<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story.png" class="attachment-large size-large wp-post-image" alt="Google Ads may be over-crediting your conversions- A 7-day test tells a different story" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google is giving advertisers more ways to evaluate creative performance and make data-driven optimization decisions.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story.png" class="attachment-large size-large wp-post-image" alt="Google Ads may be over-crediting your conversions- A 7-day test tells a different story" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story.png 1920w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/03/Google-Ads-may-be-over-crediting-your-conversions-A-7-day-test-tells-a-different-story-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is expanding experimentation capabilities in Performance Max, giving advertisers more ways to test creative assets and measure campaign performance before making large-scale changes.</p>



<p><strong>What&#8217;s happening</strong>. Google is rolling out new asset experiments for Performance Max campaigns, allowing advertisers to test how different creative assets affect results.</p>



<p>The feature enables marketers to compare entirely new asset groups, evaluate the impact of adding individual assets, or measure the performance of seasonal creative against evergreen content.</p>



<p>Advertisers will also be able to test assets generated through Google&#8217;s Asset Studio.</p>



<p><strong>The big picture</strong>. Performance Max has long automated campaign optimization across Google&#8217;s inventory, but advertisers have had limited visibility into the impact of creative changes.</p>



<p>The new experiments aim to give marketers a more controlled way to evaluate creative decisions before applying them across campaigns.</p>



<p><strong>Between the lines</strong>. The addition of a second success metric could be particularly valuable for advertisers balancing competing objectives, such as maximizing conversions while maintaining efficiency targets.</p>



<p>Rather than declaring a winner based on a single KPI, marketers will be able to evaluate how changes affect broader campaign performance.</p>



<p><strong>What else is new:</strong>



<ul class="wp-block-list">
<li>Conversion lift studies and experiments are being brought together under one Experiments page.</li>



<li>Additional experiment and measurement capabilities are planned for future releases.</li>



<li>Expanded support for manager accounts (MCCs) and the Google Ads API is expected to begin rolling out in the coming weeks.</li>
</ul>



<p><strong>Why we care.</strong> Creative remains one of the biggest levers available to Performance Max advertisers, yet testing new assets often involves risk. The new experimentation tools provide a structured way to validate creative decisions with data before fully committing budget.</p>



<p><strong>What to watch.</strong> As Google continues investing in automation and AI-generated creative, asset testing is becoming increasingly important. The ability to directly compare human-created, seasonal, evergreen, and AI-generated assets could offer advertisers deeper insight into what drives performance across Performance Max campaigns.</p>



<p><strong>The bottom line</strong>. <a href="https://business.google.com/us/accelerate/announcements/scale-results-with-new-asset-experiments-in-performance-max/">Google is giving Performance Max advertisers more sophisticated testing capabilities</a>, making it easier to evaluate creative changes, measure results across multiple KPIs, and manage experiments from a centralized location.</p>



<p><strong>First spotted. </strong>The update was first spotted by PPC News Feed. </p>


Google is expanding experimentation capabilities in Performance Max, giving advertisers more ways to test creative assets and measure campaign performance before making large-scale changes.
What’s happening. Google is rolling out new asset experiments for Performance Max campaigns, allowing advertisers to test how different creative assets affect results.
The feature enables marketers to compare entirely new asset groups, evaluate the impact of adding individual assets, or measure the performance of seasonal creative against evergreen content.
Advertisers will also be able to test assets generated through Google’s Asset Studio.
The big picture. Performance Max has long automated campaign optimization across Google’s inventory, but advertisers have had limited visibility into the impact of creative changes.
The new experiments aim to give marketers a more controlled way to evaluate creative decisions before applying them across campaigns.
Between the lines. The addition of a second success metric could be particularly valuable for advertisers balancing competing objectives, such as maximizing conversions while maintaining efficiency targets.
Rather than declaring a winner based on a single KPI, marketers will be able to evaluate how changes affect broader campaign performance.
What else is new:




Conversion lift studies and experiments are being brought together under one Experiments page.
Additional experiment and measurement capabilities are planned for future releases.
Expanded support for manager accounts (MCCs) and the Google Ads API is expected to begin rolling out in the coming weeks.
Why we care. Creative remains one of the biggest levers available to Performance Max advertisers, yet testing new assets often involves risk. The new experimentation tools provide a structured way to validate creative decisions with data before fully committing budget.
What to watch. As Google continues investing in automation and AI-generated creative, asset testing is becoming increasingly important. The ability to directly compare human-created, seasonal, evergreen, and AI-generated assets could offer advertisers deeper insight into what drives performance across Performance Max campaigns.
The bottom line. Google is giving Performance Max advertisers more sophisticated testing capabilities, making it easier to evaluate creative changes, measure results across multiple KPIs, and manage experiments from a centralized location.
First spotted. The update was first spotted by PPC News Feed.

Google is giving advertisers more ways to evaluate creative performance and make data-driven optimization decisions.

Google adds new Performance Max asset testing tools

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform.png" class="attachment-large size-large wp-post-image" alt="OpenAI ChatGPT ad platform" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform.png 1920w, https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>OpenAI is expanding ChatGPT ads to five new markets including the UK, &#038; introducing new campaign management features.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform.png" class="attachment-large size-large wp-post-image" alt="OpenAI ChatGPT ad platform" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform.png 1920w, https://searchengineland.com/wp-content/seloads/2025/09/OpenAI-ChatGPT-ad-platform-768x432.png 768w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>OpenAI is expanding its advertising ambitions inside ChatGPT, beginning an early test that allows multiple advertisers to appear within a single ad placement.</p>



<p><strong>What&#8217;s happening</strong>. The company is testing multi-advertiser ad units across a small subset of ChatGPT ads, according to a product update sent to advertisers.</p>



<p>Rather than displaying a single sponsored result, the new format will group multiple relevant ads together in one placement. Eligible ads will be sold through a second-price auction model, a common pricing mechanism used across digital advertising platforms.</p>



<p>OpenAI says the goal is to improve product discovery for users while creating more opportunities for advertisers to engage with users during high-intent conversations.</p>



<p><strong>Meanwhile, in Ads Manager Beta</strong>. OpenAI also announced several new campaign management features for advertisers:</p>



<ul class="wp-block-list">
<li>Advertisers can now convert existing campaigns from lifetime budgets to daily budgets.</li>



<li>CPM campaigns can be cloned and converted to CPC bidding with one click.</li>



<li>Impression-based campaigns now support custom CPM max bids.</li>



<li>Bulk editing is available directly within the Ads Manager interface.</li>



<li>Daily budgets will transition to an average daily budget model with weekly pacing flexibility.</li>



<li>Geographic targeting is expanding beyond the U.S., Canada, Australia, and New Zealand to include the U.K., Japan, South Korea, Brazil, and Mexico.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="539" height="990" src="https://searchengineland.com/wp-content/seloads/2026/06/chatgpt-expandsion.jpeg" alt="" class="wp-image-479657"/></figure>



<p><strong>Why we care.</strong> The updates bring OpenAI&#8217;s ad platform closer to the functionality marketers expect from mature advertising ecosystems, reducing campaign management friction while expanding targeting opportunities internationally.</p>



<p><strong>What to watch</strong>. The multi-advertiser placement test could provide an early signal of how aggressively OpenAI intends to monetize ChatGPT. If successful, the format may become a larger part of the platform&#8217;s ad inventory strategy while offering advertisers more opportunities to reach users during purchase and research journeys.</p>



<p><strong>The bottom line</strong>. OpenAI is steadily building out its advertising stack, but the biggest development may be its experiment with showing multiple advertisers in a single ChatGPT ad placement — a move that could reshape how sponsored content appears within AI conversations.</p>


OpenAI is expanding its advertising ambitions inside ChatGPT, beginning an early test that allows multiple advertisers to appear within a single ad placement.
What’s happening. The company is testing multi-advertiser ad units across a small subset of ChatGPT ads, according to a product update sent to advertisers.
Rather than displaying a single sponsored result, the new format will group multiple relevant ads together in one placement. Eligible ads will be sold through a second-price auction model, a common pricing mechanism used across digital advertising platforms.
OpenAI says the goal is to improve product discovery for users while creating more opportunities for advertisers to engage with users during high-intent conversations.
Meanwhile, in Ads Manager Beta. OpenAI also announced several new campaign management features for advertisers:
Advertisers can now convert existing campaigns from lifetime budgets to daily budgets.
CPM campaigns can be cloned and converted to CPC bidding with one click.
Impression-based campaigns now support custom CPM max bids.
Bulk editing is available directly within the Ads Manager interface.
Daily budgets will transition to an average daily budget model with weekly pacing flexibility.
Geographic targeting is expanding beyond the U.S., Canada, Australia, and New Zealand to include the U.K., Japan, South Korea, Brazil, and Mexico.




Why we care. The updates bring OpenAI’s ad platform closer to the functionality marketers expect from mature advertising ecosystems, reducing campaign management friction while expanding targeting opportunities internationally.
What to watch. The multi-advertiser placement test could provide an early signal of how aggressively OpenAI intends to monetize ChatGPT. If successful, the format may become a larger part of the platform’s ad inventory strategy while offering advertisers more opportunities to reach users during purchase and research journeys.
The bottom line. OpenAI is steadily building out its advertising stack, but the biggest development may be its experiment with showing multiple advertisers in a single ChatGPT ad placement — a move that could reshape how sponsored content appears within AI conversations.

OpenAI is expanding ChatGPT ads to five new markets including the UK, & introducing new campaign management features.

OpenAI to expand ChatGPT ads to new markets & test multi-advertiser placements

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads.jpg" class="attachment-large size-large wp-post-image" alt="Google Local Services Ads vs. Search Ads- Which drives better local leads?" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google will update its Local Services Ads policies on July 6, renaming them as "requirements" and aligning advertiser guidelines.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads.jpg" class="attachment-large size-large wp-post-image" alt="Google Local Services Ads vs. Search Ads- Which drives better local leads?" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/11/Google-Local-Services-Ads-vs.-Search-Ads-Which-drives-better-local-leads-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is changing the rules framework that governs Local Services Ads, updating policy language and aligning advertiser requirements with its new badge system.</p>



<p><strong>What&#8217;s happening</strong>. On July 6th Google will update its Local Services Ads policies to improve readability, revise terminology, and remove requirements that no longer apply to advertisers.</p>



<p>As part of the update, Google will rename &#8220;Local Services platform policies&#8221; as &#8220;Local Services Ads requirements.&#8221;</p>



<p>The changes build on the company&#8217;s recent overhaul of the Local Services Ads badge system, including updates to Google Guarantee badges and advertiser verification standards.</p>



<p><strong>Why we care. </strong>While these changes are mostly administrative, advertisers should pay attention because the new &#8220;requirements&#8221; framework could make it easier for Google to tie compliance standards directly to badge status in the future. For agencies and local businesses, it&#8217;s another indication that maintaining verification credentials and meeting platform standards will remain critical for competing in LSAs.</p>



<p><strong>The big picture</strong>. Google says the policy refresh is intended to better align advertiser requirements with the new badge framework while making compliance guidance easier to understand.</p>



<p>The company is not positioning the update as a major policy crackdown. Instead, the focus appears to be on simplifying existing rules and modernizing the way requirements are communicated to businesses.</p>



<p><strong>The bottom line</strong>. <a href="https://support.google.com/adspolicy/answer/6245891">Google is refreshing the policy framework for Local Services Ads</a>, replacing &#8220;platform policies&#8221; with &#8220;requirements&#8221; and aligning advertiser guidance with a new badge-driven approach to trust and eligibility.</p>


Google is changing the rules framework that governs Local Services Ads, updating policy language and aligning advertiser requirements with its new badge system.
What’s happening. On July 6th Google will update its Local Services Ads policies to improve readability, revise terminology, and remove requirements that no longer apply to advertisers.
As part of the update, Google will rename “Local Services platform policies” as “Local Services Ads requirements.”
The changes build on the company’s recent overhaul of the Local Services Ads badge system, including updates to Google Guarantee badges and advertiser verification standards.
Why we care. While these changes are mostly administrative, advertisers should pay attention because the new “requirements” framework could make it easier for Google to tie compliance standards directly to badge status in the future. For agencies and local businesses, it’s another indication that maintaining verification credentials and meeting platform standards will remain critical for competing in LSAs.
The big picture. Google says the policy refresh is intended to better align advertiser requirements with the new badge framework while making compliance guidance easier to understand.
The company is not positioning the update as a major policy crackdown. Instead, the focus appears to be on simplifying existing rules and modernizing the way requirements are communicated to businesses.
The bottom line. Google is refreshing the policy framework for Local Services Ads, replacing “platform policies” with “requirements” and aligning advertiser guidance with a new badge-driven approach to trust and eligibility.

Google will update its Local Services Ads policies on July 6, renaming them as "requirements" and aligning advertiser guidelines.

Google to update Local Services Ads policies in July

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more.png" class="attachment-large size-large wp-post-image" alt="The problem with AI share of voice and 3 metrics that matter more" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Many AI visibility platforms extrapolate from a small subset of prompts. Explore three metrics designed for an infinite-query environment.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more.png" class="attachment-large size-large wp-post-image" alt="The problem with AI share of voice and 3 metrics that matter more" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/The-problem-with-AI-share-of-voice-and-3-metrics-that-matter-more-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Traditional <a href="https://searchengineland.com/guides/share-of-voice">share of voice</a> (SOV) is effectively obsolete, yet many organizations have replaced it with an equally flawed successor: AI share of voice.</p>



<p>Software vendors now claim to measure brand visibility across ChatGPT, Gemini, Claude, Perplexity, and other AI platforms using a single percentage score. The problem is that these metrics rely on a hidden denominator. </p>



<p>Unlike traditional search, where visibility could be measured against a known keyword set, the universe of possible AI prompts is effectively infinite.</p>



<p>Traditional SOV had limitations, but at least its assumptions were transparent. Marketers defined a fixed keyword set, tracked visibility against competitors, and used that list as a stable denominator. Everyone understood the measurement&#8217;s boundaries.</p>



<p>That model no longer exists. Search results are dynamic and personalized, and are increasingly being replaced by conversational interfaces. Yet many AI visibility platforms continue to present precise-looking percentages that can&#8217;t be audited or validated.</p>



<p>To stop presenting fictional metrics to leadership teams, we must rethink how we define and measure visibility in AI search.</p>



<h2 class="wp-block-heading" id="h-why-traditional-sov-metrics-now-fail">Why traditional SOV metrics now fail</h2>



<p>The basic assumptions of search engine optimization and digital brand tracking have been broken by two major shifts: the disappearance of the static results page and the rapid rise of personalized, conversational answers.</p>



<p>Search engines have become highly dynamic, personalized landscapes that change shape continuously based on real-time data.</p>



<p>Between AI-generated summaries, localized results, continuous scrolling, interactive merchant grids, and real-time social feeds, no two users will encounter the same interface, even when entering the exact same query at the exact same moment. </p>



<p>Because the search environment changes constantly, attempting to calculate a precise &#8220;share&#8221; of that screen has become a mathematical impossibility.</p>



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<h2 class="wp-block-heading" id="h-the-new-volatile-normality-of-rankings">The new volatile normality of rankings</h2>



<p>Securing the top ranking position in the older marketing model meant capturing a highly predictable percentage of user click-through rates.</p>



<p>In the modern search landscape, however, ranking first organically might place a brand below several sponsored listings, an AI-generated overview, interactive question accordions, and featured discussions from community platforms.</p>



<p>Because search engines now construct layouts dynamically in response to immediate user intent and past search history, rankings fluctuate by the hour.</p>



<p>Measuring share of voice based on static positions is as unproductive as trying to measure the volume of an ocean wave with a wooden ruler.</p>



<h2 class="wp-block-heading" id="h-the-modern-ai-share-of-voice">The modern AI share of voice</h2>



<p>When marketing teams realized that traditional rank tracking was losing its utility, software vendors quickly introduced alternative metrics, branded as LLM Visibility or AI share of voice.</p>



<p>These dashboards present highly polished, authoritative percentage scores that suggest a brand&#8217;s footprint has been successfully mapped across platforms like ChatGPT, Claude, Gemini, and Perplexity.</p>



<p>These tools fail to deliver on this promise, exposing a fundamental methodology problem that we must address directly.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Legacy tracking (transparent)</strong></td><td><strong>LLM visibility (black box)</strong></td></tr><tr><td>– Define fixed keyword list (known).<br>– Measure rank on static SERPAuditable denominator.</td><td>– Infinite possible user prompts.<br>– Vendor runs small, arbitrary subset.<br>– Subjective denominator.</td></tr></tbody></table></figure>



<h2 class="wp-block-heading" id="h-the-infinite-tail">The infinite tail</h2>



<p>Legacy <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> tools relied on a user-defined keyword list that served as a transparent denominator, whereas modern conversational engines present an entirely different mathematical reality where the universe of possible user prompts is effectively infinite.</p>



<p>Buyers no longer search for solutions using simple, two-word phrases. Instead, they enter highly specific, conversational queries that describe their exact organizational context, integration needs, and feature requirements.</p>



<p>Because no marketing tool can realistically sample this infinite universe of natural language, software vendors instead select a small, arbitrary subset of static prompts, run them through AI models behind the scenes, and aggregate those limited outputs into a representative global percentage.</p>



<p>This process creates a metric that only measures share of voice within a contrived and artificial environment, presenting a closed sandbox as if it were the open web.</p>



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<h2 class="wp-block-heading" id="h-the-issue-with-black-box-metrics">The issue with black-box metrics</h2>



<p>Marketers maintained full visibility into the data they were analyzing with legacy tracking tools, which meant that if a system reported a specific percentage of visibility, the underlying keyword list could be audited and adjusted. Modern LLM visibility tools obscure their denominator within proprietary, vendor-defined systems that are almost certainly incomplete.</p>



<p>This structural flaw became incredibly clear in September 2025, when OpenAI updated to its ChatGPT 5.0 model. Following this release, the platform-wide volume of outbound citations and source links dropped.</p>



<p>For marketing teams relying on LLM tracking dashboards, this model change resulted in a sudden, sharp decline in their reported visibility metrics. The decline had nothing to do with a loss of brand relevance or a failure in marketing strategy. ChatGPT had simply changed how it presented source data to users.</p>



<p>This update demonstrates that modern AI metrics are highly volatile and largely out of your control. While software vendors are genuinely trying to solve an incredibly complex engineering problem, the underlying methodology simply cannot support the high-confidence dashboards they deliver, meaning these metrics should be treated as directional signals rather than hard numbers.</p>



<h2 class="wp-block-heading" id="h-beyond-ai-share-of-voice-3-metrics-that-matter-more">Beyond AI share of voice: 3 metrics that matter more</h2>



<p>We must shift our focus from measuring pure search volume to measuring how effectively a brand is integrated into the broader context of digital discussions.</p>



<p>As search queries morph into conversational discovery, a brand&#8217;s visibility is no longer defined by the keywords it owns, but by how deeply it is embedded in the conceptual models used by AI.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1024" height="559" src="https://searchengineland.com/wp-content/seloads/2026/06/The-modern-brand-visibility-trial.png" alt="The modern brand visibility trial" class="wp-image-479613" srcset="https://searchengineland.com/wp-content/seloads/2026/06/The-modern-brand-visibility-trial.png 1024w, https://searchengineland.com/wp-content/seloads/2026/06/The-modern-brand-visibility-trial-768x419.png 768w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure>
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<h3 class="wp-block-heading" id="h-1-share-of-mentions">1. Share of mentions</h3>



<p>AI models synthesize relationships between concepts rather than simply indexing pages, meaning a brand must exist within the model&#8217;s training data, fine-tuning datasets, or real-time retrieval sources to be surfaced at all.</p>



<p>Share of mentions tracks how frequently your brand name, products, or key executives are naturally included in the responses generated across the broader information ecosystem.</p>



<p>This metric shifts the operational focus from ranking positions to vocabulary inclusion, ensuring that a brand is recognized by the model even when it is not explicitly prompted for a vendor list.</p>



<p>To influence this metric, organizations must focus on securing organic mentions across high-trust forums, developer communities, and authoritative industry publications where AI models actively gather and update their information.</p>



<h3 class="wp-block-heading" id="h-2-share-of-recommendations">2. Share of recommendations</h3>



<p>When buyers use conversational engines to make purchasing decisions, they regularly ask for direct comparisons, shortlists, and product recommendations to simplify their research process.</p>



<p>Share of recommendations measures how often your product or service is explicitly featured when a user asks an AI engine to act as an advisor on a specific business challenge.</p>



<p>This approach shifts our focus from raw traffic acquisition to winning the buyer&#8217;s consideration set, which is critical because conversational engines filter out the noise of the web to deliver a highly curated list of options.</p>



<p>If your product positioning is overly generic, the model will struggle to categorize your offering and will default to recommending competitors that have established a much clearer, highly documented use case.</p>



<h3 class="wp-block-heading" id="h-3-share-of-narrative">3. Share of narrative</h3>



<p>Merely securing a mention in an AI response is insufficient if the context of that mention portrays your brand poorly, as high visibility within a negative framework can quickly become a strategic liability.</p>



<p>Share of narrative measures the qualitative attributes, adjectives, and associations linked to your brand name in conversational outputs, allowing you to understand how your business is being framed.</p>



<figure class="wp-block-table"><table><tbody><tr><td><strong>Narrative</strong></td><td><strong>What it tracks</strong></td><td><strong>The core strategic question</strong></td></tr><tr><td>The “best” narrative</td><td>How often you are framed as the premium, gold-standard market leader.</td><td>Is the model positioning our brand as the most capable solution available?</td></tr><tr><td>The “popular” narrative</td><td>How often you are cited as the default, widely adopted industry standard.</td><td>Is the model identifying our brand as the most commonly used option?</td></tr><tr><td>The “budget” narrative</td><td>How often you are categorized as the cost-effective, value, or entry-level alternative.</td><td>Is the model framing our brand primarily as a low-cost, entry-level alternative?</td></tr></tbody></table></figure>



<p>If an AI engine includes your brand frequently but consistently describes your product as a complex, legacy system, your high share of voice may actually be damaging your sales pipeline.</p>



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<h2 class="wp-block-heading" id="h-reframing-your-success-metrics">Reframing your success metrics</h2>



<p>Leadership teams require competitive benchmarks to evaluate market performance, meaning you cannot simply stop reporting on share of voice without offering a viable alternative.</p>



<p>Transitioning your executive reporting smoothly requires a structured, three-step plan.</p>



<p>Reframing the executive narrative involves educating your leadership team on the limitations of modern AI dashboards.</p>



<p>This means explaining the hidden denominator problem and demonstrating why treating these figures as absolute metrics introduces unnecessary risk.</p>

Traditional share of voice (SOV) is effectively obsolete, yet many organizations have replaced it with an equally flawed successor: AI share of voice.
Software vendors now claim to measure brand visibility across ChatGPT, Gemini, Claude, Perplexity, and other AI platforms using a single percentage score. The problem is that these metrics rely on a hidden denominator. 
Unlike traditional search, where visibility could be measured against a known keyword set, the universe of possible AI prompts is effectively infinite.
Traditional SOV had limitations, but at least its assumptions were transparent. Marketers defined a fixed keyword set, tracked visibility against competitors, and used that list as a stable denominator. Everyone understood the measurement’s boundaries.
That model no longer exists. Search results are dynamic and personalized, and are increasingly being replaced by conversational interfaces. Yet many AI visibility platforms continue to present precise-looking percentages that can’t be audited or validated.
To stop presenting fictional metrics to leadership teams, we must rethink how we define and measure visibility in AI search.
Why traditional SOV metrics now fail
The basic assumptions of search engine optimization and digital brand tracking have been broken by two major shifts: the disappearance of the static results page and the rapid rise of personalized, conversational answers.
Search engines have become highly dynamic, personalized landscapes that change shape continuously based on real-time data.
Between AI-generated summaries, localized results, continuous scrolling, interactive merchant grids, and real-time social feeds, no two users will encounter the same interface, even when entering the exact same query at the exact same moment. 
Because the search environment changes constantly, attempting to calculate a precise “share” of that screen has become a mathematical impossibility.
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The new volatile normality of rankings
Securing the top ranking position in the older marketing model meant capturing a highly predictable percentage of user click-through rates.
In the modern search landscape, however, ranking first organically might place a brand below several sponsored listings, an AI-generated overview, interactive question accordions, and featured discussions from community platforms.
Because search engines now construct layouts dynamically in response to immediate user intent and past search history, rankings fluctuate by the hour.
Measuring share of voice based on static positions is as unproductive as trying to measure the volume of an ocean wave with a wooden ruler.
The modern AI share of voice
When marketing teams realized that traditional rank tracking was losing its utility, software vendors quickly introduced alternative metrics, branded as LLM Visibility or AI share of voice.
These dashboards present highly polished, authoritative percentage scores that suggest a brand’s footprint has been successfully mapped across platforms like ChatGPT, Claude, Gemini, and Perplexity.
These tools fail to deliver on this promise, exposing a fundamental methodology problem that we must address directly.

Legacy tracking (transparent)LLM visibility (black box)
– Define fixed keyword list (known).
– Measure rank on static SERPAuditable denominator.– Infinite possible user prompts.
– Vendor runs small, arbitrary subset.
– Subjective denominator.





The infinite tail
Legacy SEO tools relied on a user-defined keyword list that served as a transparent denominator, whereas modern conversational engines present an entirely different mathematical reality where the universe of possible user prompts is effectively infinite.
Buyers no longer search for solutions using simple, two-word phrases. Instead, they enter highly specific, conversational queries that describe their exact organizational context, integration needs, and feature requirements.
Because no marketing tool can realistically sample this infinite universe of natural language, software vendors instead select a small, arbitrary subset of static prompts, run them through AI models behind the scenes, and aggregate those limited outputs into a representative global percentage.
This process creates a metric that only measures share of voice within a contrived and artificial environment, presenting a closed sandbox as if it were the open web.
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The issue with black-box metrics
Marketers maintained full visibility into the data they were analyzing with legacy tracking tools, which meant that if a system reported a specific percentage of visibility, the underlying keyword list could be audited and adjusted. Modern LLM visibility tools obscure their denominator within proprietary, vendor-defined systems that are almost certainly incomplete.
This structural flaw became incredibly clear in September 2025, when OpenAI updated to its ChatGPT 5.0 model. Following this release, the platform-wide volume of outbound citations and source links dropped.
For marketing teams relying on LLM tracking dashboards, this model change resulted in a sudden, sharp decline in their reported visibility metrics. The decline had nothing to do with a loss of brand relevance or a failure in marketing strategy. ChatGPT had simply changed how it presented source data to users.
This update demonstrates that modern AI metrics are highly volatile and largely out of your control. While software vendors are genuinely trying to solve an incredibly complex engineering problem, the underlying methodology simply cannot support the high-confidence dashboards they deliver, meaning these metrics should be treated as directional signals rather than hard numbers.
Beyond AI share of voice: 3 metrics that matter more
We must shift our focus from measuring pure search volume to measuring how effectively a brand is integrated into the broader context of digital discussions.
As search queries morph into conversational discovery, a brand’s visibility is no longer defined by the keywords it owns, but by how deeply it is embedded in the conceptual models used by AI.




1. Share of mentions
AI models synthesize relationships between concepts rather than simply indexing pages, meaning a brand must exist within the model’s training data, fine-tuning datasets, or real-time retrieval sources to be surfaced at all.
Share of mentions tracks how frequently your brand name, products, or key executives are naturally included in the responses generated across the broader information ecosystem.
This metric shifts the operational focus from ranking positions to vocabulary inclusion, ensuring that a brand is recognized by the model even when it is not explicitly prompted for a vendor list.
To influence this metric, organizations must focus on securing organic mentions across high-trust forums, developer communities, and authoritative industry publications where AI models actively gather and update their information.
2. Share of recommendations
When buyers use conversational engines to make purchasing decisions, they regularly ask for direct comparisons, shortlists, and product recommendations to simplify their research process.
Share of recommendations measures how often your product or service is explicitly featured when a user asks an AI engine to act as an advisor on a specific business challenge.
This approach shifts our focus from raw traffic acquisition to winning the buyer’s consideration set, which is critical because conversational engines filter out the noise of the web to deliver a highly curated list of options.
If your product positioning is overly generic, the model will struggle to categorize your offering and will default to recommending competitors that have established a much clearer, highly documented use case.
3. Share of narrative
Merely securing a mention in an AI response is insufficient if the context of that mention portrays your brand poorly, as high visibility within a negative framework can quickly become a strategic liability.
Share of narrative measures the qualitative attributes, adjectives, and associations linked to your brand name in conversational outputs, allowing you to understand how your business is being framed.

NarrativeWhat it tracksThe core strategic question
The “best” narrativeHow often you are framed as the premium, gold-standard market leader.Is the model positioning our brand as the most capable solution available?
The “popular” narrativeHow often you are cited as the default, widely adopted industry standard.Is the model identifying our brand as the most commonly used option?
The “budget” narrativeHow often you are categorized as the cost-effective, value, or entry-level alternative.Is the model framing our brand primarily as a low-cost, entry-level alternative?





If an AI engine includes your brand frequently but consistently describes your product as a complex, legacy system, your high share of voice may actually be damaging your sales pipeline.
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Reframing your success metrics
Leadership teams require competitive benchmarks to evaluate market performance, meaning you cannot simply stop reporting on share of voice without offering a viable alternative.
Transitioning your executive reporting smoothly requires a structured, three-step plan.
Reframing the executive narrative involves educating your leadership team on the limitations of modern AI dashboards.
This means explaining the hidden denominator problem and demonstrating why treating these figures as absolute metrics introduces unnecessary risk.

Many AI visibility platforms extrapolate from a small subset of prompts. Explore three metrics designed for an infinite-query environment.

The problem with AI share of voice and 3 metrics that matter more

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had.png" class="attachment-large size-large wp-post-image" alt="Google AI Brief may be the replacement keywords never had" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>After 15 years of premature obituaries, the keyword may finally have a replacement. Here's why Google AI Brief matters.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had.png" class="attachment-large size-large wp-post-image" alt="Google AI Brief may be the replacement keywords never had" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Google-AI-Brief-may-be-the-replacement-keywords-never-had-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>People have been calling the keyword dead since at least 2010. Yet here we are in 2026, still using keywords to show ads on Google. </p>



<p>Advertisers weren&#8217;t wrong to equate the loss of control with the death of the keyword. The keyword simply couldn&#8217;t disappear until Google had something better to replace it.</p>



<p>At <a href="https://searchengineland.com/google-marketing-live-2026-everything-you-need-to-know-478167">Google Marketing Live</a> (GML) last month, we may have seen that replacement. <a href="https://blog.google/products/ads-commerce/ai-max-new-features/" target="_blank" rel="noopener">AI Brief</a> is a Gemini-powered control layer that lets you steer AI Max using prompts-first language.</p>



<p>At first glance, AI Brief may seem like just another AI Max feature. AI Max is still trying to gain traction among advertisers. So couldn&#8217;t advertisers simply ignore it and stick with keywords?</p>



<p>Probably not.</p>



<p>When users shifted to mobile, Google eventually pushed advertisers toward Enhanced Campaigns. The conditions may now be in place for a similar transition, this time from keywords to prompts.</p>



<p>Consider the other announcements from GML. <a href="https://blog.google/products-and-platforms/products/search/search-io-2026/" target="_blank" rel="noopener">AI Mode</a> surpassed 1 billion monthly users. The search box is getting its biggest redesign in 25 years. Users in AI Mode are also submitting queries that are, on average, three times as long as traditional searches.</p>



<p>Whether advertisers like it or not, people are increasingly using prompts instead of keywords to find information.</p>



<p>With AI Brief, the replacement for the keyword finally exists. We can now target prompts with prompts. Combined with the consumer-driven shift away from keyword-based searches, that makes the keyword&#8217;s obituary much easier to believe.</p>



<h2 class="wp-block-heading" id="h-the-keyword-is-dying-because-users-stopped-using-it">The keyword is dying because users stopped using it</h2>



<p>Most &#8220;keywords are dead&#8221; arguments over the past decade were supply-side stories. Google reduced broad match&#8217;s control, made RSAs decide the best ad variation, and let Smart Bidding set bids to help any keyword deliver on its underlying financial goals. They also stopped showing every query in search terms reports, all steps framed as Google taking the keyword away.</p>



<p>Now it&#8217;s different. The pressure is coming from the demand side. </p>



<p>People are asking Google longer, more conversational questions because Google built a search experience that invites them to. The <a href="https://searchengineland.com/googles-new-intelligent-search-box-its-biggest-change-to-the-search-box-in-25-years-477968">new search box, the biggest upgrade in 25 years</a>, dynamically expands as you type. You no longer pick a &#8220;mode&#8221; before you ask. The interface itself is telling consumers that &#8220;running shoes&#8221; is no longer the only way to ask for what they really want.</p>



<p>If you&#8217;re an advertiser, the question stops being &#8220;Do I want to use keywords?&#8221; It becomes &#8220;How do I show up in a query a keyword can&#8217;t possibly match?&#8221; Trying to capture a paragraph of context with three positive match types and one negative is, let&#8217;s be real, increasingly absurd.</p>



<p><a href="https://www.optmyzr.com/blog/google-ads-match-type-performance/" target="_blank" rel="noopener">Optmyzr&#8217;s 2026 Match Type Study</a> shows the same pattern from the spend side. We analyzed 30,000 Google Ads accounts in February 2026 across all Search campaigns with active keyword spend. <em>(</em><strong><em>Disclosure: </em></strong><em>I’m the cofounder and CEO of Optmyzr.)</em>



<p>Exact match has lost nearly 10 percentage points of spend share since 2022, while broad match has climbed steadily to become the dominant match type by budget.&nbsp;</p>



<p>Phrase match, meanwhile, consistently punches above its weight, holding the largest share of non-branded spend and leading on conversion rate in both ecommerce and lead-gen segments.&nbsp;</p>



<p>Advertisers are clearly growing more comfortable trusting Google&#8217;s AI with broader targeting, a shift attributed to Smart Bidding&#8217;s maturation rather than exact match losing its performance edge.</p>



<p>The other tell is that Google isn&#8217;t alone here. We recently started managing ads on ChatGPT, and <a href="https://openai.com/index/testing-ads-in-chatgpt/" target="_blank" rel="noopener">OpenAI&#8217;s ad surface</a> is keyword-optional from day one.&nbsp;</p>



<p>When the company that invented keyword advertising and the company reinventing search both ship a keyword-optional product, that means something. At this point, we&#8217;re just arguing about how fast the keyword is dying.</p>



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<h2 class="wp-block-heading" id="h-ai-brief-is-a-technical-replacement-for-keywords">AI Brief is a technical replacement for keywords</h2>



<p>Unsurprisingly, AI was the topic that drove nearly every announcement at GML 2026. At I/O the day before, Sundar Pichai, Google&#8217;s CEO, even said that Google&#8217;s migration to become an <a href="https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/cloud-next-2026-sundar-pichai/" target="_blank" rel="noopener">AI-first company</a> was nearing completion, with AI agents providing the final push and rewriting the last remaining code. Downstream from all the talk about AI is the realization that consumers now prompt rather than search with keywords.</p>



<p>AI Brief is one way to operationalize the required evolution for advertisers to keep up with consumer behavior. Powered by Gemini, it lets you describe, in your own words, what your business is, what your messaging should and shouldn&#8217;t say, the searches you want to capture or avoid, and the audience you&#8217;re trying to reach.&nbsp;</p>



<p>Google calls these messaging guidelines, matching guidelines, and audience guidelines. Internally, I think of it as: tell the model what you&#8217;d tell a new media buyer on their first day.</p>



<p>Then AI Brief echoes back how it understood your requests and shows preview samples of the assets and queries it thinks you meant. You push back if it&#8217;s off. You iterate. When you&#8217;re happy, you lock in the brief.</p>



<p>That&#8217;s a meaningfully different interaction model than a keyword list. A keyword list is a static artifact. A brief is a negotiation. It can adapt as your business changes without you reuploading hundreds of new keywords.</p>



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<p>There&#8217;s a parallel in the world of coding, where AI has arguably had the biggest impact with agentic code writers and vibe-coding systems like Lovable.dev. The idea is that the code we write to have software achieve an outcome should be merely a temporary artifact reflecting the current abilities of the tech.&nbsp;</p>



<p>Coders should focus on writing the prompts that describe the goals of the web page rather than the code needed to achieve those goals. The prompt instructs the software what it should do and how to do it safely. AI can then write the code that executes the task on demand, using the latest capabilities while staying grounded in the prompts that define its purpose.</p>



<p>This is what Sam Altman called <a href="https://interestingengineering.com/innovation/openai-chatgpt-5-software-on-demand" target="_blank" rel="noopener">&#8220;software on demand&#8221; at the GPT-5 launch</a>, the idea that AI can &#8220;instantaneously create an entire piece of computer software for you.&#8221; </p>



<p>Google echoed the same vision at <a href="https://blog.google/innovation-and-ai/sundar-pichai-io-2026/" target="_blank" rel="noopener">I/O 2026</a>, where Pichai described Search using Gemini and Antigravity to build custom experiences, dynamic layouts, and persistent mini apps on the fly. Software generated in response to what each user needs, in the moment they need it.</p>



<p>People need to be purposeful about work. Your purpose at work isn&#8217;t to write emails and work with spreadsheets. It&#8217;s to achieve certain outcomes, and writing emails and using spreadsheets is how that gets done. Stop worrying about how and start thinking about the real goal: growing your ad revenue by 10% while maintaining similar margins.</p>



<p>Keywords are the “how,” not the “why.” AI Brief is actually closer to letting us manage the “why” while letting AI figure out the “how.”</p>



<h2 class="wp-block-heading" id="h-how-to-try-ai-brief-now">How to try AI brief now</h2>



<p>AI Brief is rolling out in English for AI Max for Search first, then Performance Max and AI Max for Shopping. Existing text guidelines will migrate into AI Brief automatically as messaging guidelines.&nbsp;</p>



<p>So yes, this is starting as an AI Max feature, and you may not be using AI Max because <a href="https://www.linkedin.com/posts/frederickvallaeys_ppc-google-googleads-activity-7463423763141844993-5qAU?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAAAANNEYBt9NB1q5YjxuhKuNA_mWp-rL1BQc" target="_blank" rel="noopener">several practitioners note</a> that AI Max can pull in junk traffic on lead-gen accounts, competitor-heavy verticals, and new campaigns with thin signal. Some veteran marketers have been turning AI Max off in those situations.</p>



<p>The practical playbook shared during a recent <a href="https://www.youtube.com/watch?v=FnIwHT0VuD8&amp;t=5m30s" target="_blank" rel="noopener">PPC Town Hall</a> is solid: start new campaigns in Phrase, promote the winners to Exact, and layer Broad and Smart Bidding on top once you have data.&nbsp;</p>



<p>With the advent of AI Brief&#8217;s matching guidelines, advertisers can further tweak their targeting by saying, &#8220;prioritize searches for X, avoid Y.&#8221; But this strategy still requires a human who knows the account to pull that lever. So don&#8217;t unplug your keyboard just yet.</p>



<h2 class="wp-block-heading" id="h-the-new-funnel-and-why-short-keywords-still-have-a-job">The new funnel, and why short keywords still have a job</h2>



<p>Andrew Lolk and Kirk Williams <a href="https://www.linkedin.com/feed/update/urn:li:activity:7463423763141844993?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7463423763141844993%2C7463579684237266945%29&amp;dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287463579684237266945%2Curn%3Ali%3Aactivity%3A7463423763141844993%29" target="_blank" rel="noopener">pushed me</a> on a real edge case in the LinkedIn discussion that led to this piece: the newborn photographer whose entire business depends on someone in their city typing &#8220;newborn photographer&#8221; and converting on the first ad that shows.</p>



<p>Short, transactional queries won&#8217;t disappear. So why not keep traditional search campaigns with keywords around to handle these types of queries? I think it&#8217;s reasonable to have two campaign types for different jobs. But their relationship is a funnel, not a parallel.</p>



<p>Here&#8217;s how I see it shaping up:</p>



<ul class="wp-block-list">
<li><strong>AI prompts for discovery:</strong> &#8220;I just had a baby and I want to remember this period. What are some ideas?&#8221;</li>



<li><strong>AI prompts for research:</strong> &#8220;Compare lifestyle newborn photographers to studio newborn photographers in the Bay Area.&#8221;</li>



<li><strong>Short keyword to buy:</strong> &#8220;Newborn photographer Los Altos.&#8221;</li>
</ul>



<p>If you only show up at the bottom of that funnel, you&#8217;re betting your entire business on being the first short-keyword click. If you&#8217;re not present in the discovery and research prompts above it, you&#8217;re not in the consideration mix when the short query happens.&nbsp;</p>



<p>The reason a user may do that short query is that they already know more or less who they&#8217;d buy from, and they&#8217;re now looking for the best offer from a shortlisted set of options. The conversational layer feeds the transactional layer. Ignore it, and the transactional volume eventually stops coming to you.</p>



<p>This is also why I don&#8217;t think Google maintains two parallel systems forever. The short-keyword volume will keep shrinking relative to AI prompt volume, and at some point, the economics of supporting both stop working.&nbsp;</p>



<p>Further, AI-first campaign types will soon be great at converting agentically, using the <a href="https://searchengineland.com/google-universal-commerce-protocol-ecommerce-seo-470460">Universal Commerce Protocol</a> and other new methods being developed to allow agents to transact for their humans.</p>



<h2 class="wp-block-heading" id="h-what-ai-brief-does-to-the-four-human-ppc-roles">What AI Brief does to the four human PPC roles</h2>



<p>I&#8217;ve argued for years that PPC pros take on four roles in an automated world:&nbsp;</p>



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



<li>Doctor.</li>



<li>Pilot.</li>



<li>Restaurateur.&nbsp;</li>
</ul>



<p>These roles continue to explain the PPC manager&#8217;s world quite well, but with some new nuance.</p>



<h3 class="wp-block-heading" id="h-the-teacher-nbsp">The teacher&nbsp;</h3>



<p>This role is the most direct analogy. You used to teach the machine what to target by handing it the end result: a keyword.&nbsp;</p>



<p>The funny part is that for many of us, that keyword was already generated by feeding an LLM a prompt and cleaning up the output.&nbsp;</p>



<p>AI Brief lets you skip the lossy translation step. Hand the machine the prompt itself, not the artifact it produced. The teaching gets richer because nothing gets lost.</p>



<h3 class="wp-block-heading" id="h-the-doctor">The doctor</h3>



<p>The shift is from &#8220;prescribe Drug X&#8221; to writing down, in structured language, what the patient actually needs.&nbsp;</p>



<p>The treatment can then evolve as the patient&#8217;s condition and the available solutions change. Keywords were restrictive: one symptom, one prescription.&nbsp;</p>



<p>Briefs and prompts allow freedom and evolution. That&#8217;s what good medicine looks like, and that&#8217;s what good targeting looks like now.</p>



<h3 class="wp-block-heading" id="h-the-pilot">The pilot</h3>



<p>We need a new instrument panel. If we&#8217;re not aiming at keywords anymore, the search query report stops being the right gauge of how well Google is matching intent.&nbsp;</p>



<p>We&#8217;ll probably see more search themes (buckets of intent that AI Brief is mapping into) replacing the line-by-line query list.</p>



<h3 class="wp-block-heading" id="h-the-restaurateur-nbsp">The restaurateur&nbsp;</h3>



<p>You write the menu and the concept brief so the chef (the AI) cooks. AI Brief is almost literally the concept brief.&nbsp;</p>



<p>You define the cuisine, the values, the things the chef must never serve, and the kind of guest you&#8217;re cooking for. Then you taste, correct, and iterate. The kitchen runs.</p>



<p>If you want the longer-form version of where I think digital marketing automation is heading, I wrote it up earlier this year as <a href="https://searchengineland.com/ai-skills-the-next-layer-of-marketing-automation-477308">AI skills, the next layer of marketing automation</a>.</p>



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<h2 class="wp-block-heading" id="h-why-ai-brief-feels-different">Why AI Brief feels different</h2>



<p>The keyword isn&#8217;t dying because Google decided to kill it. It&#8217;s dying because consumers stopped phrasing their needs in a couple of words.</p>



<p>AI Brief is the first structural replacement that seems to allow advertisers to express their intent in as rich a manner as consumers can now express theirs to a chatbot. That&#8217;s why this GML announcement felt like a more serious nail in the coffin of the keyword than the last several.</p>



<p>Control was about dictating keywords to Google. Leverage is about feeding the engine the right brief and letting the auction execute at a scale no human team can match.</p>



<p>We don&#8217;t have to escape automation. We have to coexist with it on better terms. AI Brief is a great eventual replacement for the keyword. Hand it your prompt. Watch what it does.&nbsp;</p>



<p>Push back. Lock it in. Then you can move on to the parts of the job a machine can&#8217;t do, like knowing your customers and working on the goals that move their business in the direction they want.</p>

People have been calling the keyword dead since at least 2010. Yet here we are in 2026, still using keywords to show ads on Google. 
Advertisers weren’t wrong to equate the loss of control with the death of the keyword. The keyword simply couldn’t disappear until Google had something better to replace it.
At Google Marketing Live (GML) last month, we may have seen that replacement. AI Brief is a Gemini-powered control layer that lets you steer AI Max using prompts-first language.
At first glance, AI Brief may seem like just another AI Max feature. AI Max is still trying to gain traction among advertisers. So couldn’t advertisers simply ignore it and stick with keywords?
Probably not.
When users shifted to mobile, Google eventually pushed advertisers toward Enhanced Campaigns. The conditions may now be in place for a similar transition, this time from keywords to prompts.
Consider the other announcements from GML. AI Mode surpassed 1 billion monthly users. The search box is getting its biggest redesign in 25 years. Users in AI Mode are also submitting queries that are, on average, three times as long as traditional searches.
Whether advertisers like it or not, people are increasingly using prompts instead of keywords to find information.
With AI Brief, the replacement for the keyword finally exists. We can now target prompts with prompts. Combined with the consumer-driven shift away from keyword-based searches, that makes the keyword’s obituary much easier to believe.
The keyword is dying because users stopped using it
Most “keywords are dead” arguments over the past decade were supply-side stories. Google reduced broad match’s control, made RSAs decide the best ad variation, and let Smart Bidding set bids to help any keyword deliver on its underlying financial goals. They also stopped showing every query in search terms reports, all steps framed as Google taking the keyword away.
Now it’s different. The pressure is coming from the demand side. 
People are asking Google longer, more conversational questions because Google built a search experience that invites them to. The new search box, the biggest upgrade in 25 years, dynamically expands as you type. You no longer pick a “mode” before you ask. The interface itself is telling consumers that “running shoes” is no longer the only way to ask for what they really want.
If you’re an advertiser, the question stops being “Do I want to use keywords?” It becomes “How do I show up in a query a keyword can’t possibly match?” Trying to capture a paragraph of context with three positive match types and one negative is, let’s be real, increasingly absurd.
Optmyzr’s 2026 Match Type Study shows the same pattern from the spend side. We analyzed 30,000 Google Ads accounts in February 2026 across all Search campaigns with active keyword spend. (Disclosure: I’m the cofounder and CEO of Optmyzr.)



Exact match has lost nearly 10 percentage points of spend share since 2022, while broad match has climbed steadily to become the dominant match type by budget. 
Phrase match, meanwhile, consistently punches above its weight, holding the largest share of non-branded spend and leading on conversion rate in both ecommerce and lead-gen segments. 
Advertisers are clearly growing more comfortable trusting Google’s AI with broader targeting, a shift attributed to Smart Bidding’s maturation rather than exact match losing its performance edge.
The other tell is that Google isn’t alone here. We recently started managing ads on ChatGPT, and OpenAI’s ad surface is keyword-optional from day one. 
When the company that invented keyword advertising and the company reinventing search both ship a keyword-optional product, that means something. At this point, we’re just arguing about how fast the keyword is dying.

  
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AI Brief is a technical replacement for keywords
Unsurprisingly, AI was the topic that drove nearly every announcement at GML 2026. At I/O the day before, Sundar Pichai, Google’s CEO, even said that Google’s migration to become an AI-first company was nearing completion, with AI agents providing the final push and rewriting the last remaining code. Downstream from all the talk about AI is the realization that consumers now prompt rather than search with keywords.
AI Brief is one way to operationalize the required evolution for advertisers to keep up with consumer behavior. Powered by Gemini, it lets you describe, in your own words, what your business is, what your messaging should and shouldn’t say, the searches you want to capture or avoid, and the audience you’re trying to reach. 
Google calls these messaging guidelines, matching guidelines, and audience guidelines. Internally, I think of it as: tell the model what you’d tell a new media buyer on their first day.
Then AI Brief echoes back how it understood your requests and shows preview samples of the assets and queries it thinks you meant. You push back if it’s off. You iterate. When you’re happy, you lock in the brief.
That’s a meaningfully different interaction model than a keyword list. A keyword list is a static artifact. A brief is a negotiation. It can adapt as your business changes without you reuploading hundreds of new keywords.
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There’s a parallel in the world of coding, where AI has arguably had the biggest impact with agentic code writers and vibe-coding systems like Lovable.dev. The idea is that the code we write to have software achieve an outcome should be merely a temporary artifact reflecting the current abilities of the tech. 
Coders should focus on writing the prompts that describe the goals of the web page rather than the code needed to achieve those goals. The prompt instructs the software what it should do and how to do it safely. AI can then write the code that executes the task on demand, using the latest capabilities while staying grounded in the prompts that define its purpose.
This is what Sam Altman called “software on demand” at the GPT-5 launch, the idea that AI can “instantaneously create an entire piece of computer software for you.” 
Google echoed the same vision at I/O 2026, where Pichai described Search using Gemini and Antigravity to build custom experiences, dynamic layouts, and persistent mini apps on the fly. Software generated in response to what each user needs, in the moment they need it.
People need to be purposeful about work. Your purpose at work isn’t to write emails and work with spreadsheets. It’s to achieve certain outcomes, and writing emails and using spreadsheets is how that gets done. Stop worrying about how and start thinking about the real goal: growing your ad revenue by 10% while maintaining similar margins.
Keywords are the “how,” not the “why.” AI Brief is actually closer to letting us manage the “why” while letting AI figure out the “how.”
How to try AI brief now
AI Brief is rolling out in English for AI Max for Search first, then Performance Max and AI Max for Shopping. Existing text guidelines will migrate into AI Brief automatically as messaging guidelines. 
So yes, this is starting as an AI Max feature, and you may not be using AI Max because several practitioners note that AI Max can pull in junk traffic on lead-gen accounts, competitor-heavy verticals, and new campaigns with thin signal. Some veteran marketers have been turning AI Max off in those situations.
The practical playbook shared during a recent PPC Town Hall is solid: start new campaigns in Phrase, promote the winners to Exact, and layer Broad and Smart Bidding on top once you have data. 
With the advent of AI Brief’s matching guidelines, advertisers can further tweak their targeting by saying, “prioritize searches for X, avoid Y.” But this strategy still requires a human who knows the account to pull that lever. So don’t unplug your keyboard just yet.
The new funnel, and why short keywords still have a job
Andrew Lolk and Kirk Williams pushed me on a real edge case in the LinkedIn discussion that led to this piece: the newborn photographer whose entire business depends on someone in their city typing “newborn photographer” and converting on the first ad that shows.
Short, transactional queries won’t disappear. So why not keep traditional search campaigns with keywords around to handle these types of queries? I think it’s reasonable to have two campaign types for different jobs. But their relationship is a funnel, not a parallel.
Here’s how I see it shaping up:
AI prompts for discovery: “I just had a baby and I want to remember this period. What are some ideas?”
AI prompts for research: “Compare lifestyle newborn photographers to studio newborn photographers in the Bay Area.”
Short keyword to buy: “Newborn photographer Los Altos.”
If you only show up at the bottom of that funnel, you’re betting your entire business on being the first short-keyword click. If you’re not present in the discovery and research prompts above it, you’re not in the consideration mix when the short query happens. 
The reason a user may do that short query is that they already know more or less who they’d buy from, and they’re now looking for the best offer from a shortlisted set of options. The conversational layer feeds the transactional layer. Ignore it, and the transactional volume eventually stops coming to you.
This is also why I don’t think Google maintains two parallel systems forever. The short-keyword volume will keep shrinking relative to AI prompt volume, and at some point, the economics of supporting both stop working. 
Further, AI-first campaign types will soon be great at converting agentically, using the Universal Commerce Protocol and other new methods being developed to allow agents to transact for their humans.
What AI Brief does to the four human PPC roles
I’ve argued for years that PPC pros take on four roles in an automated world: 
Teacher.
Doctor.
Pilot.
Restaurateur. 
These roles continue to explain the PPC manager’s world quite well, but with some new nuance.
The teacher 
This role is the most direct analogy. You used to teach the machine what to target by handing it the end result: a keyword. 
The funny part is that for many of us, that keyword was already generated by feeding an LLM a prompt and cleaning up the output. 
AI Brief lets you skip the lossy translation step. Hand the machine the prompt itself, not the artifact it produced. The teaching gets richer because nothing gets lost.
The doctor
The shift is from “prescribe Drug X” to writing down, in structured language, what the patient actually needs. 
The treatment can then evolve as the patient’s condition and the available solutions change. Keywords were restrictive: one symptom, one prescription. 
Briefs and prompts allow freedom and evolution. That’s what good medicine looks like, and that’s what good targeting looks like now.
The pilot
We need a new instrument panel. If we’re not aiming at keywords anymore, the search query report stops being the right gauge of how well Google is matching intent. 
We’ll probably see more search themes (buckets of intent that AI Brief is mapping into) replacing the line-by-line query list.
The restaurateur 
You write the menu and the concept brief so the chef (the AI) cooks. AI Brief is almost literally the concept brief. 
You define the cuisine, the values, the things the chef must never serve, and the kind of guest you’re cooking for. Then you taste, correct, and iterate. The kitchen runs.
If you want the longer-form version of where I think digital marketing automation is heading, I wrote it up earlier this year as AI skills, the next layer of marketing automation.

  
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Why AI Brief feels different
The keyword isn’t dying because Google decided to kill it. It’s dying because consumers stopped phrasing their needs in a couple of words.
AI Brief is the first structural replacement that seems to allow advertisers to express their intent in as rich a manner as consumers can now express theirs to a chatbot. That’s why this GML announcement felt like a more serious nail in the coffin of the keyword than the last several.
Control was about dictating keywords to Google. Leverage is about feeding the engine the right brief and letting the auction execute at a scale no human team can match.
We don’t have to escape automation. We have to coexist with it on better terms. AI Brief is a great eventual replacement for the keyword. Hand it your prompt. Watch what it does. 
Push back. Lock it in. Then you can move on to the parts of the job a machine can’t do, like knowing your customers and working on the goals that move their business in the direction they want.

After 15 years of premature obituaries, the keyword may finally have a replacement. Here's why Google AI Brief matters.

Google AI Brief may be the replacement keywords never had

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split.png" class="attachment-large size-large wp-post-image" alt="Stop looking for the perfect PPC budget split" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Fixed budget ratios rarely survive real-world conditions. Here's how to evaluate funnel health and adjust spend as market dynamics change.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split.png" class="attachment-large size-large wp-post-image" alt="Stop looking for the perfect PPC budget split" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Stop-looking-for-the-perfect-PPC-budget-split-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Most <a href="https://searchengineland.com/guide/what-is-paid-search">PPC</a> <a href="https://searchengineland.com/guide/ppc/budgeting-and-bidding-strategies">budget</a> discussions focus on finding the right split between brand awareness and conversion-focused campaigns. That&#8217;s usually the wrong goal.</p>



<p>The optimal balance changes constantly based on business stage, market saturation, seasonality, competitive pressure, and revenue objectives.</p>



<p>Yet many teams still treat the funnel split as a fixed decision: 40% upper funnel, 60% lower funnel, set it and forget it. That might be the right ratio today and completely wrong in six months.</p>



<p>Every budget conversation eventually comes down to the same argument. Someone wants to cut brand awareness spend because it doesn&#8217;t convert directly. Someone else warns that if you only chase conversions, the pipeline dries up in 12 months. </p>



<p>Both are right, which is what makes this so difficult.</p>



<h2 class="wp-block-heading" id="h-the-lower-funnel-case-is-easy-to-make">The lower-funnel case is easy to make</h2>



<p>When most PPC managers talk about the lower funnel, they mean Shopping, Performance Max, and high-intent Search.&nbsp;</p>



<p>Someone typing &#8220;buy running shoes new york&#8221; has already decided they want the product. Shopping shows the right SKU at the right price. PMax chases the conversion signal across every Google surface. The attribution is clean, the ROAS is visible, and the CFO is happy.</p>



<p>The problem is that this demand already exists. These campaign types harvest intent. They don&#8217;t create it. Every conversion you get from a high-intent search term or a Shopping click is the result of awareness that was built somewhere else:&nbsp;</p>



<ul class="wp-block-list">
<li>A YouTube pre-roll.</li>



<li>A friend&#8217;s recommendation.</li>



<li>A social post.</li>



<li>Years of brand presence in the market.&nbsp;</li>
</ul>



<p>You&#8217;re collecting fruit from a tree you didn&#8217;t plant.</p>



<p>Search is worth treating separately here because it doesn&#8217;t sit neatly at the bottom of the funnel. A query like &#8220;best running shoes for marathon training&#8221; is informational.&nbsp;</p>



<p>The person is researching, not buying. AI Max and broad match expansion in Google Ads are pushing Search campaigns further into this territory, meaning Search can serve both ends of the funnel depending on how it&#8217;s configured and which queries it actually captures.&nbsp;</p>



<p>It&#8217;s worth auditing your Search terms regularly through this lens: How much of your Search spend is closing existing demand versus reaching people earlier in their decision-making process?</p>



<p>This works until it stops working. And the signal that it&#8217;s stopping usually arrives too late.&nbsp;</p>



<p>When branded search volume flatlines, CPCs on your core terms keep climbing because the same pool of high-intent users is getting more expensive to reach, and new customer acquisition starts to plateau while retention holds steady. These are the symptoms of a brand that&#8217;s been living off existing demand without replenishing it.</p>



<p>Lower-funnel efficiency is real. But it&#8217;s also borrowing against the future.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/ppc-budget-planning-aligning-business-goals-ad-spend-performance-458150">PPC budget planning: Aligning business goals, ad spend, and performance</a></em></strong>



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<h2 class="wp-block-heading" id="h-the-reseller-trap-when-your-lower-funnel-depends-on-someone-else-s-brand">The reseller trap: When your lower funnel depends on someone else&#8217;s brand</h2>



<p>There is a version of this problem that&#8217;s specific to resellers and multi-brand ecommerce, and it doesn&#8217;t get discussed enough.</p>



<p>If you sell branded products you don&#8217;t own, your lower funnel can work extremely well in the short term.&nbsp;</p>



<p>Shopping and Search campaigns for established brands convert efficiently because the brand owner has already done the awareness work. You&#8217;re harvesting demand that Nike, Adidas, or whoever else has spent years and significant budgets building.</p>



<p>The structural risk is that you have no control over that demand. If the brand owner reduces its marketing investment, pulls out of a market, or simply fades in relevance, your Shopping and Search volume follows.&nbsp;</p>



<p>You can&#8217;t counter it with your own PPC spend because the underlying interest isn&#8217;t there to harvest. The tree stops producing fruit, and you never owned it.</p>



<p>This creates two strategic imperatives that are easy to deprioritize when the lower funnel is performing well.&nbsp;</p>



<ul class="wp-block-list">
<li>Own-brand development: products or lines that you control, where you own the brand equity and can invest in awareness independently.&nbsp;</li>



<li>Reseller brand building: investing in the upper funnel to make your own name well known, so customers think of you as the destination regardless of which brands you carry. A consumer who searches for your store name rather than a specific brand is much more resilient than one who only finds you through a branded product query.</li>
</ul>



<p>Both require some form of upper-funnel investment. Own-brand development needs awareness campaigns to build product recognition from scratch. Reseller brand building needs a consistent presence across Demand Gen, YouTube, and Display to make your name synonymous with the category, not just the brands within it. That&#8217;s only within Google&#8217;s ecosystem.&nbsp;</p>



<p>To complete the picture, you might also include SEO, word of mouth, pop-up events, local advertising, and more. Brand building has no limits.</p>



<p>Neither of these investments shows up in this month&#8217;s ROAS report. Both show up in next year&#8217;s business resilience.</p>



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<h2 class="wp-block-heading" id="h-upper-funnel-is-inventory-management">Upper funnel is inventory management</h2>



<p>Brand awareness spend is often framed as the soft, hard-to-measure part of the budget. The part you do when you have money left over. That framing gets it exactly backward.</p>



<p>Upper-funnel investment is how you build the pool of future converters. Every person who sees a Demand Gen ad on YouTube or Google Display today and doesn&#8217;t click isn&#8217;t a failed impression. They&#8217;re a potential high-intent searcher in three weeks. You&#8217;re filling the top of the pipeline that your Shopping and Search campaigns will harvest later.</p>



<p>Google&#8217;s Demand Gen campaigns make this dynamic particularly visible within a single platform. You can run Demand Gen to reach in-market audiences who don&#8217;t yet know your brand, then watch Search impression share and branded query volume respond over the following weeks. The lag is real and measurable.&nbsp;</p>



<p>Upper-funnel spend today shows up in lower-funnel performance next month, not this week. That delay is why it gets cut first when budgets tighten, and why cutting it tends to hurt six to eight weeks later rather than immediately.</p>



<p>Teams that manage this well think of Demand Gen not as brand spend, but as pipeline investment. The question isn&#8217;t &#8220;What is the ROAS on this campaign?&#8221; It&#8217;s &#8220;How much qualified demand am I creating for my Shopping and Search campaigns to close?&#8221;</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/paid-media-efficiency-cut-waste-improve-roas-474032">Paid media efficiency: How to cut waste and improve ROAS</a></em></strong>



<h2 class="wp-block-heading" id="h-why-a-fixed-split-is-the-wrong-answer">Why a fixed split is the wrong answer</h2>



<p>The 70/30 or 60/40 rules you read about are averages across many businesses in many contexts. They&#8217;re useful as a starting point and useless as a long-term policy.</p>



<p>Consider what changes the optimal split.</p>



<ul class="wp-block-list">
<li>A new product launch needs heavy upper-funnel investment upfront because awareness is zero.&nbsp;</li>



<li>A mature product in a saturated category needs it, too, because every competitor is also harvesting the same pool of high-intent searchers, and the only way to grow is to expand the pool.&nbsp;</li>



<li>A seasonal business approaching peak needs to have already done its upper-funnel work before the peak hits because awareness doesn&#8217;t respond fast enough to be built in-season.</li>
</ul>



<p>Equally, a business in financial distress or facing a short-term revenue target can&#8217;t afford to wait eight weeks for upper-funnel investment to mature. The right answer in that moment is to focus on the lower funnel, accept the trade-off consciously, and plan to reinvest in awareness as soon as the pressure lifts.</p>



<p>The point is that both of these decisions are correct in context. A fixed split ignores context entirely.</p>



<h2 class="wp-block-heading" id="h-building-a-dynamic-split-logic">Building a dynamic split logic</h2>



<p>Rather than a fixed ratio, the most useful framework is a set of conditions that trigger a shift in either direction.</p>



<p><strong>Shift budget toward upper funnel when:</strong>



<ul class="wp-block-list">
<li>Branded search volume is flat or declining quarter over quarter.</li>



<li>New customer acquisition cost is rising while retention metrics hold.</li>



<li>You&#8217;re entering a new market or launching a new product.</li>



<li>Competitors are visibly increasing their brand presence.</li>



<li>You&#8217;re approaching a peak season with at least six to eight weeks of runway.</li>



<li>You&#8217;re a reseller whose top brands are showing declining search interest or reduced marketing activity.</li>
</ul>



<p><strong>Shift budget toward lower funnel when:</strong>



<ul class="wp-block-list">
<li>You have a short-term revenue target that can&#8217;t wait.</li>



<li>Upper-funnel campaigns have been running long enough to build measurable awareness, and the conversion window is now.</li>



<li>Cost per acquisition on Shopping or Search is below target, and scaling makes sense.</li>



<li>Audience saturation on Demand Gen is high, meaning you&#8217;re reaching the same people repeatedly without expanding reach.</li>
</ul>



<p>Within Google Ads, the data to monitor this is available without external tools. Branded query volume in Search Terms, impression share trends on non-branded terms, Demand Gen reach and frequency metrics, and new versus returning customer segmentation in conversion data together give you a reasonable picture of where the funnel is healthy and where it isn&#8217;t.</p>



<p>The review cadence matters as much as the metrics. Monthly is the minimum for a funnel split review. Quarterly is too slow. By the time a quarterly review catches a declining branded search trend, you&#8217;ve already lost several weeks of pipeline-building time.</p>



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<h2 class="wp-block-heading" id="h-the-conversation-nobody-wants-to-have">The conversation nobody wants to have</h2>



<p>The reason funnel balance stays broken in most organizations isn&#8217;t analytical. It&#8217;s political.</p>



<p>Lower-funnel spend is easy to defend in a meeting. The ROAS is there, the conversion numbers are there, and the CFO can see a direct line between spend and revenue.&nbsp;</p>



<p>Upper-funnel spend requires a different kind of argument: &#8220;This investment will make our Shopping and Search campaigns work better in six weeks.&#8221; That argument is harder to make, easier to cut, and almost impossible to defend when someone asks for a quick win.</p>



<p>The answer isn&#8217;t to stop making the argument. It&#8217;s to change the evidence you bring to it.&nbsp;</p>



<ul class="wp-block-list">
<li>Track branded search volume as a leading indicator.&nbsp;</li>



<li>Build a view that shows Demand Gen reach in month one and Search conversion volume in month two alongside each other.&nbsp;</li>



<li>Make the lag visible and the relationship concrete. Once the data tells the story, the conversation gets easier.</li>
</ul>



<p>Budget allocation isn&#8217;t a one-time decision. It&#8217;s an ongoing signal about what kind of growth you&#8217;re building.&nbsp;</p>



<p>Optimizing purely for this month&#8217;s ROAS is a choice. So is investing in the demand that will drive next quarter&#8217;s revenue.&nbsp;</p>



<p>And if you&#8217;re a reseller, it&#8217;s also a decision about whether your business is built on a foundation you control or one you&#8217;re renting from brand owners who have their own priorities.</p>



<p>The best PPC teams do both, and they know when to lean in each direction.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/how-to-optimize-b2b-ppc-spend-when-budgets-and-confidence-are-low-454592">How to optimize B2B PPC spend when budgets and confidence are low</a></em></strong>

Most PPC budget discussions focus on finding the right split between brand awareness and conversion-focused campaigns. That’s usually the wrong goal.
The optimal balance changes constantly based on business stage, market saturation, seasonality, competitive pressure, and revenue objectives.
Yet many teams still treat the funnel split as a fixed decision: 40% upper funnel, 60% lower funnel, set it and forget it. That might be the right ratio today and completely wrong in six months.
Every budget conversation eventually comes down to the same argument. Someone wants to cut brand awareness spend because it doesn’t convert directly. Someone else warns that if you only chase conversions, the pipeline dries up in 12 months. 
Both are right, which is what makes this so difficult.
The lower-funnel case is easy to make
When most PPC managers talk about the lower funnel, they mean Shopping, Performance Max, and high-intent Search. 
Someone typing “buy running shoes new york” has already decided they want the product. Shopping shows the right SKU at the right price. PMax chases the conversion signal across every Google surface. The attribution is clean, the ROAS is visible, and the CFO is happy.
The problem is that this demand already exists. These campaign types harvest intent. They don’t create it. Every conversion you get from a high-intent search term or a Shopping click is the result of awareness that was built somewhere else: 
A YouTube pre-roll.
A friend’s recommendation.
A social post.
Years of brand presence in the market. 
You’re collecting fruit from a tree you didn’t plant.
Search is worth treating separately here because it doesn’t sit neatly at the bottom of the funnel. A query like “best running shoes for marathon training” is informational. 
The person is researching, not buying. AI Max and broad match expansion in Google Ads are pushing Search campaigns further into this territory, meaning Search can serve both ends of the funnel depending on how it’s configured and which queries it actually captures. 
It’s worth auditing your Search terms regularly through this lens: How much of your Search spend is closing existing demand versus reaching people earlier in their decision-making process?
This works until it stops working. And the signal that it’s stopping usually arrives too late. 
When branded search volume flatlines, CPCs on your core terms keep climbing because the same pool of high-intent users is getting more expensive to reach, and new customer acquisition starts to plateau while retention holds steady. These are the symptoms of a brand that’s been living off existing demand without replenishing it.
Lower-funnel efficiency is real. But it’s also borrowing against the future.
Dig deeper: PPC budget planning: Aligning business goals, ad spend, and performance




  
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The reseller trap: When your lower funnel depends on someone else’s brand
There is a version of this problem that’s specific to resellers and multi-brand ecommerce, and it doesn’t get discussed enough.
If you sell branded products you don’t own, your lower funnel can work extremely well in the short term. 
Shopping and Search campaigns for established brands convert efficiently because the brand owner has already done the awareness work. You’re harvesting demand that Nike, Adidas, or whoever else has spent years and significant budgets building.
The structural risk is that you have no control over that demand. If the brand owner reduces its marketing investment, pulls out of a market, or simply fades in relevance, your Shopping and Search volume follows. 
You can’t counter it with your own PPC spend because the underlying interest isn’t there to harvest. The tree stops producing fruit, and you never owned it.
This creates two strategic imperatives that are easy to deprioritize when the lower funnel is performing well. 
Own-brand development: products or lines that you control, where you own the brand equity and can invest in awareness independently. 
Reseller brand building: investing in the upper funnel to make your own name well known, so customers think of you as the destination regardless of which brands you carry. A consumer who searches for your store name rather than a specific brand is much more resilient than one who only finds you through a branded product query.
Both require some form of upper-funnel investment. Own-brand development needs awareness campaigns to build product recognition from scratch. Reseller brand building needs a consistent presence across Demand Gen, YouTube, and Display to make your name synonymous with the category, not just the brands within it. That’s only within Google’s ecosystem. 
To complete the picture, you might also include SEO, word of mouth, pop-up events, local advertising, and more. Brand building has no limits.
Neither of these investments shows up in this month’s ROAS report. Both show up in next year’s business resilience.
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Upper funnel is inventory management
Brand awareness spend is often framed as the soft, hard-to-measure part of the budget. The part you do when you have money left over. That framing gets it exactly backward.
Upper-funnel investment is how you build the pool of future converters. Every person who sees a Demand Gen ad on YouTube or Google Display today and doesn’t click isn’t a failed impression. They’re a potential high-intent searcher in three weeks. You’re filling the top of the pipeline that your Shopping and Search campaigns will harvest later.
Google’s Demand Gen campaigns make this dynamic particularly visible within a single platform. You can run Demand Gen to reach in-market audiences who don’t yet know your brand, then watch Search impression share and branded query volume respond over the following weeks. The lag is real and measurable. 
Upper-funnel spend today shows up in lower-funnel performance next month, not this week. That delay is why it gets cut first when budgets tighten, and why cutting it tends to hurt six to eight weeks later rather than immediately.
Teams that manage this well think of Demand Gen not as brand spend, but as pipeline investment. The question isn’t “What is the ROAS on this campaign?” It’s “How much qualified demand am I creating for my Shopping and Search campaigns to close?”
Dig deeper: Paid media efficiency: How to cut waste and improve ROAS



Why a fixed split is the wrong answer
The 70/30 or 60/40 rules you read about are averages across many businesses in many contexts. They’re useful as a starting point and useless as a long-term policy.
Consider what changes the optimal split.
A new product launch needs heavy upper-funnel investment upfront because awareness is zero. 
A mature product in a saturated category needs it, too, because every competitor is also harvesting the same pool of high-intent searchers, and the only way to grow is to expand the pool. 
A seasonal business approaching peak needs to have already done its upper-funnel work before the peak hits because awareness doesn’t respond fast enough to be built in-season.
Equally, a business in financial distress or facing a short-term revenue target can’t afford to wait eight weeks for upper-funnel investment to mature. The right answer in that moment is to focus on the lower funnel, accept the trade-off consciously, and plan to reinvest in awareness as soon as the pressure lifts.
The point is that both of these decisions are correct in context. A fixed split ignores context entirely.
Building a dynamic split logic
Rather than a fixed ratio, the most useful framework is a set of conditions that trigger a shift in either direction.
Shift budget toward upper funnel when:




Branded search volume is flat or declining quarter over quarter.
New customer acquisition cost is rising while retention metrics hold.
You’re entering a new market or launching a new product.
Competitors are visibly increasing their brand presence.
You’re approaching a peak season with at least six to eight weeks of runway.
You’re a reseller whose top brands are showing declining search interest or reduced marketing activity.
Shift budget toward lower funnel when:




You have a short-term revenue target that can’t wait.
Upper-funnel campaigns have been running long enough to build measurable awareness, and the conversion window is now.
Cost per acquisition on Shopping or Search is below target, and scaling makes sense.
Audience saturation on Demand Gen is high, meaning you’re reaching the same people repeatedly without expanding reach.
Within Google Ads, the data to monitor this is available without external tools. Branded query volume in Search Terms, impression share trends on non-branded terms, Demand Gen reach and frequency metrics, and new versus returning customer segmentation in conversion data together give you a reasonable picture of where the funnel is healthy and where it isn’t.
The review cadence matters as much as the metrics. Monthly is the minimum for a funnel split review. Quarterly is too slow. By the time a quarterly review catches a declining branded search trend, you’ve already lost several weeks of pipeline-building time.

  
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The conversation nobody wants to have
The reason funnel balance stays broken in most organizations isn’t analytical. It’s political.
Lower-funnel spend is easy to defend in a meeting. The ROAS is there, the conversion numbers are there, and the CFO can see a direct line between spend and revenue. 
Upper-funnel spend requires a different kind of argument: “This investment will make our Shopping and Search campaigns work better in six weeks.” That argument is harder to make, easier to cut, and almost impossible to defend when someone asks for a quick win.
The answer isn’t to stop making the argument. It’s to change the evidence you bring to it. 
Track branded search volume as a leading indicator. 
Build a view that shows Demand Gen reach in month one and Search conversion volume in month two alongside each other. 
Make the lag visible and the relationship concrete. Once the data tells the story, the conversation gets easier.
Budget allocation isn’t a one-time decision. It’s an ongoing signal about what kind of growth you’re building. 
Optimizing purely for this month’s ROAS is a choice. So is investing in the demand that will drive next quarter’s revenue. 
And if you’re a reseller, it’s also a decision about whether your business is built on a foundation you control or one you’re renting from brand owners who have their own priorities.
The best PPC teams do both, and they know when to lean in each direction.
Dig deeper: How to optimize B2B PPC spend when budgets and confidence are low

Fixed budget ratios rarely survive real-world conditions. Here's how to evaluate funnel health and adjust spend as market dynamics change.

Stop looking for the perfect PPC budget split

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>We've analyzed nearly 1 million queries to show you what's really happening. Most search teams are missing it completely.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-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/2026/06/Adthena-20260608-feature.png" alt="" class="wp-image-479302" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-feature-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
</div>


<p>Here&#8217;s something worth sitting with for a moment.</p>



<p>Your competitors are running ads on ChatGPT. You can&#8217;t see them. You don&#8217;t know which prompts they&#8217;re bidding on, what creative they&#8217;re running, or how their presence compares to yours. And unlike Google Ads (where Auction Insights at least gives you a rear-view mirror), there&#8217;s currently no native way to get any of that picture on ChatGPT.</p>



<p>That&#8217;s the blind spot. And it&#8217;s bigger than most search teams realize.</p>



<p>OpenAI launched advertising inside AI-generated responses earlier this year. Brands moved fast, all in within weeks. The minimum spend dropped, the Ads Manager launched, and a genuinely new ad channel was born. With ChatGPT advertising expected to expand to U.K. markets soon, the window for early-mover advantage is closing faster than it looks.</p>



<p>We&#8217;ve been watching since day one. Here&#8217;s what we&#8217;ve found.</p>



<h2 class="wp-block-heading" id="h-what-does-chatgpt-ads-actually-look-like-right-now">What does ChatGPT Ads actually look like right now?</h2>



<p>We analyzed nearly 1 million query indexes across 20 industries and five markets (the U.S., U.K., Australia, New Zealand, and Canada) between March 2026 and May 2026. The data tells a clear story.</p>



<h3 class="wp-block-heading" id="h-it-s-a-u-s-first-channel-everywhere-else-is-still-warming-up">It&#8217;s a U.S.-first channel. Everywhere else is still warming up.</h3>



<p>In the U.S., ChatGPT served ads on 4.5% of queries. Across roughly 170,000 U.K. indexes in the same period? We found zero ads. The U.S. accounts for around 90% of all ChatGPT ad placements in our dataset. Canada and New Zealand are active. Australia&#8217;s at 1.6%. The U.K. hasn&#8217;t flipped yet, but it will.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="800" height="420" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-1.png" alt="Horizontal bar chart on dark navy background showing ChatGPT ad frequency by market. Canada leads at 4.57%, U.S. at 4.47%, New Zealand 3.85%, Australia 1.61%, United Kingdom at effectively zero. Adthena branding bottom right." class="wp-image-479307" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-1.png 800w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-1-768x403.png 768w" sizes="(max-width: 800px) 100vw, 800px" /><figcaption class="wp-element-caption"><em>ChatGPT ad frequency by market</em></figcaption></figure>
</div>


<p>For U.K. search teams, that&#8217;s a two-sided finding. The channel isn&#8217;t live here yet. But your U.S. competitors have had months to figure out which prompts convert, what creative works, and where the real opportunity sits. When U.K. advertising opens up, they won&#8217;t be starting from scratch. You might be.</p>



<h3 class="wp-block-heading" id="h-there-s-only-one-ad-per-response-the-majority-of-cases">There&#8217;s only one ad per response the majority of cases</h3>



<p>In the U.S., ChatGPT averages just 1.06 ad items per ad-bearing answer. That means in the vast majority of responses, there&#8217;s a single sponsored slot. Not three. Not a carousel. One.</p>



<p>That changes the stakes completely. In Google Ads you can hold position two or three and still get clicks. On ChatGPT, you&#8217;re either in the answer or you&#8217;re not. Share of voice here is binary in a way paid search has never quite been before.</p>



<h3 class="wp-block-heading" id="h-some-industries-are-blocked-for-now">Some industries are blocked for now</h3>



<p>Four categories returned zero ChatGPT ads across the entire dataset: Legal, Pharma, Banking, and Nonprofit. Healthcare was near-zero at 0.45%. This looks like deliberate OpenAI policy rather than lack of demand. These restrictions will evolve. When they do, the teams that are already watching will have a head start.</p>



<h3 class="wp-block-heading" id="h-the-hottest-categories-aren-t-the-ones-you-d-expect">The hottest categories aren&#8217;t the ones you&#8217;d expect</h3>



<p>Logistics tops the chart at 12.4% ad frequency, followed by Home &amp; Garden at 12% and Beauty &amp; Cosmetics at 10%. These are categories building serious ground on ChatGPT right now, well above a platform average of around 3.3%. Media &amp; Entertainment (8%), Insurance (7.2%), and Energy &amp; Utilities (6.4%) aren&#8217;t far behind.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-2.png" alt="Horizontal bar chart showing ChatGPT ad frequency across 19 industries. Logistics leads at 12.41%, Home &amp; Garden 11.99%, Beauty &amp; Cosmetics 10.03%. Blocked verticals shown at zero with platform average line in lime. Dark navy Adthena-branded design." class="wp-image-479308" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-2.png 800w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-2-768x432.png 768w" sizes="(max-width: 800px) 100vw, 800px" /><figcaption class="wp-element-caption"><em>ChatGPT ad frequency by industry, all markets</em></figcaption></figure>
</div>


<h3 class="wp-block-heading" id="h-retail-is-where-the-real-money-is-flowing">Retail is where the real money is flowing</h3>



<p>Retail &amp; Fashion makes up 24% of U.S. query volume but accounts for 39% of all U.S. ad items. That&#8217;s not a scrape artifact. It&#8217;s genuine advertiser demand. With an ad frequency of 6.55% against a U.S. average of 4.5%, retail brands are competing hard for ChatGPT presence. The category accounts for more than a third of all ad placements in our full dataset.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="800" height="440" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-3.png" alt="Grouped bar chart comparing share of U.S. queries vs share of U.S. ad items. Retail &amp; Fashion over-indexes: 24.1% of queries, 38.9% of ad items. Dark navy Adthena-branded design." class="wp-image-479309" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-3.png 800w, https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-3-768x422.png 768w" sizes="(max-width: 800px) 100vw, 800px" /><figcaption class="wp-element-caption"><em>Retail &amp; Fashion: share of queries vs share of ad items, U.S.</em></figcaption></figure>
</div>


<h3 class="wp-block-heading" id="h-so-what-s-the-actual-problem"><strong>So what&#8217;s the actual problem?</strong></h3>



<p>Here&#8217;s where it gets frustrating. And we&#8217;ll be straight with you about it.</p>



<p>Every serious search practitioner lives and dies by competitive intelligence: auction Insights, competitor ad copy, impression share trends. You know this stuff because running campaigns without it isn&#8217;t a strategy. It&#8217;s guesswork.</p>



<p>None of those tools exist for ChatGPT Ads.</p>



<p>OpenAI&#8217;s native Ads Manager shows you your own data. Your spend, and basic performance metrics such as impressions, clicks, CPC, and CTR, and that&#8217;s it. What you can&#8217;t see: which competitors are showing up alongside you, which prompts are triggering their ads, what creative they&#8217;re running, or how your share of voice stacks up across the market.</p>



<p>You&#8217;re spending real budget on a channel where you can only see yourself. In a format where there&#8217;s one winner per response. That&#8217;s a problem worth taking seriously.</p>



<h2 class="wp-block-heading" id="h-what-does-full-visibility-actually-look-like">What does full visibility actually look like?</h2>



<p>That&#8217;s the gap we built <a href="https://www.adthena.com/chat-gpt-ads/?utm_campaign=38650020-FY26.SELarticles&amp;utm_source=SEL&amp;utm_medium=article&amp;utm_term=ChatGPT%20June" target="_blank" rel="noopener sponsored nofollow">ChatGPT Ads Intelligence</a> to close.</p>



<p>We monitor 300,000+ prompts daily across the markets where ChatGPT Ads are live, giving you the whole-market view that&#8217;s been missing from this channel since day one. It&#8217;s the same competitive intelligence approach that&#8217;s made Adthena essential for Google paid search, now fully applied to ChatGPT.</p>



<p>You can see which competitors are bidding on your prompts, track share of voice week on week, and find the greenfield prompts (the high-intent queries where no one&#8217;s advertising yet) before someone else does.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="652" height="231" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-4.png" alt="Adthena ChatGPT Ads Intelligence dashboard showing a brand's ad presence compared to five competitors, with a donut chart showing 88% top 10 visibility and a trend line graph tracking competitor ad impressions over time." class="wp-image-479310"/><figcaption class="wp-element-caption"><em>Share of voice vs top competitors</em></figcaption></figure>
</div>


<p>That last part matters more than it might sound. In a new channel where positions aren&#8217;t entrenched yet, first-mover advantage is still genuinely available. It won&#8217;t be forever.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="652" height="317" src="https://searchengineland.com/wp-content/seloads/2026/06/Adthena-20260608-5.png" alt="Adthena ChatGPT Ads Intelligence showing a table of prompts where ads have been detected, including each prompt's ad detection rate, competitor count, competitive rate, and top competitor, with a Greenfield filter applied." class="wp-image-479311"/><figcaption class="wp-element-caption"><em>Prompt-level ad detection and competitive data</em></figcaption></figure>
</div>


<p><a href="https://www.adthena.com/chat-gpt-ads/?utm_campaign=38650020-FY26.SELarticles&amp;utm_source=SEL&amp;utm_medium=article&amp;utm_term=ChatGPT%20June#row-329640-5" target="_blank" rel="noopener sponsored nofollow">Try ChatGPT Ads Intelligence free for 21 days</a> and see the full picture.</p>



<h2 class="wp-block-heading" id="h-why-does-this-matter-beyond-chatgpt"><strong>Why does this matter beyond ChatGPT?</strong></h2>



<p>Let&#8217;s zoom out for a second.</p>



<p>Search isn&#8217;t one place anymore. It hasn&#8217;t been for a while. Users are turning to AI-native interfaces like ChatGPT and Perplexity for exactly the kind of high-intent queries paid search was built around: product recommendations, service comparisons, purchase decisions. The intent is there. The ads are starting to follow.</p>



<p>For search practitioners, the job description is expanding whether you signed up for it or not. The same rigor you apply to Google Ads (competitive monitoring and share of voice tracking) needs to extend to wherever high-intent search is actually happening.</p>



<p>The brands paying attention to ChatGPT Ads now are the ones who&#8217;ll be hardest to displace when the channel fully matures. We&#8217;ve seen this movie before. The early Google Ads practitioners who understood the platform first built advantages that took years to close.</p>



<p>That window is open right now. Our data shows it clearly.</p>



<p>Ready to see what&#8217;s happening on ChatGPT Ads? <a href="https://www.adthena.com/chat-gpt-ads/?utm_campaign=38650020-FY26.SELarticles&amp;utm_source=SEL&amp;utm_medium=article&amp;utm_term=ChatGPT%20June#row-329640-5" target="_blank" rel="noopener sponsored nofollow">Start your free 21-day trial</a> of Adthena&#8217;s ChatGPT Ads Intelligence.</p>


Here’s something worth sitting with for a moment.
Your competitors are running ads on ChatGPT. You can’t see them. You don’t know which prompts they’re bidding on, what creative they’re running, or how their presence compares to yours. And unlike Google Ads (where Auction Insights at least gives you a rear-view mirror), there’s currently no native way to get any of that picture on ChatGPT.
That’s the blind spot. And it’s bigger than most search teams realize.
OpenAI launched advertising inside AI-generated responses earlier this year. Brands moved fast, all in within weeks. The minimum spend dropped, the Ads Manager launched, and a genuinely new ad channel was born. With ChatGPT advertising expected to expand to U.K. markets soon, the window for early-mover advantage is closing faster than it looks.
We’ve been watching since day one. Here’s what we’ve found.
What does ChatGPT Ads actually look like right now?
We analyzed nearly 1 million query indexes across 20 industries and five markets (the U.S., U.K., Australia, New Zealand, and Canada) between March 2026 and May 2026. The data tells a clear story.
It’s a U.S.-first channel. Everywhere else is still warming up.
In the U.S., ChatGPT served ads on 4.5% of queries. Across roughly 170,000 U.K. indexes in the same period? We found zero ads. The U.S. accounts for around 90% of all ChatGPT ad placements in our dataset. Canada and New Zealand are active. Australia’s at 1.6%. The U.K. hasn’t flipped yet, but it will.
ChatGPT ad frequency by market



For U.K. search teams, that’s a two-sided finding. The channel isn’t live here yet. But your U.S. competitors have had months to figure out which prompts convert, what creative works, and where the real opportunity sits. When U.K. advertising opens up, they won’t be starting from scratch. You might be.
There’s only one ad per response the majority of cases
In the U.S., ChatGPT averages just 1.06 ad items per ad-bearing answer. That means in the vast majority of responses, there’s a single sponsored slot. Not three. Not a carousel. One.
That changes the stakes completely. In Google Ads you can hold position two or three and still get clicks. On ChatGPT, you’re either in the answer or you’re not. Share of voice here is binary in a way paid search has never quite been before.
Some industries are blocked for now
Four categories returned zero ChatGPT ads across the entire dataset: Legal, Pharma, Banking, and Nonprofit. Healthcare was near-zero at 0.45%. This looks like deliberate OpenAI policy rather than lack of demand. These restrictions will evolve. When they do, the teams that are already watching will have a head start.
The hottest categories aren’t the ones you’d expect
Logistics tops the chart at 12.4% ad frequency, followed by Home & Garden at 12% and Beauty & Cosmetics at 10%. These are categories building serious ground on ChatGPT right now, well above a platform average of around 3.3%. Media & Entertainment (8%), Insurance (7.2%), and Energy & Utilities (6.4%) aren’t far behind.
ChatGPT ad frequency by industry, all markets



Retail is where the real money is flowing
Retail & Fashion makes up 24% of U.S. query volume but accounts for 39% of all U.S. ad items. That’s not a scrape artifact. It’s genuine advertiser demand. With an ad frequency of 6.55% against a U.S. average of 4.5%, retail brands are competing hard for ChatGPT presence. The category accounts for more than a third of all ad placements in our full dataset.
Retail & Fashion: share of queries vs share of ad items, U.S.



So what’s the actual problem?
Here’s where it gets frustrating. And we’ll be straight with you about it.
Every serious search practitioner lives and dies by competitive intelligence: auction Insights, competitor ad copy, impression share trends. You know this stuff because running campaigns without it isn’t a strategy. It’s guesswork.
None of those tools exist for ChatGPT Ads.
OpenAI’s native Ads Manager shows you your own data. Your spend, and basic performance metrics such as impressions, clicks, CPC, and CTR, and that’s it. What you can’t see: which competitors are showing up alongside you, which prompts are triggering their ads, what creative they’re running, or how your share of voice stacks up across the market.
You’re spending real budget on a channel where you can only see yourself. In a format where there’s one winner per response. That’s a problem worth taking seriously.
What does full visibility actually look like?
That’s the gap we built ChatGPT Ads Intelligence to close.
We monitor 300,000+ prompts daily across the markets where ChatGPT Ads are live, giving you the whole-market view that’s been missing from this channel since day one. It’s the same competitive intelligence approach that’s made Adthena essential for Google paid search, now fully applied to ChatGPT.
You can see which competitors are bidding on your prompts, track share of voice week on week, and find the greenfield prompts (the high-intent queries where no one’s advertising yet) before someone else does.
Share of voice vs top competitors



That last part matters more than it might sound. In a new channel where positions aren’t entrenched yet, first-mover advantage is still genuinely available. It won’t be forever.
Prompt-level ad detection and competitive data



Try ChatGPT Ads Intelligence free for 21 days and see the full picture.
Why does this matter beyond ChatGPT?
Let’s zoom out for a second.
Search isn’t one place anymore. It hasn’t been for a while. Users are turning to AI-native interfaces like ChatGPT and Perplexity for exactly the kind of high-intent queries paid search was built around: product recommendations, service comparisons, purchase decisions. The intent is there. The ads are starting to follow.
For search practitioners, the job description is expanding whether you signed up for it or not. The same rigor you apply to Google Ads (competitive monitoring and share of voice tracking) needs to extend to wherever high-intent search is actually happening.
The brands paying attention to ChatGPT Ads now are the ones who’ll be hardest to displace when the channel fully matures. We’ve seen this movie before. The early Google Ads practitioners who understood the platform first built advantages that took years to close.
That window is open right now. Our data shows it clearly.
Ready to see what’s happening on ChatGPT Ads? Start your free 21-day trial of Adthena’s ChatGPT Ads Intelligence.

We've analyzed nearly 1 million queries to show you what's really happening. Most search teams are missing it completely.

What ChatGPT Ads data reveals about your competitors by Adthena

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/06/google-robot-seo-working-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/google-robot-seo-working-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/google-robot-seo-working-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/google-robot-seo-working-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google added in topics about generative AI optimization, how third-party tools are not endorsed by Google and more.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/06/google-robot-seo-working-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/google-robot-seo-working-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/google-robot-seo-working-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/google-robot-seo-working-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google added a new help document to its search developer documentation named <a href="https://developers.google.com/search/docs/fundamentals/third-party-seo">Google Search&#8217;s guidance on using third-party SEO tools, services, and advice</a> and updated the older <a href="https://developers.google.com/search/docs/fundamentals/do-i-need-seo">Do you need an SEO?</a> document on Friday.  Google primarily added in topics around generative AI optimization and more guidance on trusting third-party tools and other SEO advice.</p>



<p><strong>Why. </strong>Google <a href="https://developers.google.com/search/updates#guidance-on-third-party-seo-tools,-services,-and-advice">said</a> it posted these document updates and changes because it wanted to &#8220;highlight important considerations when evaluating third-party SEO tools and advice, and to simplify some sections and remove outdated examples in existing documentation.&#8221;</p>



<p><strong>Third-party SEO tools, services and advice. </strong>Google&#8217;s new help document named <a href="https://developers.google.com/search/docs/fundamentals/third-party-seo">Google Search&#8217;s guidance on using third-party SEO tools, services, and advice</a> digs into advice from Google on how you should consider advice from third parties and using third-party SEO services and tools. </p>



<p>This includes how to:</p>



<ul class="wp-block-list">
<li>Evaluate and verify external SEO advice against official Google guidelines</li>



<li>Think critically about using third-party SEO tools and services
<ul class="wp-block-list">
<li>Assisting in sitemap generation</li>



<li>Establishing indexing directives</li>



<li>Offering to generate &#8220;SEO-optimized&#8221; content for you</li>



<li>Providing advice they claim will improve the ranking of your existing content</li>



<li>Promising improvements for AI experiences and search formats (also known as &#8220;AEO&#8221; or &#8220;GEO&#8221; tools)</li>
</ul>
</li>
</ul>



<p>Google added that it does not endorse any third-party tool and warns about tools claiming so.  It also warns about tools or services that claim &#8220;using a service or tool doesn&#8217;t guarantee ranking success.&#8221;  Google added, &#8220;Third-party tools don&#8217;t have access to our internal ranking data. They can&#8217;t guarantee performance.&#8221;</p>



<p>Google does recommend you use Google Search Console, which provides you with &#8220;key information and data directly from Google Search itself,&#8221; Google wrote.</p>



<p><strong>Hiring an SEO document update. </strong>Google also made updates to the  <a href="https://developers.google.com/search/docs/fundamentals/do-i-need-seo">Do you need an SEO?</a> help document. Google added in topics around Optimizing for generative AI, where Google references a <a href="https://searchengineland.com/google-publishes-guide-on-optimizing-for-generative-ai-features-477671">newish help document</a> on that topic.</p>



<p>Google also posted in this document new content about SEO tools including:</p>



<ul class="wp-block-list">
<li>&#8220;If your SEO uses a third-party tool, keep in mind that Google doesn&#8217;t evaluate or endorse third-party SEO tools, and these tools don&#8217;t have access to Google&#8217;s internal ranking data. Be wary of tools that claim to be &#8220;acceptable&#8221; or &#8220;approved&#8221; by Google Search.&#8221;</li>



<li>&#8220;If your SEO offers to do an SEO audit for you, be sure to carefully consider what&#8217;s involved and only grant read access to Search Console (at this stage, don&#8217;t grant them write access). An SEO audit should be about giving you realistic estimates of improvement, and an estimate of the work involved. If they guarantee you that their changes will give you first place in search results, find someone else.&#8221;</li>



<li>&#8220;<strong>Evaluate your SEO&#8217;s recommendations and tools they use.</strong> Before making significant changes to your site based on a third-party tool&#8217;s audit, be sure to check their recommendations against <a href="https://developers.google.com/search/docs/fundamentals/seo-starter-guide">official guidance from Google Search</a>, think critically about any claims or recommendations you hear, and make your own informed decisions.&#8221;
<ul class="wp-block-list">
<li>Do they cite official Google documentation as supporting evidence for their recommendations?</li>



<li>If they have advice on optimizing for AI experiences (also known as &#8220;AEO&#8221; &#8220;GEO&#8221; services), is their advice aligned with <a href="https://developers.google.com/search/docs/fundamentals/ai-optimization-guide">Google Search&#8217;s official guidance on optimizing for generative AI features</a>?</li>



<li>Do they use tools that are aligned with Google&#8217;s guidance?</li>
</ul>
</li>
</ul>



<p>The document was also cleaned up and made shorter and more concise. </p>



<p><strong>Why we care. </strong>Reviewing Google&#8217;s official documentation is always something SEOs should be doing.  New documents that Google posts are useful to read and understand.  Understanding the changes made to older help documents is also important. </p>


Google added a new help document to its search developer documentation named Google Search’s guidance on using third-party SEO tools, services, and advice and updated the older Do you need an SEO? document on Friday.  Google primarily added in topics around generative AI optimization and more guidance on trusting third-party tools and other SEO advice.
Why. Google said it posted these document updates and changes because it wanted to “highlight important considerations when evaluating third-party SEO tools and advice, and to simplify some sections and remove outdated examples in existing documentation.”
Third-party SEO tools, services and advice. Google’s new help document named Google Search’s guidance on using third-party SEO tools, services, and advice digs into advice from Google on how you should consider advice from third parties and using third-party SEO services and tools. 
This includes how to:
Evaluate and verify external SEO advice against official Google guidelines
Think critically about using third-party SEO tools and services

Assisting in sitemap generation
Establishing indexing directives
Offering to generate “SEO-optimized” content for you
Providing advice they claim will improve the ranking of your existing content
Promising improvements for AI experiences and search formats (also known as “AEO” or “GEO” tools)
Google added that it does not endorse any third-party tool and warns about tools claiming so.  It also warns about tools or services that claim “using a service or tool doesn’t guarantee ranking success.”  Google added, “Third-party tools don’t have access to our internal ranking data. They can’t guarantee performance.”
Google does recommend you use Google Search Console, which provides you with “key information and data directly from Google Search itself,” Google wrote.
Hiring an SEO document update. Google also made updates to the  Do you need an SEO? help document. Google added in topics around Optimizing for generative AI, where Google references a newish help document on that topic.
Google also posted in this document new content about SEO tools including:
“If your SEO uses a third-party tool, keep in mind that Google doesn’t evaluate or endorse third-party SEO tools, and these tools don’t have access to Google’s internal ranking data. Be wary of tools that claim to be “acceptable” or “approved” by Google Search.”
“If your SEO offers to do an SEO audit for you, be sure to carefully consider what’s involved and only grant read access to Search Console (at this stage, don’t grant them write access). An SEO audit should be about giving you realistic estimates of improvement, and an estimate of the work involved. If they guarantee you that their changes will give you first place in search results, find someone else.”
“Evaluate your SEO’s recommendations and tools they use. Before making significant changes to your site based on a third-party tool’s audit, be sure to check their recommendations against official guidance from Google Search, think critically about any claims or recommendations you hear, and make your own informed decisions.”

Do they cite official Google documentation as supporting evidence for their recommendations?
If they have advice on optimizing for AI experiences (also known as “AEO” “GEO” services), is their advice aligned with Google Search’s official guidance on optimizing for generative AI features?
Do they use tools that are aligned with Google’s guidance?
The document was also cleaned up and made shorter and more concise. 
Why we care. Reviewing Google’s official documentation is always something SEOs should be doing.  New documents that Google posts are useful to read and understand.  Understanding the changes made to older help documents is also important.

Google added in topics about generative AI optimization, how third-party tools are not endorsed by Google and more.

Google adds guidance on third-party SEO tools, services, advice and updates hiring an SEO doc

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-Demand-Gen-creative-impact-with-asset-uplift-tests.png" class="attachment-large size-large wp-post-image" alt="How to measure Demand Gen creative impact with asset uplift tests" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-Demand-Gen-creative-impact-with-asset-uplift-tests.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-Demand-Gen-creative-impact-with-asset-uplift-tests-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-Demand-Gen-creative-impact-with-asset-uplift-tests-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-Demand-Gen-creative-impact-with-asset-uplift-tests-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>The update provides additional guidance on when audience targeting restrictions could affect campaign delivery and reach.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-Demand-Gen-creative-impact-with-asset-uplift-tests.png" class="attachment-large size-large wp-post-image" alt="How to measure Demand Gen creative impact with asset uplift tests" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-Demand-Gen-creative-impact-with-asset-uplift-tests.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-Demand-Gen-creative-impact-with-asset-uplift-tests-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-Demand-Gen-creative-impact-with-asset-uplift-tests-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-Demand-Gen-creative-impact-with-asset-uplift-tests-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google updated its personalized advertising policy documentation to clarify how restricted targeting rules apply to Demand Gen and Discovery campaigns, particularly when advertisers promote products or services tied to sensitive interest categories.</p>



<p><strong>The big picture</strong>. The update appears in Google&#8217;s &#8220;Restricted targeting in Personalized Advertising&#8221; policy documentation and focuses on explaining potential ad serving limitations rather than introducing new policy restrictions.</p>



<p id="h-what-s-changing"><strong>What&#8217;s changing. </strong>Google updated its help documentation in June to provide additional guidance on how Demand Gen and Discovery campaigns interact with personalized advertising restrictions.</p>



<p>The changes specifically address campaigns that target products and services associated with sensitive interest categories.</p>



<p id="h-the-fine-print"><strong>The fine print. </strong>The update is a clarification to existing policy guidance, not a new policy announcement.</p>



<p>Google says the revised documentation includes additional information about potential serving implications when advertisers use audience targeting for products or services that fall into restricted categories.</p>



<p>Sensitive interest categories can include areas such as:</p>



<ul class="wp-block-list">
<li>Health conditions</li>



<li>Financial hardship</li>



<li>Personal difficulties</li>



<li>Other topics that Google considers sensitive under its personalized advertising policies</li>
</ul>



<p id="h-between-the-lines"><strong>Between the lines.</strong> Demand Gen campaigns rely heavily on audience signals and personalized targeting to reach users across YouTube, Discover and Gmail.</p>



<p>As adoption of Demand Gen continues to grow, advertisers have increasingly sought clarity around how Google&#8217;s sensitive interest policies affect audience eligibility, reach and campaign delivery.</p>



<p>The documentation update suggests Google is responding to those questions by providing more explicit guidance on when targeting restrictions may limit campaign performance.</p>



<p id="h-why-now-the-clarification-comes-as-demand-gen-becomes-a-larger-part-of-google-s-advertising-ecosystem-and-more-advertisers-shift-budgets-from-discovery-campaigns-into-google-s-ai-powered-audience-products"><strong>Why now. </strong>The clarification comes as <a href="https://searchengineland.com/google-expands-demand-gen-with-youtube-creator-tools-478111">Demand Gen becomes a larger part of Google&#8217;s advertising ecosystem</a> and more advertisers shift budgets from Discovery campaigns into Google&#8217;s AI-powered audience products.</p>



<p><strong>Why we care. </strong>For advertisers operating in regulated or sensitive industries, understanding these restrictions is becoming increasingly important to campaign planning and audience strategy.</p>



<p id="h-what-to-watch-advertisers-running-demand-gen-campaigns-in-healthcare-financial-services-or-other-potentially-sensitive-verticals-may-want-to-review-the-updated-guidance-to-understand-whether-targeting-choices-could-affect-reach-or-ad-delivery"><strong>What to watch.</strong> Advertisers running Demand Gen campaigns in healthcare, financial services or other potentially sensitive verticals may want to review the <a href="https://support.google.com/adspolicy/answer/143465">updated guidance</a> to understand whether targeting choices could affect reach or ad delivery.</p>


Google updated its personalized advertising policy documentation to clarify how restricted targeting rules apply to Demand Gen and Discovery campaigns, particularly when advertisers promote products or services tied to sensitive interest categories.
The big picture. The update appears in Google’s “Restricted targeting in Personalized Advertising” policy documentation and focuses on explaining potential ad serving limitations rather than introducing new policy restrictions.
What’s changing. Google updated its help documentation in June to provide additional guidance on how Demand Gen and Discovery campaigns interact with personalized advertising restrictions.
The changes specifically address campaigns that target products and services associated with sensitive interest categories.
The fine print. The update is a clarification to existing policy guidance, not a new policy announcement.
Google says the revised documentation includes additional information about potential serving implications when advertisers use audience targeting for products or services that fall into restricted categories.
Sensitive interest categories can include areas such as:
Health conditions
Financial hardship
Personal difficulties
Other topics that Google considers sensitive under its personalized advertising policies
Between the lines. Demand Gen campaigns rely heavily on audience signals and personalized targeting to reach users across YouTube, Discover and Gmail.
As adoption of Demand Gen continues to grow, advertisers have increasingly sought clarity around how Google’s sensitive interest policies affect audience eligibility, reach and campaign delivery.
The documentation update suggests Google is responding to those questions by providing more explicit guidance on when targeting restrictions may limit campaign performance.
Why now. The clarification comes as Demand Gen becomes a larger part of Google’s advertising ecosystem and more advertisers shift budgets from Discovery campaigns into Google’s AI-powered audience products.
Why we care. For advertisers operating in regulated or sensitive industries, understanding these restrictions is becoming increasingly important to campaign planning and audience strategy.
What to watch. Advertisers running Demand Gen campaigns in healthcare, financial services or other potentially sensitive verticals may want to review the updated guidance to understand whether targeting choices could affect reach or ad delivery.

The update provides additional guidance on when audience targeting restrictions could affect campaign delivery and reach.

Google clarifies sensitive audience targeting rules for Demand Gen campaigns

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/08/The-future-of-remarketing-Microsoft-bets-on-impressions-not-clicks.jpg" class="attachment-large size-large wp-post-image" alt="The future of remarketing? Microsoft bets on impressions, not clicks" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/08/The-future-of-remarketing-Microsoft-bets-on-impressions-not-clicks.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/08/The-future-of-remarketing-Microsoft-bets-on-impressions-not-clicks-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/08/The-future-of-remarketing-Microsoft-bets-on-impressions-not-clicks-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Microsoft is opening more premium ad inventory to cryptocurrency advertisers while maintaining existing compliance requirements.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/08/The-future-of-remarketing-Microsoft-bets-on-impressions-not-clicks.jpg" class="attachment-large size-large wp-post-image" alt="The future of remarketing? Microsoft bets on impressions, not clicks" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/08/The-future-of-remarketing-Microsoft-bets-on-impressions-not-clicks.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/08/The-future-of-remarketing-Microsoft-bets-on-impressions-not-clicks-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/08/The-future-of-remarketing-Microsoft-bets-on-impressions-not-clicks-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Microsoft is widening advertising opportunities for cryptocurrency exchanges by allowing them to run Audience Ads in all markets where crypto advertising is already permitted.</p>



<p><strong>The big picture:</strong> The update extends cryptocurrency advertising beyond traditional search placements, giving eligible exchanges access to Microsoft&#8217;s Audience Ads inventory across its broader network.</p>



<p id="h-what-s-changing">What&#8217;s changing. Microsoft has updated its advertising policies to allow cryptocurrency exchanges to use Audience Ads in all approved crypto advertising markets.</p>



<p>The expansion applies only to advertisers that comply with Microsoft&#8217;s Cryptocurrency and Related Products policies and any applicable local regulations.</p>



<p id="h-why-it-matters"><strong>Why we care. </strong>Audience Ads allow advertisers to reach users across Microsoft&#8217;s native advertising network, which includes placements on content, news and partner sites.</p>



<p>For cryptocurrency exchanges, the change creates additional opportunities to build awareness and engage potential customers beyond search-driven intent.</p>



<p id="h-the-fine-print"><strong>The fine print.</strong> The policy update does not represent a relaxation of Microsoft&#8217;s cryptocurrency advertising requirements.</p>



<p>Advertisers must still meet all eligibility requirements and comply with the company&#8217;s Cryptocurrency and Related Products policies, which vary by market and regulatory environment.</p>



<p id="h-what-to-watch"><strong>What to watch.</strong> Advertisers will be watching to see whether the expanded inventory leads to increased adoption of Audience Ads among cryptocurrency exchanges and whether <a href="https://help.ads.microsoft.com/?pnf=may-2026-pilot-program-policies-fdd2a452#apex/ads/en/60225/0">Microsoft further broadens crypto advertising opportunities</a> in additional markets.</p>






Microsoft is widening advertising opportunities for cryptocurrency exchanges by allowing them to run Audience Ads in all markets where crypto advertising is already permitted.
The big picture: The update extends cryptocurrency advertising beyond traditional search placements, giving eligible exchanges access to Microsoft’s Audience Ads inventory across its broader network.
What’s changing. Microsoft has updated its advertising policies to allow cryptocurrency exchanges to use Audience Ads in all approved crypto advertising markets.
The expansion applies only to advertisers that comply with Microsoft’s Cryptocurrency and Related Products policies and any applicable local regulations.
Why we care. Audience Ads allow advertisers to reach users across Microsoft’s native advertising network, which includes placements on content, news and partner sites.
For cryptocurrency exchanges, the change creates additional opportunities to build awareness and engage potential customers beyond search-driven intent.
The fine print. The policy update does not represent a relaxation of Microsoft’s cryptocurrency advertising requirements.
Advertisers must still meet all eligibility requirements and comply with the company’s Cryptocurrency and Related Products policies, which vary by market and regulatory environment.
What to watch. Advertisers will be watching to see whether the expanded inventory leads to increased adoption of Audience Ads among cryptocurrency exchanges and whether Microsoft further broadens crypto advertising opportunities in additional markets.

Microsoft is opening more premium ad inventory to cryptocurrency advertisers while maintaining existing compliance requirements.

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