<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage.avif" class="attachment-large size-large wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage.avif 1920w, https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage-768x432.avif 768w, https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage-1536x864.avif 1536w, https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage-1200x675.avif 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Centralized, tagged, governed. And still slow. Here's what your DAM was never designed to do.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage.avif" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage.avif 1920w, https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage-768x432.avif 768w, https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage-1536x864.avif 1536w, https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage-1200x675.avif 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage.avif" alt="Illustration of the content activation cycle, showing an organized DAM connected to omnichannel asset delivery through asset activation workflows." class="wp-image-482034" srcset="https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage.avif 1920w, https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage-768x432.avif 768w, https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage-1536x864.avif 1536w, https://searchengineland.com/wp-content/seloads/2026/07/ImageKit-FeaturedImage-1200x675.avif 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
</div>


<p class="wp-block-paragraph">Your DAM is working.</p>



<p class="wp-block-paragraph">Assets are centralized. Metadata is applied. Roles and permissions are in place. By every standard measure of a <a href="https://imagekit.io/guides/digital-asset-management/?utm_source=MarTech&amp;utm_medium=referral&amp;utm_campaign=Your-DAM-Solved-the-Library-Problem" target="_blank" rel="noopener sponsored nofollow">digital asset management</a> (DAM) implementation, you succeeded.</p>



<p class="wp-block-paragraph">And yet, campaigns still launch late. Engineering is still fielding requests to resize hero images. The regional team in APAC is re-uploading files into the local CMS because they can&#8217;t easily pull from the DAM. The DAM isn&#8217;t broken; the assumption is that a functioning DAM means your content is ready to work.</p>



<h3 class="wp-block-heading" id="h-a-library-and-a-supply-chain-are-not-the-same-thing">A library and a supply chain are not the same thing</h3>



<p class="wp-block-paragraph">The original promise of DAM was organization: one place for assets, consistent metadata, and governance over which ones are approved and current. That&#8217;s a library problem, and modern DAMs solve it well. Assets are searchable, versions are controlled, and expired content doesn’t go live by accident.&nbsp;</p>



<p class="wp-block-paragraph">But activation is a supply chain problem. An asset has to reach a campaign page, a product detail page, a social post, an email, and a partner&#8217;s CMS, in the right format, at the right quality, at the right moment. And the systems running that chain are increasingly AI agents and automations, not humans. Libraries were not built to run supply chains.</p>



<p class="wp-block-paragraph"><a href="https://business.adobe.com/blog/71-percent-of-marketers-say-content-demand-to-increase-5x" target="_blank" rel="noopener sponsored nofollow">Adobe&#8217;s 2025 research</a> surveyed more than 1,600 marketers and found that 62% say content demand has already increased 5x or more over the last two years. At the same time, <a href="https://learn.g2.com/ai-in-digital-asset-management" target="_blank" rel="noopener sponsored nofollow">G2&#8217;s 2026 DAM report</a> found eight out of 10 DAM vendors now cite exponential asset growth as their primary operational pressure. More content, multiplied by more channels, equals more strain on the activation side. And yet, content activation workflows are stuck where they were five years ago.</p>



<p class="wp-block-paragraph">The distance between an asset sitting in your DAM and that asset arriving in front of a customer —&nbsp; in the right state, at the right moment — is the Content Activation Gap. Closing it requires five specific shifts that most DAM implementations haven&#8217;t made:</p>



<h3 class="wp-block-heading" id="h-from-portal-navigation-to-headless-integration">From portal navigation to headless integration</h3>



<p class="wp-block-paragraph">Most DAMs were built with a portal in mind. A user logs in, navigates a folder structure, finds an asset, downloads it, and uploads it into the next system. Every interaction is manual, in both directions.&nbsp;</p>



<p class="wp-block-paragraph">That model breaks at scale. Content moves in and out of systems faster than any portal can mediate. <a href="https://imagekit.io/solutions/headless-dam/?utm_source=MarTech&amp;utm_medium=referral&amp;utm_campaign=Your-DAM-Solved-the-Library-Problem" target="_blank" rel="noopener sponsored nofollow">Headless</a> API access lets any authorized system write to or read from the DAM directly. An ecommerce platform pulls product images from the DAM at the moment a webpage is rendered. A video production tool uploads rendered files to the DAM the moment a job completes.  </p>



<p class="wp-block-paragraph">Native integrations bring the DAM into the tools teams already use. A Figma plugin pushes designs straight into the campaign folder. A Slack integration shares assets and approval status directly in the channel where the team already talks.&nbsp;</p>



<p class="wp-block-paragraph">A DAM disconnected from the stack becomes a workaround.&nbsp;</p>



<h3 class="wp-block-heading" id="h-from-stored-exports-to-on-demand-variants-and-versions">From stored exports to on-demand variants and versions</h3>



<p class="wp-block-paragraph">Every time a new channel, size, or format is needed, the same asset gets downloaded, resized, and re-uploaded. A <a href="https://digitalassetmanagementnews.org/features/designers-extremely-frustrated-with-resizing-process/" target="_blank" rel="noopener sponsored nofollow">2023 survey by Santa Cruz Software</a> found that 76% of designers spend at least 20 hours per week resizing graphics. That isn&#8217;t a design capacity problem. It&#8217;s a file architecture problem.</p>



<p class="wp-block-paragraph">The alternative is URL-based <a href="https://imagekit.io/solutions/publish-assets-digital-asset-management/?utm_source=MarTech&amp;utm_medium=referral&amp;utm_campaign=Your-DAM-Solved-the-Library-Problem" target="_blank" rel="noopener sponsored nofollow">transformations</a> that work in real time. Add parameters for size, format, or edits, and the variant comes back without anyone pre-generating it. A 6MB original at 4000×3000 serves a 1920×1080 hero image, a 400×400 thumbnail, a 1200×630 social preview card, and a 750×1000 mobile variant, all from the same asset. And with AI, transformations go further. The same source file delivers background swaps, generative fill, prompt-based edits, and AI-generated variations on demand.</p>



<p class="wp-block-paragraph"><a href="https://imagekit.io/docs/dam/asset-versioning?utm_source=MarTech&amp;utm_medium=referral&amp;utm_campaign=Your-DAM-Solved-the-Library-Problem" target="_blank" rel="noopener sponsored nofollow">Versioning</a> works on the same principle. The URL stays stable, the file behind it changes, and one update reaches every system that references it. Update once. Reflect everywhere. This is the model DAM platforms like <a href="https://imagekit.io/digital-asset-management/?utm_source=MarTech&amp;utm_medium=referral&amp;utm_campaign=Your-DAM-Solved-the-Library-Problem" target="_blank" rel="noopener sponsored nofollow">ImageKit</a> are built on.</p>



<h3 class="wp-block-heading" id="h-from-manual-upkeep-to-autonomous-ai-agents">From manual upkeep to autonomous AI agents </h3>



<p class="wp-block-paragraph">A growing library doesn&#8217;t stay clean on its own. Tags drift as people leave, metadata grows inconsistent, and file formats sneak in that shouldn&#8217;t. Manual housekeeping doesn&#8217;t scale with volume.</p>



<p class="wp-block-paragraph">Autonomous <a href="https://imagekit.io/blog/introducing-dam-agent-ai-powered-media-management/?utm_source=MarTech&amp;utm_medium=referral&amp;utm_campaign=Your-DAM-Solved-the-Library-Problem" target="_blank" rel="noopener sponsored nofollow">AI agents</a> change that. They run <a href="https://imagekit.io/solutions/digital-asset-management-governance/?utm_source=MarTech&amp;utm_medium=referral&amp;utm_campaign=Your-DAM-Solved-the-Library-Problem" target="_blank" rel="noopener sponsored nofollow">quality control</a> on every upload, apply controlled vocabulary against business-specific taxonomies, enforce format and metadata requirements, and hold drafts unpublished until approved. The library stays clean without anyone scheduling a cleanup sprint.</p>



<p class="wp-block-paragraph">This becomes essential when downstream consumers are themselves agents. An AI agent retrieving an asset for a product page needs the file to be correctly tagged, in an approved format, and published rather than still a draft. If autonomous agents have already done the upkeep, the retrieving agent finds a folder where the rules have already been applied.</p>



<h3 class="wp-block-heading" id="h-from-hopeful-search-to-ai-powered-discovery">From hopeful search to AI-powered discovery</h3>



<p class="wp-block-paragraph">At scale, <a href="https://imagekit.io/solutions/digital-asset-management-organize-search/?utm_source=MarTech&amp;utm_medium=referral&amp;utm_campaign=Your-DAM-Solved-the-Library-Problem" target="_blank" rel="noopener sponsored nofollow">search</a> in a DAM becomes a gamble. One team tags a product image &#8220;T-shirt.&#8221; Another tags it &#8220;TShirt.&#8221; A third uses a different tag entirely. Search for any one term and you&#8217;ll find a fraction of what the library actually holds.</p>



<p class="wp-block-paragraph">AI agents are now searching alongside humans, and that changes what a missed match costs. A wrong result used to mean another search. Now it can mean a wrong asset shipping into production.&nbsp;</p>



<p class="wp-block-paragraph">AI-powered discovery closes the gap. Natural-language queries return results based on meaning, not keyword match. Visual search surfaces similar assets regardless of how they were named. The same approach extends to video, where AI can index visual content and spoken dialogue rather than depending on a manually-typed title. Discovery isn&#8217;t about better keywords anymore. It&#8217;s about a library queryable by what assets contain.</p>



<h3 class="wp-block-heading" id="h-from-a-standalone-dam-to-an-mcp-connected-stack">From a standalone DAM to an MCP-connected stack</h3>



<p class="wp-block-paragraph">A modern DAM doesn&#8217;t sit on its own. Creative apps, AI coding assistants, marketing copilots, and campaign automation agents all need to interact with the asset library directly.</p>



<p class="wp-block-paragraph"><a href="https://imagekit.io/docs/build-with-ai?utm_source=MarTech&amp;utm_medium=referral&amp;utm_campaign=Your-DAM-Solved-the-Library-Problem" target="_blank" rel="noopener sponsored nofollow">MCP</a> (Model Context Protocol) servers make this possible. They expose the DAM as a service that any compliant AI tool can call. A developer in Cursor pulls approved product images without leaving their IDE. A marketer in Claude pulls brand-cleared hero images mid-conversation. An automation agent building a product launch email pulls the right assets without anyone selecting them. The DAM stops being a destination people switch to. It becomes a layer that the rest of the stack reaches into.</p>



<h3 class="wp-block-heading" id="h-the-question-has-changed">The question has changed</h3>



<p class="wp-block-paragraph">For years, content operations revolved around one question. Where do we store our assets? Building a DAM was the answer.</p>



<p class="wp-block-paragraph">That question is largely settled. Most enterprise teams have a functioning library. The next question is harder. How fast can those assets reach customers, formatted for every channel, correctable at the source, and ready for both human teams and AI agents to act on?</p>



<p class="wp-block-paragraph">Together, the five shifts answer it. They turn the DAM from a tool that teams visit into infrastructure that the rest of the stack runs on. <a href="https://imagekit.io/solutions/ai-imagekit-dam/?utm_source=MarTech&amp;utm_medium=referral&amp;utm_campaign=Your-DAM-Solved-the-Library-Problem" target="_blank" rel="noopener sponsored nofollow">AI</a> compounds the change: agents handle the upkeep, drive the discovery, and cut the time between a finished asset and a live channel.</p>



<p class="wp-block-paragraph">The next generation of DAM won&#8217;t be judged by how well it stores and organizes assets. It will be judged by how quickly those assets move across channels, teams, and AI workflows. The library was the foundation. Activation is the building on top of it.</p>


Your DAM is working.
Assets are centralized. Metadata is applied. Roles and permissions are in place. By every standard measure of a digital asset management (DAM) implementation, you succeeded.
And yet, campaigns still launch late. Engineering is still fielding requests to resize hero images. The regional team in APAC is re-uploading files into the local CMS because they can’t easily pull from the DAM. The DAM isn’t broken; the assumption is that a functioning DAM means your content is ready to work.
A library and a supply chain are not the same thing
The original promise of DAM was organization: one place for assets, consistent metadata, and governance over which ones are approved and current. That’s a library problem, and modern DAMs solve it well. Assets are searchable, versions are controlled, and expired content doesn’t go live by accident. 
But activation is a supply chain problem. An asset has to reach a campaign page, a product detail page, a social post, an email, and a partner’s CMS, in the right format, at the right quality, at the right moment. And the systems running that chain are increasingly AI agents and automations, not humans. Libraries were not built to run supply chains.
Adobe’s 2025 research surveyed more than 1,600 marketers and found that 62% say content demand has already increased 5x or more over the last two years. At the same time, G2’s 2026 DAM report found eight out of 10 DAM vendors now cite exponential asset growth as their primary operational pressure. More content, multiplied by more channels, equals more strain on the activation side. And yet, content activation workflows are stuck where they were five years ago.
The distance between an asset sitting in your DAM and that asset arriving in front of a customer —  in the right state, at the right moment — is the Content Activation Gap. Closing it requires five specific shifts that most DAM implementations haven’t made:
From portal navigation to headless integration
Most DAMs were built with a portal in mind. A user logs in, navigates a folder structure, finds an asset, downloads it, and uploads it into the next system. Every interaction is manual, in both directions. 
That model breaks at scale. Content moves in and out of systems faster than any portal can mediate. Headless API access lets any authorized system write to or read from the DAM directly. An ecommerce platform pulls product images from the DAM at the moment a webpage is rendered. A video production tool uploads rendered files to the DAM the moment a job completes.  
Native integrations bring the DAM into the tools teams already use. A Figma plugin pushes designs straight into the campaign folder. A Slack integration shares assets and approval status directly in the channel where the team already talks. 
A DAM disconnected from the stack becomes a workaround. 
From stored exports to on-demand variants and versions
Every time a new channel, size, or format is needed, the same asset gets downloaded, resized, and re-uploaded. A 2023 survey by Santa Cruz Software found that 76% of designers spend at least 20 hours per week resizing graphics. That isn’t a design capacity problem. It’s a file architecture problem.
The alternative is URL-based transformations that work in real time. Add parameters for size, format, or edits, and the variant comes back without anyone pre-generating it. A 6MB original at 4000×3000 serves a 1920×1080 hero image, a 400×400 thumbnail, a 1200×630 social preview card, and a 750×1000 mobile variant, all from the same asset. And with AI, transformations go further. The same source file delivers background swaps, generative fill, prompt-based edits, and AI-generated variations on demand.
Versioning works on the same principle. The URL stays stable, the file behind it changes, and one update reaches every system that references it. Update once. Reflect everywhere. This is the model DAM platforms like ImageKit are built on.
From manual upkeep to autonomous AI agents 
A growing library doesn’t stay clean on its own. Tags drift as people leave, metadata grows inconsistent, and file formats sneak in that shouldn’t. Manual housekeeping doesn’t scale with volume.
Autonomous AI agents change that. They run quality control on every upload, apply controlled vocabulary against business-specific taxonomies, enforce format and metadata requirements, and hold drafts unpublished until approved. The library stays clean without anyone scheduling a cleanup sprint.
This becomes essential when downstream consumers are themselves agents. An AI agent retrieving an asset for a product page needs the file to be correctly tagged, in an approved format, and published rather than still a draft. If autonomous agents have already done the upkeep, the retrieving agent finds a folder where the rules have already been applied.
From hopeful search to AI-powered discovery
At scale, search in a DAM becomes a gamble. One team tags a product image “T-shirt.” Another tags it “TShirt.” A third uses a different tag entirely. Search for any one term and you’ll find a fraction of what the library actually holds.
AI agents are now searching alongside humans, and that changes what a missed match costs. A wrong result used to mean another search. Now it can mean a wrong asset shipping into production. 
AI-powered discovery closes the gap. Natural-language queries return results based on meaning, not keyword match. Visual search surfaces similar assets regardless of how they were named. The same approach extends to video, where AI can index visual content and spoken dialogue rather than depending on a manually-typed title. Discovery isn’t about better keywords anymore. It’s about a library queryable by what assets contain.
From a standalone DAM to an MCP-connected stack
A modern DAM doesn’t sit on its own. Creative apps, AI coding assistants, marketing copilots, and campaign automation agents all need to interact with the asset library directly.
MCP (Model Context Protocol) servers make this possible. They expose the DAM as a service that any compliant AI tool can call. A developer in Cursor pulls approved product images without leaving their IDE. A marketer in Claude pulls brand-cleared hero images mid-conversation. An automation agent building a product launch email pulls the right assets without anyone selecting them. The DAM stops being a destination people switch to. It becomes a layer that the rest of the stack reaches into.
The question has changed
For years, content operations revolved around one question. Where do we store our assets? Building a DAM was the answer.
That question is largely settled. Most enterprise teams have a functioning library. The next question is harder. How fast can those assets reach customers, formatted for every channel, correctable at the source, and ready for both human teams and AI agents to act on?
Together, the five shifts answer it. They turn the DAM from a tool that teams visit into infrastructure that the rest of the stack runs on. AI compounds the change: agents handle the upkeep, drive the discovery, and cut the time between a finished asset and a live channel.
The next generation of DAM won’t be judged by how well it stores and organizes assets. It will be judged by how quickly those assets move across channels, teams, and AI workflows. The library was the foundation. Activation is the building on top of it.

Centralized, tagged, governed. And still slow. Here's what your DAM was never designed to do.

Your DAM solved the library problem. The activation problem is next. by ImageKit

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-brand-topic-ownership.png" class="attachment-large size-large wp-post-image" alt="ChatGPT brand topic ownership" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-brand-topic-ownership.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-brand-topic-ownership-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-brand-topic-ownership-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-brand-topic-ownership-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Mentions matter more than citations, and narrow leads often change, revealing the instability of AI search brand authority, Semrush finds.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-brand-topic-ownership.png" class="attachment-large size-large wp-post-image" alt="ChatGPT brand topic ownership" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-brand-topic-ownership.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-brand-topic-ownership-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-brand-topic-ownership-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-brand-topic-ownership-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p class="wp-block-paragraph">Only 15.2% of ChatGPT topic categories had a clear brand owner across related buyer questions, according to a Semrush analysis.</p>
</blockquote>



<p class="wp-block-paragraph">That means about 85% of categories had no dominant brand. Appearing in one prompt didn&#8217;t mean a brand appeared consistently across related questions in the same topic.</p>



<p class="wp-block-paragraph"><strong>How ownership was defined.</strong> Semrush defined a category owner as the brand with the highest share of mentions, appearing in at least four of five related prompts and leading the runner-up by at least 5 percentage points.</p>



<ul class="wp-block-list">
<li>Only 15.2% of the 1,094 categories met that standard.</li>



<li>Another 31.2% had an emerging leader, with one brand appearing in at least three prompts but without the required lead.</li>



<li>The remaining 53.7% were unsettled, with no brand appearing in at least three of five prompts.</li>
</ul>



<p class="wp-block-paragraph"><strong>Popular topics</strong>,<strong> few clear owners. </strong>Semrush split categories into two groups by AI search demand. Only 11.3% of topics in the top half had a clear owner, compared with 19% in the bottom half.</p>



<ul class="wp-block-list">
<li>The top half accounted for 98% of the AI search volume in the sample.</li>



<li>Most ChatGPT demand Semrush tracked was in categories without a clear brand owner.</li>
</ul>



<p class="wp-block-paragraph"><strong>Broad SEO metrics had limited predictive value</strong>. Traditional SEO strength didn&#8217;t reliably predict which brands owned ChatGPT topics. “Traditional SEO metrics aren&#8217;t enough to explain who owns a topic. While they play their role, there’s more to it,” wrote Kevin Indig, founder of Growth Memo. </p>



<ul class="wp-block-list">
<li>Semrush compared category owners with runners-up across branded search volume, organic traffic, and Authority Score.</li>



<li>Owners had higher branded search volume in 55.7% of comparisons, higher organic traffic in 48.4%, and a higher Authority Score in 52.5%. Branded search volume was the only metric that reached statistical significance.</li>
</ul>



<p class="wp-block-paragraph"><strong>Most-cited rarely meant most-mentioned.</strong> Semrush measured ownership by brand mentions in ChatGPT’s answer text, not by the links cited below.</p>



<ul class="wp-block-list">
<li>Mentions and citations often didn&#8217;t align. Only 21% of the most-cited domains in a category were also the most-mentioned brand.</li>
</ul>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0102ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        If AI can’t find you, <span>customers won’t either</span>.
      </div>
      <p id="semrush-one-subhead">
       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">See your AI visibility</span>
    </div>
    </div>
    </a>




<p class="wp-block-paragraph"><strong>Why we care</strong>. Semrush found that prompt-level visibility didn&#8217;t always carry across related buyer questions. That suggests topic-level visibility is more useful for assessing whether ChatGPT consistently associates a brand with a category.</p>



<p class="wp-block-paragraph"><strong>Brands that owned a topic usually kept it.</strong> Clear category owners stayed in first place in 90.4% of month-over-month comparisons. Weaker leaders changed more often, with the top brand switching in 1,950 of 5,470 comparisons across emerging and unsettled categories.</p>



<p class="wp-block-paragraph"><strong>Narrow leads were less stable. </strong>When a top brand later lost first place, its typical lead was just 1.3 percentage points. When it stayed on top, its typical lead was 2.9 points, more than twice as large.</p>



<ul class="wp-block-list">
<li>Semrush said the data didn&#8217;t explain why leaders changed. It showed only that brands with smaller leads were more likely to lose first place.</li>
</ul>



<p class="wp-block-paragraph"><strong>About the data.</strong> Semrush and Kevin Indig analyzed 1,094 U.S. categories in ChatGPT from January through June 2026 using the Semrush AI Visibility Toolkit. The dataset covered more than 50,000 brands, 220,000 domains, 600,000 citations, and 220,000 URLs. Each category included five prompts tied to common buyer questions, including definitions, comparisons, alternatives, use cases, and buying decisions.</p>



<p class="wp-block-paragraph"><strong>The study.</strong> <a href="https://www.semrush.com/blog/chatgpt-topic-authority-study/" target="_blank" rel="noopener">AI visibility is a topic-level game: A study of 50,000 brands in ChatGPT [Study]</a>


Only 15.2% of ChatGPT topic categories had a clear brand owner across related buyer questions, according to a Semrush analysis.
That means about 85% of categories had no dominant brand. Appearing in one prompt didn’t mean a brand appeared consistently across related questions in the same topic.
How ownership was defined. Semrush defined a category owner as the brand with the highest share of mentions, appearing in at least four of five related prompts and leading the runner-up by at least 5 percentage points.
Only 15.2% of the 1,094 categories met that standard.
Another 31.2% had an emerging leader, with one brand appearing in at least three prompts but without the required lead.
The remaining 53.7% were unsettled, with no brand appearing in at least three of five prompts.
Popular topics, few clear owners. Semrush split categories into two groups by AI search demand. Only 11.3% of topics in the top half had a clear owner, compared with 19% in the bottom half.
The top half accounted for 98% of the AI search volume in the sample.
Most ChatGPT demand Semrush tracked was in categories without a clear brand owner.
Broad SEO metrics had limited predictive value. Traditional SEO strength didn’t reliably predict which brands owned ChatGPT topics. “Traditional SEO metrics aren’t enough to explain who owns a topic. While they play their role, there’s more to it,” wrote Kevin Indig, founder of Growth Memo. 
Semrush compared category owners with runners-up across branded search volume, organic traffic, and Authority Score.
Owners had higher branded search volume in 55.7% of comparisons, higher organic traffic in 48.4%, and a higher Authority Score in 52.5%. Branded search volume was the only metric that reached statistical significance.
Most-cited rarely meant most-mentioned. Semrush measured ownership by brand mentions in ChatGPT’s answer text, not by the links cited below.
Mentions and citations often didn’t align. Only 21% of the most-cited domains in a category were also the most-mentioned brand.
customers won’t either.
      
See your AI visibility
    
Why we care. Semrush found that prompt-level visibility didn’t always carry across related buyer questions. That suggests topic-level visibility is more useful for assessing whether ChatGPT consistently associates a brand with a category.
Brands that owned a topic usually kept it. Clear category owners stayed in first place in 90.4% of month-over-month comparisons. Weaker leaders changed more often, with the top brand switching in 1,950 of 5,470 comparisons across emerging and unsettled categories.
Narrow leads were less stable. When a top brand later lost first place, its typical lead was just 1.3 percentage points. When it stayed on top, its typical lead was 2.9 points, more than twice as large.
Semrush said the data didn’t explain why leaders changed. It showed only that brands with smaller leads were more likely to lose first place.
About the data. Semrush and Kevin Indig analyzed 1,094 U.S. categories in ChatGPT from January through June 2026 using the Semrush AI Visibility Toolkit. The dataset covered more than 50,000 brands, 220,000 domains, 600,000 citations, and 220,000 URLs. Each category included five prompts tied to common buyer questions, including definitions, comparisons, alternatives, use cases, and buying decisions.
The study. AI visibility is a topic-level game: A study of 50,000 brands in ChatGPT [Study]

Mentions matter more than citations, and narrow leads often change, revealing the instability of AI search brand authority, Semrush finds.

ChatGPT topic ownership is rare, and SEO alone doesn’t explain it

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-recommendation-tab.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-recommendation-tab.png 1672w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-recommendation-tab-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-recommendation-tab-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-recommendation-tab-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google Ads' new beta estimates the clicks, conversions, and revenue you may be missing due to limited budgets and low bids.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-recommendation-tab.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-recommendation-tab.png 1672w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-recommendation-tab-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-recommendation-tab-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-recommendation-tab-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google Ads is bringing its &#8220;Missed Growth Opportunity&#8221; insights into the main Recommendations tab as a new beta, giving advertisers a clearer view of the traffic, conversions, and revenue they may be missing because of budget constraints or low bids.</p>



<p class="wp-block-paragraph">The move makes these opportunity estimates more visible, surfacing them directly alongside Google&#8217;s existing optimization recommendations.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s new</strong>. A new beta recommendation estimates the potential impact of increasing budgets or raising bids.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="565" height="431" src="https://searchengineland.com/wp-content/seloads/2026/07/Screenshot-2026-07-21-at-18.55.09.png" alt="" class="wp-image-482918"/></figure>



<p class="wp-block-paragraph">The feature highlights:</p>



<ul class="wp-block-list">
<li>Estimated clicks left on the table.</li>



<li>Missed conversions.</li>



<li>Unrealized conversion value.</li>



<li>Whether those missed opportunities were primarily caused by budget limitations or low bids.</li>
</ul>



<p class="wp-block-paragraph">Previously, these insights were available as the &#8220;Missed Growth Opportunity&#8221; tool within Google Ads Labs. They&#8217;re now being integrated into the Recommendations section for eligible advertisers.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. Budget allocation and bid adjustments are among the biggest levers advertisers have for driving growth. By quantifying the potential impact of budget or bid constraints, Google is giving advertisers another data point to prioritize where additional spend could deliver the greatest return. That said, these are still modeled estimates—not guarantees. Advertisers should treat them as directional insights and validate them against their own performance data before increasing budgets or bids.</p>



<p class="wp-block-paragraph"><strong>What they&#8217;re seeing</strong>. The beta displays estimated missed performance directly within the Recommendations tab, breaking down unrealized results by whether they were caused by insufficient budgets or bids.</p>



<p class="wp-block-paragraph">The update was spotted by PPC expert Thomas Eccel, who noted that the feature is essentially a rebranded version of the former <a href="https://www.linkedin.com/posts/thomaseccel_ppc-googleads-share-7484255810085269504-42wP/">&#8220;Missed Growth Opportunity&#8221; tool from Google Ads Labs</a>.</p>



<p class="wp-block-paragraph"><strong>Bottom line</strong>. Google is giving advertisers a more prominent way to evaluate the cost of budget and bid constraints. If the underlying estimates are reliable, the new beta could become a useful planning tool for identifying untapped growth opportunities—but advertisers should verify the recommendations before acting on them.</p>


<a href="https://www.semrush.com/analytics/traffic/paid-search?utm_campaign=ic_sel_0102ppc&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        Every click they win is <span>a customer you lose</span>.
      </div>
      <p id="semrush-one-subhead">
        See where competitors are investing, which keywords drive their results, and how to capture more of the market.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">See who’s stealing your traffic</span>
    </div>
    </div>
    </a>



Google Ads is bringing its “Missed Growth Opportunity” insights into the main Recommendations tab as a new beta, giving advertisers a clearer view of the traffic, conversions, and revenue they may be missing because of budget constraints or low bids.
The move makes these opportunity estimates more visible, surfacing them directly alongside Google’s existing optimization recommendations.
What’s new. A new beta recommendation estimates the potential impact of increasing budgets or raising bids.




The feature highlights:
Estimated clicks left on the table.
Missed conversions.
Unrealized conversion value.
Whether those missed opportunities were primarily caused by budget limitations or low bids.
Previously, these insights were available as the “Missed Growth Opportunity” tool within Google Ads Labs. They’re now being integrated into the Recommendations section for eligible advertisers.
Why we care. Budget allocation and bid adjustments are among the biggest levers advertisers have for driving growth. By quantifying the potential impact of budget or bid constraints, Google is giving advertisers another data point to prioritize where additional spend could deliver the greatest return. That said, these are still modeled estimates—not guarantees. Advertisers should treat them as directional insights and validate them against their own performance data before increasing budgets or bids.
What they’re seeing. The beta displays estimated missed performance directly within the Recommendations tab, breaking down unrealized results by whether they were caused by insufficient budgets or bids.
The update was spotted by PPC expert Thomas Eccel, who noted that the feature is essentially a rebranded version of the former “Missed Growth Opportunity” tool from Google Ads Labs.
Bottom line. Google is giving advertisers a more prominent way to evaluate the cost of budget and bid constraints. If the underlying estimates are reliable, the new beta could become a useful planning tool for identifying untapped growth opportunities—but advertisers should verify the recommendations before acting on them.
a customer you lose.
      
See who’s stealing your traffic

Google Ads' new beta estimates the clicks, conversions, and revenue you may be missing due to limited budgets and low bids.

Google Ads adds missed growth estimates to the Recommendations tab

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-GA4-setup.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-GA4-setup.png 1672w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-GA4-setup-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-GA4-setup-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-GA4-setup-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google Ads now lets advertisers bulk link multiple accounts to a single GA4 property reducing manual account management.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-GA4-setup.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-GA4-setup.png 1672w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-GA4-setup-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-GA4-setup-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Google-Ads-GA4-setup-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google is rolling out a new way for advertisers to link multiple Google Ads accounts to a single Google Analytics 4 (GA4) property, making it easier to manage data sharing across accounts from one place.</p>



<p class="wp-block-paragraph">The update is particularly useful for advertisers and agencies that oversee multiple Google Ads accounts tied to the same GA4 property, reducing the need to link each account individually.</p>



<p class="wp-block-paragraph"><strong>What&#8217;s new</strong>. A new bulk linking option has been added under Data Manager &gt; Google Analytics 4 &gt; Link Setup in Google Ads.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="569" height="403" src="https://searchengineland.com/wp-content/seloads/2026/07/Screenshot-2026-07-21-at-18.31.03.png" alt="" class="wp-image-482910"/></figure>



<p class="wp-block-paragraph">Advertisers can use a drop-down menu to select or deselect multiple Google Ads accounts they want connected to a GA4 property. Once linked, the selected accounts can share data with the chosen analytics property, streamlining account management.</p>



<p class="wp-block-paragraph"><strong>Why we care</strong>. For advertisers managing multiple Google Ads accounts, this update removes a tedious administrative task and makes it easier to keep measurement setups consistent across campaigns. Agencies, franchises, and enterprise marketing teams can quickly update account links as clients are added or removed, reducing the risk of missing conversion data or creating reporting gaps.</p>



<p class="wp-block-paragraph"><strong>Bottom line</strong>. Google&#8217;s latest update simplifies the relationship between Google Ads and GA4 by allowing advertisers to manage multiple account links from a single interface, saving time and making data integration easier at scale.</p>



<p class="wp-block-paragraph"><strong>First spotted.</strong> This update was spotted by Paid Search expert Thomas Eccel, who shared spotting the update on <a href="https://www.linkedin.com/posts/thomaseccel_ppc-googleads-share-7483127201417162752-wxZ4/">LinkedIn</a>.</p>


<a href="https://www.semrush.com/analytics/traffic/paid-search?utm_campaign=ic_sel_0101ppc&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        See exactly how <span>your competitors win</span>.
      </div>
      <p id="semrush-one-subhead">
        Uncover the keywords, ads, landing pages, and strategies driving your competitors&#8217; paid search success—and find your next opportunity to outperform them.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">Analyze your competitors</span>
    </div>
    </div>
    </a>



Google is rolling out a new way for advertisers to link multiple Google Ads accounts to a single Google Analytics 4 (GA4) property, making it easier to manage data sharing across accounts from one place.
The update is particularly useful for advertisers and agencies that oversee multiple Google Ads accounts tied to the same GA4 property, reducing the need to link each account individually.
What’s new. A new bulk linking option has been added under Data Manager > Google Analytics 4 > Link Setup in Google Ads.




Advertisers can use a drop-down menu to select or deselect multiple Google Ads accounts they want connected to a GA4 property. Once linked, the selected accounts can share data with the chosen analytics property, streamlining account management.
Why we care. For advertisers managing multiple Google Ads accounts, this update removes a tedious administrative task and makes it easier to keep measurement setups consistent across campaigns. Agencies, franchises, and enterprise marketing teams can quickly update account links as clients are added or removed, reducing the risk of missing conversion data or creating reporting gaps.
Bottom line. Google’s latest update simplifies the relationship between Google Ads and GA4 by allowing advertisers to manage multiple account links from a single interface, saving time and making data integration easier at scale.
First spotted. This update was spotted by Paid Search expert Thomas Eccel, who shared spotting the update on LinkedIn.
your competitors win.
      
Analyze your competitors

Google Ads now lets advertisers bulk link multiple accounts to a single GA4 property reducing manual account management.

Google Ads simplifies GA4 setup with bulk account linking

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/07/google-flash-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-flash-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-flash-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-flash-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google said Gemini 3.5 Flash-Lite is designed for both low-latency tasks and tasks where high throughput is critical for developers workflows, like agentic search.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/07/google-flash-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-flash-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-flash-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-flash-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Google has released three new AI models today Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber and one of those models is rolling out now for Google Search.  3.5 Flash-Lite is Google&#8217;s &#8220;fastest, most cost-effective 3.5-class model, delivering 350 output tokens per second according to the Artificial Analysis Index, also significantly outperforming prior Flash-Lite generations in agentic workflows,&#8221; the company <a href="https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber/">announced</a>.</p>



<p class="wp-block-paragraph">Google said 3.5 Flash-Lite is rolling out now for Google Search. Where in Google Search? Google did mention in the blog post that it is used for agentic search. But it might be used for Google AI Overviews and Google AI Mode as well.</p>



<p class="wp-block-paragraph"><strong>Agentic Search. </strong>Google announced at Google I/O <a href="https://searchengineland.com/google-search-gains-information-agents-and-improved-agentic-experiences-477979">back in May</a> about Google Search&#8217;s information agents and improved agentic experiences. “We’re entering the era of Search agents, where you can easily create, customize and manage multiple Al agents for your many tasks, right in Search,” Liz Reid, the head of Google Search said a couple of months back.</p>



<p class="wp-block-paragraph"><strong>Rollout. </strong>3.5 Flash-Lite is rolling out for everyone in the Gemini app and also within Google Search, the company announced.</p>



<p class="wp-block-paragraph"><strong>Why we care. </strong>Google will continue to improve its AI models; the latest improvements are on its lower-end models but also faster models. Those faster models are often uses for Google Search and you soon may see those changes in Google AI Overviews, AI Mode and the agentic experiences within Google Search.</p>



<p class="wp-block-paragraph"><strong>Postscript. </strong>Robby Stein from Google later said on <a href="https://x.com/rmstein/status/2079683273962492388">X</a>, &#8220;It offers stronger instruction following and better understands user intent, so conversations flow much more seamlessly.&#8221;</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0102ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        If AI can’t find you, <span>customers won’t either</span>.
      </div>
      <p id="semrush-one-subhead">
       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">See your AI visibility</span>
    </div>
    </div>
    </a>



Google has released three new AI models today Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber and one of those models is rolling out now for Google Search.  3.5 Flash-Lite is Google’s “fastest, most cost-effective 3.5-class model, delivering 350 output tokens per second according to the Artificial Analysis Index, also significantly outperforming prior Flash-Lite generations in agentic workflows,” the company announced.
Google said 3.5 Flash-Lite is rolling out now for Google Search. Where in Google Search? Google did mention in the blog post that it is used for agentic search. But it might be used for Google AI Overviews and Google AI Mode as well.
Agentic Search. Google announced at Google I/O back in May about Google Search’s information agents and improved agentic experiences. “We’re entering the era of Search agents, where you can easily create, customize and manage multiple Al agents for your many tasks, right in Search,” Liz Reid, the head of Google Search said a couple of months back.
Rollout. 3.5 Flash-Lite is rolling out for everyone in the Gemini app and also within Google Search, the company announced.
Why we care. Google will continue to improve its AI models; the latest improvements are on its lower-end models but also faster models. Those faster models are often uses for Google Search and you soon may see those changes in Google AI Overviews, AI Mode and the agentic experiences within Google Search.
Postscript. Robby Stein from Google later said on X, “It offers stronger instruction following and better understands user intent, so conversations flow much more seamlessly.”
customers won’t either.
      
See your AI visibility

Google said Gemini 3.5 Flash-Lite is designed for both low-latency tasks and tasks where high throughput is critical for developers workflows, like agentic search.

3.5 Flash-Lite rolling out in Google Search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-curiosity-into-high-performing-social-ads.png" class="attachment-large size-large wp-post-image" alt="How to build curiosity into high-performing social ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-curiosity-into-high-performing-social-ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-curiosity-into-high-performing-social-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-curiosity-into-high-performing-social-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-curiosity-into-high-performing-social-ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A strong hook only gets you so far. Learn how open loops, storytelling, and gradual reveals can keep viewers watching.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-curiosity-into-high-performing-social-ads.png" class="attachment-large size-large wp-post-image" alt="How to build curiosity into high-performing social ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-curiosity-into-high-performing-social-ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-curiosity-into-high-performing-social-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-curiosity-into-high-performing-social-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-curiosity-into-high-performing-social-ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Capturing attention is no longer enough to create high-performing social ads. You also need to create curiosity that keeps people watching.</p>



<p class="wp-block-paragraph">Stopping the scroll and hooking viewers in the first three seconds still matter. But as video-heavy platforms like <a href="https://searchengineland.com/meta-ai-driven-advertising-system-andromeda-gem-468020">Meta</a> and <a href="https://searchengineland.com/tiktok-advertising-450817">TikTok</a> continue investing in AI-powered delivery systems that optimize for engagement, watch time, and downstream conversions, what happens next matters, too.</p>



<p class="wp-block-paragraph">Attention is the moment someone notices your ad. Curiosity is the reason they stick around instead of scrolling. Understanding that difference can help you build creative that earns both attention and consideration.</p>



<h2 class="wp-block-heading" id="h-attention-is-only-the-beginning">Attention is only the beginning</h2>



<p class="wp-block-paragraph">Many creative briefs I’ve seen begin with questions like “what’s our hook?” or “how do we stop the scroll?” While these questions matter, they focus almost entirely on the first few seconds of the ad, not how to maintain that fleeting attention in the seconds that follow.</p>



<p class="wp-block-paragraph">Every piece of content on social media is competing for attention, and platforms are becoming increasingly better at predicting what users are most likely to engage with next.</p>



<p class="wp-block-paragraph">A flashy visual, a bold headline, or an unexpected opening can earn a pause, but if the rest of your creative immediately answers every question or transitions into a predictable sales pitch, people will scroll onward.</p>



<p class="wp-block-paragraph">High-performing creative doesn’t just interrupt the scroll. It creates a gap in information that people want to close. That’s curiosity.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/measure-paid-social-impact-paid-search-performance-480936">How to measure paid social’s impact on paid search performance</a></em></strong>


<a href="https://www.semrush.com/analytics/traffic/paid-search?utm_campaign=ic_sel_0101ppc&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        See exactly how <span>your competitors win</span>.
      </div>
      <p id="semrush-one-subhead">
        Uncover the keywords, ads, landing pages, and strategies driving your competitors&#8217; paid search success—and find your next opportunity to outperform them.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">Analyze your competitors</span>
    </div>
    </div>
    </a>




<h2 class="wp-block-heading" id="h-curiosity-keeps-the-algorithms-working-for-you">Curiosity keeps the algorithms working for you</h2>



<p class="wp-block-paragraph">As user behavior changes and algorithms evolve, platforms increasingly reward signals that indicate genuine user interest. These signals include:</p>



<ul class="wp-block-list">
<li>Longer watch times.</li>



<li>Meaningful engagement (e.g., saves and sharing videos via DMs).</li>



<li>Higher video completion rates.</li>



<li>Clicks that occur after viewing the content.</li>



<li>Replays.</li>
</ul>



<p class="wp-block-paragraph">Platforms use these signals to identify content that people choose to spend their time with. Human curiosity naturally produces these signals because it encourages viewers to keep watching rather than scroll until something else grabs their attention.</p>



<p class="wp-block-paragraph">The highest-performing creator ads often don’t feel rushed. They gradually reveal information through conversation, yapper-style content, demonstrations, storytelling, and personal experiences.</p>



<p class="wp-block-paragraph">While the goal may be to secure a conversion quickly, sometimes slow and steady wins the race. A thoughtfully curated approach invites viewers to discover something new rather than asking them to purchase right away.</p>



<h2 class="wp-block-heading" id="h-the-rise-of-curiosity-driven-creators">The rise of curiosity-driven creators</h2>



<p class="wp-block-paragraph">One recent creative shift is the growing popularity of longer-form, conversational creator ads called “yapper ads.” These ads are longer, less polished, more authentic, often unscripted, and sometimes don’t even mention the product immediately.</p>



<p class="wp-block-paragraph">They seem to violate nearly every traditional best practice, but they perform well because they stand out, feel like conversations rather than commercials, and create curiosity throughout the video. They often address what happened next, why something solved a problem, and whether something would work well for the viewer.</p>



<p class="wp-block-paragraph">Creators who do this well reward the attention they’ve earned with more interesting information throughout the content, and brands that boost these ads are seeing impressive results.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="387" height="900" src="https://searchengineland.com/wp-content/seloads/2026/07/Source-Gratsi-Meta-Ads.png" alt="A screenshot of a Meta video ad from Gratsi Wine and creator, Kayce Smith" class="wp-image-482762"/><figcaption class="wp-element-caption"><em>Source: <a href="https://www.facebook.com/ads/library/?id=1521275346037736" target="_blank" rel="noopener">Gratsi Meta Ads</a></em></figcaption></figure>
</div>


<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/ugly-ads-outperforming-polished-creative-475007">Why ugly ads outperform polished creative and how to test them</a></em></strong>



<h2 class="wp-block-heading" id="h-don-t-improvise-curiosity-build-it">Don’t improvise curiosity, build it</h2>



<p class="wp-block-paragraph">Curiosity should be intentionally designed because most creators can’t naturally create it on the fly. The creative strategy behind these successful ads is to delay the obvious and gradually reveal information.</p>



<p class="wp-block-paragraph">To do this, create open loops with a surprising statement, an unexpected result, or a relatable problem that encourages viewers to stay for the outcome. Demonstrate something rather than just describing it, or show a transformation rather than simply listing features.</p>



<p class="wp-block-paragraph">While this type of content is strategic, it can feel more human and natural by telling stories, answering questions along the way, and perhaps even jumping between thoughts. That conversational rhythm creates authenticity, trust, and ultimately, the momentum you’re aiming for.</p>



<h2 class="wp-block-heading" id="h-curiosity-builds-trust-while-clickbait-breaks-it">Curiosity builds trust while clickbait breaks it</h2>



<p class="wp-block-paragraph">There’s an important distinction between curiosity and manipulation. Clickbait typically withholds information without delivering meaningful value, whereas curiosity rewards attention by eventually answering questions in a way that satisfies the viewer.</p>



<p class="wp-block-paragraph">Every promise should be fulfilled, and every story should reach a worthwhile conclusion. Don’t trick people into watching longer and create an unpleasant experience. Make every second genuinely worth the viewer’s time because prospective customers will recognize your intent, and their behavior will send signals to the platforms.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/paid-search-roas-paid-social-482040">Why search ROAS depends on paid social more than you think</a></em></strong>



<h2 class="wp-block-heading" id="h-optimize-beyond-the-first-three-seconds">Optimize beyond the first three seconds</h2>



<p class="wp-block-paragraph">The advice has long been to obsess over the first three seconds of every video, and while that advice isn’t wrong, it may be incomplete. Instead of asking only, “Will someone stop scrolling?” creative teams should also ask, “Why would someone keep watching?”</p>



<p class="wp-block-paragraph">Considering both can improve how creative is written, filmed, edited, and evaluated. Every transition should introduce another point of interest, and every scene should answer one question while creating another without overwhelming the viewer. It should also deliver on the promise to close earlier loops. Don’t just earn attention. Sustain it.</p>



<h2 class="wp-block-heading" id="h-creative-is-what-you-still-control">Creative is what you still control</h2>



<p class="wp-block-paragraph">AI has made audience targeting on social media advertising platforms smarter and easier than ever. Campaign setup continues to become more automated and streamlined, while machine learning handles more optimization.</p>



<p class="wp-block-paragraph">Creative strategy and development remain in your hands. Algorithms can decide who sees an ad, but you can decide whether it’s worth watching. You have the ability to create compelling creative and develop novel ideas that tap into people&#8217;s natural curiosity.</p>



<p class="wp-block-paragraph">That doesn’t mean you should stop trying to earn attention. You also need to sustain it because curiosity is what keeps the conversation going.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/structure-paid-social-creative-testing-478963">How to structure paid social creative testing for better performance</a></em></strong>


Capturing attention is no longer enough to create high-performing social ads. You also need to create curiosity that keeps people watching.
Stopping the scroll and hooking viewers in the first three seconds still matter. But as video-heavy platforms like Meta and TikTok continue investing in AI-powered delivery systems that optimize for engagement, watch time, and downstream conversions, what happens next matters, too.
Attention is the moment someone notices your ad. Curiosity is the reason they stick around instead of scrolling. Understanding that difference can help you build creative that earns both attention and consideration.
Attention is only the beginning
Many creative briefs I’ve seen begin with questions like “what’s our hook?” or “how do we stop the scroll?” While these questions matter, they focus almost entirely on the first few seconds of the ad, not how to maintain that fleeting attention in the seconds that follow.
Every piece of content on social media is competing for attention, and platforms are becoming increasingly better at predicting what users are most likely to engage with next.
A flashy visual, a bold headline, or an unexpected opening can earn a pause, but if the rest of your creative immediately answers every question or transitions into a predictable sales pitch, people will scroll onward.
High-performing creative doesn’t just interrupt the scroll. It creates a gap in information that people want to close. That’s curiosity.
Dig deeper: How to measure paid social’s impact on paid search performance



  
your competitors win.
      
Analyze your competitors
    
Curiosity keeps the algorithms working for you
As user behavior changes and algorithms evolve, platforms increasingly reward signals that indicate genuine user interest. These signals include:
Longer watch times.
Meaningful engagement (e.g., saves and sharing videos via DMs).
Higher video completion rates.
Clicks that occur after viewing the content.
Replays.
Platforms use these signals to identify content that people choose to spend their time with. Human curiosity naturally produces these signals because it encourages viewers to keep watching rather than scroll until something else grabs their attention.
The highest-performing creator ads often don’t feel rushed. They gradually reveal information through conversation, yapper-style content, demonstrations, storytelling, and personal experiences.
While the goal may be to secure a conversion quickly, sometimes slow and steady wins the race. A thoughtfully curated approach invites viewers to discover something new rather than asking them to purchase right away.
The rise of curiosity-driven creators
One recent creative shift is the growing popularity of longer-form, conversational creator ads called “yapper ads.” These ads are longer, less polished, more authentic, often unscripted, and sometimes don’t even mention the product immediately.
They seem to violate nearly every traditional best practice, but they perform well because they stand out, feel like conversations rather than commercials, and create curiosity throughout the video. They often address what happened next, why something solved a problem, and whether something would work well for the viewer.
Creators who do this well reward the attention they’ve earned with more interesting information throughout the content, and brands that boost these ads are seeing impressive results.
Source: Gratsi Meta Ads



Dig deeper: Why ugly ads outperform polished creative and how to test them



Don’t improvise curiosity, build it
Curiosity should be intentionally designed because most creators can’t naturally create it on the fly. The creative strategy behind these successful ads is to delay the obvious and gradually reveal information.
To do this, create open loops with a surprising statement, an unexpected result, or a relatable problem that encourages viewers to stay for the outcome. Demonstrate something rather than just describing it, or show a transformation rather than simply listing features.
While this type of content is strategic, it can feel more human and natural by telling stories, answering questions along the way, and perhaps even jumping between thoughts. That conversational rhythm creates authenticity, trust, and ultimately, the momentum you’re aiming for.
Curiosity builds trust while clickbait breaks it
There’s an important distinction between curiosity and manipulation. Clickbait typically withholds information without delivering meaningful value, whereas curiosity rewards attention by eventually answering questions in a way that satisfies the viewer.
Every promise should be fulfilled, and every story should reach a worthwhile conclusion. Don’t trick people into watching longer and create an unpleasant experience. Make every second genuinely worth the viewer’s time because prospective customers will recognize your intent, and their behavior will send signals to the platforms.
Dig deeper: Why search ROAS depends on paid social more than you think



Optimize beyond the first three seconds
The advice has long been to obsess over the first three seconds of every video, and while that advice isn’t wrong, it may be incomplete. Instead of asking only, “Will someone stop scrolling?” creative teams should also ask, “Why would someone keep watching?”
Considering both can improve how creative is written, filmed, edited, and evaluated. Every transition should introduce another point of interest, and every scene should answer one question while creating another without overwhelming the viewer. It should also deliver on the promise to close earlier loops. Don’t just earn attention. Sustain it.
Creative is what you still control
AI has made audience targeting on social media advertising platforms smarter and easier than ever. Campaign setup continues to become more automated and streamlined, while machine learning handles more optimization.
Creative strategy and development remain in your hands. Algorithms can decide who sees an ad, but you can decide whether it’s worth watching. You have the ability to create compelling creative and develop novel ideas that tap into people’s natural curiosity.
That doesn’t mean you should stop trying to earn attention. You also need to sustain it because curiosity is what keeps the conversation going.
Dig deeper: How to structure paid social creative testing for better performance

A strong hook only gets you so far. Learn how open loops, storytelling, and gradual reveals can keep viewers watching.

How to build curiosity into high-performing social ads

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/The-new-SEO-rules-for-bloggers-in-2026-Why-clarity-matters-in-AI-search.png" class="attachment-large size-large wp-post-image" alt="The new SEO rules for bloggers in 2026: Why clarity matters in AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/The-new-SEO-rules-for-bloggers-in-2026-Why-clarity-matters-in-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/The-new-SEO-rules-for-bloggers-in-2026-Why-clarity-matters-in-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/The-new-SEO-rules-for-bloggers-in-2026-Why-clarity-matters-in-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/The-new-SEO-rules-for-bloggers-in-2026-Why-clarity-matters-in-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Forget AI plugins, word counts, SEO scores, and fake freshness. Focus on better content, stronger connections, and less friction.

Forget AI plugins, word counts, SEO scores, and fake freshness. Focus on better content, stronger connections, and less friction.

The new SEO rules for bloggers in 2026: Why clarity matters in AI search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-schema-markup-matters-beyond-rich-results.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-schema-markup-matters-beyond-rich-results.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-schema-markup-matters-beyond-rich-results-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-schema-markup-matters-beyond-rich-results-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-schema-markup-matters-beyond-rich-results-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Schema markup does more than power rich results. Build a knowledge graph, assess entity coverage, and find gaps in AI understanding.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-schema-markup-matters-beyond-rich-results.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-schema-markup-matters-beyond-rich-results.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-schema-markup-matters-beyond-rich-results-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-schema-markup-matters-beyond-rich-results-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-schema-markup-matters-beyond-rich-results-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">After presenting a custom schema my team built to evaluate <a href="https://searchengineland.com/knowledge-graphs-introduction-448128">knowledge graphs</a> for university programs, I saw firsthand how differently SEOs view the role of <a href="https://searchengineland.com/guide/what-is-schema-markup">schema markup</a>. Our approach used 23 existing Schema.org <a href="https://searchengineland.com/entities-topics-keywords-relationships-seo-431696">entities</a> and more than 60 additional entities to assess gaps in entity coverage.</p>



<p class="wp-block-paragraph">Schema connects entities (objects, people, concepts, and ideas) to build knowledge graphs that provide deeper semantic understanding. It can also serve as a framework for evaluating a site’s vector embeddings and identifying gaps in entity coverage.</p>



<p class="wp-block-paragraph">Let’s look at how you can use schema and knowledge graphs to identify and prioritize those gaps.</p>



<h2 class="wp-block-heading" id="h-how-knowledge-graphs-turn-entities-into-context">How knowledge graphs turn entities into context</h2>



<p class="wp-block-paragraph">A knowledge graph stores entities as nodes and relationships as edges, so machines can understand context and meaning rather than matching keywords. It’s how a system knows that the Tulane Freeman School of Business is an organization that offers business courses taught by a person rather than a string of text.</p>



<p class="wp-block-paragraph">Enterprise-level businesses use knowledge graphs to remove data silos. In the book &#8220;The Knowledge Graph Cookbook: Recipes That Work&#8221; by Andreas Blumauer and Helmut Nagy, knowledge graphs are used as the “ultimate linking engine” to create a semantic data fabric for business intelligence.</p>



<p class="wp-block-paragraph">In this context, your website uses the same recipe that enterprise-level businesses use for internal data. Your website is essentially functioning as a public API through which your brand’s entities, such as organization, location, products, positioning, values, features, and key benefits, are connected to search engines and LLMs.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/knowledge-graphs-entities-seo-when-how-452584">When and how to use knowledge graphs and entities for SEO</a></em></strong>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0101ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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        Be the brand <span>AI recommends</span>.
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        See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
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<h2 class="wp-block-heading" id="h-treat-schema-as-the-on-ramp-to-the-graph">Treat schema as the on-ramp to the graph</h2>



<p class="wp-block-paragraph">Schema markup declares entities in a language that search engines and LLMs already speak. JSON-LD explicitly asserts both entities and the relationships between them rather than relying on bots to infer them from website copy.</p>



<p class="wp-block-paragraph">For example, an executive Master of Business Administration (MBA) degree from New York University should present a coherent body of information rather than a loose collection of unrelated facts. </p>



<p class="wp-block-paragraph">The organization is NYU, which offers a Brand and Digital Strategy course taught by professor and author Scott Galloway, a faculty member in the MBA program at the NYU Stern School of Business.</p>



<p class="wp-block-paragraph">Stop padding FAQ schema and optimizing for rich results. Website pages are vehicles for nodes and edges that feed the graph. This approach provides critical context about your business, helping search engines and LLMs better connect your products and services with relevant audiences.</p>



<h2 class="wp-block-heading" id="h-build-the-graph-with-markup-vectors-and-agents">Build the graph with markup, vectors, and agents</h2>



<p class="wp-block-paragraph">Our custom schema is a framework for explicitly asserting all the entities and relationships a prospective student engages with on their enrollment journey. The schema prioritizes these entities by importance.&nbsp;</p>



<p class="wp-block-paragraph">That declared truth is what we use to analyze the vectorized version of a partner’s website. Vector embeddings of .edu website content are measured against the schema to capture semantic proximity and context that the markup doesn’t state outright.</p>



<p class="wp-block-paragraph">We use agents to compare the static custom schema markup, semantic proximity, and vector embeddings to ensure comprehensive coverage of all the entities surrounding a university program.&nbsp;</p>



<p class="wp-block-paragraph">The results surface entity gaps, which can inform content strategy across websites, social media properties, and earned media. The final output is a knowledge graph that highlights both covered and missing entities related to a university program.</p>



<p class="wp-block-paragraph">We aim to provide robust context that helps shape and frame the value of our programs through a search or conversation. This approach is directly aligned with Google’s patents on understanding entities.&nbsp;</p>



<p class="wp-block-paragraph">Google’s need to understand context is driven by a desire to deliver a more relevant answer to its end user. Building your knowledge graph adds context that can help search engines and AI systems better understand your business.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/google-llm-patent-seo-teaching-ai-480625"><strong><em>Google’s LLM patent suggests a new goal for SEO: Teaching AI who you are</em></strong></a>



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<h2 class="wp-block-heading" id="h-get-honest-about-schema-and-ai-visibility">Get honest about schema and AI visibility</h2>



<p class="wp-block-paragraph">In <a href="https://searchengineland.com/microsoft-bing-copilot-use-schema-for-its-llms-453455">March 2025</a> at SMX Munich, Fabrice Canel, principal product manager at Microsoft Bing, confirmed that Copilot uses schema markup to understand content. </p>



<p class="wp-block-paragraph">When we consider how other LLMs use search indexes for grounding, schema markup becomes an important factor in connecting entities back to your site. Semrush has also published guides showing correlations among cited pages.</p>



<p class="wp-block-paragraph">Other research points in a different direction. A 2025 study by Search Atlas found that <a href="https://searchatlas.com/research/the-limits-of-schema-markup-for-ai-search/" target="_blank" rel="noopener">schema doesn’t affect LLM visibility</a>, while a study by Mark William-Cook showed that <a href="https://www.linkedin.com/posts/markseo_seo-activity-7424067360497770496-s3ks" target="_blank" rel="noopener">LLMs don’t read on-page schema</a>. However, those studies look at performance and citations rather than entity connections.</p>



<p class="wp-block-paragraph">These findings may seem conflicting, but they point to a distinction between using JSON-LD as infrastructure for machine understanding and treating it as a GEO hack for increasing citations. JSON-LD provides explicit information about entities and relationships that can help machines understand your site. Visibility is a byproduct of being understood.</p>



<h2 class="wp-block-heading" id="h-use-schema-and-vector-embeddings-to-find-entity-gaps">Use schema and vector embeddings to find entity gaps</h2>



<p class="wp-block-paragraph">The custom schema I discussed above is specifically designed for higher education, but you can build your own. Use Schema.org as your library to build a robust collection of entities that matter to your product, service, or business.&nbsp;</p>



<p class="wp-block-paragraph">Does Schema.org map to all your important entities? In our case, Schema.org covered only 23 higher education-specific entities, leaving significant gaps. We filled the gaps by creating our own entities.</p>



<p class="wp-block-paragraph">Start with the ideal entity model. In a perfect world, what does that complete set of information look like? This exercise will help you determine what can be built using custom JSON-LD in addition to established entities.&nbsp;</p>



<p class="wp-block-paragraph">With that custom schema, you can compare it against vector embeddings to understand entity coverage. Prioritize your entity gaps by focusing on what drives the most value.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/schema-markup-ai-search-no-hype-472339">How schema markup fits into AI search — without the hype</a></em></strong>



<h2 class="wp-block-heading" id="h-measure-entity-visibility-not-just-rich-results">Measure entity visibility, not just rich results</h2>



<p class="wp-block-paragraph">As you develop a content strategy for entity coverage, it’s important to use prompt-tracking and brand sentiment tools to monitor AI visibility. Monitor your priority entities to understand how you appear across different models.</p>



<p class="wp-block-paragraph">Brand sentiment tracking is especially important because it allows you to see shifts among your covered entities. Is your content matching how people are actually talking about the entity? </p>



<p class="wp-block-paragraph">AI visibility and brand perception aren’t complete metrics on their own. As you see shifts in visibility and perception, compare them with conversions and performance. As citations grow, does the volume or quality of leads rise?</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0102ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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        If AI can’t find you, <span>customers won’t either</span>.
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       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
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<h2 class="wp-block-heading" id="h-schema-as-infrastructure-for-ai-understanding">Schema as infrastructure for AI understanding</h2>



<p class="wp-block-paragraph">Schema markup isn’t an overnight solution for driving AI citations. It’s infrastructure for helping search engines and LLMs understand your site by declaring entities and relationships, building your knowledge graph, and uncovering gaps in entity coverage.</p>



<p class="wp-block-paragraph">

After presenting a custom schema my team built to evaluate knowledge graphs for university programs, I saw firsthand how differently SEOs view the role of schema markup. Our approach used 23 existing Schema.org entities and more than 60 additional entities to assess gaps in entity coverage.
Schema connects entities (objects, people, concepts, and ideas) to build knowledge graphs that provide deeper semantic understanding. It can also serve as a framework for evaluating a site’s vector embeddings and identifying gaps in entity coverage.
Let’s look at how you can use schema and knowledge graphs to identify and prioritize those gaps.
How knowledge graphs turn entities into context
A knowledge graph stores entities as nodes and relationships as edges, so machines can understand context and meaning rather than matching keywords. It’s how a system knows that the Tulane Freeman School of Business is an organization that offers business courses taught by a person rather than a string of text.
Enterprise-level businesses use knowledge graphs to remove data silos. In the book “The Knowledge Graph Cookbook: Recipes That Work” by Andreas Blumauer and Helmut Nagy, knowledge graphs are used as the “ultimate linking engine” to create a semantic data fabric for business intelligence.
In this context, your website uses the same recipe that enterprise-level businesses use for internal data. Your website is essentially functioning as a public API through which your brand’s entities, such as organization, location, products, positioning, values, features, and key benefits, are connected to search engines and LLMs.
Dig deeper: When and how to use knowledge graphs and entities for SEO



  
AI recommends.
      
See your AI visibility
    
Treat schema as the on-ramp to the graph
Schema markup declares entities in a language that search engines and LLMs already speak. JSON-LD explicitly asserts both entities and the relationships between them rather than relying on bots to infer them from website copy.
For example, an executive Master of Business Administration (MBA) degree from New York University should present a coherent body of information rather than a loose collection of unrelated facts. 
The organization is NYU, which offers a Brand and Digital Strategy course taught by professor and author Scott Galloway, a faculty member in the MBA program at the NYU Stern School of Business.
Stop padding FAQ schema and optimizing for rich results. Website pages are vehicles for nodes and edges that feed the graph. This approach provides critical context about your business, helping search engines and LLMs better connect your products and services with relevant audiences.
Build the graph with markup, vectors, and agents
Our custom schema is a framework for explicitly asserting all the entities and relationships a prospective student engages with on their enrollment journey. The schema prioritizes these entities by importance. 
That declared truth is what we use to analyze the vectorized version of a partner’s website. Vector embeddings of .edu website content are measured against the schema to capture semantic proximity and context that the markup doesn’t state outright.
We use agents to compare the static custom schema markup, semantic proximity, and vector embeddings to ensure comprehensive coverage of all the entities surrounding a university program. 
The results surface entity gaps, which can inform content strategy across websites, social media properties, and earned media. The final output is a knowledge graph that highlights both covered and missing entities related to a university program.
We aim to provide robust context that helps shape and frame the value of our programs through a search or conversation. This approach is directly aligned with Google’s patents on understanding entities. 
Google’s need to understand context is driven by a desire to deliver a more relevant answer to its end user. Building your knowledge graph adds context that can help search engines and AI systems better understand your business.
Dig deeper: Google’s LLM patent suggests a new goal for SEO: Teaching AI who you are




	
	
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Get honest about schema and AI visibility
In March 2025 at SMX Munich, Fabrice Canel, principal product manager at Microsoft Bing, confirmed that Copilot uses schema markup to understand content. 
When we consider how other LLMs use search indexes for grounding, schema markup becomes an important factor in connecting entities back to your site. Semrush has also published guides showing correlations among cited pages.
Other research points in a different direction. A 2025 study by Search Atlas found that schema doesn’t affect LLM visibility, while a study by Mark William-Cook showed that LLMs don’t read on-page schema. However, those studies look at performance and citations rather than entity connections.
These findings may seem conflicting, but they point to a distinction between using JSON-LD as infrastructure for machine understanding and treating it as a GEO hack for increasing citations. JSON-LD provides explicit information about entities and relationships that can help machines understand your site. Visibility is a byproduct of being understood.
Use schema and vector embeddings to find entity gaps
The custom schema I discussed above is specifically designed for higher education, but you can build your own. Use Schema.org as your library to build a robust collection of entities that matter to your product, service, or business. 
Does Schema.org map to all your important entities? In our case, Schema.org covered only 23 higher education-specific entities, leaving significant gaps. We filled the gaps by creating our own entities.
Start with the ideal entity model. In a perfect world, what does that complete set of information look like? This exercise will help you determine what can be built using custom JSON-LD in addition to established entities. 
With that custom schema, you can compare it against vector embeddings to understand entity coverage. Prioritize your entity gaps by focusing on what drives the most value.
Dig deeper: How schema markup fits into AI search — without the hype



Measure entity visibility, not just rich results
As you develop a content strategy for entity coverage, it’s important to use prompt-tracking and brand sentiment tools to monitor AI visibility. Monitor your priority entities to understand how you appear across different models.
Brand sentiment tracking is especially important because it allows you to see shifts among your covered entities. Is your content matching how people are actually talking about the entity? 
AI visibility and brand perception aren’t complete metrics on their own. As you see shifts in visibility and perception, compare them with conversions and performance. As citations grow, does the volume or quality of leads rise?
customers won’t either.
      
See your AI visibility
    
Schema as infrastructure for AI understanding
Schema markup isn’t an overnight solution for driving AI citations. It’s infrastructure for helping search engines and LLMs understand your site by declaring entities and relationships, building your knowledge graph, and uncovering gaps in entity coverage.

Schema markup does more than power rich results. Build a knowledge graph, assess entity coverage, and find gaps in AI understanding.

Schema for AI search: How to identify and prioritize entity gaps

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Your-customers-query-wording-determines-your-AI-visibility-not-your-brand-strength.png" class="attachment-large size-large wp-post-image" alt="Your customers&#039; query wording determines your AI visibility, not your brand strength" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Your-customers-query-wording-determines-your-AI-visibility-not-your-brand-strength.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Your-customers-query-wording-determines-your-AI-visibility-not-your-brand-strength-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Your-customers-query-wording-determines-your-AI-visibility-not-your-brand-strength-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Your-customers-query-wording-determines-your-AI-visibility-not-your-brand-strength-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>The language your customers use to search can shape your brand's AI visibility. Learn what new research reveals about category coding.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Your-customers-query-wording-determines-your-AI-visibility-not-your-brand-strength.png" class="attachment-large size-large wp-post-image" alt="Your customers&#039; query wording determines your AI visibility, not your brand strength" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Your-customers-query-wording-determines-your-AI-visibility-not-your-brand-strength.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Your-customers-query-wording-determines-your-AI-visibility-not-your-brand-strength-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Your-customers-query-wording-determines-your-AI-visibility-not-your-brand-strength-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Your-customers-query-wording-determines-your-AI-visibility-not-your-brand-strength-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Most brands approaching AI visibility ask the wrong question: How do we get stronger as an entity so that <a href="https://searchengineland.com/llm-optimization-tracking-visibility-ai-discovery-463860">LLMs</a> recommend us more?</p>



<p class="wp-block-paragraph">In <a href="https://searchengineland.com/guide/entity-first-content-optimization">entity SEO</a>, we tend to say, “Build the Knowledge Graph, add schema, and get more press.” But that logic assumes the LLM is evaluating the brand and deciding whether it&#8217;s good enough to recommend for any query related to what the brand sells. The LLM evaluates the query and matches it against whatever category associations it has built for the brand from third-party content.</p>



<p class="wp-block-paragraph">The difference matters enormously in practice. </p>



<p class="wp-block-paragraph">As we&#8217;ve seen in multiple scenarios, <a href="https://searchengineland.com/brand-ai-recommendation-set-477229">recognition isn&#8217;t the same as recommendation</a>. So being a recognized brand isn&#8217;t synonymous with being a strong brand. </p>



<p class="wp-block-paragraph">What matters is whether the category your customers are using to search for you matches the category the LLM has coded you into.</p>



<h2 class="wp-block-heading" id="h-what-the-data-showed">What the data showed</h2>



<p class="wp-block-paragraph">João da Silva and I conducted a study of 12 athletic apparel brands in the U.K. over seven days, with 14,140 API runs across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews. We tested the same brands using two different category framings: athleisure and athletic footwear.</p>



<p class="wp-block-paragraph">After looking at the <a href="https://searchengineland.com/co-mentions-ai-recommendation-gap-479829">results derived from co-mentions</a> and putting numbers on the impact of framing on category recognition for LLMs, we took the test one step further and changed the category register in the prompt.</p>



<p class="wp-block-paragraph">The results were symmetric to a degree that rules out noise:</p>



<figure class="wp-block-table"><table><thead><tr><th><strong>Brand</strong></th><th><strong>Knowledge Graph (KG) score</strong></th><th><strong>Athleisure rate</strong></th><th><strong>Footwear rate</strong></th><th><strong>Δ</strong></th><th><strong>Verdict</strong></th></tr></thead><tbody><tr><td>New Balance</td><td>64,235</td><td>1%</td><td>90%</td><td>+89</td><td>Jumped (footwear-coded)</td></tr><tr><td>Nike</td><td>25,996</td><td>77%</td><td>90%</td><td>+13</td><td>Small shift (footwear-coded with strong athleisure co-mentions)</td></tr><tr><td>Alo Yoga</td><td>3,062</td><td>63%</td><td>0%</td><td>-63</td><td>Dropped (athleisure-coded)</td></tr><tr><td>lululemon</td><td>810</td><td>90%</td><td>0%</td><td>-90</td><td>Dropped (athleisure-coded)</td></tr><tr><td>Sweaty Betty</td><td>751</td><td>9%</td><td>0%</td><td>-9</td><td>Stable</td></tr><tr><td>Reebok</td><td>665</td><td>1%</td><td>20%</td><td>+19</td><td>Small shift</td></tr><tr><td>Outdoor Voices</td><td>455</td><td>26%</td><td>0%</td><td>-26</td><td>Small shift</td></tr><tr><td>Rhone Apparel</td><td>400</td><td>5%</td><td>0%</td><td>-5</td><td>Stable</td></tr><tr><td>Varley</td><td>381</td><td>6%</td><td>0%</td><td>-6</td><td>Stable</td></tr><tr><td>TALA</td><td>356</td><td>5%</td><td>0%</td><td>-5</td><td>Stable</td></tr><tr><td>Gymshark</td><td>277</td><td>37%</td><td>0%</td><td>-37</td><td>Dropped (athleisure-coded)</td></tr><tr><td>LNDR</td><td>2</td><td>0%</td><td>0%</td><td>0</td><td>Stable</td></tr></tbody></table></figure>



<p class="wp-block-paragraph"><strong>Notes</strong>:</p>



<ul class="wp-block-list">
<li>New Balance goes from 1% to 90%.</li>



<li>lululemon goes from 90% to 0%.</li>
</ul>



<p class="wp-block-paragraph">The variation is approximately 0.9 points in both directions simultaneously.</p>



<p class="wp-block-paragraph">We&#8217;re not talking about correlation here, but a controlled observation: what happens when we change only one variable — the category word in the prompt.</p>


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<h2 class="wp-block-heading" id="h-why-this-happens-category-coding">Why this happens: Category coding</h2>



<p class="wp-block-paragraph">Nike, New Balance, and Reebok share the exact same Google Knowledge Graph (KG) description: “Footwear company,” so all three are recognized perfectly by every LLM we tested. From an entity standpoint (recognition), they start from an identical position. However, their behavior under different category framings isn&#8217;t identical at all.</p>



<p class="wp-block-paragraph">The reason is what the paper formalizes as category coding: the combination of the KG description field and the third-party content corpus that has accumulated around a brand in a given category.</p>



<p class="wp-block-paragraph">The KG description anchors a brand to a category in the model&#8217;s representation (impacts recognition).</p>



<p class="wp-block-paragraph">The third-party corpus — articles, reviews, editorial comparisons, and roundups — fills in the detail of what that category association actually looks like (impacts recommendation).</p>



<h3 class="wp-block-heading" id="h-looking-at-the-example-from-new-balance">Looking at the example from New Balance</h3>



<p class="wp-block-paragraph">New Balance&#8217;s KG description says “Footwear company,” and the third-party corpus that has accumulated around it corroborates the category by focusing on topics related to running shoes, performance footwear, and athletic training.&nbsp;</p>



<p class="wp-block-paragraph">When a user asks about athleisure brands, the model doesn&#8217;t find New Balance in that corpus because there is no third-party association. But it does find lululemon, Alo Yoga, and Gymshark: all brands whose corpus is built from fashion publications, lifestyle editorial, and activewear roundups.</p>



<p class="wp-block-paragraph">When we changed the query to athletic footwear, the retrieval flipped: New Balance is suddenly in the right corpus, and lululemon is not.</p>



<p class="wp-block-paragraph">The model itself can&#8217;t and isn&#8217;t making a judgment about brand quality or belonging. What an LLM does is pattern-match a query category against a content category. If those two things align, the brand surfaces. If they don&#8217;t, it doesn&#8217;t, regardless of how established the brand is.</p>



<h3 class="wp-block-heading" id="h-so-can-you-just-recode-your-kg-description-nbsp">So, can you just recode your KG description?&nbsp;</h3>



<p class="wp-block-paragraph">Some brands reading this will consider the obvious shortcut: Change the KG description. If “Footwear company” is anchoring you to the wrong category, recode it to “Apparel company,” and the problem is solved.</p>



<p class="wp-block-paragraph">However, the KG description is only half of what determines category coding. The other half is the third-party content corpus that has accumulated around your brand, and that doesn&#8217;t change because you updated a field in the Knowledge Graph. If your entire external content history is performance footwear, running, and athletic training, changing the description gives the model a new anchor with nothing attached to it. The corpus still says what it always said.</p>



<p class="wp-block-paragraph">The corrective lever is third-party content investment in the specific category framing your customers are using: in the publications the model retrieves from, alongside the brands that already define that space. The KG description can support that work once the corpus exists.</p>



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<h2 class="wp-block-heading" id="h-what-this-means-for-your-geo-strategy">What this means for your GEO strategy</h2>



<p class="wp-block-paragraph">The standard <a href="https://searchengineland.com/what-is-generative-engine-optimization-geo-444418">GEO</a> advice is to strengthen your entity: a consistent name, clean schema, a strong About page, and more press coverage. That advice is correct for getting recognized and even recommended within the brand’s coded category, but it isn&#8217;t sufficient for getting recommended in adjacent category queries.</p>



<p class="wp-block-paragraph">What determines recommendation in adjacent categories is whether the third-party content corpus around your brand matches the category framing your customers are actually using.</p>



<p class="wp-block-paragraph">The questions worth asking about any brand are:</p>



<ul class="wp-block-list">
<li>Are we visible in AI?</li>



<li>What category has the LLM coded us into?</li>



<li>Is that the category our customers are querying?</li>
</ul>



<p class="wp-block-paragraph">If a brand is strong in one category, but its customers are increasingly using adjacent category language to search (for example, athleisure instead of sportswear, or performance wellness instead of fitness), and the brand&#8217;s third-party corpus hasn&#8217;t kept pace with that language shift, the brand will be invisible in exactly the queries customers are using.</p>



<p class="wp-block-paragraph">Nike is the study&#8217;s clearest positive case, surfacing in both athleisure (77%) and athletic footwear (90%) queries, despite being KG-coded as a footwear brand. </p>



<p class="wp-block-paragraph">The reason is that Nike has accumulated enough athleisure-coded third-party content, including editorial coverage in fashion publications, inclusion in activewear roundups, and co-mentions with other athleisure brands, to register as category-eligible in both framings. It built a sub-stream in the adjacent category that New Balance didn&#8217;t.</p>


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<h2 class="wp-block-heading" id="h-what-is-the-audit-question-everyone-should-be-asking">What is the audit question everyone should be asking?</h2>



<p class="wp-block-paragraph">Before investing further in entity optimization, it&#8217;s worth running a simple diagnostic: Take the five or six different ways your customers might phrase a category query for what you do, and test each one across two or three LLMs. Note which formulations surface your brand and which don&#8217;t.</p>



<p class="wp-block-paragraph">For the ones that don&#8217;t, the questions to ask are:</p>



<ul class="wp-block-list">
<li>Does third-party content about your brand actually use that language?</li>



<li>Are you being written about in publications that cover that category?</li>



<li>Are you appearing in editorial roundups that use that phrasing?</li>
</ul>



<p class="wp-block-paragraph">If the answer is no, you know where to start: getting into the external conversations that speak the language of that query.</p>



<p class="wp-block-paragraph">Closing that gap means becoming a participant in the category comparison content that defines who belongs in that space.</p>



<p class="wp-block-paragraph"><em>This article is based on findings from “</em><a href="https://doi.org/10.5281/zenodo.20331344" target="_blank" rel="noopener"><em>The recognition-recommendation gap: Empirical evidence that category coding, not knowledge-graph strength, determines brand visibility in generative AI output</em></a><em>,” co-authored with João da Silva and published open access on Zenodo.</em>

Most brands approaching AI visibility ask the wrong question: How do we get stronger as an entity so that LLMs recommend us more?
In entity SEO, we tend to say, “Build the Knowledge Graph, add schema, and get more press.” But that logic assumes the LLM is evaluating the brand and deciding whether it’s good enough to recommend for any query related to what the brand sells. The LLM evaluates the query and matches it against whatever category associations it has built for the brand from third-party content.
The difference matters enormously in practice. 
As we’ve seen in multiple scenarios, recognition isn’t the same as recommendation. So being a recognized brand isn’t synonymous with being a strong brand. 
What matters is whether the category your customers are using to search for you matches the category the LLM has coded you into.
What the data showed
João da Silva and I conducted a study of 12 athletic apparel brands in the U.K. over seven days, with 14,140 API runs across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews. We tested the same brands using two different category framings: athleisure and athletic footwear.
After looking at the results derived from co-mentions and putting numbers on the impact of framing on category recognition for LLMs, we took the test one step further and changed the category register in the prompt.
The results were symmetric to a degree that rules out noise:

BrandKnowledge Graph (KG) scoreAthleisure rateFootwear rateΔVerdict

New Balance64,2351%90%+89Jumped (footwear-coded)
Nike25,99677%90%+13Small shift (footwear-coded with strong athleisure co-mentions)
Alo Yoga3,06263%0%-63Dropped (athleisure-coded)
lululemon81090%0%-90Dropped (athleisure-coded)
Sweaty Betty7519%0%-9Stable
Reebok6651%20%+19Small shift
Outdoor Voices45526%0%-26Small shift
Rhone Apparel4005%0%-5Stable
Varley3816%0%-6Stable
TALA3565%0%-5Stable
Gymshark27737%0%-37Dropped (athleisure-coded)
LNDR20%0%0Stable





Notes:
New Balance goes from 1% to 90%.
lululemon goes from 90% to 0%.
The variation is approximately 0.9 points in both directions simultaneously.
We’re not talking about correlation here, but a controlled observation: what happens when we change only one variable — the category word in the prompt.
AI recommends.
      
See your AI visibility
    
Why this happens: Category coding
Nike, New Balance, and Reebok share the exact same Google Knowledge Graph (KG) description: “Footwear company,” so all three are recognized perfectly by every LLM we tested. From an entity standpoint (recognition), they start from an identical position. However, their behavior under different category framings isn’t identical at all.
The reason is what the paper formalizes as category coding: the combination of the KG description field and the third-party content corpus that has accumulated around a brand in a given category.
The KG description anchors a brand to a category in the model’s representation (impacts recognition).
The third-party corpus — articles, reviews, editorial comparisons, and roundups — fills in the detail of what that category association actually looks like (impacts recommendation).
Looking at the example from New Balance
New Balance’s KG description says “Footwear company,” and the third-party corpus that has accumulated around it corroborates the category by focusing on topics related to running shoes, performance footwear, and athletic training. 
When a user asks about athleisure brands, the model doesn’t find New Balance in that corpus because there is no third-party association. But it does find lululemon, Alo Yoga, and Gymshark: all brands whose corpus is built from fashion publications, lifestyle editorial, and activewear roundups.
When we changed the query to athletic footwear, the retrieval flipped: New Balance is suddenly in the right corpus, and lululemon is not.
The model itself can’t and isn’t making a judgment about brand quality or belonging. What an LLM does is pattern-match a query category against a content category. If those two things align, the brand surfaces. If they don’t, it doesn’t, regardless of how established the brand is.
So, can you just recode your KG description? 
Some brands reading this will consider the obvious shortcut: Change the KG description. If “Footwear company” is anchoring you to the wrong category, recode it to “Apparel company,” and the problem is solved.
However, the KG description is only half of what determines category coding. The other half is the third-party content corpus that has accumulated around your brand, and that doesn’t change because you updated a field in the Knowledge Graph. If your entire external content history is performance footwear, running, and athletic training, changing the description gives the model a new anchor with nothing attached to it. The corpus still says what it always said.
The corrective lever is third-party content investment in the specific category framing your customers are using: in the publications the model retrieves from, alongside the brands that already define that space. The KG description can support that work once the corpus exists.
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See terms.
		
What this means for your GEO strategy
The standard GEO advice is to strengthen your entity: a consistent name, clean schema, a strong About page, and more press coverage. That advice is correct for getting recognized and even recommended within the brand’s coded category, but it isn’t sufficient for getting recommended in adjacent category queries.
What determines recommendation in adjacent categories is whether the third-party content corpus around your brand matches the category framing your customers are actually using.
The questions worth asking about any brand are:
Are we visible in AI?
What category has the LLM coded us into?
Is that the category our customers are querying?
If a brand is strong in one category, but its customers are increasingly using adjacent category language to search (for example, athleisure instead of sportswear, or performance wellness instead of fitness), and the brand’s third-party corpus hasn’t kept pace with that language shift, the brand will be invisible in exactly the queries customers are using.
Nike is the study’s clearest positive case, surfacing in both athleisure (77%) and athletic footwear (90%) queries, despite being KG-coded as a footwear brand. 
The reason is that Nike has accumulated enough athleisure-coded third-party content, including editorial coverage in fashion publications, inclusion in activewear roundups, and co-mentions with other athleisure brands, to register as category-eligible in both framings. It built a sub-stream in the adjacent category that New Balance didn’t.
customers won’t either.
      
See your AI visibility
    
What is the audit question everyone should be asking?
Before investing further in entity optimization, it’s worth running a simple diagnostic: Take the five or six different ways your customers might phrase a category query for what you do, and test each one across two or three LLMs. Note which formulations surface your brand and which don’t.
For the ones that don’t, the questions to ask are:
Does third-party content about your brand actually use that language?
Are you being written about in publications that cover that category?
Are you appearing in editorial roundups that use that phrasing?
If the answer is no, you know where to start: getting into the external conversations that speak the language of that query.
Closing that gap means becoming a participant in the category comparison content that defines who belongs in that space.
This article is based on findings from “The recognition-recommendation gap: Empirical evidence that category coding, not knowledge-graph strength, determines brand visibility in generative AI output,” co-authored with João da Silva and published open access on Zenodo.

The language your customers use to search can shape your brand's AI visibility. Learn what new research reveals about category coding.

How category framing changes which brands AI recommends

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>As identity signals decline, match rate has become a critical metric for improving audience reach, advertising efficiency, and ROAS.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1.png" alt="" class="wp-image-482417" srcset="https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/20260721_rokt-1-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
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<p class="wp-block-paragraph">Ask a performance marketer which numbers they check every morning and you’ll get the same list: CPM, CTR, CVR, ROAS. Ask them their match rate on Meta or Google, the share of an audience they uploaded that the platform could actually recognize and target, and you’ll usually get a pause. Most teams don’t track it. Plenty don’t know it’s a number at all.</p>



<p class="wp-block-paragraph">That pause is expensive. Build an audience of 100,000 customers, upload it to a platform that matches 55% of them, and the campaign is running against 55,000 people. The other 45,000 are invisible to it, no matter how good the targeting or the creative is. And match rate sits upstream of every metric that teams do track.&nbsp;</p>



<p class="wp-block-paragraph">When a platform recognizes only part of the audience you built, every number that follows (reach, frequency, conversions, return on spend) is quietly computed against that smaller matched audience. You can rework creative, retune bids, and rebuild your conversion model forever, and none of it touches the slice of your audience the platform never saw. The part worth examining is where matching breaks, why it’s getting harder, and how much reach teams lose without ever seeing it on a dashboard.</p>



<h3 class="wp-block-heading" id="h-the-gap-between-the-audience-you-build-and-the-audience-you-reach">The gap between the audience you build and the audience you reach</h3>



<p class="wp-block-paragraph">Here’s the mechanic most teams never examine. When you push a first-party audience to a paid platform, the platform doesn’t target “your customers.” It targets the subset of your list it can resolve to its own logged-in users, usually by matching hashed emails and phone numbers against the identifiers on its accounts. Every record it can’t resolve simply falls out, with no error and no warning. The campaign runs against whoever survived.</p>



<p class="wp-block-paragraph">Every privacy shift of the past few years has widened that gap. Third-party cookie deprecation removed the connective tissue that bridged identities across sites. Apple’s App Tracking Transparency cut off device identifiers. The walled gardens keep tightening their matching logic. And the mundane failure modes never went away: the customer who signs up with a work email but uses a personal one on social, the phone number formatted differently on each side, the record that’s three years stale. Identifiers are splintering faster than most CRMs and CDPs can consolidate them, so the distance between the audience you build and the audience you can reach is growing, not shrinking, no matter how clean your data is.</p>



<p class="wp-block-paragraph">The insidious part: platforms report performance against the matched portion. So the campaign looks fine. You’re measuring the efficiency of the audience the platform found, not the audience you built, and the difference between the two never shows up in any report you open.</p>



<h3 class="wp-block-heading" id="h-four-places-it-s-costing-you-right-now">Four places it’s costing you right now</h3>



<p class="wp-block-paragraph">Most marketers who have thought about match rate file it under “retargeting problem.” It’s far broader than that.</p>



<ul class="wp-block-list">
<li><strong>Acquisition.</strong> Seed and exclusion lists that only partially match make prospecting less precise, and a platform training on partial signal has to guess more. That usually surfaces as inflated CAC, and never gets traced back to matching.</li>



<li><strong>Retargeting.</strong> The obvious one, but state the math plainly: if your CRM list matches at 45%, more than half the customers you meant to re-engage never see the campaign. The program runs at less than half capacity, and its reported numbers say nothing about the people it never reached.</li>



<li><strong>Suppression.</strong> The sneakiest. Suppression lists only suppress the customers a platform recognizes. Every existing customer who doesn’t match is invisible to your exclusions, so you pay acquisition prices to re-buy people you already have, and some of them get served the new-customer discount your loyal buyers never see. Low match rates don’t just waste budget; they fund your own margin erosion.</li>



<li><strong>Lookalike seeding.</strong> Lookalike models expand from the matched portion of your seed, not the seed you uploaded. A weak match rate means the model learns from a skewed sub-sample of your best customers, and that error compounds as the platform extrapolates across millions of impressions.</li>
</ul>



<p class="wp-block-paragraph">Add it up, and match rate isn’t a data team curiosity. It’s a tax on every dollar of paid spend, and almost nobody has measured how big it is.</p>



<h3 class="wp-block-heading" id="h-what-happens-when-you-close-the-gap">What happens when you close the gap</h3>



<p class="wp-block-paragraph">This isn&#8217;t just theoretical. CKE Restaurants, the company behind Carl&#8217;s Jr. and Hardee&#8217;s, ran their audiences through Rokt mParticle&#8217;s Match Boost to enrich identifiers for ad platforms. Match rates rose up to 117% on Google Ads and 29% on Meta.</p>



<p class="wp-block-paragraph">Note what didn’t change: the budget, the creative, the campaign structure. The same spend simply reached more of the audience the brands had already built, and ROAS improved on that same spend. That’s the signature of a match-rate problem. When recognition goes up, efficiency follows, because the waste you’re removing was never visible to begin with.</p>



<h3 class="wp-block-heading" id="h-this-used-to-be-a-procurement-project-now-it-s-a-setting">This used to be a procurement project. Now it’s a setting.</h3>



<p class="wp-block-paragraph">If match rate has been ignored, part of the reason is that fixing it used to be genuinely painful. Improving recognition meant licensing third-party data: vendor evaluations, procurement cycles, legal review, an integration build, and months before you could measure anything. The cost of the fix outweighed an upside most teams weren’t even quantifying.</p>



<p class="wp-block-paragraph">That’s no longer the shape of the problem. Enrichment increasingly happens at the point where audiences leave your customer-data infrastructure for the ad platform, a setting on the connection rather than a system you build.&nbsp;</p>



<p class="wp-block-paragraph">Done well, it inherits the governance you already have: identifiers you’ve deliberately excluded for privacy or compliance stay excluded, and the enriched data is used only to sharpen the match in flight, never written back into your profiles or stored in the destination platform. Closing the gap has become a configuration decision, not a data-strategy overhaul. That doesn’t mean it’s solved for everyone. It means the excuse for not looking is gone.</p>



<h3 class="wp-block-heading" id="h-how-to-check-your-own-match-rate">How to check your own match rate</h3>



<p class="wp-block-paragraph">Measure the gap. It takes about thirty minutes.</p>



<ul class="wp-block-list">
<li>Pick your top three paid destinations by spend.</li>



<li>For each, compare the size of the list you uploaded against what the platform actually matched. Google Ads reports a match rate on Customer Match uploads (bucketed, but close enough); Meta shows the resulting audience size, which you can hold against the list you sent. Most brands land somewhere in the 40–60% range for email-only lists, well below what most teams assume.</li>



<li>Run the same check on your largest suppression list. That’s the one that will sting.</li>
</ul>



<p class="wp-block-paragraph">If your numbers come back north of 70%, go back to optimizing creative. If you’re like most brands, you’ll find you’ve been paying full price to reach a fraction of your audience. Every metric you already track is downstream of that one number, and most teams have never looked at it.</p>


Ask a performance marketer which numbers they check every morning and you’ll get the same list: CPM, CTR, CVR, ROAS. Ask them their match rate on Meta or Google, the share of an audience they uploaded that the platform could actually recognize and target, and you’ll usually get a pause. Most teams don’t track it. Plenty don’t know it’s a number at all.
That pause is expensive. Build an audience of 100,000 customers, upload it to a platform that matches 55% of them, and the campaign is running against 55,000 people. The other 45,000 are invisible to it, no matter how good the targeting or the creative is. And match rate sits upstream of every metric that teams do track. 
When a platform recognizes only part of the audience you built, every number that follows (reach, frequency, conversions, return on spend) is quietly computed against that smaller matched audience. You can rework creative, retune bids, and rebuild your conversion model forever, and none of it touches the slice of your audience the platform never saw. The part worth examining is where matching breaks, why it’s getting harder, and how much reach teams lose without ever seeing it on a dashboard.
The gap between the audience you build and the audience you reach
Here’s the mechanic most teams never examine. When you push a first-party audience to a paid platform, the platform doesn’t target “your customers.” It targets the subset of your list it can resolve to its own logged-in users, usually by matching hashed emails and phone numbers against the identifiers on its accounts. Every record it can’t resolve simply falls out, with no error and no warning. The campaign runs against whoever survived.
Every privacy shift of the past few years has widened that gap. Third-party cookie deprecation removed the connective tissue that bridged identities across sites. Apple’s App Tracking Transparency cut off device identifiers. The walled gardens keep tightening their matching logic. And the mundane failure modes never went away: the customer who signs up with a work email but uses a personal one on social, the phone number formatted differently on each side, the record that’s three years stale. Identifiers are splintering faster than most CRMs and CDPs can consolidate them, so the distance between the audience you build and the audience you can reach is growing, not shrinking, no matter how clean your data is.
The insidious part: platforms report performance against the matched portion. So the campaign looks fine. You’re measuring the efficiency of the audience the platform found, not the audience you built, and the difference between the two never shows up in any report you open.
Four places it’s costing you right now
Most marketers who have thought about match rate file it under “retargeting problem.” It’s far broader than that.
Acquisition. Seed and exclusion lists that only partially match make prospecting less precise, and a platform training on partial signal has to guess more. That usually surfaces as inflated CAC, and never gets traced back to matching.
Retargeting. The obvious one, but state the math plainly: if your CRM list matches at 45%, more than half the customers you meant to re-engage never see the campaign. The program runs at less than half capacity, and its reported numbers say nothing about the people it never reached.
Suppression. The sneakiest. Suppression lists only suppress the customers a platform recognizes. Every existing customer who doesn’t match is invisible to your exclusions, so you pay acquisition prices to re-buy people you already have, and some of them get served the new-customer discount your loyal buyers never see. Low match rates don’t just waste budget; they fund your own margin erosion.
Lookalike seeding. Lookalike models expand from the matched portion of your seed, not the seed you uploaded. A weak match rate means the model learns from a skewed sub-sample of your best customers, and that error compounds as the platform extrapolates across millions of impressions.
Add it up, and match rate isn’t a data team curiosity. It’s a tax on every dollar of paid spend, and almost nobody has measured how big it is.
What happens when you close the gap
This isn’t just theoretical. CKE Restaurants, the company behind Carl’s Jr. and Hardee’s, ran their audiences through Rokt mParticle’s Match Boost to enrich identifiers for ad platforms. Match rates rose up to 117% on Google Ads and 29% on Meta.
Note what didn’t change: the budget, the creative, the campaign structure. The same spend simply reached more of the audience the brands had already built, and ROAS improved on that same spend. That’s the signature of a match-rate problem. When recognition goes up, efficiency follows, because the waste you’re removing was never visible to begin with.
This used to be a procurement project. Now it’s a setting.
If match rate has been ignored, part of the reason is that fixing it used to be genuinely painful. Improving recognition meant licensing third-party data: vendor evaluations, procurement cycles, legal review, an integration build, and months before you could measure anything. The cost of the fix outweighed an upside most teams weren’t even quantifying.
That’s no longer the shape of the problem. Enrichment increasingly happens at the point where audiences leave your customer-data infrastructure for the ad platform, a setting on the connection rather than a system you build. 
Done well, it inherits the governance you already have: identifiers you’ve deliberately excluded for privacy or compliance stay excluded, and the enriched data is used only to sharpen the match in flight, never written back into your profiles or stored in the destination platform. Closing the gap has become a configuration decision, not a data-strategy overhaul. That doesn’t mean it’s solved for everyone. It means the excuse for not looking is gone.
How to check your own match rate
Measure the gap. It takes about thirty minutes.
Pick your top three paid destinations by spend.
For each, compare the size of the list you uploaded against what the platform actually matched. Google Ads reports a match rate on Customer Match uploads (bucketed, but close enough); Meta shows the resulting audience size, which you can hold against the list you sent. Most brands land somewhere in the 40–60% range for email-only lists, well below what most teams assume.
Run the same check on your largest suppression list. That’s the one that will sting.
If your numbers come back north of 70%, go back to optimizing creative. If you’re like most brands, you’ll find you’ve been paying full price to reach a fraction of your audience. Every metric you already track is downstream of that one number, and most teams have never looked at it.

As identity signals decline, match rate has become a critical metric for improving audience reach, advertising efficiency, and ROAS.

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Read the Latest Trends and Headlines in Digital Marketing.

Here are some of the latest headlines on digital marketing insights, search, and ecommerce:

Free Consultation

Reach out with any questions you might have regarding how RSO can help you with digital marketing.

All About
Digital Marketing