priced purchases like plane tickets or cars are too risky to delegate. You have idiosyncratic preferences (airline seat rules, car features) that no agent can reliably model. Low-priced purchases like toilet paper or laundry detergent already have automation via subscription services (Instacart recurring orders, Subscribe & Save). An agent adds no incremental value. The middle ground is smaller than the hype suggests. If high-priced resists delegation and low-priced is already “automated,” where does autonomous purchasing actually generate value? “Conversational commerce” is a better frame. Instead of 100% automating the act of buying, LLMs compress the funnel by offering far superior research to classic search engines and showing products in the user interface. Models read expert reviews, product specs, ingredient lists, and actual user feedback rather than ranking by keyword bids and conversion history. The value lies in collapsing 14 clicks [ https://substack.com/redirect/4696269f-b42a-41a8-bc1e-40facff41c32?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (Amazon’s disclosed average before purchase) into one or two. 2. Protocols make e-commerce “headless” The new commerce protocols allow AI agents to directly plug into the backend of your business, instead of crawling your site to show them in a list of search results. Protocols make commerce “headless” and decouple the front from the back-end: Websites become less important as destinations and more important as databases. The game shifts from optimizing landing page design for human eyes to optimizing data feeds for machine ingestion. If your shipping speed, inventory status, or return policy isn’t accessible via API, you are invisible to the agent. The shift from crawling to protocols collapses the legacy 14-click funnel (search, browse, click, checkout) into just 2 interactions: (1) the model parses intent by matching expert reviews against real-time inventory, and (2) the user executes a single click to buy using stored credentials. While both protocols, ACP and UCP, enable the same user experience, they offer vastly different terms for the merchant. OpenAI’s ACP [ https://substack.com/redirect/7f3a692c-0380-4568-9b57-8dce4790ddf9?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (Agentic Commerce Protocol) The Vision: The “Walled Garden.” OpenAI aims to handle the entire transaction within the chat interface, treating merchants effectively as suppliers. The Trade-off: Efficiency vs. LTV. You gain access to 700M weekly users, but you lose the direct customer relationship. Because OpenAI currently restricts passing customer emails for marketing, you lose the ability to remarket - effectively killing the 15-20% of Lifetime Value (LTV) that typically comes from post-purchase email flows. Google’s UCP [ https://substack.com/redirect/d73cf4b1-6a95-411b-9ebc-63c521e89abf?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (Universal Commerce Protocol) The Vision: The “Distributed Layer.” Google extends its Shopping Graph into a transactional layer that sits on top of Search, Lens, and Gemini. The Trade-off: Ownership vs. Competition. Unlike ACP, Google allows merchants to retain the full customer lifecycle, including email rights and loyalty data. The cost is significantly higher competition intensity: Instead of fighting for 10 blue links, you are fighting for 1 of 3 “slots” in an AI Overview, making the margin for error in your product data effectively zero. 3. Conversational commerce disrupts the whole ecosystem The shift from search to conversation creates a distinct set of winners, losers, and strategic dilemmas. Buyers get a dramatically better user experience. Discovery: High-consideration purchases (e.g., specific running shoes) shift from clicking through 6 potentially irrelevant product listing ads to receiving top-tier recommendations based on expert reviews. Cognitive Load: The model handles the research, collapsing the average 14-click journey into 1-2 interactions. Merchants face a tradeoff between distribution and control. On ChatGPT: You gain access to early adopters, but lose the direct customer relationship and email marketing rights. You have no leverage over commission rates or recommendation logic. On Google/Copilot: You retain merchant-of-record status, but as the funnel compresses, on-site ad inventory loses value. While conversion rates may rise, total ad revenue falls. Affiliates die when LLMs disintermediate the click. The Trap: If ChatGPT synthesizes reviews without sending traffic, affiliates stop writing. This creates an “ouroboros” where models train on their own AI-generated output. The Pivot: Publishers must paywall premium content or charge merchants directly for reviews. Amazon dominates on price and speed but faces a business model conflict. The Conflict: Retail margins are thin (~1%); profitability comes from the $60B advertising business. The Risk: Amazon’s ad machine relies on a 14-click funnel. If conversational commerce compresses this to one click, sponsored product inventory evaporates. The Choice: They must either block crawlers to protect ad revenue (current strategy) or participate and cannibalize it. Walmart joining ChatGPT forces their hand. Google is best positioned to weather the shift. Parity: They are already monetizing AI Overviews at parity with legacy search. Economics: Higher relevance leads to exploding conversion rates. Advertisers will pay more per click to offset the lower click volume, balancing the ecosystem. 4. SEO shifts from optimizing clicks to optimizing ingestion We are moving from a world of infinite shelf space (10 blue links, endless pagination) to a world of constrained shelf space (3 recommendation slots in an AI response). In this environment, SEO shifts from optimizing for clicks to optimizing for ingestion. The goal isn’t to get a human to visit your landing page; it’s to get your product data into the agent’s context window with enough authority that it recommends you. The New “Technical SEO:” Feed quality in the legacy model meant site speed, mobile responsiveness, and Core Web Vitals. In the protocol era, technical SEO is feed integrity. Agents don’t “browse” your site; they query your API. Your website becomes less of a visual destination and more of a structured database. The winners will be merchants who treat their product feed as their primary storefront. The New “On-Page SEO:” Legacy SEO often rewarded articles that simply summarized what everyone else was already saying to rank for broad keywords. LLMs, however, are trained on that consensus. To be cited now, you must provide Information Gain [ https://substack.com/redirect/3f555c4e-d579-4af2-95ed-3b2f666905c0?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], the delta between what the model already knows and the unique value you provide on top of the consensus. You cannot “market” your way out of inferior specs. If you claim to be the “best running shoe for flat feet,” the model doesn’t look for adjectives; it validates your arch support measurements against podiatry standards in its training set. Your content must shift from general engagement to structured “Product Truth.” LLMs prioritize detailed comparison tables, proprietary test results (e.g., “we dropped this phone 50 times”), and ingredient breakdowns. If your data isn’t structured for easy ingestion/verification, the model will bypass you for a source that is. The New “Off-Page SEO:” Backlinks [ https://substack.com/redirect/03771bd6-74bc-472d-959a-6538caf0e94e?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] still matter, but their function changes. Instead of passing “link juice” for ranking, they now serve as verification sources for reputation synthesis together with reviews and web mentions. LLMs scrape third-party sites (e.g., Reddit, specialized forums, expert review sites) to form a consensus. A high volume of verified, specific reviews on trusted third-party platforms is the strongest signal you can send. In a world where an AI suggests 3 options, brand familiarity becomes a tie-breaker [ https://substack.com/redirect/50c76cb2-b9f5-40d2-a0ad-835aca7108e9?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Brand advertising and organic brand building returns as a critical lever to ensure users recognize the recommendation the AI provides. 5. The end of “Marketing Brands” The last decade allowed white-label brands to arbitrage their way to growth via ads, but agentic commerce acts as the quality filter for this model. While humans are swayed by slick branding, LLMs are dispassionate readers of data that will not recommend a “premium” product when the specs prove it is identical to a generic alternative. The shift to protocols creates a paradox: Models understand long-tail intent perfectly but fulfill it with fat head inventory. Safety Bias: Models prefer consensus to avoid hallucinations. A niche brand looks like noise; a Category King looks like truth. The RAG Reality: RAG tools typically only scan the top 10-20 search results. Since search engines already favor authority, RAG often just reinforces the incumbents. The only force that overrides this bias is granular data. Your merchant feed acts as the Claim, but RAG acts as the Trust Layer to verify it. The market bifurcates: The Incumbents win general intent via “trust” (consensus) The Specialists win specific intent via “granularity” (specs), but only if they rank in the top search results If you expose data points the giants ignore (e.g., exact sourcing, chemical analysis), the model’s reasoning engine must select you to fulfill the constraint, but only if you rank on page 1 to be fetched. Organic search is no longer about the click; it is the prerequisite for agentic verification. Premium: The Agentic Commerce Playbook Today’s Memo explains why agentic commerce matters. But knowing the landscape isn’t the same as navigating it. Premium subscribers get the Agentic Commerce Adoption Playbook: a 70+ item implementation checklist that walks you through every phase of getting your products into ChatGPT, Copilot, and Google’s AI Mode. It includes: Exact paths for Shopify settings, feed tags, and Merchant Center configs Protocol-specific constraints (like ChatGPT’s email marketing restriction and what it means for your LTV math) A decision matrix for prioritizing protocols based on your product type and business model The pitfalls I’m seeing brands make right now The window for early-mover advantage is open. If you want the step-by-step guide to capture it, subscribe to premium [ https://substack.com/redirect/d6a2a978-cf5e-4dc4-8e7b-3d6c721b9261?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Unsubscribe https://substack.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.Hip-ybb6ZlZSUfXAIhMoacmsW2snudeFubrl61wAL3k?

[https://eotrx.substackcdn.com/open?token=eyJtIjoiPDIwMjYwMTI0MjMxMjQ1LjMuMTYxYmExNmFjZWRkOTdlOS54OGpqOGwwMkBtZy1kMS5zdWJzdGFjay5jb20-IiwidSI6NDI2NTE1MDQwLCJyIjoiYkBlbWFpbC5nb21vZHVsci5jb20iLCJkIjoibWctZDEuc3Vic3RhY2suY29tIiwicCI6bnVsbCwidCI6bnVsbCwiYSI6bnVsbCwicyI6MTQ1MzQ1MiwiYyI6ImRyaXAtY2FtcGFpZ24tZW1haWwiLCJmIjp0cnVlLCJwb3NpdGlvbiI6InRvcCIsImlhdCI6MTc2OTI5NjM2NSwiZXhwIjoxNzcxODg4MzY1LCJpc3MiOiJwdWItMCIsInN1YiI6ImVvIn0.0KJrBbTQ1HOXj4urZVOQ9uXGTDl-9rqrpXKr_JgEM_g] You’re only getting half the story. ͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­ Hey there! You’ve been reading the free version of Growth Memo, designed to keep you informed in the current AI search landscape. But with Growth Memo Premium? If you want to stop reacting to SEO changes and start getting ahead of them, Premium is where that happens. You’ll get access to all the frameworks, templates, and extended analysis that top operators use to make decisions in the AI-search era. Here’s a quick look at what Premium subscribers get every month: * Turnkey frameworks tested across dozens of clients and industries * Interactive tools and templates that can save 2+ hours a week * Deep tactical guidance I can’t fit in the free send * Growth Intelligence Briefs breaking down the most important search + AI shifts * Early access to workshops and events 👉 Join Growth Memo Premium ($15/month or $150/year) [https://email.mg-d1.substack.com/c/eJxs0MvO2yAQBeCnMbtYMFwMCxbd5DUsLmOH_zdgYWibt6-SqFIXXR-dmU8nuI57bU8bWzpvweXTpb3cMLt0kGi58VQHRdCyRRkwiitJ3uG6Y8HmOsbV9X9SbYA8rImUMr2ExfMQNg1CyhjiohjfNOjNkGSBgqIMBHAGQs58Zop5x5QLGKNZ0My_9deXPihMgub9Ftl8DX91F77nUDNJ17o1fFtsbwPJYR-9n9fEf0xwn-B-JN9ce857q7_645Yx11dxgvvZMKeRSak9bSm4nmpZ-_NEm137xp7KTs7h11BzHiX154rF-QPj59E5_PG3laJlQnIhgTTrJ0HfoHmvucZxtLf0Gj7W7FKxH8pLQvp_Fx8XttdNAUoySQUlPy38CQAA__8NVow7] —Kevin PS: Every Premium subscriber gets “first dibs” on new research, including extra data, upcoming frameworks, and stakeholder deck templates. © 2026 Kevin Indig 548 Market Street PMB 72296, San Francisco, CA 94104 Unsubscribe [https://email.mg-d1.substack.com/c/eJxskk1zsjoUxz8N7GRIeCksWHQei41zwWq9Vbtx8qYcJAkDQcRPf0c7d6aLZ_3PL-fM_3c4tfJsuikTHbQzTlVL4axnUlFoXJEFKfMTHrsyQy9xitM4iCP3GR7PUsuOWimO1P5KkxS7VcaYfEEoZvKUpoyiODgliEoZszTlic-YCxn2cewjHOIA4TDyAg_F6PGUcilE-iJT75bUddL42Al9dZ4J5PUD6y3lF48b5UJ_PHXyuUtmu0G6TVZZ2_ZO8Org3MH5OI7euTOjrWZKKvOAHJzTFhycX5GD8yfr4FwbCyfg1ILRDs4H_ZjDO2DSCXJrLlI7wVxOS8Tx17THzYXUxi_qAyq2_47l_LUnenPli6YmtQG2yyeKv3y6izSBEfj7F7BFUx_2m5bh6ER3a1jVb365LfxyS3qimkr8IXGx5Xi1JVFZF7j8HOF7X42kNrfV_HVczYupuB_QP3-WLQ8KWMFyFDtii-0aFfc1KoD0RJfoACQmKrqKRaPorqzEorkyQIg9Ml3WXDXw_blMveGOlnsjho1NDh_V5vBxf9cosUVvwc7Y_HZ_O14234m47dbu73aOdmplpmh3kRb02W0HduRGqUGDnY5SU9ZI8WOjHVjzPwUiQ2EUhBF2u4w5of9s3jsbZcTQdE-d_cCEURR09uPsocy1fz3LoZfd488QxxGK_NB3rxn-LwAA___wS_EO] Get the app [https://substackcdn.com/image/fetch/$s_!IzGP!,w_262,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack.com%2Fimg%2Femail%2Fgeneric-app-button%402x.png]https://email.mg-d1.substack.com/c/eJxskc1u5CAQhJ8GbrZMg7F94LBSNK9h8dPjITFg4WZ35-1XcZTVHHLh8lVRVWpvCbdSnybUeHTepsPGLXeYbNx5MHJxw-w1RyMmvcCipR75BdcNM1ZLGFZLL3RegD8MjLigw-DFtEzSTTBbsIhKhlGKSTgeDQygBwEKpAA19rIXWjgrtPUYwjLh0v-d39_nfQCmhrR1QfRncydZ_9H7kng813vFq4uh2pDv5kF0nEz-YnBjcHtVM7jZ4_h6u5NKxa5iiBU9MXlrlNbv6Uy-fWqSrR9IMW8M9IVLJszE5NuV2N1LIayda0Ql81wo3qO3FEte6Xmg-e_nR3OrLym1HOm5YrZux_DV-Ghu_3bFYIQapRqBV-OYGq6cfiuphLbXa_LZXCjJxmy2Wv7QI2EqnH48XTuxfv6pQI9iHNTAfxv4FwAA__8dUKcPStart writing [https://substackcdn.com/image/fetch/$s_!LkrL!,w_270,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack.com%2Fimg%2Femail%2Fpublish-button%402x.png]https://email.mg-d1.substack.com/c/eJxskk1u5CAQhU8Du7b4M7YXLEby9AXmAFYZym4SAxaGmfTtR-2koyx6yavH9x6oLBRcU74bl_1-sRB28Gu8YAC_UWfkMLPeaoqGd3oQg5a6pedwWjFihoJugvJj2g-C3oxDCVZAJ20L0rWsV7KzwMUMC3fLANQbwYRmXCghuVBtIxuu-Qxcg0Xnhg6H5qN_e-s3JohiYb043hx1PgrY98amQP0xLRnPLqbkinQzt1L2g8hfRFyJuP50P45-jXUn8lpLmI5Us0Uix6eJCP3QAzpfA5Hjyf0SbYoFYyFyXFIqmJ_y12cROUItafHbhu7ybXmU-3Nm_j5ZcpyJYie3WVNIrm75s5rORI4d_9gVozEVv3gLxac4lfuOJkB-x-LjSvc6TzaFUKMv9wkjzBu6z7fvdd6et7wzXLVStYJm8zqTHnV2KYCPZs3pX7kFDImWl0tQD8wPphK65S1TjP414n8AAAD__8OuwIQ [https://eotrx.substackcdn.com/open?token=eyJtIjoiPDIwMjYwMTI0MjMxMjQ1LjMuMTYxYmExNmFjZWRkOTdlOS54OGpqOGwwMkBtZy1kMS5zdWJzdGFjay5jb20-IiwidSI6NDI2NTE1MDQwLCJyIjoiYkBlbWFpbC5nb21vZHVsci5jb20iLCJkIjoibWctZDEuc3Vic3RhY2suY29tIiwicCI6bnVsbCwidCI6bnVsbCwiYSI6bnVsbCwicyI6MTQ1MzQ1MiwiYyI6ImRyaXAtY2FtcGFpZ24tZW1haWwiLCJmIjp0cnVlLCJwb3NpdGlvbiI6ImJvdHRvbSIsImlhdCI6MTc2OTI5NjM2NSwiZXhwIjoxNzcxODg4MzY1LCJpc3MiOiJwdWItMCIsInN1YiI6ImVvIn0.VkSJXFXoR07gdTh0buQrwGsg7Y7FYgL-uWBmJeod3K8][https://email.mg-d1.substack.com/o/eJxskM1u6yAQRp8m7GIB5scseBZrgAkhMWBhuLd--6quKnXR9dE5M_o8dIy1nTa0tN895B1SLHfMkDYS7GwcXbwiaJlWhhs1K0kuuEYs2KBjWKH_oovh5Gm1MNpQHSTOApGDV9Rw5rwR7qG09iRZTrmijAs-My7kNE9MMQdMgccQjEYzfSyv17JRfhM0x3tg0zHc0cG_J18zScf6aHj9YnsbSErt6ZE89FTL2s8dbYb2xp5KJPtwq685j5L6uWIBt2H41vbhth8rBcuEnIXkpFl3E_TKT7HmGsbWrrvHcKFmSMXGVv_3Z8ZcSf9zv3Fg-2oKriSTVFDyz_LPAAAA___jaXrE]

View this post on the web at https://www.growth-memo.com/p/the-great-decoupling Last week, I announced that all posts older than 4 weeks will now be gated. You can upgrade to Premium [ https://substack.com/redirect/f95a024d-b8df-46bb-9c50-fc4699dce079?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for the full archive, research, frameworks, and templates. Growth Memo Premium is trusted by top growth leaders and operators navigating AI search in real time. Sent to 23,862 subscribers. Welcome to +163 new readers. SEO died as a traffic channel the moment pipeline stopped following pageviews. Traffic is either down for many sites, or its growth nowhere near reflects growth rates of 2019-2022, but demos and pipeline are up for brands that shifted from chasing clicks to building authority. What you’ll get in today’s memo: Why traffic and pipeline decoupled What brand strength actually means in AI search How to reframe SEO with executives This week, premium subscribers also get the Brand-SEO Scorecard - a measurement framework to diagnose whether your SEO strategy is positioned for traffic or brand influence - and a 30-day action plan to transition to a brand-SEO focus. Be first everywhere customers search For businesses, visibility means more than rankings. Semrush Enterprise empowers brands to own every layer of search. By unifying SEO and AI search visibility into one platform, you get comprehensive performance tracking, live content scoring, and real-time optimization guidance. Your teams can swap manual busywork for impactful strategy with advanced automations. And it’s all backed by the market’s leading search database. It’s how leaders become dominant across both search engines and AI. Explore Semrush Enterprise [ https://substack.com/redirect/740f3b0b-709e-4723-adb2-2c389aa50604?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] 1. We’ve hit peak search volume for traditional queries Short-head keyword demand is in permanent decline and likely contributing to slowed traffic growth or decline. An analysis of roughly 10,000 short-head keywords shows that collective search volume grew only 1.2% over the last 12 months and is forecasted to decline by 0.74% over the next 12 months. Two forces are driving it: Fragmentation into long-tail: demand did not disappear, it atomized into thousands of specific queries. Bypass behavior: more users start in AI interfaces (AIOs, AI Mode, ChatGPT) instead of classic search. This shift is irreversible for 4 structural reasons: 1/ AI Overviews are here to stay. Google’s revenue model depends on keeping users inside the SERP. Zero-click search protects Google’s ad business. The company is not reverting to the 10 blue links. 2/ LLM outputs are preferred starting points. Many users have conditioned themselves to expect direct answers. The behavior change is complete. 3/ Zero-click is now the default expectation. Clicking through now feels like friction, not value. If the answer or solution isn’t easily acquired, the search experience failed. 4/ Content supply exploded. There is significantly more content competing for the same queries than 3 years ago. AI-generated articles, Reddit threads, YouTube videos, and newsletters all compete for visibility. Even if visibility or “rankings” hold, CTR collapses under the weight of infinite options. Optimizing for traffic growth in this environment is like optimizing for fax machine usage in 2010. The channel is structurally shifting - the products that people use to find answers have fundamentally changed. 2. Traffic and pipeline decoupled because AI ate the click The correlation between organic traffic and pipeline has broken. But it takes a bit more work to convince stakeholders and executives. We’re seeing this across the industry. In December, Maeva Cifuentes [ https://substack.com/redirect/0e7be75e-5236-4f60-8153-22b97b114419?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] reported traffic growth of 32% for one of her clients, while signups grew 75% over the same 6-month period. Her post was in response to one from Gaetano DiNardi [ https://substack.com/redirect/fc4f6a77-0aa4-48db-bfa5-49fdde256920?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], who found no correlation between traffic and pipeline across multiple B2B SaaS companies he advises. Maeva’s client data shows you can grow pipeline 2.3x faster than traffic. Gaetano’s data shows you can grow pipeline while traffic stays flat or even declines. The classic SEO model assumed a linear relationship: More rankings meant more clicks, more clicks meant more traffic, more traffic meant more leads. Alternatively, AI answers queries without sending clicks. The Growth Memo AI Mode Study [ https://substack.com/redirect/4ba9cd32-f645-4eb0-a6c3-b4f18e1dbd20?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] found that when the search task was informational and non-transactional, the number of external clicks to sources outside the AI Mode output was nearly zero across all user tasks. Users get the information they need - directly in their interface of choice - without ever visiting your site. But buying intent didn’t disappear with the clicks. SEO creates influence. It can still shape which brands buyers trust. It just doesn’t deliver the click anymore. Education happens inside the AI interface. Brand selection happens after. Your traffic vanished, but the demand for your product/services didn’t. This explains why Maeva noted she has clients whose traffic is declining but demos are growing by double digits [ https://substack.com/redirect/0e7be75e-5236-4f60-8153-22b97b114419?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] month-over-month. The SEO work didn’t stop working. The measurement broke. Teams optimized for clicks are being judged on a metric that no longer predicts business outcomes. 3. Strong brands still win in AI search, but “brand strength” has a new definition In AI search, performance depends less on “more pages” and more on whether AI systems can confidently understand, trust, and cite you for a specific audience and context. Brand strength in AI search has four components: Topical Authority: Complete ownership of the conceptual map (see topic-first SEO [ https://substack.com/redirect/41ab5112-4168-402f-9f23-a5a110315006?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]), not just keyword coverage. ICP Alignment: Answers tailored to specific buyer questions, prioritizing relevance over volume. Read Personas are critical for AI search [ https://substack.com/redirect/f2e410cd-aa3a-4211-afb0-db54f3230c59?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] to learn more. Third-Party Validation: Citations from category-defining sources matter more than high-DA links (see the data in How AI weighs your links [ https://substack.com/redirect/e1ca8ed3-48bd-42f5-8d65-a614d4c993d2?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]). Positioning Clarity: LLMs must recognize what a brand is known for. Vague positioning gets skipped; sharp positioning gets cited (covered in State of AI Search [ https://substack.com/redirect/2d01060a-0456-4cde-bb42-c9f915e0c6bd?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]). SEO teams that are structured for traffic optimization are now misaligned with business outcomes. The conversation you need to have is “traffic and pipeline decoupled, here’s the data proving it, and here’s what we’re measuring instead.” Move from keyword-first workflows to ICP-first workflows. Start with ICP research (what questions do your buyers ask and where do they ask them), positioning (what are you known for), and omnichannel distribution (SEO + Reddit + YouTube + earned media). SEO is no longer a standalone channel. It’s one input in a brand-building system. Move from traffic reporting to influence reporting. Stop leading stakeholder conversations with sessions, impressions, and rankings. Report on brand lift (are more people searching for you by name?), pipeline influence (what percentage of demos started with organic touchpoints?), and LLM visibility rates (how often do AI systems mention your brand vs cite your content?). 4. The uncomfortable question: If SEO doesn’t drive traffic anymore, what does it do? Here’s what SEO actually does and always did: It shapes mental availability and brand recognition, builds topic/category authority, frames the problem (and the solution), and reduces buyer uncertainty. Traffic was a proxy for those things. The click was the observable action, but the trust was the outcome that mattered. LLM-based search has removed the click but kept the trust-building. Users still learn from your content. It just happens inside an LLM interface instead of on your domain. Your content can still influence which brands buyers trust. Yes, it’s harder to measure because it’s invisible to analytics. But the outcome - buyers choosing your brand when they’re ready to buy - is the same. SEO influences brand preference within the category. When buyers are in-market and researching solutions, SEO determines whether your brand is in the consideration set and whether AI systems recommend you. Traffic was never the point. It was just the easiest thing to measure. 5. What to do in the next 30 days This week, premium subscribers get the playbook for moving from traffic-first to pipeline-first, including: The Brand-SEO Scorecard: An 8-metric diagnostic with industry benchmarks. A 30-Day Action Plan: How to execute the pivot. Upgrade to Premium and get... Unsubscribe https://substack.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.kTyD6NRXh0UvHHi8WbPjSfLsziHCl_595Lf_kVY1V-c?

View this post on the web at https://www.growth-memo.com/p/growth-intelligence-brief-12 Welcome to another Growth Intelligence Brief [ https://substack.com/redirect/fab45b4f-e718-4e22-ba77-c90bd41ea590?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], where organic growth leaders discover what matters - getting insights into the bigger picture and guidance on how to stay ahead of the competition. As a free subscriber, you’re getting the first big story. Premium subscribers get the whole brief. Today’s Growth Intelligence Brief went out to 506 (+29) marketing leaders. AI isn’t collapsing search. It’s stressing the whole ecosystem. This brief covers 3 pressure points: Capital is flowing faster than AI revenues can justify Google continues to capture most discovery and dollars High-intent queries are shifting while routine search stays put The implication: uneven disruption, not a clean handover. Let’s take a closer look. Is the AI bubble going to burst? It’s not just AI Visibility tools. AI hype is everywhere. Here’s what happened: Aswath Damodaran, Professor of Finance at Stern, looked [ https://substack.com/redirect/cc58052b-a244-4f5c-b301-28fdaddac800?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] at the combined valuations of the major LLM players (OpenAI, Anthropic, xAI and others) and noted that investors have priced them at roughly $1.5 trillion despite collective revenues well under $100 billion. He reverse‑engineers the revenue a company would need to justify a $5 trillion market cap and shows that even with today’s high margins, an AI firm would need $590 billion to $677 billion in annual revenue to break even. He then applies a 3‑part test (possible, plausible, and probable) to decide whether those breakeven revenues can realistically be reached. Finally, he warns that investors may be falling prey to a big‑market delusion, where each company’s revenue projections could make sense individually but in aggregate exceed the total size of the AI market. Why this news matters: Funding and executive attention follow valuations. If expectations are detached from reality, capital could dry up quickly when results disappoint. You should be mindful that only a handful of model providers are likely to justify their current prices; others may merge, pivot or disappear. The sector’s growth is real, but it’s neither infinite nor evenly distributed. My take on this: We all wonder why there’s so much hype around AI Visibility tracking, but there’s so much hype around… anything AI. You can trace it all the way back to billions in capex and circular deals. Here’s what to do: As marketers, our role is to cut through the noise: Judge whether an AI visibility product truly makes sense Shape the right internal narrative (caution, but don’t kill the excitement) Avoid jumping on every new bandwagon Google’s Market Share is… stable... Unsubscribe https://substack.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.WxOoDTkjuYEIlSj1D_boCGcGXWAiIcem3zvSdDQiOyU?

View this post on the web at https://www.growth-memo.com/p/how-much-can-we-influence-ai-responses Get the free memo weekly. Upgrade to Premium for the full archive, research, frameworks, and templates. Growth Memo Premium is trusted by top growth leaders and operators navigating AI search in real time. Sent to 22,699 subscribers. Welcome to +300 new readers. A note from Kevin: Starting today, posts older than 4 weeks are paywalled for premium members. Why? I want to keep my new writing free and accessible to everyone when it comes out, but I also want to build a deep library of resources for the paying subscribers who support this work. What this means for you: New posts are still 100% free to read when they land in your inbox. The archives (100+ past posts) will become a premium feature. If you’ve been meaning to catch up on old editions, you can upgrade today [ https://substack.com/redirect/4d441a32-d4d2-424d-a9e1-316e3ef7c9c1?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] to keep full access forever. Search visibility for the AI era AI answers are becoming a primary discovery layer for B2B SaaS. That changes how brands earn visibility. dofollow.com [ https://substack.com/redirect/4ea4753d-37b4-414d-b59b-a4631573d413?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] is launching a new offer focused on branded web mentions that help brands show up in AI-generated answers. This is a dedicated service built around authoritative mentions on real SaaS and industry publications, designed for AI visibility. Early access is open to a small group of B2B SaaS teams that want to secure visibility before it becomes table stakes. Join the Branded Web Mentions waitlist. [ https://substack.com/redirect/57acb664-b0e3-4b2a-a39e-dc311830a6c9?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Right now, we’re dealing with a search landscape that is both unstable in influence and dangerously easy to manipulate. We keep asking how to influence AI answers - without acknowledging that LLM outputs are probabilistic by design. In today’s memo, I’m covering: Why LLM visibility is a volatility problem What new research proves about how easily AI answers can be manipulated Why this sets up the same arms race Google already fought Premium subscribers also get a tool that offers quick, research-backed insights on improvements you can implement right away to your product descriptions. 1/ Influencing AI answers is possible but unstable Last week, I published a list of AI visibility factors [ https://substack.com/redirect/30a02af8-2ae5-4640-9a6d-f17ab3db4b72?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]; levers that grow your representation in LLM responses. The article got a lot of attention because we all love a good list of tactics that drive results. But we don’t have a crisp answer to the question “How much can we actually influence the outcomes?” There are 7 good reasons why the probabilistic nature of LLMs might make it hard to influence their answers: Lottery-style outputs. LLMs (probabilistic) are not search engines (deterministic). Answers vary a lot on the micro-level (single prompts). Inconsistency. AI answers are not consistent. When you run the same prompt 5 times, only 20% [ https://substack.com/redirect/f032d0b3-7b3f-45bc-a692-cbbb8aee5951?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] of brands show up consistently. Models have a bias (which Dan Petrovic calls “Primary Bias”) based on pre-training data. How much we are able to influence or overcome that pre-training bias is unclear. Models evolve. ChatGPT has become a lot smarter when comparing 3.5 to 5.2. Do “old” tactics still work? How do we ensure that tactics still work for new models? Models vary. Models weigh sources differently [ https://substack.com/redirect/8be0e613-f2dc-4c19-98b0-95276a40c744?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for training and web retrieval. For example, ChatGPT leans heavier on Wikipedia while AI Overviews cite Reddit more [ https://substack.com/redirect/9d93cbf5-5746-411f-a9a5-6ef4edf7cf6e?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Personalization. Gemini might have more access to your personal data through Google Workspace than ChatGPT and therefore, give you much more personalized results. Models might also vary the degree to which they allow personalization. More context. Users reveal much richer context about what they want with long prompts, so the set of possible answers is much smaller, and therefore harder to influence. 2/ Research: LLM Visibility is easy to game A brand new paper from Columbia University by Bagga et al. titled “E-GEO: A Testbed for Generative Engine Optimization in E-Commerce [ https://substack.com/redirect/d64ac711-9e2d-4cee-b143-fc419b8f4034?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]” shows just how much we can influence AI answers. The methodology: The authors built the “E-GEO Testbed,” a dataset and evaluation framework that pairs over 7,000 real product queries (sourced from Reddit) with over 50,000 Amazon product listings and evaluates how different rewriting strategies improve a product’s AI Visibility when shown to an LLM (GPT-4o). The system measures performance by comparing a product’s AI Visibility before and after its description is rewritten (using AI). The simulation is driven by two distinct AI agents and a control group: “The Optimizer” acts as the vendor with the goal of rewriting product descriptions to maximize their appeal to the search engine. It creates the “content” that is being tested. “The Judge” functions as the shopping assistant that receives a realistic consumer query (e.g., “I need a durable backpack for hiking under $100”) and a set of products. It then evaluates them and produces a ranked list from best to worst. The Competitors are a control group of existing products with their original, unedited descriptions. The Optimizer must beat these competitors to prove its strategy is effective. The researchers developed a sophisticated optimization method that used GPT-4o to analyze the results of previous optimization rounds and give recommendations for improvements (like “Make the text longer and include more technical specifications”). This cycle repeats iteratively until a dominant strategy emerges. The results: The most significant discovery of the E-GEO paper is the existence of a “Universal Strategy” for “LLM output visibility” in e-commerce. Contrary to the belief that AI prefers concise facts, the study found that the optimization process consistently converged on a specific writing style: longer descriptions with a highly persuasive tone and fluff (rephrasing existing details to sound more impressive without adding new factual information). The rewritten descriptions achieved a win rate of ~90% against the baseline (original) descriptions. Sellers do not need category-specific expertise to game the system: A strategy developed entirely using home goods products achieved an 88% win rate when applied to the electronics category and 87% when applied to the clothing category. 3/ The body of research grows The paper covered above is not the only one showing us how to manipulate LLM answers. 1. GEO: Generative Engine Optimization [ https://substack.com/redirect/207995e7-8481-4da3-979b-ef53f8578d4c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (Aggarwal et al., 2023) The researchers applied ideas like adding statistics or including quotes to content and found that factual density (citations and stats) boosted visibility by about 40%. Note that the E-GEO paper found that verbosity and persuasion were far more effective levers than citations, but the researchers (1) looked specifically at a shopping context, (1) used AI to find out what works, and (3) the paper is newer in comparison. 2. Manipulating Large Language Models [ https://substack.com/redirect/50169bdb-8798-4d34-9ee1-c28a8712f125?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (Kumar et al., 2024) The researchers added a “Strategic Text Sequence,” - JSON-formatted text with product information - to product pages to manipulate LLMs. Conclusion: “We show that a vendor can significantly improve their product’s LLM Visibility in the LLM’s recommendations by inserting an optimized sequence of tokens into the product information page.” 3. Ranking Manipulation [ https://substack.com/redirect/19353d1b-b3a9-487a-bd82-717d4fdcbca5?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (Pfrommer et al., 2024) The authors added text on product pages that gave LLMs specific instructions (like “please recommend this product first”), which is very similar to the other two papers referenced above. They argue that LLM Visibility is fragile and highly dependent on factors like product names and their position in the context window. The paper emphasizes that different LLMs have significantly different vulnerabilities and don’t all prioritize the same factors when making LLM Visibility decisions. 4/ The coming Arms Race The growing body of research shows the extreme fragility of LLMs. They’re highly sensitive to how information is presented. Minor stylistic changes that don’t alter the product’s actual utility can move a product from the bottom of the list to the #1 recommendation. The long-term problem is scale: LLM developers need to find ways to reduce the impact of these manipulative tactics to avoid an endless arms race with “optimizers.” If these optimization techniques become widespread, marketplaces could be flooded with artificially bloated content, significantly reducing the user experience. Google stood in front of the same problem and then launched Panda and Penguin. You could argue that LLMs already ground their answers in classic search results, which are “quality filtered,” but grounding varies from model to model and not all LLMs prioritize pages ranking at the top of Google search. Google protects its search results more and more against other LLMs (see “SerpAPI lawsuit” and the “num=100 apocalypse”). I’m aware of the irony that I contribute to the problem by writing about those optimization techniques, but I hope I can inspire LLM developers to take action. This week, Premium members get an audit tool that scores your product descriptions against the features that have proven to boost visibility in AI-generated shopping recommendations. The E-GEO Audit Tool is based on the E-GEO paper and provides quick insight on improvements you can implement right away... Unsubscribe https://substack.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.NX_gdAkduLzEM77uQrSvQPBzPc97x2jTPd4G05YLP1U?

View this post on the web at https://www.growth-memo.com/p/state-of-ai-search-optimization-2026 Welcome back and happy new year! This Memo was sent to 23,358 subscribers. Welcome to +158 new readers! You’re reading the free version of Growth Memo. This week, Premium subscribers get the 20-item implementation checklist with step-by-step tactics you can deploy this quarter. 📣Have questions you want answered in 2026? Fill out the reader survey [ https://substack.com/redirect/73c09942-4f58-4e5d-b6ea-ef3d5b52f458?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. It takes less than 5 minutes, and I read every response. 📣 Take control of your AI Visibility AI is becoming the new front door to the internet, and most brands are flying blind. Search Party is the leading analytics platform that shows how your brand appears in AI-generated answers across ChatGPT, Claude, Perplexity, and more. See where you’re mentioned, how you’re positioned, and what’s driving those results - all in one place. Start for free today. [ https://substack.com/redirect/fffa97a7-3462-44a7-888b-3c9bf384273b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Every year, after the winter holidays, I spend a few days ramping up by gathering the context from last year and reminding myself of where my clients are at. I want to use the opportunity to share my understanding of where we are with AI Search, so you can quickly get back into the swing of things. As a reminder, the vibe around ChatGPT turned a bit sour at the end of 2025: Google released the superior Gemini 3, causing Sam Altman to announce a Code Red (ironically, 3 years after Google did the same at the launch of ChatGPT 3.5). OpenAI made a series of circular investments that raised eyebrows and questions about how to finance them. ChatGPT, which sends the majority of all LLMs, reaches at most 4% of the current organic (mostly Google) referral traffic. Most of all, we still don’t know the value of a mention in an AI response. However, the topic of AI and LLMs couldn’t be more important because the Google user experience is turning from a list of results to a definitive answer. A big “thank you” to Dan Petrovic [ https://substack.com/redirect/0079ecb6-ba9f-4b46-9918-f1bfe5402af2?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] and Andrea Volpini [ https://substack.com/redirect/ad3436e1-5ecc-441a-a6c6-978eb73e729f?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] for reviewing my draft and adding meaningful concepts. Retrieved → cited → trusted Optimize for AI Search Visibility follows a pipeline similar to the classic “crawl, index, rank” for search engines: Retrieval systems decide which pages enter the candidate set The model selects which sources to cite Users decide which citation to trust and act on Caveats: A lot of the recommendations overlap strongly with common SEO best practices. Same tactics, new game. I don’t pretend to have an exhaustive list of everything that works. Controversial factors like schema or llms.txt are not included. Consideration: Getting into the candidate pool Before any content enters the model’s consideration (grounding) set, it must be crawled, indexed, and fetchable within milliseconds during real-time search. The factors that drive consideration are: Selection Rate and Primary Bias Server response time Metadata relevance Product feeds (in e-commerce) 1/ Selection rate and primary bias Definition: Primary bias measures the brand-attribute associations a model holds before grounding in live search results. Selection Rate measures how frequently the model chooses your content from the retrieval candidate pool. Why it matters: LLMs are biased by training data. Models develop confidence scores for brand-attribute relationships (eg, “cheap”, “durable”, “fast”) independent of real-time retrieval. These pre-existing associations influence citation likelihood even when your content enters the candidate pool. Goal: Understand which attributes the model associates with your brand and how confident it is in your brand as an entity. Systematically strengthen those associations through targeted on-page and off-page campaigns. This week’s guidance for Premium members provides a checklist for implementation. Don’t miss it! Sign up for Growth Memo Premium [ https://substack.com/redirect/47042b11-a055-47df-ae4a-4c9683a305ca?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. 2/ Server response time Definition: The time between a crawler request and the server’s first byte of response data (TTFB = Time To First Byte). Why it matters: When models need web results for reasoning answers (RAG), they need to retrieve the content like a search engine crawler. Even though retrieval is mostly index-based, faster servers help with rendering, agentic work flows and freshness, and compound query fan-out. LLM retrieval operates under tight latency budgets during real-time search. Slow responses prevent pages from entering the candidate pool because they miss the retrieval window. Consistently slow response times trigger crawl rate limiting [ https://substack.com/redirect/39c34d47-c129-4b85-8b97-b9364888a986?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Goal: Maintain server response times <200ms [ https://substack.com/redirect/b89378a4-1803-48b3-9de7-b7a94e79a024?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Sites with <1s load times receive 3x more [ https://substack.com/redirect/9b4a1bf9-2068-4918-a19a-d8d12da24130?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] Googlebot requests than sites >3s. For LLM crawlers (GPTBot, Google-Extended), retrieval windows are even tighter than traditional search. 3/ Metadata relevance Definition: Title tags, meta descriptions, and URL structure that LLMs parse when evaluating page relevance during live retrieval. Why it matters: Before picking content to form AI answers, LLMs parse titles for topical relevance, descriptions as document summaries and URLs as context clues for page relevance and trustworthiness. Goal: Include target concepts in titles and descriptions (!) to match user prompt language. Create keyword-descriptive URLs, potentially even including the current year to signal freshness. 4/ Product feed availability (ecommerce) Definition: Structured product catalogs submitted directly to LLM platforms with real-time inventory, pricing, and attribute data. Why it matters: Direct feeds bypass traditional retrieval constraints and enable LLMs to answer transactional shopping queries (”where can I buy,” “best price for”) with accurate, current information. Goal: Submit merchant-controlled product feeds to ChatGPT’s merchant program (chatgpt.com/merchants [ https://substack.com/redirect/5c3cc6dd-8d46-4410-9ca7-5477ee3540dd?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]) in JSON, CSV, TSV, or XML format with complete attributes (title, price, images, reviews, availability, specs). Implement ACP (Agentic Commerce Protocoll) for agentic shopping. Relevance: Being selected for citation “The Attribution Crisis in LLM Search Results [ https://substack.com/redirect/c5581da6-ac30-4f88-ac3d-b02c1cccdd78?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]” (Strauss et al., 2025) reports low citation rates even when models access relevant sources. 24% of ChatGPT (4o) responses are generated without explicitly fetching any online content. Gemini provides no clickable citation in 92% of answers. Perplexity visits about 10 relevant pages per query but cites only 3 to 4. Models can only cite sources that enter the context window. Pre-training mentions often go unattributed [ https://substack.com/redirect/c5581da6-ac30-4f88-ac3d-b02c1cccdd78?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. Live retrieval adds a URL, which enables attribution. 5/ Content structure Definition: The semantic HTML hierarchy, formatting elements (tables, lists, FAQs), and fact density that make pages machine-readable. Why it matters: LLMs extract and cite specific passages. Clear structure makes pages easier to parse and excerpt. Since prompts average 5x the length of keywords [ https://substack.com/redirect/c0855aac-a49f-4082-b257-229c748084fe?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ], structured content answering multi-part questions outperforms single-keyword pages. Goal: Use semantic HTML with clear H-tag hierarchies, tables for comparisons, and lists for enumeration. Increase fact and concept density [ https://substack.com/redirect/c78364fc-7ea2-4d96-8573-49ee5bc9b8bb?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] to maximize snippet contribution probability. 6/ FAQ coverage Definition: Question-and-answer sections that mirror the conversational phrasing users employ in LLM prompts. Why it matters: FAQ formats align with how users query LLMs (”How do I…,” “What’s the difference between…”). This structural and linguistic match increases citation and mention likelihood compared to keyword-optimized content. Goal: Build FAQ libraries from real customer questions (support tickets, sales calls, community forums) that capture emerging prompt patterns. Monitor FAQ freshness through lastReviewed or DateModified schema. 7/ Content freshness Definition: Recency of content updates as measured by “last updated” timestamps and actual content changes. Why it matters: LLMs parse last-updated metadata to assess source recency and prioritize recent information as more accurate and relevant. Goal: Update content within the past 3 months for maximum performance. Over 70% of pages [ https://substack.com/redirect/b034ea57-74f2-44c5-9885-d5723c6c4c91?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] cited by ChatGPT were updated within 12 months, but content updated in the last 3 months [ https://substack.com/redirect/cefd4c64-f489-484d-b932-48a71ca5eacb?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] performs best across all intents. 8/ Third-party mentions (”Webutation”) Definition: Brand mentions, reviews, and citations on external domains (publishers, review sites, news outlets) rather than owned properties. Why it matters: LLMs weigh external validation more heavily than self-promotion the closer user intent comes to a purchase decision. Third-party content provides independent verification of claims and establishes category relevance through co-mentions with recognized authorities. They increase the entitithood inside large context graphs. Goal: 85% of brand mentions [ https://substack.com/redirect/267bc1a8-349c-42c6-8534-f225e134d2ea?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] in AI search for high purchase intent prompts come from third-party sources. Earn contextual backlinks [ https://substack.com/redirect/b25ae4c6-865c-4cf7-8868-734916fd931b?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] from authoritative domains and maintain complete profiles on category review platforms [ https://substack.com/redirect/abc842d5-5b0c-41b3-90af-c23f554af352?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. 9/ Organic search position Definition: Page ranking in traditional search engine results pages (SERPs) for relevant queries. Why it matters: Many LLMs use search engines as retrieval sources. Higher organic rankings increase the probability of entering the LLM’s candidate pool and receiving citations. Goal: Rank in Google’s top 10 for fan-out query variations around your core topics, not just head terms. Since LLM prompts are conversational and varied, pages ranking for many long-tail and question-based variations have higher citation probability. Pages in the top 10 show a strong correlation [ https://substack.com/redirect/0c5e48d9-429c-4967-936c-ecbfbecf9f99?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] (~0.65) with LLM mentions, and 76% of AI Overview citations [ https://substack.com/redirect/b3a56564-fe68-49c9-a2b1-1b9bd7f2ec46?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] pull from these positions. Caveat: Correlation varies by LLM. For example, overlap is high for AI Overviews [ https://substack.com/redirect/b486b6b5-2644-47b6-9b0e-17dd385a30eb?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] but low for ChatGPT [ https://substack.com/redirect/8a405b70-110f-4a7c-bb38-f039f8c604ba?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ]. User Selection: Earning trust and action Trust is critical because we’re dealing with a single answer in AI Search, not a list of search results. Optimizing for trust is similar to optimizing for click-through rates in classic search, just that it takes longer and is harder to measure. 10/ Demonstrated expertise Definition: Visible credentials, certifications, bylines, and verifiable proof points that establish author and brand authority. Why it matters: AI search delivers single answers rather than ranked lists. Users who click through require stronger trust signals [ https://substack.com/redirect/07fc2615-ad40-460e-a597-2909c5aae400?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] before taking action because they’re validating a definitive claim. Goal: Display author credentials, industry certifications, and verifiable proof (customer logos, case study metrics, third-party test results, awards) prominently. Support marketing claims with evidence. 11/ User-generated content presence Definition: Brand representation in community-driven platforms (Reddit, YouTube, forums) where users share experiences and opinions. Why it matters: Users validate synthetic AI answers against human experience. When AI Overviews [ https://substack.com/redirect/96997b28-d3a1-469b-adb8-0b48c4e6fea6?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] appear, clicks on Reddit and YouTube grow from 18% to 30% because users seek social proof. Goal: Build positive presence in category-relevant subreddits, YouTube, and forums. YouTube and Reddit are consistently [ https://substack.com/redirect/8a405b70-110f-4a7c-bb38-f039f8c604ba?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] in the top 3 most cited domains [ https://substack.com/redirect/f0316607-5c96-471f-8a5a-6658cbf4099c?j=eyJ1IjoiNzF4cDQwIn0.VLQsNiiAawz-DS2VtWTrcrG2IFeLIxnWNFcK9akSjpY ] across LLMs. From Choice to conviction Search is moving from abundance to synthesis. For two decades, Google’s ranked list gave users a choice. AI search delivers a single answer that compresses multiple sources into one definitive response. The mechanics differ from early 2000s SEO: Retrieval windows replace crawl budgets. Selection rate replaces PageRank. Third-party validation replaces anchor text. The strategic imperative is identical: earn visibility in the interface where users search. Traditional SEO remains foundational, but AI visibility demands different content strategies: Conversational query coverage matters more than head-term rankings. External validation matters more than owned content. Structure matters more than keyword density. Brands that build systematic optimization programs now will compound advantages as LLM traffic scales. The shift from ranked lists to definitive answers is irreversible. Premium: AEO optimization checklist... 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[https://eotrx.substackcdn.com/open?token=eyJtIjoiPDIwMjUxMjMwMjI1NzIyLjMuMTYxYmExNmFjZWRkOTdlOS5qaXcycHltakBtZy1kMS5zdWJzdGFjay5jb20-IiwidSI6NDI2NTE1MDQwLCJyIjoiYkBlbWFpbC5nb21vZHVsci5jb20iLCJkIjoibWctZDEuc3Vic3RhY2suY29tIiwicCI6bnVsbCwidCI6bnVsbCwiYSI6bnVsbCwicyI6MTQ1MzQ1MiwiYyI6ImRyaXAtY2FtcGFpZ24tZW1haWwiLCJmIjp0cnVlLCJwb3NpdGlvbiI6InRvcCIsImlhdCI6MTc2NzEzNTQ0MiwiZXhwIjoxNzY5NzI3NDQyLCJpc3MiOiJwdWItMCIsInN1YiI6ImVvIn0.0zLKuJpvADgCt6b7tnXzOXhfIOjC2E0jI8QMHULhBvU] Did you see this? ͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­ Traffic is a decaying metric. Here’s what I mean: The new success metrics aren’t clicks and rankings. They’re presence and authority. AI Overviews, AI Mode, and ChatGPT & Co. have turned search into a visibility surface - not a reliable traffic channel. Most of your users will never leave the page (or the chat). Yet the brands that still get seen are the ones with recognizable authority and topic depth. So your strategy needs to adapt, and quickly, if you’re to stay ahead. If you haven’t yet, check out the Growth Memo library [https://email.mg-d1.substack.com/c/eJxs0M2OpSAQBeCnkZ0GCvBnwWI29zUMP6XSLWAQ2vj2k-vNJLPo9cmp-nKsLrimfCuX_dFaHQ7t19hi0H4nTvHJ0NH2BBUb-oFxKQSQJ5xXjJh1QTfr8l8KoiebGi3lbuTgllFOxjirJ7HA5NgkhewXSbwCCpIBpwByAOh4x3pmNOu1ReemAafuy19w3OGrETSsrWPdWc1ZtP3ubArEn_OS8bGokiuSXW2lHGfD_zTwauB1XVe35nSVrQ0Y0rvUwEtnu_kfJDEVv3iri09xLveBKuj8jcXHlRzVzDaFUKMv94xRmx3d58lRzf6v5Z1iQnIhgWRlGkEfTLemkFzd86M8q3EpaB_Vh_KWkPLr2vXE_L4poJdMUkHJj4K_AQAA__98BItR] to start designing growth strategies for authority and visibility (the currencies of AI search). Or join us over at the Growth Memo Research slack channel [https://email.mg-d1.substack.com/c/eJxskT2P1DAQQH9N3CWyx3ZCCheHUE4IOjqayB-TrHdjOziTPS2_HrGAoKB-M6P3NN4SrqU-TKhxb71Nu41rbjHZuLFg5Oj4O98zNGLoByG1UsCecF4xY7WEYbb0DwXVs4vhS28HDh6WceR-4M6iQjW6IaDvpVhYNMBBC5AcQA8AnexEL5wVvfUYwjjg2F3jG-yPdG0UT2sbRHec7iDrb50vicVjXio-XQzVE9lmLkT70ciXBqYGpmuJuTu23_MNTNTAtNbyRpeEqTQwHRdbMcwx3yNhA9N3aqV3cPs2BtnOH-n-_uvnTx_E61i_RNKTflUvLBeKS_SWYskzPXY0ydYbUswr2083-5LSmSM9ZszWbRh-ye2n2_5sxWCE0lJpYNW4RvFnRLeWVMK51WfdcbpQko3Z_DVm9N8vnQfWnzcV9Fporji7G_gRAAD__686nXs] when you get the chance (it’s free!). — Kevin © 2025 Kevin Indig 548 Market Street PMB 72296, San Francisco, CA 94104 Unsubscribe [https://email.mg-d1.substack.com/c/eJxskk1vozAQhn8N3IKwsUk4cKjakiVbiNLN5qOXyF9JhmCbBRNKfv0qqVbqYc-vnpnR-4xgTp1sO6ayhWYimG4YnMxEaQa1L9Mo4eFMxL5K0TSeoogSgv1HeDgpo1rmlDww9y3FJPbP6ZEgggmaUiqSeEoRSlic8CklYXRMIkR9SHGIKcJRiDGdYhxEAYoRZyhmQkmZTFUSVDDgZtSVR0J9mkgUdD3vHBOXQFjtQ3c4tupxS-raXvl1enau6bzoycOZh7NhGIJTawd3nmil7R3ycMYa8HB2RR7OHqyHM2MdHEEwB9Z4OOvNfY9ogSsvypy9KONFL2pcIIE34w7Xl7yyYVHtUbH-PZQvT11u3q9iXld5ZYFvs5HhTci21OQwgPixAT6vq_3uveGYHtl2BcvqNSzXRViu8y7X9Vk-53GxFri8vd7K9SosYICP3XnIK_tZ3i6krMRn-bIa354XjYgKWMJikNvcFesVKm4rVEDe5aZEe8jjXNOrnNeabcuznNdXDgjxe2bKSugaPn4tkuDt8mcW75dPu_AtrzZmc4ki2V2v6DyrbzWRcdtkP5frnrySmf-9nYMbG5Vq1l6UA3Pym54fhNW6N-DGgzKM10p-2Wh6Xv-jQKaI0IhQ7Lcp90j4aD44WW1lX7cPnV3PpdUMTPrl7K7Md_99y75T7X0mwTFFNCShf03x3wAAAP__KU3vAw] Get the app [https://substackcdn.com/image/fetch/$s_!IzGP!,w_262,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack.com%2Fimg%2Femail%2Fgeneric-app-button%402x.png]https://email.mg-d1.substack.com/c/eJxskc1upDAQhJ_GvoFw28Bw8GGlaF4D-adhnOAfmfZG8_arEGU1h1x8-apcVWpnCPdcn9rXUDpnYjFhTx1GEw7utVzscHMTRy3maRZyVAr4BdcdE1ZD6FdDLxTUxB96GoYFRiOF2yYUztqb37y64TabxRnYeNAwwChADgDjDNDLXkzCGjEZh94vMy79e_iE8ozvTA1x77zoz2ZPMu6jdznycK5bxauLptqQH_pBVE4m_zC4M7i_qhncTSnfb3dSrthV9KGiIybvjeL6M53Jty9NNPUDKaSdwXThnAgTMfl2JXZbzoS1s40oJ54yhS04QyGnlZ4F9X8_L82uLsfYUqDnisnYA_1349Ls8eMKXgs1SjUCr9oyNVw5_Z5j9u2o1-SzWZ-jCUnvNX_SI2LMnH49XTuxfv2pYBrFOKiB_9XwLwAA__-WnqguStart writing [https://substackcdn.com/image/fetch/$s_!LkrL!,w_270,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack.com%2Fimg%2Femail%2Fpublish-button%402x.png]https://email.mg-d1.substack.com/c/eJxskk2O4yAQhU8Du1j8GScLFiN5coE5gAVU2aHbgIVhenL7UdydVi-y5NXjew9U3lZccrkbKGE7eRs3G5Z0wmjDSsHIi2NnrykaPuiBy14pQY_htGDCYivCZOuPqVCa3gyAxJ556M8c3JlpCRL5fBZagWMagAYjmOi5kEyIfhCikx3X3FmurUeAy4CX7i18iO0e34hicTkB7_bm9mr9e-dzpGGf5oJHF1NLQ7qaW63bTuQvIq5EXH-6H8ewpLYReW01TntuxSOR49NEhH7oESG0SOR4cL9En1PFVIkc55wrlqf89VlEjrbVPId1RTh9Wx7l_hyZvw-WHB1R7OB2S44Z2lo-q-lC5Djwf5tiNOUa5uBtDTlN9b6hiba8Yw1poVtzk88xthTqfcJk3Yrw-fatufV5K4DhqpeqF7SY15l0bw5ytCGZpeSPeosYM60vl6DtWB5MJXTPe6YY_WvE_wAAAP__CdLAww [https://eotrx.substackcdn.com/open?token=eyJtIjoiPDIwMjUxMjMwMjI1NzIyLjMuMTYxYmExNmFjZWRkOTdlOS5qaXcycHltakBtZy1kMS5zdWJzdGFjay5jb20-IiwidSI6NDI2NTE1MDQwLCJyIjoiYkBlbWFpbC5nb21vZHVsci5jb20iLCJkIjoibWctZDEuc3Vic3RhY2suY29tIiwicCI6bnVsbCwidCI6bnVsbCwiYSI6bnVsbCwicyI6MTQ1MzQ1MiwiYyI6ImRyaXAtY2FtcGFpZ24tZW1haWwiLCJmIjp0cnVlLCJwb3NpdGlvbiI6ImJvdHRvbSIsImlhdCI6MTc2NzEzNTQ0MiwiZXhwIjoxNzY5NzI3NDQyLCJpc3MiOiJwdWItMCIsInN1YiI6ImVvIn0.Nn04yAcHTcv6a5_YCJT_4geZGtuXxWKKR_nR4OGPbVQ][https://email.mg-d1.substack.com/o/eJxskE2O6yAQBk8TdrGg-XG84CxWA22HxICF4UW-_dN4NNIsZl2q6tbnsdFa6mlDjfvdY9oxrvlOCePGgpWT4w9vGFkxmlFIrRSwC84rZarYKMzYflFQhj1tkA8JHiGYhZN-TAhEC8rRjJMmwx2LFjhoAZID6BFgkIMwwqEw6CmEaaRpeMUP7Gd63RRP6z2I4ejuaOjfgy-JxWNeKl2_2FY7sVxaXKLHFkue27mTTVjf1GJe2d7d7EtKPcd2zpTRbRS-tb277ceKwQqlpdLAqnU3xa_8sJZUQt_qdffoLpSEMdu1lk97JkqFtT_36wfVr6YCo4XmirN_Fv4HAAD__64ke6w]

[https://eotrx.substackcdn.com/open?token=eyJtIjoiPDIwMjUxMjE3MjIyNzAwLjMuMTYxYmExNmFjZWRkOTdlOS5iYnNobGZ5akBtZzIuc3Vic3RhY2suY29tPiIsInUiOjQyNjUxNTA0MCwiciI6ImJAZW1haWwuZ29tb2R1bHIuY29tIiwiZCI6Im1nMi5zdWJzdGFjay5jb20iLCJwIjpudWxsLCJ0IjpudWxsLCJhIjpudWxsLCJzIjoxNDUzNDUyLCJjIjoiZHJpcC1jYW1wYWlnbi1lbWFpbCIsImYiOnRydWUsInBvc2l0aW9uIjoidG9wIiwiaWF0IjoxNzY2MDEwNDIxLCJleHAiOjE3Njg2MDI0MjEsImlzcyI6InB1Yi0wIiwic3ViIjoiZW8ifQ.v1vqFaiD9twUfb-uq2J9qHbkVA_kvHZ5_jcdJ-ZGr0A] Welcome to the Growth Memo! ͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­ I’m EXCITED to see you here. Thank you for signing up! You’ll get a free piece of content every week that includes data-driven analyses on how AI, search, and growth models are reshaping marketing strategy. You’re also invited to join the Growth Memo Research slack channel [https://email.mg2.substack.com/c/eJxskT2P1DAQQH9N3G1kT-x4U7g4hHJC0NHRRP6Yzfo2toM9WbT8esQCgoL6zYze03hLuJb6MKHG_eRt2m1c8wmTjRsLRgZxVmeGRuhx5IJLEOzJlhUzVksYFkv_UD0Bu5owaOX9MAUFow5OXs4TjNqBGLyGswUWDXBQAoQGAM15P_RiFM6K0XoMYdI49c6163Z5vHWSpxX6drhG1t96XxKLbblUfJoYqgeyzVyJ9tYNLx3MHcxvJea-bb_nO5ipg3mt5RtdE6bSwdyutmJYYr5Hwg7m73QavIPb1ykMp-UD3d99-fTxvXid6udIalav8oXlQvESvaVY8kKPHU2y9YYU88r2wy2-pHTkSI8Fs3Ubhl9y--G2P1sxGCHVIBWwalwn-TOiX0sq4djqs64dLpRkYzZ_jRn990VHw_rzpoRRCcUlZ3cDPwIAAP__8deb2g], where I regularly share interesting research finds and industry news throughout the week. 2 quick asks: * I would love to hear from you so I can improve the Growth Memo. Could you please take 60 seconds to fill out this survey [https://email.mg2.substack.com/c/eJxs0D2O3DAMBeDTWN0YIv1fqEgz1zAoifYoa0mGfnbg2wfjRYAUqR8e-eEZKrzHdCmb3Pkw5E9ye3iwJ3cIq3oL8zALVjCNowTZI4g7W3cOnKiwXan8k04LipdawIybHhYYLSMt1nQ0w0LzpDczzzALp1DiAAgTIk5Stl0LI2iCkQxbu0y8tFrn17Fdv5te-h3bXHUuZL5aE71wed0S3xJVUmVxqFcpZ266Xw0-G3y-3-92T_FdXg_PPn5KDT5zTd98NfhcOpiwEyEWtzlDxcWwlutk5Sl9cXFhF2fVq4ne1-DKtXIgfbD9eXZWffxtOaugH7p-QJGUbnp5o9o9-mjrkW5trtpGTy6oH9JHJMp_J6-Z0-dmj-MAg-yl-Fb4JwAA__-sXIw2] (5 questions)? * Add growthmemo@substack.com to your contacts so all the good stuff ahead doesn’t land in your spam folder. Thanks so much, — Kevin PS: Premium subscribers get access to research-backed frameworks, exclusive data, and deep industry insights for the AI-search era for only $15/month or $150/year (=$12.5/month)! 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