<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm.png" class="attachment-large size-large wp-post-image" alt="Instagram Your Algorithm" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Topic controls give users more say in what they see, pushing brands to create content tied to clear audience interests.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm.png" class="attachment-large size-large wp-post-image" alt="Instagram Your Algorithm" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Instagram expanded its Your Algorithm controls to the main feed. This update gives Instagram users a way to manage the topics that influence recommendations across Feed, Reels, and Explore.</p>



<p><strong>About Your Algorithm.</strong> The Your Algorithm feature lets people view the topics Instagram associates with their interests, remove topics they don&#8217;t want, and add topics they want to see more often. <a href="https://searchengineland.com/instagram-your-algorithm-465987">Instagram introduced Your Algorithm for Reels in December</a> and has since expanded topic controls to more recommendation surfaces.</p>



<p><strong>Feed joins Reels and Explore</strong>. The update brings topic-level controls to Instagram’s main feed, where recommended posts from accounts users don’t follow now make up a larger share of the experience.</p>



<ul class="wp-block-list">
<li>Users start with a list Instagram generates from their activity. </li>



<li>Changes to that list help the system adjust future recommendations.</li>
</ul>



<p><strong>More user control</strong>. Instagram head Adam Mosseri said the change addresses the lack of control users often feel in recommendation-driven feeds.</p>



<ul class="wp-block-list">
<li>“The system learns from what you tap, watch, and share, but you don’t really get to tell it what you want,” Mosseri wrote.</li>



<li>Large language models now allow Instagram to describe content clusters in plain language, giving users a clearer way to shape what the system believes they like, Mosseri added.</li>
</ul>



<p><strong>Interest media. </strong><a href="https://garyvee.substack.com/p/social-media-is-dead-interest-media" target="_blank" rel="noopener">Gary Vaynerchuk described this shift</a> from follower-based feeds (social media) to interest-based discovery (interest media). Platforms now prioritize content based on engagement rather than who users follow. Instagram’s update makes that model more transparent by showing users the interests behind their recommendations.</p>



<p><strong>Why we care</strong>. Instagram discovery increasingly prioritizes matching user interests over follower relationships. Your content must signal clear topics and audience intent because those signals help determine where recommendations appear.</p>



<p><strong>More controls are planned</strong>. Topics are just the beginning. Mosseri said Instagram is developing controls for people, moods, content types, and other signals.</p>


Instagram expanded its Your Algorithm controls to the main feed. This update gives Instagram users a way to manage the topics that influence recommendations across Feed, Reels, and Explore.
About Your Algorithm. The Your Algorithm feature lets people view the topics Instagram associates with their interests, remove topics they don’t want, and add topics they want to see more often. Instagram introduced Your Algorithm for Reels in December and has since expanded topic controls to more recommendation surfaces.
Feed joins Reels and Explore. The update brings topic-level controls to Instagram’s main feed, where recommended posts from accounts users don’t follow now make up a larger share of the experience.
Users start with a list Instagram generates from their activity. 
Changes to that list help the system adjust future recommendations.
More user control. Instagram head Adam Mosseri said the change addresses the lack of control users often feel in recommendation-driven feeds.
“The system learns from what you tap, watch, and share, but you don’t really get to tell it what you want,” Mosseri wrote.
Large language models now allow Instagram to describe content clusters in plain language, giving users a clearer way to shape what the system believes they like, Mosseri added.
Interest media. Gary Vaynerchuk described this shift from follower-based feeds (social media) to interest-based discovery (interest media). Platforms now prioritize content based on engagement rather than who users follow. Instagram’s update makes that model more transparent by showing users the interests behind their recommendations.
Why we care. Instagram discovery increasingly prioritizes matching user interests over follower relationships. Your content must signal clear topics and audience intent because those signals help determine where recommendations appear.
More controls are planned. Topics are just the beginning. Mosseri said Instagram is developing controls for people, moods, content types, and other signals.

Topic controls give users more say in what they see, pushing brands to create content tied to clear audience interests.

Instagram now lets users tell the algorithm what they want

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google and Walmart team up to let advertisers target Walmart shoppers on YouTube and measure the sales within DV360.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Google and Walmart are partnering to bring Walmart Connect audience targeting and sales measurement into Display &amp; Video 360, starting with YouTube campaigns.</p>



<p>The integration gives advertisers access to Walmart&#8217;s shopper data while allowing them to measure whether YouTube ads drive actual sales at the retailer.</p>



<p><strong>What&#8217;s happening</strong>. Brands can now activate Walmart Connect audiences within Display &amp; Video 360 and use those insights to reach shoppers across YouTube.</p>



<p>The partnership also enables closed-loop measurement, allowing advertisers to connect ad exposure with purchases made at Walmart.</p>



<p><strong>Why we care. </strong>The partnership combines Walmart&#8217;s valuable first-party shopper data with YouTube&#8217;s massive reach, allowing brands to target consumers based on actual retail behavior rather than inferred interests alone.</p>



<p>More importantly, it closes a longstanding measurement gap. Instead of simply tracking views or clicks, advertisers can see whether their YouTube campaigns drove sales at Walmart, making it easier to connect media spend to business outcomes and justify investment in video advertising.</p>



<p><strong>The big picture</strong>. Retail media networks are increasingly extending beyond their owned properties, bringing shopper data and measurement capabilities to broader digital advertising channels where brands are investing more of their budgets.</p>



<p><strong>Between the lines.</strong> The deal reflects Walmart Connect&#8217;s push to make its audience and measurement tools available across more advertising platforms. Last year, Walmart ended the exclusivity arrangement tied to its partnership with The Trade Desk, opening the door to additional ad tech integrations.</p>



<p><strong>What advertisers get</strong>. Access to Walmart&#8217;s audience insights, reach to the retailer&#8217;s 150 million weekly U.S. customers through YouTube campaigns, and sales attribution tied to Walmart purchases — all within the Display &amp; Video 360 workflow.</p>



<p><strong>What&#8217;s next</strong>. The rollout is beginning with YouTube campaigns, with Google and Walmart planning to expand the integration to additional inventory over time.</p>



<p><strong>The bottom line</strong>. The partnership brings retail media data, media activation and sales measurement closer together, giving advertisers a more direct way to connect YouTube campaigns with in-store and online purchasing behavior at Walmart.</p>



<p><strong>Dig deeper.</strong>



<ul class="wp-block-list">
<li><a href="https://blog.google/products/marketingplatform/360/walmart-connect/">We’re bringing Walmart Connect to Display &amp; Video 360.</a></li>



<li><a href="https://www.adweek.com/commerce/walmart-challenges-amazon-with-google-deal/">Walmart Challenges Amazon With New Google Deal</a></li>
</ul>


Google and Walmart are partnering to bring Walmart Connect audience targeting and sales measurement into Display & Video 360, starting with YouTube campaigns.
The integration gives advertisers access to Walmart’s shopper data while allowing them to measure whether YouTube ads drive actual sales at the retailer.
What’s happening. Brands can now activate Walmart Connect audiences within Display & Video 360 and use those insights to reach shoppers across YouTube.
The partnership also enables closed-loop measurement, allowing advertisers to connect ad exposure with purchases made at Walmart.
Why we care. The partnership combines Walmart’s valuable first-party shopper data with YouTube’s massive reach, allowing brands to target consumers based on actual retail behavior rather than inferred interests alone.
More importantly, it closes a longstanding measurement gap. Instead of simply tracking views or clicks, advertisers can see whether their YouTube campaigns drove sales at Walmart, making it easier to connect media spend to business outcomes and justify investment in video advertising.
The big picture. Retail media networks are increasingly extending beyond their owned properties, bringing shopper data and measurement capabilities to broader digital advertising channels where brands are investing more of their budgets.
Between the lines. The deal reflects Walmart Connect’s push to make its audience and measurement tools available across more advertising platforms. Last year, Walmart ended the exclusivity arrangement tied to its partnership with The Trade Desk, opening the door to additional ad tech integrations.
What advertisers get. Access to Walmart’s audience insights, reach to the retailer’s 150 million weekly U.S. customers through YouTube campaigns, and sales attribution tied to Walmart purchases — all within the Display & Video 360 workflow.
What’s next. The rollout is beginning with YouTube campaigns, with Google and Walmart planning to expand the integration to additional inventory over time.
The bottom line. The partnership brings retail media data, media activation and sales measurement closer together, giving advertisers a more direct way to connect YouTube campaigns with in-store and online purchasing behavior at Walmart.
Dig deeper.




We’re bringing Walmart Connect to Display & Video 360.
Walmart Challenges Amazon With New Google Deal

Google and Walmart team up to let advertisers target Walmart shoppers on YouTube and measure the sales within DV360.

Walmart Connect audiences come to Google Display & Video 360

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Amazon is weaving advertising into AI-powered shopping conversations, giving brands new ways to reach consumers.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Amazon is making the case that conversational and agentic shopping aren&#8217;t future concepts — they&#8217;re already reshaping how consumers discover, compare and buy products.</p>



<p>As AI assistants become shopping destinations, advertisers have a new opportunity to reach consumers at the moment intent is expressed, rather than relying on traditional search queries or passive browsing.</p>



<p><strong>What&#8217;s happening</strong>. Amazon has combined its AI shopping assistant Rufus with Alexa+ to create Alexa for Shopping, a unified experience that helps customers research products, compare options, track prices, build carts and automate purchases.</p>



<p>The company says advertising is a key part of that experience, with sponsored products, sponsored brands and conversational ad formats integrated directly into shopping conversations.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1430" height="1008" src="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-brand_nws_Picture_bodyy1._TTW_.jpg" alt="" class="wp-image-479906" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-brand_nws_Picture_bodyy1._TTW_.jpg 1430w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-brand_nws_Picture_bodyy1._TTW_-768x541.jpg 768w" sizes="(max-width: 1430px) 100vw, 1430px" /></figure>



<p><strong>What advertisers get</strong>. Existing sponsored ads campaigns are automatically eligible to appear in Alexa for Shopping, while conversational ad formats can help brands engage customers throughout the buying journey. Amazon also highlighted closed-loop measurement, first-party data signals and AI-powered campaign tools designed to simplify campaign creation and optimization.</p>



<p><strong>Why we care.</strong> The integration of ads into Alexa for Shopping gives advertisers access to intent-rich conversations at multiple points along the customer journey, from product discovery to purchase.</p>



<p>The update also highlights how commerce and advertising are converging inside AI assistants, potentially shortening the path from product discovery to conversion while providing advertisers with closed-loop measurement from impression to purchase.</p>



<p><strong>By the numbers</strong>. More than 300 million customers used Rufus in 2025, according to Amazon. The company also says nearly 20% of shoppers who interact with a Sponsored Brands prompt continue the conversation about that brand, while prompts drive a 6% increase in conversions.</p>



<p><strong>Between the lines.</strong> Amazon&#8217;s pitch to advertisers is that conversational AI creates richer intent signals than traditional advertising environments. Instead of inferring what a customer wants from clicks or searches, Alexa can respond to explicit requests, preferences and purchase goals shared in natural language.</p>



<p><strong>The bottom line</strong>. As shopping becomes more conversational, <a href="https://advertising.amazon.com/library/news/agentic-shopping-advertising">Amazon is positioning advertising within the same interfaces consumers use to research products, compare options and complete purchases</a>.</p>






Amazon is making the case that conversational and agentic shopping aren’t future concepts — they’re already reshaping how consumers discover, compare and buy products.
As AI assistants become shopping destinations, advertisers have a new opportunity to reach consumers at the moment intent is expressed, rather than relying on traditional search queries or passive browsing.
What’s happening. Amazon has combined its AI shopping assistant Rufus with Alexa+ to create Alexa for Shopping, a unified experience that helps customers research products, compare options, track prices, build carts and automate purchases.
The company says advertising is a key part of that experience, with sponsored products, sponsored brands and conversational ad formats integrated directly into shopping conversations.




What advertisers get. Existing sponsored ads campaigns are automatically eligible to appear in Alexa for Shopping, while conversational ad formats can help brands engage customers throughout the buying journey. Amazon also highlighted closed-loop measurement, first-party data signals and AI-powered campaign tools designed to simplify campaign creation and optimization.
Why we care. The integration of ads into Alexa for Shopping gives advertisers access to intent-rich conversations at multiple points along the customer journey, from product discovery to purchase.
The update also highlights how commerce and advertising are converging inside AI assistants, potentially shortening the path from product discovery to conversion while providing advertisers with closed-loop measurement from impression to purchase.
By the numbers. More than 300 million customers used Rufus in 2025, according to Amazon. The company also says nearly 20% of shoppers who interact with a Sponsored Brands prompt continue the conversation about that brand, while prompts drive a 6% increase in conversions.
Between the lines. Amazon’s pitch to advertisers is that conversational AI creates richer intent signals than traditional advertising environments. Instead of inferring what a customer wants from clicks or searches, Alexa can respond to explicit requests, preferences and purchase goals shared in natural language.
The bottom line. As shopping becomes more conversational, Amazon is positioning advertising within the same interfaces consumers use to research products, compare options and complete purchases.

Amazon is weaving advertising into AI-powered shopping conversations, giving brands new ways to reach consumers.

Amazon turns Alexa into a shopping agent — and an advertising platform

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>OpenAI is allowing retail advertisers connect shoppers with relevant products without manually building ads for each item.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>OpenAI has introduced a new Ads Manager beta feature that allows retail advertisers to upload product feeds and automatically create ads from individual catalog items.</p>



<p>The update gives brands a way to scale advertising campaigns inside ChatGPT while serving products that are more relevant to users during purchase-focused conversations.</p>



<p><strong>What&#8217;s happening</strong>. Advertisers can now upload their full product catalogs and generate ads directly from feed data rather than building campaigns item by item.</p>



<p>According to OpenAI, feed-based ads have been among the strongest-performing formats in its Ads Beta so far.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="999" height="963" src="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-Porduct-feeds.jpeg" alt="" class="wp-image-479901" srcset="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-Porduct-feeds.jpeg 999w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-Porduct-feeds-768x740.jpeg 768w" sizes="(max-width: 999px) 100vw, 999px" /></figure>



<p><strong>Why we care. </strong>OpenAI bringing product feed ads to ChatGPT gives retailers a scalable way to match catalog inventory with high-intent conversations and potentially improve ad performance.</p>



<p><strong>Between the lines</strong>. The move mirrors a strategy long used by Google and Meta, where dynamic product feeds allow advertisers to match inventory with user intent at scale.</p>



<p><strong>What&#8217;s next</strong>. Retail advertisers in the beta can review feed requirements and begin creating campaigns directly from uploaded product catalogs.</p>



<p><strong>The bottom line</strong>. OpenAI is expanding its advertising toolkit with a format designed to make ChatGPT ads more scalable, automated and performance-driven for retailers.</p>



<p><strong>First spotted. </strong>This update was shared by Founder of JXT Group Menachem Ani, who shared the email he got from OpenAI on <a href="https://x.com/MenachemAni/status/2064778385113375047">X</a>.</p>


OpenAI has introduced a new Ads Manager beta feature that allows retail advertisers to upload product feeds and automatically create ads from individual catalog items.
The update gives brands a way to scale advertising campaigns inside ChatGPT while serving products that are more relevant to users during purchase-focused conversations.
What’s happening. Advertisers can now upload their full product catalogs and generate ads directly from feed data rather than building campaigns item by item.
According to OpenAI, feed-based ads have been among the strongest-performing formats in its Ads Beta so far.




Why we care. OpenAI bringing product feed ads to ChatGPT gives retailers a scalable way to match catalog inventory with high-intent conversations and potentially improve ad performance.
Between the lines. The move mirrors a strategy long used by Google and Meta, where dynamic product feeds allow advertisers to match inventory with user intent at scale.
What’s next. Retail advertisers in the beta can review feed requirements and begin creating campaigns directly from uploaded product catalogs.
The bottom line. OpenAI is expanding its advertising toolkit with a format designed to make ChatGPT ads more scalable, automated and performance-driven for retailers.
First spotted. This update was shared by Founder of JXT Group Menachem Ani, who shared the email he got from OpenAI on X.

OpenAI is allowing retail advertisers connect shoppers with relevant products without manually building ads for each item.

OpenAI launches product feed ads in Ads Manager beta

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-to-estimate-the-traffic-impact-of-SEO-fixes.png" class="attachment-large size-large wp-post-image" alt="How to estimate the traffic impact of SEO fixes" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-to-estimate-the-traffic-impact-of-SEO-fixes.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-estimate-the-traffic-impact-of-SEO-fixes-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-estimate-the-traffic-impact-of-SEO-fixes-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-estimate-the-traffic-impact-of-SEO-fixes-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A practical framework for forecasting SEO outcomes, prioritizing opportunities, and defending your roadmap with data instead of gut instinct.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-to-estimate-the-traffic-impact-of-SEO-fixes.png" class="attachment-large size-large wp-post-image" alt="How to estimate the traffic impact of SEO fixes" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-to-estimate-the-traffic-impact-of-SEO-fixes.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-estimate-the-traffic-impact-of-SEO-fixes-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-estimate-the-traffic-impact-of-SEO-fixes-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-estimate-the-traffic-impact-of-SEO-fixes-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Your backlog is full. Your developer wants to know what to tackle first. Your boss wants to know why three months of <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> work haven&#8217;t moved the needle. And you&#8217;re stuck defending your roadmap with gut feelings.</p>



<p>If you can&#8217;t tell someone how much traffic a fix is worth before you ship it, you&#8217;re guessing. Guessing doesn&#8217;t hold up in budget meetings.</p>



<p>This is a framework for turning messy data into directional estimates. Not perfect, but good enough to prioritize with confidence and explain your thinking to anyone in the room.</p>



<h2 class="wp-block-heading" id="h-why-every-recommendation-can-t-be-high-priority">Why every recommendation can&#8217;t be high priority</h2>



<p>When everything is urgent, nothing gets done well.</p>



<p>I&#8217;ve watched teams spend two sprints fixing an obscure schema issue affecting three pages while a title tag template bug quietly dragged down thousands of product pages. Both were flagged &#8220;high priority&#8221; in the audit. One was worth maybe 50 clicks a month. The other? Closer to 15,000.</p>



<p>Traffic is the lens that reveals true priority. It doesn&#8217;t mean you ignore brand visibility or UX, but it gives you a common unit to compare recommendations. Without quantified impact, you&#8217;re letting the squeakiest stakeholder, or the most interesting technical puzzle, drive your roadmap instead of actual business value.</p>



<p>One more thing to factor in: The SERP landscape has shifted dramatically underneath your forecasts. SparkToro&#8217;s latest research found that <a href="https://searchengineland.com/google-zero-click-searches-2026-study-479717">68%</a> of U.S. Google searches in the first four months of 2026 ended without a click, up from 58.5% just two years ago.</p>



<p>AI Overviews, featured snippets, and answer boxes are intercepting traffic before anyone reaches your site, and it&#8217;s accelerating. Research from Seer Interactive found organic CTR for informational queries with AI Overviews fell <a href="https://searchengineland.com/google-ai-overviews-drive-drop-organic-paid-ctr-464212">61%</a> between mid-2024 and late 2025.</p>



<p>That means the same ranking improvement can deliver wildly different results depending on SERP layout. </p>



<p>A jump from position eight to three on a commercial keyword might triple your traffic. The same move on an informational query dominated by an AI Overview? You might see nothing. </p>



<p>Your forecasts need to account for this, or you&#8217;ll overpromise every quarter.</p>



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<h2 class="wp-block-heading" id="h-step-1-define-the-scope">Step 1: Define the scope</h2>



<p>Before you estimate anything, nail down the scope. Is this a sitewide technical change, a template fix, or a single-page optimization? The answer completely changes your math.</p>



<h3 class="wp-block-heading" id="h-sitewide-technical-fixes">Sitewide technical fixes</h3>



<p>These include site speed, mobile usability, HTTPS migrations, and Core Web Vitals. Touch every page, but not every page benefits equally.&nbsp;</p>



<p>A CWV fix might move pages that were borderline failing while doing nothing for pages that were already passing. Segment by current performance before you estimate exposure.</p>



<h3 class="wp-block-heading" id="h-template-level-changes">Template-level changes</h3>



<p>Rewriting title tags across all product pages or fixing a broken canonical pattern across blog posts can have an outsized impact, but weigh your forecast by where traffic actually lives. Your product templates might drive 80% of organic clicks while your blog drives 10%.</p>



<h3 class="wp-block-heading" id="h-individual-page-optimizations">Individual page optimizations</h3>



<p>Examples include refreshing meta descriptions, adding internal links, and updating old content. These are easier to model but harder to scale. Even a 50% lift on five pages might not move a business needle. Great for quick wins, but don&#8217;t let them crowd out the bigger opportunities.</p>



<h2 class="wp-block-heading" id="h-step-2-calculate-your-current-traffic-exposure">Step 2: Calculate your current traffic exposure</h2>



<p>Once you know which pages are in scope, pull the numbers from <a href="https://searchengineland.com/guide/google-search-console">Google Search Console</a>.</p>



<ul class="wp-block-list">
<li>Organic clicks give you your baseline. Filter by the affected URLs, look at the last 90 days, and note the trend. If clicks are declining, the fix may be urgent, or the keyword itself might be softening. Context matters.</li>



<li>Impressions and near-win rankings tell you where the real upside lives. <a href="https://searchengineland.com/how-to-use-google-search-console-to-unlock-easy-seo-wins-445389">Pages ranking positions 8-15 are your low-hanging fruit</a>. They&#8217;re already visible, just not getting clicked. Push them into the top five and CTR jumps. Filter for pages with high impressions and low CTR. That&#8217;s your opportunity zone.</li>



<li>SERP feature prevalence matters more than it used to. Check whether your target keywords trigger AI Overviews or other features. Google now <a href="https://searchengineland.com/google-ai-mode-traffic-data-search-console-457076">reports AI Mode impression data</a> directly in Search Console, which gives you a clearer picture of where your visibility is showing up, even if clicks aren&#8217;t flowing from it. If AI features dominate a keyword&#8217;s SERP, temper your click expectations accordingly.</li>
</ul>



<h2 class="wp-block-heading" id="h-step-3-estimate-potential-lift">Step 3: Estimate potential lift</h2>



<p>Now comes the educated part.</p>



<h3 class="wp-block-heading" id="h-your-own-history">Your own history</h3>



<p>If you optimized a batch of category pages six months ago and saw an average 18% traffic bump, use that as your baseline for the next batch. Keep a running log of past projects and their outcomes.&nbsp;</p>



<p>Over time, you&#8217;ll build a benchmark library that makes every forecast faster and more credible to the people you&#8217;re presenting to.</p>



<h3 class="wp-block-heading" id="h-competitor-benchmarks-and-serp-analysis">Competitor benchmarks and SERP analysis</h3>



<p>Look at who&#8217;s ranking above you. Are they doing something obviously better: longer content, stronger UX, or more authoritative backlinks?&nbsp;</p>



<p>If your fix closes that gap, you can reasonably forecast a ranking gain. Tools like Ahrefs or Semrush let you compare page metrics side by side.</p>



<h3 class="wp-block-heading" id="h-ai-influenced-ctr-assumptions">AI-influenced CTR assumptions</h3>



<p>This is where many forecasts quietly fall apart. CTR curves from even a few years ago no longer hold.&nbsp;</p>



<p>Organic CTR on informational queries with AI Overviews dropped from <a href="https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update" target="_blank" rel="noopener">1.76% to 0.61%</a>, research from Seer Interactive found. That’s a 61% decline between mid-2024 and late 2025.&nbsp;</p>



<p>There&#8217;s some encouraging news: Seer&#8217;s 2026 update found CTR on AI Overview queries climbed back to <a href="https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-2026-update" target="_blank" rel="noopener">2.4%</a> by February 2026, suggesting the steepest period of compression may be leveling off.&nbsp;</p>



<p>Still, I apply a meaningful CTR discount to informational queries and keep commercial queries closer to legacy benchmarks, then adjust based on what I&#8217;m actually seeing in Search Console for that specific keyword set.</p>



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<h2 class="wp-block-heading" id="h-step-4-build-three-scenarios-not-one-number">Step 4: Build three scenarios, not one number</h2>



<p>A single forecast number feels confident. It&#8217;s also a lie.</p>



<p>You don&#8217;t know if Google will index your changes this week or next quarter. You don&#8217;t know if a competitor will rebrand and steal your featured snippet. You don&#8217;t know if your dev will ship the fix with a bug that tanks page speed. So stop pretending you do.</p>



<p>Give stakeholders three numbers: conservative, expected, and aggressive. These models provide estimates, not exact figures, and as long as that&#8217;s clearly communicated, any model you build adds value.</p>



<p>Here&#8217;s how to construct the three scenarios:</p>



<ul class="wp-block-list">
<li><strong>Conservative:</strong> Assume partial implementation, delayed indexing, and that competitors improve at the same time. Apply the lowest lift you&#8217;ve seen from comparable past fixes. This is your &#8220;we still made progress&#8221; floor.</li>



<li><strong>Expected:</strong> Use your best historical benchmark, assume normal rollout timelines, and factor in current SERP dynamics. This is the number you&#8217;d bet on if you had to pick one.</li>



<li><strong>Aggressive:</strong> Assume clean implementation, faster-than-usual indexing, and that you pick up a featured snippet or two. This is your &#8220;stars align&#8221; ceiling. It happens. Just not every time.</li>
</ul>



<p>Apply AI impact to each scenario, too. In your conservative case, assume AI Overviews expand further into informational queries. In your expected case, assume the current SERP feature prevalence holds. In your aggressive case, assume you capture snippet features that offset some CTR loss.</p>



<p>For example, you&#8217;re forecasting a title tag template fix across 300 product pages pulling 22,000 clicks per month. Based on past template work, you&#8217;ve seen 12% to 20% CTR lifts after ranking improvements.</p>



<ul class="wp-block-list">
<li><strong>Conservative:</strong> 12% lift → ~2,600 additional monthly clicks.</li>



<li><strong>Expected:</strong> 16% lift → ~3,500 additional monthly clicks.</li>



<li><strong>Aggressive:</strong> 20% lift → ~4,400 additional monthly clicks.</li>
</ul>



<p>Now multiply by your average conversion rate and order value, and that range becomes a revenue conversation, not an SEO conversation. That&#8217;s what gets your fix prioritized.</p>



<h2 class="wp-block-heading" id="h-step-5-use-the-forecast-to-build-your-roadmap">Step 5: Use the forecast to build your roadmap</h2>



<p>Here&#8217;s where it pays off. You&#8217;ve got 20 items in your backlog. You&#8217;ve forecasted traffic impact for each. Now sort by expected value, weight by effort (developer hours, content hours, etc.), and build a simple impact-versus-effort matrix.&nbsp;</p>



<p><a href="https://searchengineland.com/seo-strategy-in-2026-where-discipline-meets-results-463255">Prioritization frameworks</a> like RICE (reach, impact, confidence, effort) can help you rank competing initiatives more systematically.</p>



<p>High impact, low effort? Ship it now. High impact, high effort? Plan it next quarter and get buy-in early. Low impact, high effort? Drop it or save it for a slow month.</p>



<p>Suddenly, your roadmap makes sense to everyone, not just you.</p>



<p>A few traps to avoid as you do this:&nbsp;</p>



<ul class="wp-block-list">
<li>Don&#8217;t overcount affected pages (check how many are noindexed before you run the math).&nbsp;</li>



<li>Don&#8217;t ignore seasonality (a December forecast in retail lives in a different universe than June).&nbsp;</li>



<li>Never treat your baseline as static. If your site loses backlinks or a competitor launches a major content push, your projections shift.</li>
</ul>



<p>It&#8217;s also worth noting that <a href="https://searchengineland.com/why-google-search-console-impressions-dropped-interpret-data-463677">GSC reporting itself changed in late 2025</a> when Google stopped supporting the <code>&amp;num=100</code> parameter, which affected how impressions and position data appear. Treat data from that point forward as your new normal.</p>



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<h2 class="wp-block-heading" id="h-the-goal-is-directional-accuracy">The goal is directional accuracy</h2>



<p>When you present a forecast, say the quiet part out loud: &#8220;I&#8217;m highly confident we&#8217;ll hit the conservative number. I&#8217;m moderately confident we&#8217;ll hit the expected. Aggressive depends on factors outside our control.&#8221;</p>



<p>That framing protects you when reality comes in 20% lower. More importantly, it makes you look like the person in the room who&#8217;s actually thought things through, not the one selling fantasy numbers to win approval.</p>



<p>Nobody expects you to predict the future. They just want to know you&#8217;ve done the work and that your roadmap is built on something real. </p>



<p>Directional accuracy gets you there. Perfect precision is a distraction.</p>

Your backlog is full. Your developer wants to know what to tackle first. Your boss wants to know why three months of SEO work haven’t moved the needle. And you’re stuck defending your roadmap with gut feelings.
If you can’t tell someone how much traffic a fix is worth before you ship it, you’re guessing. Guessing doesn’t hold up in budget meetings.
This is a framework for turning messy data into directional estimates. Not perfect, but good enough to prioritize with confidence and explain your thinking to anyone in the room.
Why every recommendation can’t be high priority
When everything is urgent, nothing gets done well.
I’ve watched teams spend two sprints fixing an obscure schema issue affecting three pages while a title tag template bug quietly dragged down thousands of product pages. Both were flagged “high priority” in the audit. One was worth maybe 50 clicks a month. The other? Closer to 15,000.
Traffic is the lens that reveals true priority. It doesn’t mean you ignore brand visibility or UX, but it gives you a common unit to compare recommendations. Without quantified impact, you’re letting the squeakiest stakeholder, or the most interesting technical puzzle, drive your roadmap instead of actual business value.
One more thing to factor in: The SERP landscape has shifted dramatically underneath your forecasts. SparkToro’s latest research found that 68% of U.S. Google searches in the first four months of 2026 ended without a click, up from 58.5% just two years ago.
AI Overviews, featured snippets, and answer boxes are intercepting traffic before anyone reaches your site, and it’s accelerating. Research from Seer Interactive found organic CTR for informational queries with AI Overviews fell 61% between mid-2024 and late 2025.
That means the same ranking improvement can deliver wildly different results depending on SERP layout. 
A jump from position eight to three on a commercial keyword might triple your traffic. The same move on an informational query dominated by an AI Overview? You might see nothing. 
Your forecasts need to account for this, or you’ll overpromise every quarter.

  
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Step 1: Define the scope
Before you estimate anything, nail down the scope. Is this a sitewide technical change, a template fix, or a single-page optimization? The answer completely changes your math.
Sitewide technical fixes
These include site speed, mobile usability, HTTPS migrations, and Core Web Vitals. Touch every page, but not every page benefits equally. 
A CWV fix might move pages that were borderline failing while doing nothing for pages that were already passing. Segment by current performance before you estimate exposure.
Template-level changes
Rewriting title tags across all product pages or fixing a broken canonical pattern across blog posts can have an outsized impact, but weigh your forecast by where traffic actually lives. Your product templates might drive 80% of organic clicks while your blog drives 10%.
Individual page optimizations
Examples include refreshing meta descriptions, adding internal links, and updating old content. These are easier to model but harder to scale. Even a 50% lift on five pages might not move a business needle. Great for quick wins, but don’t let them crowd out the bigger opportunities.
Step 2: Calculate your current traffic exposure
Once you know which pages are in scope, pull the numbers from Google Search Console.
Organic clicks give you your baseline. Filter by the affected URLs, look at the last 90 days, and note the trend. If clicks are declining, the fix may be urgent, or the keyword itself might be softening. Context matters.
Impressions and near-win rankings tell you where the real upside lives. Pages ranking positions 8-15 are your low-hanging fruit. They’re already visible, just not getting clicked. Push them into the top five and CTR jumps. Filter for pages with high impressions and low CTR. That’s your opportunity zone.
SERP feature prevalence matters more than it used to. Check whether your target keywords trigger AI Overviews or other features. Google now reports AI Mode impression data directly in Search Console, which gives you a clearer picture of where your visibility is showing up, even if clicks aren’t flowing from it. If AI features dominate a keyword’s SERP, temper your click expectations accordingly.
Step 3: Estimate potential lift
Now comes the educated part.
Your own history
If you optimized a batch of category pages six months ago and saw an average 18% traffic bump, use that as your baseline for the next batch. Keep a running log of past projects and their outcomes. 
Over time, you’ll build a benchmark library that makes every forecast faster and more credible to the people you’re presenting to.
Competitor benchmarks and SERP analysis
Look at who’s ranking above you. Are they doing something obviously better: longer content, stronger UX, or more authoritative backlinks? 
If your fix closes that gap, you can reasonably forecast a ranking gain. Tools like Ahrefs or Semrush let you compare page metrics side by side.
AI-influenced CTR assumptions
This is where many forecasts quietly fall apart. CTR curves from even a few years ago no longer hold. 
Organic CTR on informational queries with AI Overviews dropped from 1.76% to 0.61%, research from Seer Interactive found. That’s a 61% decline between mid-2024 and late 2025. 
There’s some encouraging news: Seer’s 2026 update found CTR on AI Overview queries climbed back to 2.4% by February 2026, suggesting the steepest period of compression may be leveling off. 
Still, I apply a meaningful CTR discount to informational queries and keep commercial queries closer to legacy benchmarks, then adjust based on what I’m actually seeing in Search Console for that specific keyword set.
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Step 4: Build three scenarios, not one number
A single forecast number feels confident. It’s also a lie.
You don’t know if Google will index your changes this week or next quarter. You don’t know if a competitor will rebrand and steal your featured snippet. You don’t know if your dev will ship the fix with a bug that tanks page speed. So stop pretending you do.
Give stakeholders three numbers: conservative, expected, and aggressive. These models provide estimates, not exact figures, and as long as that’s clearly communicated, any model you build adds value.
Here’s how to construct the three scenarios:
Conservative: Assume partial implementation, delayed indexing, and that competitors improve at the same time. Apply the lowest lift you’ve seen from comparable past fixes. This is your “we still made progress” floor.
Expected: Use your best historical benchmark, assume normal rollout timelines, and factor in current SERP dynamics. This is the number you’d bet on if you had to pick one.
Aggressive: Assume clean implementation, faster-than-usual indexing, and that you pick up a featured snippet or two. This is your “stars align” ceiling. It happens. Just not every time.
Apply AI impact to each scenario, too. In your conservative case, assume AI Overviews expand further into informational queries. In your expected case, assume the current SERP feature prevalence holds. In your aggressive case, assume you capture snippet features that offset some CTR loss.
For example, you’re forecasting a title tag template fix across 300 product pages pulling 22,000 clicks per month. Based on past template work, you’ve seen 12% to 20% CTR lifts after ranking improvements.
Conservative: 12% lift → ~2,600 additional monthly clicks.
Expected: 16% lift → ~3,500 additional monthly clicks.
Aggressive: 20% lift → ~4,400 additional monthly clicks.
Now multiply by your average conversion rate and order value, and that range becomes a revenue conversation, not an SEO conversation. That’s what gets your fix prioritized.
Step 5: Use the forecast to build your roadmap
Here’s where it pays off. You’ve got 20 items in your backlog. You’ve forecasted traffic impact for each. Now sort by expected value, weight by effort (developer hours, content hours, etc.), and build a simple impact-versus-effort matrix. 
Prioritization frameworks like RICE (reach, impact, confidence, effort) can help you rank competing initiatives more systematically.
High impact, low effort? Ship it now. High impact, high effort? Plan it next quarter and get buy-in early. Low impact, high effort? Drop it or save it for a slow month.
Suddenly, your roadmap makes sense to everyone, not just you.
A few traps to avoid as you do this: 
Don’t overcount affected pages (check how many are noindexed before you run the math). 
Don’t ignore seasonality (a December forecast in retail lives in a different universe than June). 
Never treat your baseline as static. If your site loses backlinks or a competitor launches a major content push, your projections shift.
It’s also worth noting that GSC reporting itself changed in late 2025 when Google stopped supporting the &num=100 parameter, which affected how impressions and position data appear. Treat data from that point forward as your new normal.

  
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The goal is directional accuracy
When you present a forecast, say the quiet part out loud: “I’m highly confident we’ll hit the conservative number. I’m moderately confident we’ll hit the expected. Aggressive depends on factors outside our control.”
That framing protects you when reality comes in 20% lower. More importantly, it makes you look like the person in the room who’s actually thought things through, not the one selling fantasy numbers to win approval.
Nobody expects you to predict the future. They just want to know you’ve done the work and that your roadmap is built on something real. 
Directional accuracy gets you there. Perfect precision is a distraction.

A practical framework for forecasting SEO outcomes, prioritizing opportunities, and defending your roadmap with data instead of gut instinct.

How to estimate the traffic impact of SEO fixes

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Google-LSA-for-real-estate.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Google-LSA-for-real-estate.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Google-LSA-for-real-estate-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Google-LSA-for-real-estate-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Google-LSA-for-real-estate-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google is bringing richer home listing ads to Search, helping agents connect with high-intent buyers earlier in the journey.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Google-LSA-for-real-estate.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Google-LSA-for-real-estate.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Google-LSA-for-real-estate-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Google-LSA-for-real-estate-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Google-LSA-for-real-estate-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Google is rolling out enhanced Local Services Ads (LSAs) for Home Listings across all 50 U.S. states, giving homebuyers more property information directly within ads and creating new lead opportunities for real estate agents.</p>



<p>Homebuyers increasingly begin their search on Google, and the expanded ad format is designed to connect them with local agents earlier in the buying journey.</p>



<p><strong>What&#8217;s new</strong>. The updated LSA experience now includes property details within the ad itself, including pricing, photos and key home features.</p>



<p>The experience is powered through a partnership with HouseCanary, whose property data platform supplies listing information shown in the ads.</p>



<p><strong>Why we care. </strong>Google is moving further down the purchase funnel, allowing real estate advertisers to showcase actual listings — complete with photos, pricing and property details — directly within Local Services Ads. That means buyers can evaluate homes and contact agents without leaving Search, potentially increasing lead quality and conversion rates.</p>



<p><strong>How it works</strong>. Buyers searching for homes can contact agents directly from the ad by calling, messaging or booking an appointment.</p>



<p><strong>Who benefits</strong>. Existing LSA advertisers will automatically be included in the new experience. Real estate professionals not currently using Local Services Ads can sign up to begin receiving leads, while portal partners can enroll agents through Google&#8217;s managed partner program.</p>



<p><strong>The bottom line</strong>. Google is betting that <a href="https://blog.google/products/ads-commerce/new-real-estate-ads-formats">richer listing information combined with direct agent connections</a> will make Search a more useful starting point for homebuyers — and a valuable source of high-intent leads for agents.</p>


Google is rolling out enhanced Local Services Ads (LSAs) for Home Listings across all 50 U.S. states, giving homebuyers more property information directly within ads and creating new lead opportunities for real estate agents.
Homebuyers increasingly begin their search on Google, and the expanded ad format is designed to connect them with local agents earlier in the buying journey.
What’s new. The updated LSA experience now includes property details within the ad itself, including pricing, photos and key home features.
The experience is powered through a partnership with HouseCanary, whose property data platform supplies listing information shown in the ads.
Why we care. Google is moving further down the purchase funnel, allowing real estate advertisers to showcase actual listings — complete with photos, pricing and property details — directly within Local Services Ads. That means buyers can evaluate homes and contact agents without leaving Search, potentially increasing lead quality and conversion rates.
How it works. Buyers searching for homes can contact agents directly from the ad by calling, messaging or booking an appointment.
Who benefits. Existing LSA advertisers will automatically be included in the new experience. Real estate professionals not currently using Local Services Ads can sign up to begin receiving leads, while portal partners can enroll agents through Google’s managed partner program.
The bottom line. Google is betting that richer listing information combined with direct agent connections will make Search a more useful starting point for homebuyers — and a valuable source of high-intent leads for agents.

Google is bringing richer home listing ads to Search, helping agents connect with high-intent buyers earlier in the journey.

Google expands richer Local Services Ads for real estate nationwide

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience.png" class="attachment-large size-large wp-post-image" alt="AI can write SEO content, but it can’t replace real experience" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Generic advice is everywhere. Learn why firsthand knowledge and real-world experience are becoming more valuable for SEO.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience.png" class="attachment-large size-large wp-post-image" alt="AI can write SEO content, but it can’t replace real experience" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/AI-can-write-SEO-content-but-it-cant-replace-real-experience-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p><a href="https://searchengineland.com/guide/what-is-seo">SEO</a> content is starting to sound painfully repetitive.</p>



<p>Search for almost anything online and you’ll find pages repeating the same advice in slightly different ways. Now that AI tools can create blog posts in seconds, the problem is getting worse.</p>



<p>There’s more content than ever, but much of it feels forgettable. This homogeneity is becoming a real issue for SEO.</p>



<h2 class="wp-block-heading" id="h-real-experience-is-becoming-a-bigger-seo-differentiator">Real experience is becoming a bigger SEO differentiator</h2>



<p>As AI-generated content floods search results, businesses need something that helps them stand out. Right now, one of the biggest things separating good content from bad content is real experience.</p>



<p>AI can write content. But it can’t replace lived experience.</p>



<p>It can’t tell people what happened when a strategy failed. It can’t explain the lessons learned from working with real clients. It can’t share the small details that come from years of doing the work.</p>



<p>That human side matters more than many businesses realize.</p>



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<h2 class="wp-block-heading" id="h-so-much-seo-content-sounds-the-same">So much SEO content sounds the same</h2>



<p>For years, SEO focused heavily on creating content around keywords and search terms. Businesses were encouraged to publish more and more articles to increase visibility.</p>



<p>The problem is that many websites ended up producing content that sounded identical.</p>



<p>Now AI has made that even easier.</p>



<p>Anyone can create a blog called &#8220;10 SEO tips&#8221; or &#8220;How to rank higher on Google&#8221; in under a minute. The internet already has thousands of them, and most add nothing new.</p>



<p>People are tired of reading content that feels copied, even when it technically isn’t.</p>



<p>The pages that stand out now usually feel more human.</p>



<p>They include:</p>



<ul class="wp-block-list">
<li>Real examples.</li>



<li>Honest opinions.</li>



<li>Lessons learned.</li>



<li>Client stories.</li>



<li>Testing and results.</li>



<li>Personal insight.</li>
</ul>



<p>In short, they sound like they were written by someone who has done the job. And that distinction is becoming increasingly important as search changes.</p>



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<h2 class="wp-block-heading" id="h-search-has-evolved-your-content-needs-to-as-well">Search has evolved. Your content needs to as well.</h2>



<p>Google has spent years talking about trust and experience in content. At the same time, AI search tools are making it easier for people to get quick answers without clicking through endless websites.</p>



<p>That evolution means basic information alone is becoming less valuable. If AI can already summarize general advice, businesses need to offer something more. This is where real experience becomes especially valuable for SEO.</p>



<p>A business owner sharing what genuinely worked for them will often build more trust than another polished article filled with generic advice. A case study showing real results carries more weight than a page stuffed with keywords.</p>



<p>Specific details make content feel believable. And that level of detail is something AI still struggles to do well because it can only work from existing information. It can’t truly speak from experience.</p>



<p>For small businesses, that distinction can be especially valuable. Big brands often rely on their name and reputation. Smaller businesses usually win customers because people trust them, like them, and feel a connection with them. That human side can become a real SEO advantage.</p>



<h2 class="wp-block-heading" id="h-ai-works-best-when-paired-with-real-expertise">AI works best when paired with real expertise</h2>



<p>None of this advice means businesses should stop using AI.</p>



<p>When used properly, AI can help with research, planning, idea generation, and speeding up content creation. Most marketers already use it in some way, and that use will continue.</p>



<p>But the businesses getting the best results aren’t relying on AI alone. They’re combining AI with real knowledge, personality, and experience. They’re adding opinions, stories, and insights that can’t simply be generated in seconds. That’s what makes people pay attention.</p>



<p>SEO isn’t about publishing the most content. It’s about creating content people remember and trust. As more websites fill up with AI-generated articles that all sound the same, genuinely human content becomes more valuable.</p>



<p>Because while AI can write words, it still can’t replace real experience.</p>

SEO content is starting to sound painfully repetitive.
Search for almost anything online and you’ll find pages repeating the same advice in slightly different ways. Now that AI tools can create blog posts in seconds, the problem is getting worse.
There’s more content than ever, but much of it feels forgettable. This homogeneity is becoming a real issue for SEO.
Real experience is becoming a bigger SEO differentiator
As AI-generated content floods search results, businesses need something that helps them stand out. Right now, one of the biggest things separating good content from bad content is real experience.
AI can write content. But it can’t replace lived experience.
It can’t tell people what happened when a strategy failed. It can’t explain the lessons learned from working with real clients. It can’t share the small details that come from years of doing the work.
That human side matters more than many businesses realize.

  
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So much SEO content sounds the same
For years, SEO focused heavily on creating content around keywords and search terms. Businesses were encouraged to publish more and more articles to increase visibility.
The problem is that many websites ended up producing content that sounded identical.
Now AI has made that even easier.
Anyone can create a blog called “10 SEO tips” or “How to rank higher on Google” in under a minute. The internet already has thousands of them, and most add nothing new.
People are tired of reading content that feels copied, even when it technically isn’t.
The pages that stand out now usually feel more human.
They include:
Real examples.
Honest opinions.
Lessons learned.
Client stories.
Testing and results.
Personal insight.
In short, they sound like they were written by someone who has done the job. And that distinction is becoming increasingly important as search changes.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Search has evolved. Your content needs to as well.
Google has spent years talking about trust and experience in content. At the same time, AI search tools are making it easier for people to get quick answers without clicking through endless websites.
That evolution means basic information alone is becoming less valuable. If AI can already summarize general advice, businesses need to offer something more. This is where real experience becomes especially valuable for SEO.
A business owner sharing what genuinely worked for them will often build more trust than another polished article filled with generic advice. A case study showing real results carries more weight than a page stuffed with keywords.
Specific details make content feel believable. And that level of detail is something AI still struggles to do well because it can only work from existing information. It can’t truly speak from experience.
For small businesses, that distinction can be especially valuable. Big brands often rely on their name and reputation. Smaller businesses usually win customers because people trust them, like them, and feel a connection with them. That human side can become a real SEO advantage.
AI works best when paired with real expertise
None of this advice means businesses should stop using AI.
When used properly, AI can help with research, planning, idea generation, and speeding up content creation. Most marketers already use it in some way, and that use will continue.
But the businesses getting the best results aren’t relying on AI alone. They’re combining AI with real knowledge, personality, and experience. They’re adding opinions, stories, and insights that can’t simply be generated in seconds. That’s what makes people pay attention.
SEO isn’t about publishing the most content. It’s about creating content people remember and trust. As more websites fill up with AI-generated articles that all sound the same, genuinely human content becomes more valuable.
Because while AI can write words, it still can’t replace real experience.

Generic advice is everywhere. Learn why firsthand knowledge and real-world experience are becoming more valuable for SEO.

AI can write SEO content, but it can’t replace real experience

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap.png" class="attachment-large size-large wp-post-image" alt="What co-mentions reveal about the AI recommendation gap" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Being understood by AI isn't the same as being recommended by it. New research reveals the gap between the two.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap.png" class="attachment-large size-large wp-post-image" alt="What co-mentions reveal about the AI recommendation gap" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/What-co-mentions-reveal-about-the-AI-recommendation-gap-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>We&#8217;ve spent the last two years optimizing for AI visibility by focusing on what we say about ourselves: writing better About pages, adding clear schema and SameAs markup, structuring content more effectively, and providing more direct answers.</p>



<p>All of these principles still apply and are essential for the <a href="https://searchengineland.com/brand-ai-recommendation-set-477229">qualification phase</a> of an LLM&#8217;s brand processing (clarity + relevance). But a study João da Silva and I conducted using Friction AI&#8217;s platform puts a number on a factor the industry has been circling around but couldn&#8217;t prove.</p>



<p>Among brands that were already recognized (where the LLM could describe them accurately), Knowledge Graph (KG) strength predicted visibility within the category each brand was coded to. What it didn&#8217;t predict was whether a brand would surface in an adjacent category query, even if it belonged there from a business perspective. In other words, recognition didn&#8217;t guarantee recommendation. That’s <a href="https://searchengineland.com/framing-gap-brand-position-ai-475715">the framing gap</a>.</p>



<h2 class="wp-block-heading" id="h-what-brands-did-we-test-and-how-did-we-test-them">What brands did we test and how did we test them?</h2>



<p>For this case study, we tested 12 athleisure and activewear brands across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews: 14,140 API runs over seven days, using UK geography with web search enabled.</p>



<p>For each brand, we ran two types of prompts:</p>



<ul class="wp-block-list">
<li>Recognition prompts (&#8220;What is [Brand]?&#8221; and &#8220;Describe [Brand]&#8221;)</li>



<li>Recommendation prompts (&#8220;Best athleisure brands,&#8221; &#8220;Top 10 athleisure brands,&#8221; and &#8220;Which athletic apparel brands are worth buying in 2026?&#8221;)</li>
</ul>



<p>The brands spanned three Knowledge Graph tiers, assigned by Google KG resultScore (the raw score returned by Google&#8217;s Knowledge Graph Search API — a proxy for how strongly an entity is established in Google&#8217;s index), so we could test whether KG strength predicted recommendation visibility:</p>



<ul class="wp-block-list">
<li><strong>Low KG: </strong>LNDR, TALA, Gymshark, Varley.</li>



<li><strong>Mid KG: </strong>Reebok, Outdoor Voices, Rhone Apparel, Sweaty Betty.</li>



<li><strong>High KG: </strong>Alo Yoga, Nike, lululemon, New Balance.</li>
</ul>



<p>Spoiler ahead: The high-KG brands didn&#8217;t dominate recommendations. The mid-KG tier showed the largest average gap between recognition and recommendation.</p>



<p>Within the high-KG tier, some brands were universally recommended, while others were nearly invisible in recommendation prompts, despite being perfectly recognized across every LLM we tested.</p>



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<h2 class="wp-block-heading" id="h-recommendations-what-the-co-mention-data-showed">Recommendations: What the co-mention data showed</h2>



<p>We mapped how often brands appeared together in athleisure content across external sources (articles, reviews, comparison pieces, and editorial lists) crawled via API from UK-indexed sources.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1562" height="680" src="https://searchengineland.com/wp-content/seloads/2026/06/co-mentions_frictionai.png" alt="Co-mentions Frictional" class="wp-image-479832" srcset="https://searchengineland.com/wp-content/seloads/2026/06/co-mentions_frictionai.png 1562w, https://searchengineland.com/wp-content/seloads/2026/06/co-mentions_frictionai-768x334.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/co-mentions_frictionai-1536x669.png 1536w" sizes="(max-width: 1562px) 100vw, 1562px" /></figure>
</div>


<p>Some of the most interesting results include:</p>



<ul class="wp-block-list">
<li>lululemon + Alo Yoga: 534 co-mentions.</li>



<li>lululemon + Nike: 482 co-mentions.</li>



<li>Alo Yoga + Nike: 449 co-mentions.</li>



<li>Gymshark + lululemon: 264 co-mentions.</li>



<li>Gymshark + Alo Yoga: 252 co-mentions.</li>
</ul>



<p>These brands appear together repeatedly in the same articles, roundups, and editorial comparisons across independent sources. Together, they form a cluster that the LLM treats as &#8220;athleisure.&#8221;</p>



<p>Now look at the other end of the spectrum. New Balance co-occurs with lululemon in athleisure content so rarely that it doesn&#8217;t appear in the top pairs at all. Nike co-occurs with lululemon roughly 50 times more often than New Balance does.</p>



<p>Nike, New Balance, and Reebok share the exact same Google Knowledge Graph description: &#8220;Footwear company.&#8221; From an entity standpoint, they start from the same position. But Nike is inside the athleisure cluster. New Balance and Reebok are entirely outside it.</p>



<p>The LLM isn&#8217;t evaluating these brands independently and deciding which ones fit athleisure. It&#8217;s pattern-matching against associations built from external content. If a brand hasn&#8217;t appeared consistently alongside lululemon, Alo Yoga, and Gymshark in the content the model trained on — or retrieves from — it doesn&#8217;t belong in that cluster because the semantic association was never built.</p>



<h2 class="wp-block-heading" id="h-nike-the-hero-same-kg-description-completely-different-results">Nike, the hero: Same KG description, completely different results</h2>



<p>Nike, New Balance, and Reebok share the same KG entity description: &#8220;Footwear company.&#8221; LLM probing across all five systems assigns all three unanimously to the athletic_footwear category, so from a pure entity-clarity standpoint, they start from the same position.</p>



<p>However, their recommendation rates in athleisure queries aren’t remotely equivalent.</p>



<p>Nike surfaces in 71% of athleisure recommendation prompts, while New Balance and Reebok appear in 0% across all five LLMs and all 14,140 runs.</p>



<p>The difference isn&#8217;t how they&#8217;re defined (&#8220;Footwear company&#8221;). It&#8217;s which conversations they appear in and which other brands appear alongside them.</p>



<p>LLMs don&#8217;t infer category adjacency. If a brand hasn&#8217;t been consistently mentioned alongside the relevant players in a category — in press, reviews, editorial content, and comparison pieces — the model doesn&#8217;t make the leap. Jason Barnard describes this well: if A plus B should equal J, you have to construct that path explicitly. The model won&#8217;t build it for you.</p>



<p>New Balance&#8217;s co-mention density lives in running and performance content. Nobody built the semantic bridge from running → athletic lifestyle → athleisure in external content, so the model doesn&#8217;t cross it. The Knowledge Graph says &#8220;Footwear company,&#8221; and the third-party corpus confirms footwear. Athleisure queries retrieve the athleisure corpus, and New Balance isn&#8217;t in it.</p>



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<hr class="wp-block-separator has-text-color has-cyan-bluish-gray-color has-css-opacity has-cyan-bluish-gray-background-color has-background"/>



<h2 class="wp-block-heading" id="h-the-third-party-citation-weight-in-recommendation-vs-recognition-data">The third-party citation weight in recommendation vs. recognition data</h2>



<p>When we split citations by prompt type — recognition vs. recommendation — a pattern emerges that should reframe where most GEO budgets are being spent.</p>



<p>For recognition prompts — where the user has already typed your brand name — own-brand content is the dominant source:</p>



<ul class="wp-block-list">
<li>ChatGPT cited own-brand content 49% of the time.</li>



<li>Perplexity: 36%.</li>



<li>Claude: 23%.</li>
</ul>



<p>This is where your About page and homepage are used for clarity, and your services, category, and guide pages are used for relevance.</p>



<p>Recommendation prompts give us completely different results. When the user hasn&#8217;t named your brand and is asking for the best option in a category, own-brand citations drop to 18% on ChatGPT and to effectively zero on Gemini, Claude, Perplexity, and Google AI Overviews. Third-party sources account for 82% to 100% of what gets cited across all five systems.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1565" height="311" src="https://searchengineland.com/wp-content/seloads/2026/06/owned-content-citation.png" alt="the citation rate of owned content on LLM recommendation vs recognition answers." class="wp-image-479833" srcset="https://searchengineland.com/wp-content/seloads/2026/06/owned-content-citation.png 1565w, https://searchengineland.com/wp-content/seloads/2026/06/owned-content-citation-768x153.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/owned-content-citation-1536x305.png 1536w" sizes="(max-width: 1565px) 100vw, 1565px" /></figure>
</div>


<p>The GEO community has argued for some time that external signals matter more than on-site optimization for recommendation visibility, and this data puts specific numbers behind that argument. It also shows that external signals aren&#8217;t all the same thing.</p>



<ul class="wp-block-list">
<li>Entity clarity gets a brand recognized. That&#8217;s a problem you solve on your own site.</li>



<li>External credibility gets it considered. That&#8217;s a PR and corroboration problem.</li>



<li>Co-mention density in the right category cluster places a brand in the concept graph for a specific recommendation query. That&#8217;s a category-positioning problem. </li>
</ul>



<p>These are three separate problems that require different solutions. Conflating them is why many GEO recommendations stop short.</p>



<p>The practical addition to any GEO audit is this: after checking entity clarity and external credibility, audit where you appear in relation to others.</p>



<ul class="wp-block-list">
<li>Are your press mentions listing you alongside your actual category competitors?</li>



<li>Do the roundups that include you also name the brands that dominate your target category? </li>
</ul>



<p>If not, the LLM has probably never learned to associate you with that category because it has never seen you in that &#8220;company.&#8221; Unlike entity clarity or schema, it&#8217;s not something you can fix on your own website. That&#8217;s the gap.</p>



<h2 class="wp-block-heading" id="h-what-the-co-mention-structure-means-for-pr-and-content-strategy">What the co-mention structure means for PR and content strategy</h2>



<p>As we&#8217;ve seen so far, being mentioned in a category isn&#8217;t enough. Being mentioned alongside the right brands in a category is what places you in the concept graph for that cluster.</p>



<p>A press mention that describes a brand as &#8220;performance apparel&#8221; in isolation does little to advance its athleisure concept graph placement.&nbsp;</p>



<p>A press mention that lists it alongside lululemon, Alo Yoga, and Gymshark in an editorial comparison does considerably more because it builds the co-occurrence signal the model needs to associate the brand with that cluster.</p>



<p>The same logic applies across content type.</p>



<h3 class="wp-block-heading" id="h-editorial-roundups-and-comparison-pieces-nbsp">Editorial roundups and comparison pieces&nbsp;</h3>



<p>Being included in &#8220;best of&#8221; lists that name your category competitors is worth more to your concept graph than a standalone brand profile. The cluster signal comes from appearing in the same article as the brands that define the category.</p>



<h3 class="wp-block-heading" id="h-podcast-appearances">Podcast appearances</h3>



<p>If the host introduces you in relation to specific named brands, or compares your approach to a category leader, that co-occurrence gets indexed.&nbsp;</p>



<p>A bio that says &#8220;founder of [Brand], which competes with lululemon and Gymshark in the premium athleisure space&#8221; does different work than a bio that says &#8220;founder of [Brand], a performance apparel company.&#8221;</p>



<h3 class="wp-block-heading" id="h-analyst-and-industry-reports">Analyst and industry reports</h3>



<p>Category-level reports that group brands together are high-signal co-mention sources. Being included in a sector analysis alongside your category peers places you in the concept graph in a way that standalone coverage doesn&#8217;t.</p>



<h3 class="wp-block-heading" id="h-retailer-and-comparison-taxonomy">Retailer and comparison taxonomy</h3>



<p>Being stocked and categorized alongside category leaders in a major retailer&#8217;s taxonomy is a co-mention signal. The retailer&#8217;s category page is external content that places your brand in a cluster.</p>



<p>The goal is visibility in the right company.</p>



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<h2 class="wp-block-heading" id="h-a-note-on-the-data-and-what-comes-next">A note on the data and what comes next</h2>



<p>This study covers a single category — athleisure and activewear — with 12 brands tested in the UK. The co-mention figures are raw co-occurrence counts from UK-indexed sources crawled via API, covering content indexed at the time of the study in May 2026. Cross-category validation and additional geography testing are in progress.</p>



<p>The full paper, &#8220;<a href="https://zenodo.org/records/20445151" target="_blank" rel="noopener">The Recognition-Recommendation Gap: Empirical Evidence That Category Coding, Not Knowledge-Graph Strength, Determines Brand Visibility in Generative AI Output</a>,&#8221; has been published by João da Silva and me on Zenodo and documents the methodology, brand sample, prompt set, and extraction code in sufficient detail for independent replication.</p>



<p>But the pattern in the co-mention data is clear enough to act on now. Three brands share the same Knowledge Graph description: one appears in 71% of athleisure recommendation responses, and two appear in 0%. The structural difference is co-mention density in category-aligned third-party content.</p>



<p>The question worth asking about any brand is this: In the content that talks about your category, are you in the room, and are you in the right company?</p>

We’ve spent the last two years optimizing for AI visibility by focusing on what we say about ourselves: writing better About pages, adding clear schema and SameAs markup, structuring content more effectively, and providing more direct answers.
All of these principles still apply and are essential for the qualification phase of an LLM’s brand processing (clarity + relevance). But a study João da Silva and I conducted using Friction AI’s platform puts a number on a factor the industry has been circling around but couldn’t prove.
Among brands that were already recognized (where the LLM could describe them accurately), Knowledge Graph (KG) strength predicted visibility within the category each brand was coded to. What it didn’t predict was whether a brand would surface in an adjacent category query, even if it belonged there from a business perspective. In other words, recognition didn’t guarantee recommendation. That’s the framing gap.
What brands did we test and how did we test them?
For this case study, we tested 12 athleisure and activewear brands across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews: 14,140 API runs over seven days, using UK geography with web search enabled.
For each brand, we ran two types of prompts:
Recognition prompts (“What is [Brand]?” and “Describe [Brand]”)
Recommendation prompts (“Best athleisure brands,” “Top 10 athleisure brands,” and “Which athletic apparel brands are worth buying in 2026?”)
The brands spanned three Knowledge Graph tiers, assigned by Google KG resultScore (the raw score returned by Google’s Knowledge Graph Search API — a proxy for how strongly an entity is established in Google’s index), so we could test whether KG strength predicted recommendation visibility:
Low KG: LNDR, TALA, Gymshark, Varley.
Mid KG: Reebok, Outdoor Voices, Rhone Apparel, Sweaty Betty.
High KG: Alo Yoga, Nike, lululemon, New Balance.
Spoiler ahead: The high-KG brands didn’t dominate recommendations. The mid-KG tier showed the largest average gap between recognition and recommendation.
Within the high-KG tier, some brands were universally recommended, while others were nearly invisible in recommendation prompts, despite being perfectly recognized across every LLM we tested.
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Recommendations: What the co-mention data showed
We mapped how often brands appeared together in athleisure content across external sources (articles, reviews, comparison pieces, and editorial lists) crawled via API from UK-indexed sources.




Some of the most interesting results include:
lululemon + Alo Yoga: 534 co-mentions.
lululemon + Nike: 482 co-mentions.
Alo Yoga + Nike: 449 co-mentions.
Gymshark + lululemon: 264 co-mentions.
Gymshark + Alo Yoga: 252 co-mentions.
These brands appear together repeatedly in the same articles, roundups, and editorial comparisons across independent sources. Together, they form a cluster that the LLM treats as “athleisure.”
Now look at the other end of the spectrum. New Balance co-occurs with lululemon in athleisure content so rarely that it doesn’t appear in the top pairs at all. Nike co-occurs with lululemon roughly 50 times more often than New Balance does.
Nike, New Balance, and Reebok share the exact same Google Knowledge Graph description: “Footwear company.” From an entity standpoint, they start from the same position. But Nike is inside the athleisure cluster. New Balance and Reebok are entirely outside it.
The LLM isn’t evaluating these brands independently and deciding which ones fit athleisure. It’s pattern-matching against associations built from external content. If a brand hasn’t appeared consistently alongside lululemon, Alo Yoga, and Gymshark in the content the model trained on — or retrieves from — it doesn’t belong in that cluster because the semantic association was never built.
Nike, the hero: Same KG description, completely different results
Nike, New Balance, and Reebok share the same KG entity description: “Footwear company.” LLM probing across all five systems assigns all three unanimously to the athletic_footwear category, so from a pure entity-clarity standpoint, they start from the same position.
However, their recommendation rates in athleisure queries aren’t remotely equivalent.
Nike surfaces in 71% of athleisure recommendation prompts, while New Balance and Reebok appear in 0% across all five LLMs and all 14,140 runs.
The difference isn’t how they’re defined (“Footwear company”). It’s which conversations they appear in and which other brands appear alongside them.
LLMs don’t infer category adjacency. If a brand hasn’t been consistently mentioned alongside the relevant players in a category — in press, reviews, editorial content, and comparison pieces — the model doesn’t make the leap. Jason Barnard describes this well: if A plus B should equal J, you have to construct that path explicitly. The model won’t build it for you.
New Balance’s co-mention density lives in running and performance content. Nobody built the semantic bridge from running → athletic lifestyle → athleisure in external content, so the model doesn’t cross it. The Knowledge Graph says “Footwear company,” and the third-party corpus confirms footwear. Athleisure queries retrieve the athleisure corpus, and New Balance isn’t in it.
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The third-party citation weight in recommendation vs. recognition data
When we split citations by prompt type — recognition vs. recommendation — a pattern emerges that should reframe where most GEO budgets are being spent.
For recognition prompts — where the user has already typed your brand name — own-brand content is the dominant source:
ChatGPT cited own-brand content 49% of the time.
Perplexity: 36%.
Claude: 23%.
This is where your About page and homepage are used for clarity, and your services, category, and guide pages are used for relevance.
Recommendation prompts give us completely different results. When the user hasn’t named your brand and is asking for the best option in a category, own-brand citations drop to 18% on ChatGPT and to effectively zero on Gemini, Claude, Perplexity, and Google AI Overviews. Third-party sources account for 82% to 100% of what gets cited across all five systems.




The GEO community has argued for some time that external signals matter more than on-site optimization for recommendation visibility, and this data puts specific numbers behind that argument. It also shows that external signals aren’t all the same thing.
Entity clarity gets a brand recognized. That’s a problem you solve on your own site.
External credibility gets it considered. That’s a PR and corroboration problem.
Co-mention density in the right category cluster places a brand in the concept graph for a specific recommendation query. That’s a category-positioning problem. 
These are three separate problems that require different solutions. Conflating them is why many GEO recommendations stop short.
The practical addition to any GEO audit is this: after checking entity clarity and external credibility, audit where you appear in relation to others.
Are your press mentions listing you alongside your actual category competitors?
Do the roundups that include you also name the brands that dominate your target category? 
If not, the LLM has probably never learned to associate you with that category because it has never seen you in that “company.” Unlike entity clarity or schema, it’s not something you can fix on your own website. That’s the gap.
What the co-mention structure means for PR and content strategy
As we’ve seen so far, being mentioned in a category isn’t enough. Being mentioned alongside the right brands in a category is what places you in the concept graph for that cluster.
A press mention that describes a brand as “performance apparel” in isolation does little to advance its athleisure concept graph placement. 
A press mention that lists it alongside lululemon, Alo Yoga, and Gymshark in an editorial comparison does considerably more because it builds the co-occurrence signal the model needs to associate the brand with that cluster.
The same logic applies across content type.
Editorial roundups and comparison pieces 
Being included in “best of” lists that name your category competitors is worth more to your concept graph than a standalone brand profile. The cluster signal comes from appearing in the same article as the brands that define the category.
Podcast appearances
If the host introduces you in relation to specific named brands, or compares your approach to a category leader, that co-occurrence gets indexed. 
A bio that says “founder of [Brand], which competes with lululemon and Gymshark in the premium athleisure space” does different work than a bio that says “founder of [Brand], a performance apparel company.”
Analyst and industry reports
Category-level reports that group brands together are high-signal co-mention sources. Being included in a sector analysis alongside your category peers places you in the concept graph in a way that standalone coverage doesn’t.
Retailer and comparison taxonomy
Being stocked and categorized alongside category leaders in a major retailer’s taxonomy is a co-mention signal. The retailer’s category page is external content that places your brand in a cluster.
The goal is visibility in the right company.
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A note on the data and what comes next
This study covers a single category — athleisure and activewear — with 12 brands tested in the UK. The co-mention figures are raw co-occurrence counts from UK-indexed sources crawled via API, covering content indexed at the time of the study in May 2026. Cross-category validation and additional geography testing are in progress.
The full paper, “The Recognition-Recommendation Gap: Empirical Evidence That Category Coding, Not Knowledge-Graph Strength, Determines Brand Visibility in Generative AI Output,” has been published by João da Silva and me on Zenodo and documents the methodology, brand sample, prompt set, and extraction code in sufficient detail for independent replication.
But the pattern in the co-mention data is clear enough to act on now. Three brands share the same Knowledge Graph description: one appears in 71% of athleisure recommendation responses, and two appear in 0%. The structural difference is co-mention density in category-aligned third-party content.
The question worth asking about any brand is this: In the content that talks about your category, are you in the room, and are you in the right company?

Being understood by AI isn't the same as being recommended by it. New research reveals the gap between the two.

What co-mentions reveal about the AI recommendation gap

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image.png" class="attachment-large size-large wp-post-image" alt="Interviewing SEOs in an AI-first world" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>One group of SEO talent is becoming dramatically more valuable. The other is watching their skill set become a commodity.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image.png" class="attachment-large size-large wp-post-image" alt="Interviewing SEOs in an AI-first world" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Interviewing-SEOs-in-an-AI-first-world-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The search industry is experiencing something many of us have never seen before.</p>



<p><em>The supply of search talent now exceeds the demand for search talent.</em>



<p>Blame artificial intelligence. Blame the economy. Blame years of <a href="https://www.seoforlunch.com/i/168015503/business-priorities-over-seo-checkboxes" target="_blank" rel="noopener">checkbox SEO</a> execution becoming increasingly commoditized.</p>



<p>Whatever the reason, the result is the same.</p>



<p>Search layoffs are up. Job openings are down. The market is more competitive than at any point in my 15+ year career.</p>



<p>The uncomfortable reality is that many of the skills that once made an SEO valuable are becoming easier to <a href="https://www.seoforlunch.com/p/compromise" target="_blank" rel="noopener">automate, outsource, or generate with AI</a>.</p>



<p>Pull up a chair.</p>



<p>Let&#8217;s talk about why this is happening, what skills are becoming table stakes, and what employers should actually be looking for when hiring SEO talent in 2026.</p>



<p><a href="https://www.seoforlunch.com/embed" rel="noopener noreferrer">View embedded content</a>



<h2 class="wp-block-heading" id="h-the-first-seo-jobs-ai-comes-for">The first SEO jobs AI comes for</h2>



<p>One of the biggest misconceptions surrounding AI is that it’s coming for SEO jobs.</p>



<p><strong>I don’t believe that’s what’s happening.</strong>



<p>What I do believe is happening is that AI is changing which SEO skills employers are willing to pay a premium for.</p>



<p>Historically, a significant portion of SEO work revolved around gathering information and generating recommendations. Technical audits, content briefs, keyword clustering, schema markup, metadata recommendations, and competitive analyses all required time, experience, and effort.</p>



<p>Those activities still matter today.</p>



<p>The difference is that they are becoming dramatically easier to produce.</p>



<p>An SEO can now use AI to generate a first-pass audit, content brief, or optimization recommendation in minutes. What once took hours can often be accomplished in seconds.</p>



<p>This doesn’t make the output worthless. It simply changes where the value resides.</p>



<p>For years, many organizations treated recommendations as the deliverable. The audit was the deliverable. The roadmap was the deliverable. The deck was the deliverable.</p>



<p>But recommendations were never the end goal.</p>



<p>Recommendations only create value when they lead to prioritization, implementation, and measurable business outcomes.</p>



<p>AI helps solve the problem of idea generation.</p>



<p>It does very little to solve the implementation problem.</p>



<p>That’s why I believe the first SEO jobs AI comes for are those centered around producing recommendations rather than driving outcomes.</p>



<p>As the cost of generating recommendations approaches zero, employers naturally place greater value on the people who can determine which recommendations matter and implement them.</p>



<p>In other words, AI is commoditizing parts of SEO execution.</p>



<p>It is not <a href="https://www.seoforlunch.com/p/blind-trust" target="_blank" rel="noopener">commoditizing judgment</a>.</p>



<h2 class="wp-block-heading" id="h-what-ai-still-struggles-to-do">What AI still struggles to do</h2>



<p>As AI becomes better at generating recommendations, the value of SEO talent shifts elsewhere.</p>



<p>Prioritization. Testing. Communication. Influence. Judgment.</p>



<p>These aren’t new skills. They’ve always mattered.</p>



<p>The difference is that they are quickly becoming the primary differentiators.</p>



<p>Most organizations don’t suffer from a shortage of ideas. They suffer from a shortage of alignment, execution, and good decision-making.</p>



<p><strong>Finally, there is judgment.</strong>



<p>A few weeks ago, I found myself disagreeing with Gemini about a topic I know extremely well. The answer sounded reasonable. The explanation was polished. <a href="https://www.seoforlunch.com/p/ai-in-the-wild" target="_blank" rel="noopener">The problem was that it was wrong.</a>



<p>As AI becomes more capable, the ability to identify when it is confidently wrong becomes a skill in itself.</p>



<p>The future SEO isn’t the person who can generate the most recommendations.</p>



<p>It’s the person who knows which recommendations actually matter.</p>



<h2 class="wp-block-heading" id="h-the-new-seo-career-framework">The new SEO career framework</h2>



<p>For years, the career progression of an SEO was fairly straightforward.</p>



<ul class="wp-block-list">
<li>Learn more about SEO. <strong>Get promoted.</strong></li>



<li>Learn technical SEO. <strong>Get promoted.</strong></li>



<li>Learn content strategy. <strong>Get promoted.</strong></li>



<li>Learn analytics. <strong>Get promoted.</strong></li>
</ul>



<p>While those skills are still important, AI is rapidly reducing the value of knowledge for knowledge’s sake. The ability to produce SEO recommendations is becoming more accessible every day.</p>



<p>That doesn’t mean expertise no longer matters. It means the skills layered on top of that expertise matter more.</p>



<p>Today’s most valuable search professionals understand search. They understand AI. They understand how <a href="https://www.seoforlunch.com/p/domain-and-brand-built-to-rank-not" target="_blank" rel="noopener">businesses operate</a>. Most importantly, they know how to align people, priorities, and resources around a common objective.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1155" height="243" src="https://searchengineland.com/wp-content/seloads/2026/06/old-vs-new.png" alt="Old vs new" class="wp-image-479686" srcset="https://searchengineland.com/wp-content/seloads/2026/06/old-vs-new.png 1155w, https://searchengineland.com/wp-content/seloads/2026/06/old-vs-new-768x162.png 768w" sizes="(max-width: 1155px) 100vw, 1155px" /><figcaption class="wp-element-caption"><em>Image generated via human-led AI</em></figcaption></figure>
</div>


<p>The higher you climb within an organization, the less your success depends on your <em>ability</em> to identify problems and the more it depends on your ability to <span>solve them</span>.</p>



<p>AI scales execution. <a href="https://www.seoforlunch.com/i/194122154/the-middle-ground-nobody-wants" target="_blank" rel="noopener"><strong>People scale vision.</strong></a>



<h2 class="wp-block-heading" id="h-what-i-d-look-for-if-i-were-hiring-today">What I’d look for if I were hiring today</h2>



<p>If I were hiring an SEO in 2026, I wouldn’t spend much time asking about canonical tags, title tags, or XML sitemaps.</p>



<p>Not because those topics are unimportant, but because I can quickly determine whether someone understands the fundamentals.</p>



<p>What I’d really want to understand is how that person operates when things become messy.</p>



<p><strong>I’d ask them to share a recommendation nobody agreed with.</strong>



<p>Years ago, I argued that H1 tags provided virtually no ranking benefit.</p>



<p><em>People laughed. Some openly disagreed. Eventually, John Mueller echoed a similar position, and Bill Slawski had been discussing the concept for years.</em>



<p>I don’t care whether the candidate was right.</p>



<p>I care whether they had the conviction to challenge assumptions and the communication skills to navigate disagreement.</p>



<p><strong>I’d ask about a failed test.</strong>



<p>Every experienced SEO has watched a seemingly great initiative die somewhere between recommendation and implementation. The difference is what happened next. Did they move on to the next project, or did they find a way to remove blockers and maintain momentum?</p>



<p><strong>I’d ask about a project that stalled.</strong>



<p>Every experienced SEO has watched a seemingly great initiative die somewhere between recommendation and implementation. The difference is what happened next. Did they move on to the next project, or did they find a way to remove blockers and keep the momentum going?</p>



<p><strong>I’d also ask where AI gave them bad advice.</strong>



<p>The common theme across all of these questions is simple.</p>



<p>I’m not looking for someone who knows SEO. I’m looking for someone who can turn SEO knowledge into outcomes.</p>



<p>The easiest part of SEO has always been knowing what to do. The hard part is getting it done, generating tangible results.</p>



<p>AI won’t replace SEOs, but the lazy ones are toast.</p>



<p><em>This post first appeared on the author’s website and is republished here with permission.</em>


<div class="wp-block-image">
<figure class="aligncenter"><a href="https://seoforlunch.com/?utm_source=searchengineland.com&amp;utm_medium=social"><img decoding="async" width="1774" height="887" src="https://searchengineland.com/wp-content/seloads/2026/05/leroy2.jpg.webp" alt="Leroy2" class="wp-image-476987" srcset="https://searchengineland.com/wp-content/seloads/2026/05/leroy2.jpg 1774w, https://searchengineland.com/wp-content/seloads/2026/05/leroy2-768x384.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/leroy2-1536x768.jpg 1536w" sizes="(max-width: 1774px) 100vw, 1774px" /></a></figure>
</div>

The search industry is experiencing something many of us have never seen before.
The supply of search talent now exceeds the demand for search talent.



Blame artificial intelligence. Blame the economy. Blame years of checkbox SEO execution becoming increasingly commoditized.
Whatever the reason, the result is the same.
Search layoffs are up. Job openings are down. The market is more competitive than at any point in my 15+ year career.
The uncomfortable reality is that many of the skills that once made an SEO valuable are becoming easier to automate, outsource, or generate with AI.
Pull up a chair.
Let’s talk about why this is happening, what skills are becoming table stakes, and what employers should actually be looking for when hiring SEO talent in 2026.
View embedded content



The first SEO jobs AI comes for
One of the biggest misconceptions surrounding AI is that it’s coming for SEO jobs.
I don’t believe that’s what’s happening.



What I do believe is happening is that AI is changing which SEO skills employers are willing to pay a premium for.
Historically, a significant portion of SEO work revolved around gathering information and generating recommendations. Technical audits, content briefs, keyword clustering, schema markup, metadata recommendations, and competitive analyses all required time, experience, and effort.
Those activities still matter today.
The difference is that they are becoming dramatically easier to produce.
An SEO can now use AI to generate a first-pass audit, content brief, or optimization recommendation in minutes. What once took hours can often be accomplished in seconds.
This doesn’t make the output worthless. It simply changes where the value resides.
For years, many organizations treated recommendations as the deliverable. The audit was the deliverable. The roadmap was the deliverable. The deck was the deliverable.
But recommendations were never the end goal.
Recommendations only create value when they lead to prioritization, implementation, and measurable business outcomes.
AI helps solve the problem of idea generation.
It does very little to solve the implementation problem.
That’s why I believe the first SEO jobs AI comes for are those centered around producing recommendations rather than driving outcomes.
As the cost of generating recommendations approaches zero, employers naturally place greater value on the people who can determine which recommendations matter and implement them.
In other words, AI is commoditizing parts of SEO execution.
It is not commoditizing judgment.
What AI still struggles to do
As AI becomes better at generating recommendations, the value of SEO talent shifts elsewhere.
Prioritization. Testing. Communication. Influence. Judgment.
These aren’t new skills. They’ve always mattered.
The difference is that they are quickly becoming the primary differentiators.
Most organizations don’t suffer from a shortage of ideas. They suffer from a shortage of alignment, execution, and good decision-making.
Finally, there is judgment.



A few weeks ago, I found myself disagreeing with Gemini about a topic I know extremely well. The answer sounded reasonable. The explanation was polished. The problem was that it was wrong.



As AI becomes more capable, the ability to identify when it is confidently wrong becomes a skill in itself.
The future SEO isn’t the person who can generate the most recommendations.
It’s the person who knows which recommendations actually matter.
The new SEO career framework
For years, the career progression of an SEO was fairly straightforward.
Learn more about SEO. Get promoted.
Learn technical SEO. Get promoted.
Learn content strategy. Get promoted.
Learn analytics. Get promoted.
While those skills are still important, AI is rapidly reducing the value of knowledge for knowledge’s sake. The ability to produce SEO recommendations is becoming more accessible every day.
That doesn’t mean expertise no longer matters. It means the skills layered on top of that expertise matter more.
Today’s most valuable search professionals understand search. They understand AI. They understand how businesses operate. Most importantly, they know how to align people, priorities, and resources around a common objective.
Image generated via human-led AI



The higher you climb within an organization, the less your success depends on your ability to identify problems and the more it depends on your ability to solve them.
AI scales execution. People scale vision.



What I’d look for if I were hiring today
If I were hiring an SEO in 2026, I wouldn’t spend much time asking about canonical tags, title tags, or XML sitemaps.
Not because those topics are unimportant, but because I can quickly determine whether someone understands the fundamentals.
What I’d really want to understand is how that person operates when things become messy.
I’d ask them to share a recommendation nobody agreed with.



Years ago, I argued that H1 tags provided virtually no ranking benefit.
People laughed. Some openly disagreed. Eventually, John Mueller echoed a similar position, and Bill Slawski had been discussing the concept for years.



I don’t care whether the candidate was right.
I care whether they had the conviction to challenge assumptions and the communication skills to navigate disagreement.
I’d ask about a failed test.



Every experienced SEO has watched a seemingly great initiative die somewhere between recommendation and implementation. The difference is what happened next. Did they move on to the next project, or did they find a way to remove blockers and maintain momentum?
I’d ask about a project that stalled.



Every experienced SEO has watched a seemingly great initiative die somewhere between recommendation and implementation. The difference is what happened next. Did they move on to the next project, or did they find a way to remove blockers and keep the momentum going?
I’d also ask where AI gave them bad advice.



The common theme across all of these questions is simple.
I’m not looking for someone who knows SEO. I’m looking for someone who can turn SEO knowledge into outcomes.
The easiest part of SEO has always been knowing what to do. The hard part is getting it done, generating tangible results.
AI won’t replace SEOs, but the lazy ones are toast.
This post first appeared on the author’s website and is republished here with permission.

One group of SEO talent is becoming dramatically more valuable. The other is watching their skill set become a commodity.

Interviewing SEOs in an AI-first world

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Ever wonder how many sites are using a specific type of structured data element? Now you can know.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/statistics-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The folks over at Schema.org are now providing aggregate usage statistics for Schema.org terms across the public web. This will show you how many domains are using a specific schema/structured data element.</p>



<p>Schema.org <a href="https://blog.schema.org/2026/06/04/announcing-the-schema-org-usage-statistics-dataset/">announced</a>, &#8220;we are pleased to share a new dataset providing aggregate usage statistics for Schema.org terms across the public web.&#8221; The dataset are updated monthly and they are aggregated at the domain level and presented in popularity range buckets. &#8220;This approach helps filter daily noise while highlighting meaningful adoption trends for researchers and toolmakers,&#8221; Schema.org wrote. </p>



<p><strong>What it looks like. </strong>Here is a screenshot of two of these Schema.org pages,  <a href="https://schema.org/author">author schema</a> and <a href="https://schema.org/Event">event schema</a> showing the usage statistics towards the top:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="2048" height="1337" src="https://searchengineland.com/wp-content/seloads/2026/06/schema-author-vs-event-usage-stats-9K1hAxbR-scaled.png" alt="" class="wp-image-479848" srcset="https://searchengineland.com/wp-content/seloads/2026/06/schema-author-vs-event-usage-stats-9K1hAxbR-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/06/schema-author-vs-event-usage-stats-9K1hAxbR-768x501.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/schema-author-vs-event-usage-stats-9K1hAxbR-1536x1003.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<p><strong>More about the data. </strong>Schema.org shared more information about the <a href="https://schema.org/docs/usage_stats.html">usage statistics over here</a>, here is a summary:</p>



<ul class="wp-block-list">
<li>Schema.org term frequencies are measured within Google&#8217;s public web crawling infrastructure. The data is aggregated at the domain level (like example.com), not by individual pages. This means if you use the same term on 100 pages of your site, it still only counts as one domain using it.</li>



<li>Instead of showing exact, raw numbers (which change daily and can be noisy), websites are grouped into range &#8220;buckets&#8221; (like &#8220;10K &#8211; 100K&#8221; domains). This keeps the data more stable and protects website privacy.</li>



<li>HubThe raw files are available on the GitHub site at <a href="https://github.com/schemaorg/schemaorg/tree/main/data/public_stats/google">Google Public Stats dataset on GitHub</a>. They are available in JSON and CSV with the same data and in a JSON summary format with aggregated bucket distributions. They are updated monthly.</li>



<li><strong>Term Type</strong>: The type of term. This is either <code>Type</code> (like &#8220;Person&#8221; or &#8220;Event&#8221;) or <code>Property</code> (like &#8220;price&#8221; or &#8220;telephone&#8221;).</li>



<li><strong>URI</strong>: The official URI of the term (for example: <code>http://schema.org/Person</code>).</li>



<li><strong>Domain Count Bucket</strong>: The range of unique domains using the term (for example: <code>100K - 1M</code> domains).</li>
</ul>



<p>Here is a screenshot of GitHub:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1980" height="1006" src="https://searchengineland.com/wp-content/seloads/2026/06/schema-usage-git-u5cVFO4A.jpg" alt="" class="wp-image-479849" srcset="https://searchengineland.com/wp-content/seloads/2026/06/schema-usage-git-u5cVFO4A.jpg 1980w, https://searchengineland.com/wp-content/seloads/2026/06/schema-usage-git-u5cVFO4A-768x390.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/schema-usage-git-u5cVFO4A-1536x780.jpg 1536w" sizes="(max-width: 1980px) 100vw, 1980px" /></figure>



<p><strong>Why we care. </strong>Because we love data. Okay, outside of just loving data, knowing if a specific schema element is popular or not may be enough to convince your development team to implement that schema code on your website.</p>






The folks over at Schema.org are now providing aggregate usage statistics for Schema.org terms across the public web. This will show you how many domains are using a specific schema/structured data element.
Schema.org announced, “we are pleased to share a new dataset providing aggregate usage statistics for Schema.org terms across the public web.” The dataset are updated monthly and they are aggregated at the domain level and presented in popularity range buckets. “This approach helps filter daily noise while highlighting meaningful adoption trends for researchers and toolmakers,” Schema.org wrote. 
What it looks like. Here is a screenshot of two of these Schema.org pages,  author schema and event schema showing the usage statistics towards the top:




More about the data. Schema.org shared more information about the usage statistics over here, here is a summary:
Schema.org term frequencies are measured within Google’s public web crawling infrastructure. The data is aggregated at the domain level (like example.com), not by individual pages. This means if you use the same term on 100 pages of your site, it still only counts as one domain using it.
Instead of showing exact, raw numbers (which change daily and can be noisy), websites are grouped into range “buckets” (like “10K – 100K” domains). This keeps the data more stable and protects website privacy.
HubThe raw files are available on the GitHub site at Google Public Stats dataset on GitHub. They are available in JSON and CSV with the same data and in a JSON summary format with aggregated bucket distributions. They are updated monthly.
Term Type: The type of term. This is either Type (like “Person” or “Event”) or Property (like “price” or “telephone”).
URI: The official URI of the term (for example: http://schema.org/Person).
Domain Count Bucket: The range of unique domains using the term (for example: 100K - 1M domains).
Here is a screenshot of GitHub:




Why we care. Because we love data. Okay, outside of just loving data, knowing if a specific schema element is popular or not may be enough to convince your development team to implement that schema code on your website.

Ever wonder how many sites are using a specific type of structured data element? Now you can know.

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