<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-to-structure-paid-social-creative-testing-for-better-performance.png" class="attachment-large size-large wp-post-image" alt="How to structure paid social creative testing for better performance" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-to-structure-paid-social-creative-testing-for-better-performance.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-structure-paid-social-creative-testing-for-better-performance-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-structure-paid-social-creative-testing-for-better-performance-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-structure-paid-social-creative-testing-for-better-performance-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Platforms can recognize when creative variations are nearly identical, making differentiated concepts more valuable than cosmetic tweaks.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-to-structure-paid-social-creative-testing-for-better-performance.png" class="attachment-large size-large wp-post-image" alt="How to structure paid social creative testing for better performance" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-to-structure-paid-social-creative-testing-for-better-performance.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-structure-paid-social-creative-testing-for-better-performance-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-structure-paid-social-creative-testing-for-better-performance-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-structure-paid-social-creative-testing-for-better-performance-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Creative testing has become a volume game in paid social, but producing more ads doesn’t automatically improve performance. When accounts become flooded with minor variations, budgets fragment, learning phases stretch longer, and performance insights become harder to interpret.</p>



<p>The strongest advertisers today are focusing less on creative quantity and more on differentiated concepts. They’re testing concepts built around audience psychology, emotional resonance, messaging angles, and formats that give algorithms stronger signals to optimize against.</p>



<h2 class="wp-block-heading" id="h-what-meaningful-creative-testing-actually-looks-like">What meaningful creative testing actually looks like</h2>



<p>One of the biggest misconceptions about creative testing is that every new asset automatically becomes a fresh test in the algorithm’s eyes. That’s not necessarily true.</p>



<p>Uploading a high volume of ad variations doesn’t automatically create meaningful differentiation. If the only difference between five creatives is the color of the overlay text, the platform can still recognize that the core message, intended audience, and visuals are nearly identical.</p>



<p>Platforms like Meta typically won’t find new audience pockets when this happens, so your creatives compete with one another, leading to delivery overlap. One or two ads may even cannibalize your budget, leaving some variations with little to no impressions.</p>



<p>Meaningful creative testing is rooted in psychology, messaging, emotional triggers, and differentiated creative angles that change how people experience the ad and how algorithms interpret it.</p>



<p>Creative testing works best when concepts truly differ. Lean into different hooks, emotional drivers, positioning, motivations, and formats. That’s where you’ll see meaningful performance shifts.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/b2b-paid-social-creative-optimization-454162">A testing primer for B2B paid social creative optimization</a></em></strong>



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<h2 class="wp-block-heading" id="h-the-hidden-costs-of-creative-volume">The hidden costs of creative volume</h2>



<p>If creative volume is prioritized too heavily over creative value, it can create performance inefficiencies, waste resources, and add operational drag to your advertising processes.&nbsp;</p>



<p>When your account is flooded with high-volume, low-value creatives, analysis becomes more complicated and pulls you away from higher-level strategic thinking.</p>



<h3 class="wp-block-heading" id="h-fragmented-budgets-and-longer-learning-phases">Fragmented budgets and longer learning phases</h3>



<p>Every time a new asset is introduced, the platform needs data to determine who to show it to, how to optimize delivery, and where it’s most likely to drive results.&nbsp;</p>



<p>When budgets are spread across too many creatives with minor variations, data becomes fragmented, and the algorithm struggles to gather enough conversion signals for each asset to move through the learning phase properly.</p>



<p>Instead of concentrating spend on stronger concepts, your budget becomes diluted across micro-test assets that are unlikely to achieve statistical significance.&nbsp;</p>



<p>Don’t waste budget accumulating inconclusive data that provides little guidance for future creative variations.</p>



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



<p>When an account is flooded with assets that contain only minor variations, advertisers get pulled away from macro-level strategy and trapped in the minutiae of the data.</p>



<p>Save yourself time parsing small differences in performance metrics to determine whether the red overlay text outperformed the blue one. Instead, analyze higher-level creative trends.</p>



<h3 class="wp-block-heading" id="h-misaligned-kpis">Misaligned KPIs</h3>



<p>While creative production speed and output matter, they shouldn’t be the primary indicators of success. When volume becomes the primary KPI, teams optimize for asset delivery instead of strategic differentiation. There should be a balance between production efficiency and a deeper strategy.</p>



<p>Produce meaningful ads that create measurable impact for both your account and the business.</p>



<h2 class="wp-block-heading" id="h-how-to-build-higher-value-creatives">How to build higher-value creatives</h2>



<p>If flooding the system with creative variations that contain only minor tweaks isn’t producing meaningful results, the next question becomes: How do you build high-value creative that actually scales?</p>



<p>Shift away from chasing trends, viral formats, and trendy audio. Instead, use real audience insights from reviews, customer service tickets, social media comments, survey results, and conversations in online forums like Reddit or Quora. Some of the strongest creative inputs already exist within your business.</p>



<p>Look for recurring themes. Are there recurring frustrations, objections, or emotional language patterns? Use AI to analyze them, save time, and uncover messaging insights that will resonate more deeply with your audience.</p>



<p>Once you identify your audience’s vocabulary and pain points, use those findings to shape your messaging and creative concepts. That’s where the real value lies.</p>



<p>High-value creative also doesn’t need high production quality or large budgets. <a href="https://searchengineland.com/ugly-ads-outperforming-polished-creative-475007">Raw, low-fi content</a> captured on a phone can perform extremely well. I’ve also found that founder-led ad content often performs best because it feels more native and less like polished advertising.</p>



<p>After all, value comes from the message, not the production quality.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="434" height="1029" src="https://searchengineland.com/wp-content/seloads/2026/05/An-image-of-a-Facebook-founder-led-ad-showing-a-wooden-product.png" alt="An image of a Facebook founder-led ad showing a wooden product" class="wp-image-478971"/><figcaption class="wp-element-caption"><em>Source: <a href="https://www.facebook.com/ads/library/?id=822377927560518" target="_blank" rel="noopener">AEKSA (Meta Ad)</a></em></figcaption></figure>
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<h2 class="wp-block-heading" id="h-strategically-feed-the-machine">Strategically feed the machine</h2>



<p>Emphasizing creative value doesn’t mean abandoning testing volume entirely. It means sequencing your testing intentionally by using a two-phase framework that separates the pursuit of value from volume.</p>



<h3 class="wp-block-heading" id="h-phase-1-macro-testing-for-value">Phase 1: Macro-testing for value</h3>



<p>This initial phase focuses on concept discovery. The goal is to test creative hypotheses.</p>



<p>For example, test three different concepts against one another and identify the winner. Use different formats, emotional angles, and creative styles across those concepts.</p>



<h3 class="wp-block-heading" id="h-phase-2-micro-testing-for-volume">Phase 2: Micro-testing for volume</h3>



<p>Once you have a clear winner from Phase 1, introduce volume.</p>



<p>If a founder-led video ad delivers a significantly lower CAC and a spike in hook rate, that creates an ideal opportunity to lean into volume. Take that winning creative and iterate on its components to maximize efficiency and extend its shelf life.</p>



<p>Test:</p>



<ul class="wp-block-list">
<li>Three different hooks in the first three seconds.</li>



<li>Two variations of pacing or music.</li>



<li>Several CTAs at the end.</li>
</ul>



<p>By structuring your workflow this way, volume serves to optimize a concept that has already proven valuable.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/ppc-tests-nuance-465494">Why PPC tests in 2026 call for nuance, not winners</a></em></strong>



<h2 class="wp-block-heading" id="h-the-weekly-creative-audit">The weekly creative audit</h2>



<p>Shifting from a volume-first approach to a value-first strategy can help pull your organization out of the content mill trap.</p>



<p>Once you implement this process, review your ad accounts each week with these questions in mind:</p>



<ul class="wp-block-list">
<li><strong>Are we launching three unique ads or the same ad three times?</strong> Review your latest batch of creatives and make sure they contain distinct psychological angles, not just visual changes.</li>



<li><strong>What customer insights drove the last three winning creatives?</strong> If you can’t identify the exact objection, review keyword, audience phrase, or use case that inspired a winning ad, you’re scaling on luck and using a strategy that isn’t repeatable.</li>



<li><strong>Is the data telling us a story? </strong>Step back from individual metrics and evaluate creative trends holistically. Are videos outperforming images? Are founder-led ads holding attention longer than UGC? Use your metrics systematically to guide your next creative strategy decisions.</li>
</ul>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/how-to-read-meta-ads-metrics-like-a-system-not-a-scoreboard-470061"><strong><em>How to read Meta Ads metrics like a system, not a scoreboard</em></strong></a>



<h2 class="wp-block-heading" id="h-slow-down-the-content-treadmill">Slow down the content treadmill</h2>



<p>Algorithms are powerful and, in many ways, mirror human behavior. They can’t manufacture interest where it doesn’t exist, nor can they turn weak messaging into profits through repetition.</p>



<p>No amount of creative volume will compensate for a lack of strategic value in your ads. Step back, assess the data, identify which concepts are actually working, and give the algorithm something meaningful to learn from to help drive business growth.</p>

Creative testing has become a volume game in paid social, but producing more ads doesn’t automatically improve performance. When accounts become flooded with minor variations, budgets fragment, learning phases stretch longer, and performance insights become harder to interpret.
The strongest advertisers today are focusing less on creative quantity and more on differentiated concepts. They’re testing concepts built around audience psychology, emotional resonance, messaging angles, and formats that give algorithms stronger signals to optimize against.
What meaningful creative testing actually looks like
One of the biggest misconceptions about creative testing is that every new asset automatically becomes a fresh test in the algorithm’s eyes. That’s not necessarily true.
Uploading a high volume of ad variations doesn’t automatically create meaningful differentiation. If the only difference between five creatives is the color of the overlay text, the platform can still recognize that the core message, intended audience, and visuals are nearly identical.
Platforms like Meta typically won’t find new audience pockets when this happens, so your creatives compete with one another, leading to delivery overlap. One or two ads may even cannibalize your budget, leaving some variations with little to no impressions.
Meaningful creative testing is rooted in psychology, messaging, emotional triggers, and differentiated creative angles that change how people experience the ad and how algorithms interpret it.
Creative testing works best when concepts truly differ. Lean into different hooks, emotional drivers, positioning, motivations, and formats. That’s where you’ll see meaningful performance shifts.
Dig deeper: A testing primer for B2B paid social creative optimization




  
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The hidden costs of creative volume
If creative volume is prioritized too heavily over creative value, it can create performance inefficiencies, waste resources, and add operational drag to your advertising processes. 
When your account is flooded with high-volume, low-value creatives, analysis becomes more complicated and pulls you away from higher-level strategic thinking.
Fragmented budgets and longer learning phases
Every time a new asset is introduced, the platform needs data to determine who to show it to, how to optimize delivery, and where it’s most likely to drive results. 
When budgets are spread across too many creatives with minor variations, data becomes fragmented, and the algorithm struggles to gather enough conversion signals for each asset to move through the learning phase properly.
Instead of concentrating spend on stronger concepts, your budget becomes diluted across micro-test assets that are unlikely to achieve statistical significance. 
Don’t waste budget accumulating inconclusive data that provides little guidance for future creative variations.
The analysis tax
When an account is flooded with assets that contain only minor variations, advertisers get pulled away from macro-level strategy and trapped in the minutiae of the data.
Save yourself time parsing small differences in performance metrics to determine whether the red overlay text outperformed the blue one. Instead, analyze higher-level creative trends.
Misaligned KPIs
While creative production speed and output matter, they shouldn’t be the primary indicators of success. When volume becomes the primary KPI, teams optimize for asset delivery instead of strategic differentiation. There should be a balance between production efficiency and a deeper strategy.
Produce meaningful ads that create measurable impact for both your account and the business.
How to build higher-value creatives
If flooding the system with creative variations that contain only minor tweaks isn’t producing meaningful results, the next question becomes: How do you build high-value creative that actually scales?
Shift away from chasing trends, viral formats, and trendy audio. Instead, use real audience insights from reviews, customer service tickets, social media comments, survey results, and conversations in online forums like Reddit or Quora. Some of the strongest creative inputs already exist within your business.
Look for recurring themes. Are there recurring frustrations, objections, or emotional language patterns? Use AI to analyze them, save time, and uncover messaging insights that will resonate more deeply with your audience.
Once you identify your audience’s vocabulary and pain points, use those findings to shape your messaging and creative concepts. That’s where the real value lies.
High-value creative also doesn’t need high production quality or large budgets. Raw, low-fi content captured on a phone can perform extremely well. I’ve also found that founder-led ad content often performs best because it feels more native and less like polished advertising.
After all, value comes from the message, not the production quality.
Source: AEKSA (Meta Ad)




	
	
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Strategically feed the machine
Emphasizing creative value doesn’t mean abandoning testing volume entirely. It means sequencing your testing intentionally by using a two-phase framework that separates the pursuit of value from volume.
Phase 1: Macro-testing for value
This initial phase focuses on concept discovery. The goal is to test creative hypotheses.
For example, test three different concepts against one another and identify the winner. Use different formats, emotional angles, and creative styles across those concepts.
Phase 2: Micro-testing for volume
Once you have a clear winner from Phase 1, introduce volume.
If a founder-led video ad delivers a significantly lower CAC and a spike in hook rate, that creates an ideal opportunity to lean into volume. Take that winning creative and iterate on its components to maximize efficiency and extend its shelf life.
Test:
Three different hooks in the first three seconds.
Two variations of pacing or music.
Several CTAs at the end.
By structuring your workflow this way, volume serves to optimize a concept that has already proven valuable.
Dig deeper: Why PPC tests in 2026 call for nuance, not winners



The weekly creative audit
Shifting from a volume-first approach to a value-first strategy can help pull your organization out of the content mill trap.
Once you implement this process, review your ad accounts each week with these questions in mind:
Are we launching three unique ads or the same ad three times? Review your latest batch of creatives and make sure they contain distinct psychological angles, not just visual changes.
What customer insights drove the last three winning creatives? If you can’t identify the exact objection, review keyword, audience phrase, or use case that inspired a winning ad, you’re scaling on luck and using a strategy that isn’t repeatable.
Is the data telling us a story? Step back from individual metrics and evaluate creative trends holistically. Are videos outperforming images? Are founder-led ads holding attention longer than UGC? Use your metrics systematically to guide your next creative strategy decisions.
Dig deeper: How to read Meta Ads metrics like a system, not a scoreboard



Slow down the content treadmill
Algorithms are powerful and, in many ways, mirror human behavior. They can’t manufacture interest where it doesn’t exist, nor can they turn weak messaging into profits through repetition.
No amount of creative volume will compensate for a lack of strategic value in your ads. Step back, assess the data, identify which concepts are actually working, and give the algorithm something meaningful to learn from to help drive business growth.

Platforms can recognize when creative variations are nearly identical, making differentiated concepts more valuable than cosmetic tweaks.

How to structure paid social creative testing for better performance

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/ai-filtration-pipeline.png" class="attachment-large size-large wp-post-image" alt="AI filtration pipeline" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/ai-filtration-pipeline.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/ai-filtration-pipeline-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/ai-filtration-pipeline-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/ai-filtration-pipeline-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI search has outgrown simple RAG. Learn how today’s hidden AI retrieval systems decide whether your content gets surfaced or filtered out.

AI search has outgrown simple RAG. Learn how today’s hidden AI retrieval systems decide whether your content gets surfaced or filtered out.

Beyond RAG: Why every AI search platform is now agentic and what that means for your content

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Why-your-B2B-PPC-metrics-may-be-lying-to-you.png" class="attachment-large size-large wp-post-image" alt="Why your B2B PPC metrics may be lying to you" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Why-your-B2B-PPC-metrics-may-be-lying-to-you.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Why-your-B2B-PPC-metrics-may-be-lying-to-you-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Why-your-B2B-PPC-metrics-may-be-lying-to-you-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Why-your-B2B-PPC-metrics-may-be-lying-to-you-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>More conversions and higher ROAS don’t always translate to more pipeline or revenue. Here’s how to measure incremental value more accurately.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Why-your-B2B-PPC-metrics-may-be-lying-to-you.png" class="attachment-large size-large wp-post-image" alt="Why your B2B PPC metrics may be lying to you" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Why-your-B2B-PPC-metrics-may-be-lying-to-you.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Why-your-B2B-PPC-metrics-may-be-lying-to-you-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Why-your-B2B-PPC-metrics-may-be-lying-to-you-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Why-your-B2B-PPC-metrics-may-be-lying-to-you-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>B2B <a href="https://searchengineland.com/guide/what-is-paid-search">PPC</a> advertisers have more options than ever for measuring success. In the past, you had to rely on form-fill data alone. Now, you can feed a wealth of <a href="https://searchengineland.com/offline-conversion-tracking-best-practices-testing-strategies-434211">offline conversion</a> data into Google Ads and Microsoft Ads.</p>



<p>It’s tempting to measure every possible metric, but optimizing toward all of them isn’t a good idea. If you’re optimizing toward everything, you’ll probably end up succeeding at nothing.</p>



<p>So how do you know whether you’ve actually driven incremental value, and what are the right success metrics for B2B PPC campaigns? The metrics that matter might not be the ones you’re focused on.</p>



<h2 class="wp-block-heading" id="h-the-advertising-metrics-that-matter">The advertising metrics that matter</h2>



<p>I’ve seen advertisers set up offline conversions and get excited because their total conversions increased. Then frustration sets in because they don’t see corresponding increases in their bottom line.</p>



<p>Usually, those conversion increases happen because the advertiser added more conversion actions and <a href="https://support.google.com/google-ads/answer/11461796?hl=en&amp;sjid=1023226550902365970-NA" target="_blank" rel="noopener">set them all to primary</a>. Before making changes, they were only tracking form fills. Afterward, they were tracking form fills, leads, marketing qualified leads (MQLs), sales qualified leads (SQLs), and opportunities.</p>



<p>Instead of one conversion action, they now have four. But the same person could complete all four, meaning leads are being quadruple-counted. A similar issue can happen with platform-reported return on ad spend (ROAS). If you’ve attached conversion values to each action — which you absolutely should do — you’ll also see ROAS increase. Both are false signals created by faulty math.</p>



<p>Focusing only on average cost per action (CPA) can also be misleading. Average CPA can mask your marginal CPA — the cost of acquiring one additional conversion as marketing spend increases. You might be overspending on incremental conversions as you scale your account.</p>



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<h2 class="wp-block-heading" id="h-set-up-conversion-values-correctly">Set up conversion values correctly</h2>



<p>Setting up conversion values is a must for offline conversions. A lot of B2B advertisers get hung up on this step. They say, “We don’t know the value of the conversion at the time it happens. We won’t know that until it works its way through the system.”</p>



<p>While using actual conversion values is ideal, don’t worry if you can’t. Just assign relative values to each conversion action. Here’s a simple example:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="601" height="360" src="https://searchengineland.com/wp-content/seloads/2026/05/Microconversion-value.jpeg" alt="Microconversion value" class="wp-image-478932"/></figure>
</div>


<p>In this case, the advertiser is measuring four conversion actions: video views, ungated asset downloads, form fills, and MQLs. MQLs come from offline conversions. The rest are measured through Google Tag conversions.</p>



<p>Each conversion is worth 10x the previous action. Ungated asset downloads are worth 10x a video view, and so on. MQLs are worth 1,000x a video view. The advertiser would rather get one MQL than 999 video views.</p>



<p>If you set arbitrary values, make sure to validate them against real data to ensure they’re directing bidding algorithms accurately. Setting values that are too high for lower-funnel actions can cause the algorithm to favor those easier-to-generate actions while deprioritizing lower-funnel actions.</p>



<p>This happened to us recently with a client who was getting a lot of leads, but very few MQLs and SQLs. We reduced the value of leads by a factor of 10, which made MQLs and SQLs look more valuable to the algorithm and increased MQLs and SQLs relative to leads.</p>



<p>Within two weeks, MQL and SQL volume increased significantly, while leads stayed flat. That might not sound like a good thing, but it was. The client was getting higher-quality leads for the same cost.</p>



<p>By using the right conversion values, even relative ones, you can measure incremental conversion value more effectively.</p>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/why-incrementality-is-the-only-metric-that-proves-marketings-real-impact-463439"><strong><em>Why incrementality is the only metric that proves marketing’s real impact</em></strong></a>



<h2 class="wp-block-heading" id="h-get-more-specific-with-campaign-specific-goals">Get more specific with campaign-specific goals</h2>



<p>If you want Smart Bidding to focus on down-funnel actions, you can use <a href="https://searchengineland.com/campaign-specific-goals-google-ads-456653">campaign-specific goals</a>. You can assign conversion actions at the campaign level, so Smart Bidding only optimizes toward those actions.</p>



<p>You can find the feature in campaign settings in both Google Ads and Microsoft Ads.</p>



<p>Here’s what that looks like in Google:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="769" height="347" src="https://searchengineland.com/wp-content/seloads/2026/05/Google-Ads-campaign-specific-goals.jpeg" alt="Google Ads campaign-specific goals" class="wp-image-478933"/></figure>
</div>


<p>And here’s what it looks like for Microsoft:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="524" height="336" src="https://searchengineland.com/wp-content/seloads/2026/05/Microsoft-Ads-campaign-specific-goals.png" alt="Microsoft Ads campaign-specific goals" class="wp-image-478934"/></figure>
</div>


<p>Let’s say you have a campaign that’s driving a lot of leads, but few MQLs and SQLs. You could use campaign-specific goals to optimize only for MQLs and SQLs and ignore leads, even though leads are a primary conversion in the account.</p>



<p>Note: If lower-funnel actions have low volume, this technique may not work. Automation still needs a clear enough signal to optimize toward. So if you only have one or two MQLs in a month, the automation might struggle.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/google-ads-ga4-crm-numbers-never-match-476978">Why Google Ads, GA4 and CRM numbers never match</a></em></strong>



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<h2 class="wp-block-heading" id="h-the-success-metrics-for-measuring-incremental-conversions">The success metrics for measuring incremental conversions</h2>



<p>Looking at simple CPA and ROAS metrics isn’t enough to measure success. You also need to look at each conversion action and measure incremental conversions. A basic way to measure incremental conversions is to establish your baseline first and then measure the CPA and ROAS of conversions at a higher spend level.</p>



<p>For example, let’s say you’re currently spending $5,000 per week and getting an average of 50 conversions, so your CPA is $100.</p>



<p>Now let’s say you increase your weekly spend to $7,500 and end up getting 70 conversions, for an average CPA of $107 — not much higher than the previous CPA of $100.</p>



<p>Your marginal spend is $2,500, marginal conversions are 20, and marginal CPA is $125 — 25% higher than the original CPA.</p>



<p>The difference in CPA may or may not be acceptable for your goals. It might make sense to invest more to increase sales. But you need to understand where your upper limit is. At some point, you may exceed the amount you’re willing to invest for an additional lead while still making fiscal sense.</p>



<p>If you want to get more sophisticated, you can <a href="https://searchengineland.com/marketing-mix-modeling-crucial-448348">use a marketing mix modeling tool</a> (MMM) to run an incrementality test.</p>



<p>There are several MMM tools you could use to do this. Some are expensive, and some are low-cost or even free.&nbsp;</p>



<p>For example, <a href="https://searchengineland.com/google-brings-meridian-marketing-mix-modeling-into-analytics-360-478110">Google’s Meridian</a> is an open-source tool and it’s free, but the trade-off is that it requires technical know-how to set up and use.&nbsp;</p>



<p>MMMs also require a <a href="https://searchengineland.com/marketing-measurement-crawl-sprint-473986">significant amount of historical data </a>— two or more years’ worth — but once the data is ingested, they’re fantastic for measuring incrementality.&nbsp;</p>



<p><strong><em>Dig deeper: </em></strong><a href="https://searchengineland.com/avoid-marketing-mix-modeling-mistakes-463576"><strong><em>How to avoid marketing mix modeling mistakes that derail results</em></strong></a>



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<h2 class="wp-block-heading" id="h-the-true-test-of-whether-your-success-metrics-are-working">The true test of whether your success metrics are working</h2>



<p>MQLs, SQLs, and closed deals are important. But the true measure of incremental value is revenue or pipeline. That means you need to make sure you’re actually measuring for it.</p>



<p>Not all sales are created equal. You might see one deal for $5,000 and another for $2 million. Both register as closed sales in the CRM, but the two are nothing alike. Which would you rather have?</p>



<p>It’s easy to undervalue conversion actions if you’re using proxy values like the ones described above. Yet pipeline and revenue are often determined outside the 90-day conversion window required for offline conversions.</p>



<p>It’s crucial to look at the data in your CRM and map it back to your paid search campaigns. Are there campaigns or content assets driving relatively few leads and MQLs, but a lot of pipeline? If so, don’t devalue them. Make sure you keep them running and give them enough budget to succeed.</p>



<p>Also, don’t forget about incremental revenue.</p>



<p>If you’re scaling your spend, keep an eye on incremental revenue to find the point of diminishing returns. Doing this can prevent overspending on campaigns or channels once they’re no longer cost-effective.</p>

B2B PPC advertisers have more options than ever for measuring success. In the past, you had to rely on form-fill data alone. Now, you can feed a wealth of offline conversion data into Google Ads and Microsoft Ads.
It’s tempting to measure every possible metric, but optimizing toward all of them isn’t a good idea. If you’re optimizing toward everything, you’ll probably end up succeeding at nothing.
So how do you know whether you’ve actually driven incremental value, and what are the right success metrics for B2B PPC campaigns? The metrics that matter might not be the ones you’re focused on.
The advertising metrics that matter
I’ve seen advertisers set up offline conversions and get excited because their total conversions increased. Then frustration sets in because they don’t see corresponding increases in their bottom line.
Usually, those conversion increases happen because the advertiser added more conversion actions and set them all to primary. Before making changes, they were only tracking form fills. Afterward, they were tracking form fills, leads, marketing qualified leads (MQLs), sales qualified leads (SQLs), and opportunities.
Instead of one conversion action, they now have four. But the same person could complete all four, meaning leads are being quadruple-counted. A similar issue can happen with platform-reported return on ad spend (ROAS). If you’ve attached conversion values to each action — which you absolutely should do — you’ll also see ROAS increase. Both are false signals created by faulty math.
Focusing only on average cost per action (CPA) can also be misleading. Average CPA can mask your marginal CPA — the cost of acquiring one additional conversion as marketing spend increases. You might be overspending on incremental conversions as you scale your account.

  
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Set up conversion values correctly
Setting up conversion values is a must for offline conversions. A lot of B2B advertisers get hung up on this step. They say, “We don’t know the value of the conversion at the time it happens. We won’t know that until it works its way through the system.”
While using actual conversion values is ideal, don’t worry if you can’t. Just assign relative values to each conversion action. Here’s a simple example:




In this case, the advertiser is measuring four conversion actions: video views, ungated asset downloads, form fills, and MQLs. MQLs come from offline conversions. The rest are measured through Google Tag conversions.
Each conversion is worth 10x the previous action. Ungated asset downloads are worth 10x a video view, and so on. MQLs are worth 1,000x a video view. The advertiser would rather get one MQL than 999 video views.
If you set arbitrary values, make sure to validate them against real data to ensure they’re directing bidding algorithms accurately. Setting values that are too high for lower-funnel actions can cause the algorithm to favor those easier-to-generate actions while deprioritizing lower-funnel actions.
This happened to us recently with a client who was getting a lot of leads, but very few MQLs and SQLs. We reduced the value of leads by a factor of 10, which made MQLs and SQLs look more valuable to the algorithm and increased MQLs and SQLs relative to leads.
Within two weeks, MQL and SQL volume increased significantly, while leads stayed flat. That might not sound like a good thing, but it was. The client was getting higher-quality leads for the same cost.
By using the right conversion values, even relative ones, you can measure incremental conversion value more effectively.
Dig deeper: Why incrementality is the only metric that proves marketing’s real impact



Get more specific with campaign-specific goals
If you want Smart Bidding to focus on down-funnel actions, you can use campaign-specific goals. You can assign conversion actions at the campaign level, so Smart Bidding only optimizes toward those actions.
You can find the feature in campaign settings in both Google Ads and Microsoft Ads.
Here’s what that looks like in Google:




And here’s what it looks like for Microsoft:




Let’s say you have a campaign that’s driving a lot of leads, but few MQLs and SQLs. You could use campaign-specific goals to optimize only for MQLs and SQLs and ignore leads, even though leads are a primary conversion in the account.
Note: If lower-funnel actions have low volume, this technique may not work. Automation still needs a clear enough signal to optimize toward. So if you only have one or two MQLs in a month, the automation might struggle.
Dig deeper: Why Google Ads, GA4 and CRM numbers never match




	
	
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
The success metrics for measuring incremental conversions
Looking at simple CPA and ROAS metrics isn’t enough to measure success. You also need to look at each conversion action and measure incremental conversions. A basic way to measure incremental conversions is to establish your baseline first and then measure the CPA and ROAS of conversions at a higher spend level.
For example, let’s say you’re currently spending $5,000 per week and getting an average of 50 conversions, so your CPA is $100.
Now let’s say you increase your weekly spend to $7,500 and end up getting 70 conversions, for an average CPA of $107 — not much higher than the previous CPA of $100.
Your marginal spend is $2,500, marginal conversions are 20, and marginal CPA is $125 — 25% higher than the original CPA.
The difference in CPA may or may not be acceptable for your goals. It might make sense to invest more to increase sales. But you need to understand where your upper limit is. At some point, you may exceed the amount you’re willing to invest for an additional lead while still making fiscal sense.
If you want to get more sophisticated, you can use a marketing mix modeling tool (MMM) to run an incrementality test.
There are several MMM tools you could use to do this. Some are expensive, and some are low-cost or even free. 
For example, Google’s Meridian is an open-source tool and it’s free, but the trade-off is that it requires technical know-how to set up and use. 
MMMs also require a significant amount of historical data — two or more years’ worth — but once the data is ingested, they’re fantastic for measuring incrementality. 
Dig deeper: How to avoid marketing mix modeling mistakes that derail results




  
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The true test of whether your success metrics are working
MQLs, SQLs, and closed deals are important. But the true measure of incremental value is revenue or pipeline. That means you need to make sure you’re actually measuring for it.
Not all sales are created equal. You might see one deal for $5,000 and another for $2 million. Both register as closed sales in the CRM, but the two are nothing alike. Which would you rather have?
It’s easy to undervalue conversion actions if you’re using proxy values like the ones described above. Yet pipeline and revenue are often determined outside the 90-day conversion window required for offline conversions.
It’s crucial to look at the data in your CRM and map it back to your paid search campaigns. Are there campaigns or content assets driving relatively few leads and MQLs, but a lot of pipeline? If so, don’t devalue them. Make sure you keep them running and give them enough budget to succeed.
Also, don’t forget about incremental revenue.
If you’re scaling your spend, keep an eye on incremental revenue to find the point of diminishing returns. Doing this can prevent overspending on campaigns or channels once they’re no longer cost-effective.

More conversions and higher ROAS don’t always translate to more pipeline or revenue. Here’s how to measure incremental value more accurately.

Why your B2B PPC metrics may be lying to you

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-brad-geddes-cover-.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-brad-geddes-cover-.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-brad-geddes-cover--768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-brad-geddes-cover--1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-brad-geddes-cover--1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Brad Geddes reflects on the evolution of search marketing and why human creativity will remain essential in an AI-driven future.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-brad-geddes-cover-.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-brad-geddes-cover-.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-brad-geddes-cover--768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-brad-geddes-cover--1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/20-for-20-brad-geddes-cover--1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<p><a href="https://www.youtube.com/embed/8pT-WP0tbkI?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>



<p id="h-brad-geddes-is-an-industry-veteran-who-began-his-career-in-seo-in-1996-and-paid-search-in-1998-after-experiencing-burnout-in-a-completely-different-industry-he-learned-website-design-and-started-as-an-at-home-affiliate-marketer-for-companies-like-amazon-and-ebay-in-the-1990s-today-he-is-a-prominent-educator-a-leading-voice-in-the-ppc-community-and-the-creator-of-the-adalysis-platform-having-navigated-every-major-shift-in-the-search-marketing-landscape-over-the-last-two-decades">Brad Geddes began his career in the digital marketing industry by starting in SEO in 1996 and 1997, and then expanding into paid search in 1998. After experiencing burnout in a different field, he taught himself website design and entered the digital space as an at-home affiliate marketer for major companies like Amazon and eBay. </p>



<p id="h-brad-geddes-is-an-industry-veteran-who-began-his-career-in-seo-in-1996-and-paid-search-in-1998-after-experiencing-burnout-in-a-completely-different-industry-he-learned-website-design-and-started-as-an-at-home-affiliate-marketer-for-companies-like-amazon-and-ebay-in-the-1990s-today-he-is-a-prominent-educator-a-leading-voice-in-the-ppc-community-and-the-creator-of-the-adalysis-platform-having-navigated-every-major-shift-in-the-search-marketing-landscape-over-the-last-two-decades">Geddes identifies the 1998 launch of Goto.com by Bill Gross as the true inception of the pay-per-click system. This platform, which later became Overture and Yahoo Search Marketing, introduced a revolutionary pricing model that placed a financial value on clicks rather than just impressions.</p>



<h2 class="wp-block-heading" id="h-google-s-rise-to-dominance-and-the-changing-industry-culture"><strong>Google&#8217;s rise to dominance and the changing industry culture</strong> </h2>



<p>Google did not firmly establish itself as the accepted industry leader until around 2006 or 2007, and advertisers initially disliked its complex system. Google introduced the concept of &#8220;ad groups,&#8221; which forced marketers to transition from spending just a few hours a year on traditional advertising to managing digital campaigns on a weekly basis. </p>



<p>Ultimately, advertisers only adopted Google&#8217;s platform because its superior, user-centric search engine attracted the vast majority of internet traffic. Around the time Search Engine Land launched in 2006, the culture of the search industry was rapidly shifting from casual, basement-run operations into a mainstream corporate environment fueled by venture capital money, oversized salaries, and lavish private yacht parties. </p>



<p>Additionally, earlier days of the industry saw greater information sharing among professionals because there were fewer corporate lock-downs and NDAs compared to today.</p>



<h2 class="wp-block-heading" id="h-major-milestones-that-changed-ppc-forever"><strong>Major milestones that changed PPC forever</strong> </h2>



<p>Geddes points to two major turning points that permanently altered the paid search landscape. First, Google&#8217;s organic algorithm updates—Panda, Penguin, and Pigeon—made organic search highly complex, forcing marketers to realize they could no longer be generalists and had to specialize in either SEO or paid search. </p>



<p>The second major milestone was the successful implementation of automated bidding. Before this technology, bidding required tedious, transient work using Excel formulas. Automation freed up massive amounts of time for marketers to focus on creativity and strategic account management instead. </p>



<p>Furthermore, Geddes highlights a massive change in 2005 when Google decided to only allow one ad per domain on a search results page, which forced affiliate marketers to build dedicated landing pages and add real value to the user experience in order to survive.</p>



<h2 class="wp-block-heading" id="h-outdated-tactics-and-missed-platform-features"><strong>Outdated tactics and missed platform features</strong> </h2>



<p>Reflecting on older practices, Geddes expresses a strong dislike for Single Keyword Ad Groups (SKAGs), an over-segmentation strategy that once forced advertisers to build thousands of campaigns due to early platform limitations. </p>



<p>However, he does miss several features that are no longer available, such as the original Enhanced Cost-Per-Click (ECPC) that allowed advertisers to dictate the exact price they wanted to pay per click and have Google do the math. </p>



<p>He also misses hyper-specific geo-targeting tools that allowed marketers to draw custom radius points around specific interstates, as well as features that allowed custom ad adjustments for individual business locations.</p>



<h2 class="wp-block-heading" id="h-ai-misconceptions-and-the-next-20-years-of-search"><strong>AI misconceptions and the next 20 years of search</strong> </h2>



<p>Looking toward the future, Geddes warns against the widespread misconception that artificial intelligence can completely run an advertising account. He argues that just as marketers shouldn&#8217;t blindly hit &#8220;accept all&#8221; on Google&#8217;s auto-applied recommendations, they shouldn&#8217;t hand total control over to AI because it can write both good and bad ads. </p>



<p>Over the next two decades, he predicts the industry will reward creativity, strategic tactics, and business optimization rather than basic &#8220;button-pushers&#8221;. Because marketing fundamentally relies on connecting with human beings—who often make illogical choices contrary to how an AI thinks—human creativity will remain critical.</p>



<h2 class="wp-block-heading" id="h-rapid-fire-retrospective"><strong>Rapid-fire retrospective</strong> </h2>



<p>When asked for quick thoughts on the industry, Geddes noted that a major prediction he got wrong was thinking mobile adoption would happen much faster than it actually did. </p>



<p>Conversely, he accurately predicted that voice search was overblown and would simply become part of normal search queries rather than a completely separate channel. He observed that Google rarely admits how the world actually functions today, preferring instead to market ideal future scenarios based on massive amounts of data that most advertisers don&#8217;t have. </p>



<p>Furthermore, he believes that PPC professionals rarely test their campaigns as much as they claim to. Finally, if he could give his younger self one piece of advice from 10 to 15 years ago, it would simply be to buy more Google stock.</p>


Watch this video on YouTube




Brad Geddes began his career in the digital marketing industry by starting in SEO in 1996 and 1997, and then expanding into paid search in 1998. After experiencing burnout in a different field, he taught himself website design and entered the digital space as an at-home affiliate marketer for major companies like Amazon and eBay. 
Geddes identifies the 1998 launch of Goto.com by Bill Gross as the true inception of the pay-per-click system. This platform, which later became Overture and Yahoo Search Marketing, introduced a revolutionary pricing model that placed a financial value on clicks rather than just impressions.
Google’s rise to dominance and the changing industry culture 
Google did not firmly establish itself as the accepted industry leader until around 2006 or 2007, and advertisers initially disliked its complex system. Google introduced the concept of “ad groups,” which forced marketers to transition from spending just a few hours a year on traditional advertising to managing digital campaigns on a weekly basis. 
Ultimately, advertisers only adopted Google’s platform because its superior, user-centric search engine attracted the vast majority of internet traffic. Around the time Search Engine Land launched in 2006, the culture of the search industry was rapidly shifting from casual, basement-run operations into a mainstream corporate environment fueled by venture capital money, oversized salaries, and lavish private yacht parties. 
Additionally, earlier days of the industry saw greater information sharing among professionals because there were fewer corporate lock-downs and NDAs compared to today.
Major milestones that changed PPC forever 
Geddes points to two major turning points that permanently altered the paid search landscape. First, Google’s organic algorithm updates—Panda, Penguin, and Pigeon—made organic search highly complex, forcing marketers to realize they could no longer be generalists and had to specialize in either SEO or paid search. 
The second major milestone was the successful implementation of automated bidding. Before this technology, bidding required tedious, transient work using Excel formulas. Automation freed up massive amounts of time for marketers to focus on creativity and strategic account management instead. 
Furthermore, Geddes highlights a massive change in 2005 when Google decided to only allow one ad per domain on a search results page, which forced affiliate marketers to build dedicated landing pages and add real value to the user experience in order to survive.
Outdated tactics and missed platform features 
Reflecting on older practices, Geddes expresses a strong dislike for Single Keyword Ad Groups (SKAGs), an over-segmentation strategy that once forced advertisers to build thousands of campaigns due to early platform limitations. 
However, he does miss several features that are no longer available, such as the original Enhanced Cost-Per-Click (ECPC) that allowed advertisers to dictate the exact price they wanted to pay per click and have Google do the math. 
He also misses hyper-specific geo-targeting tools that allowed marketers to draw custom radius points around specific interstates, as well as features that allowed custom ad adjustments for individual business locations.
AI misconceptions and the next 20 years of search 
Looking toward the future, Geddes warns against the widespread misconception that artificial intelligence can completely run an advertising account. He argues that just as marketers shouldn’t blindly hit “accept all” on Google’s auto-applied recommendations, they shouldn’t hand total control over to AI because it can write both good and bad ads. 
Over the next two decades, he predicts the industry will reward creativity, strategic tactics, and business optimization rather than basic “button-pushers”. Because marketing fundamentally relies on connecting with human beings—who often make illogical choices contrary to how an AI thinks—human creativity will remain critical.
Rapid-fire retrospective 
When asked for quick thoughts on the industry, Geddes noted that a major prediction he got wrong was thinking mobile adoption would happen much faster than it actually did. 
Conversely, he accurately predicted that voice search was overblown and would simply become part of normal search queries rather than a completely separate channel. He observed that Google rarely admits how the world actually functions today, preferring instead to market ideal future scenarios based on massive amounts of data that most advertisers don’t have. 
Furthermore, he believes that PPC professionals rarely test their campaigns as much as they claim to. Finally, if he could give his younger self one piece of advice from 10 to 15 years ago, it would simply be to buy more Google stock.

Brad Geddes reflects on the evolution of search marketing and why human creativity will remain essential in an AI-driven future.

Brad Geddes on 20 Years of Paid Search Evolution

<div><img width="1658" height="949" src="https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center.png 1658w, https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center-768x440.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center-1536x879.png 1536w" sizes="(max-width: 1658px) 100vw, 1658px" /></div>Google is adding new AI shopping insights to Merchant Center, giving retailers visibility into how products perform across their AI platforms. 

<div><img width="1658" height="949" src="https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center.png 1658w, https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center-768x440.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center-1536x879.png 1536w" sizes="(max-width: 1658px) 100vw, 1658px" /></div>
<p>Google is rolling out new AI performance insights inside Merchant Center to help retailers track how products appear across AI-powered shopping experiences.</p>



<p><strong>What’s happening.</strong> The new reporting tools are designed to show advertisers how their products perform across Google’s AI-powered shopping surfaces.</p>



<p>The reports will include:</p>



<ul class="wp-block-list">
<li>share of voice insights benchmarking brand visibility against similar retailers,</li>



<li>shopping funnel performance across discovery, evaluation and purchase stages,</li>



<li>product term insights showing popular conversational shopping queries,</li>



<li>and product attribute insights highlighting missing product specifications.</li>
</ul>



<p>Google says advertisers will also be able to identify which structured product attributes, such as color, material or style, are incomplete across their product feeds.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="2048" height="1152" src="https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center-1.png" alt="" class="wp-image-478990" srcset="https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center-1.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center-1-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center-1-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/AI-shopping-visibility-insights-to-Merchant-Center-1-1200x675.png 1200w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<p><strong>Why we care. </strong>AI-powered shopping experiences are changing how consumers discover products across Search and Gemini, making visibility inside conversational results increasingly important. The new Merchant Center insights give brands early access to performance data that could help improve product feed quality, optimize discoverability and better understand how Google’s AI surfaces are ranking and recommending products.</p>



<p><strong>The bigger picture.</strong> Merchant Center is steadily evolving from a product feed management tool into an AI commerce optimization platform.</p>



<p>As Search, Gemini and AI Overviews become more conversational, retailers may need to optimize product feeds more like SEO content — focusing on completeness, context and natural language discoverability.</p>



<p><strong>What to watch. </strong><a href="https://support.google.com/merchants/answer/17117204">The new insights</a> could give advertisers an early look into how Google measures “share of voice” inside AI shopping environments — an area likely to become increasingly important as AI-generated commerce experiences expand.</p>



<p><strong>Availability.</strong> AI performance insights will roll out to advertisers in the U.S., Canada, Australia, India and New Zealand in the coming months.</p>


Google is rolling out new AI performance insights inside Merchant Center to help retailers track how products appear across AI-powered shopping experiences.
What’s happening. The new reporting tools are designed to show advertisers how their products perform across Google’s AI-powered shopping surfaces.
The reports will include:
share of voice insights benchmarking brand visibility against similar retailers,
shopping funnel performance across discovery, evaluation and purchase stages,
product term insights showing popular conversational shopping queries,
and product attribute insights highlighting missing product specifications.
Google says advertisers will also be able to identify which structured product attributes, such as color, material or style, are incomplete across their product feeds.




Why we care. AI-powered shopping experiences are changing how consumers discover products across Search and Gemini, making visibility inside conversational results increasingly important. The new Merchant Center insights give brands early access to performance data that could help improve product feed quality, optimize discoverability and better understand how Google’s AI surfaces are ranking and recommending products.
The bigger picture. Merchant Center is steadily evolving from a product feed management tool into an AI commerce optimization platform.
As Search, Gemini and AI Overviews become more conversational, retailers may need to optimize product feeds more like SEO content — focusing on completeness, context and natural language discoverability.
What to watch. The new insights could give advertisers an early look into how Google measures “share of voice” inside AI shopping environments — an area likely to become increasingly important as AI-generated commerce experiences expand.
Availability. AI performance insights will roll out to advertisers in the U.S., Canada, Australia, India and New Zealand in the coming months.

Google is adding new AI shopping insights to Merchant Center, giving retailers visibility into how products perform across their AI platforms.

Google adds AI shopping visibility insights to Merchant Center

<div><img width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/05/vanessa-fox-2020.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/vanessa-fox-2020.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/05/vanessa-fox-2020-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/vanessa-fox-2020-1200x675.jpg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></div>From the super early days of Google through using AI today for SEO - we covered a lot in this interview.

<div><img width="1280" height="720" src="https://searchengineland.com/wp-content/seloads/2026/05/vanessa-fox-2020.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/vanessa-fox-2020.jpg 1280w, https://searchengineland.com/wp-content/seloads/2026/05/vanessa-fox-2020-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/vanessa-fox-2020-1200x675.jpg 1200w" sizes="(max-width: 1280px) 100vw, 1280px" /></div>
<p>Vanessa Fox was the individual who was instrumental in what we call Google Search Console today. I sat down with Vanessa Fox for a one-on-one interview to discuss the early days of Google, how Search Console came about and the early days of the industry, through what the industry is now.</p>



<p>We spoke about what it was like to work at Google in the early days, how XML Sitemaps turned into Webmaster Tools, which turned into Search Console, what it was like working with Matt Cutts. We also spoke about how she sold her Google stock options way too soon and then how she left Google and found herself for a period of time writing at Search Engine Land, this site.</p>



<p>Vanessa spoke about the early days of SEO misconceptions, her Panda SEO audits and recoveries, how AI is changing search and SEO, and much more.</p>



<p>Here is the interview:</p>



<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<p><a href="https://www.youtube.com/embed/RCNwbpTX5oA?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>



<p>Here is an outline of what we spoke about:</p>



<ul class="wp-block-list">
<li>The early days of XML sitemaps and the beginnings of Google Search Console. </li>



<li>Vanessa’s professional background in UX and technical writing before joining Google. </li>



<li>Joining Google: The Kirkland office culture and working with 200 employees worldwide. </li>



<li>Collaborating with Matt Cutts and using help center data to educate site owners. </li>



<li>A &#8220;sad story&#8221; about selling Google stock options too early due to a past experience at AOL. </li>



<li>Leaving Google in 2007 and joining Search Engine Land to provide a unique technical perspective. </li>



<li>Debunking early SEO misconceptions: The reality of the Google spam team vs. &#8220;&#8221;sneaky&#8221;&#8221; tactics. </li>



<li>Investigating Super Bowl search trends and the disconnect between brands and searchers. </li>



<li>Deep dive into the Panda algorithm: Analyzing sitewide quality over individual pages. </li>



<li>Thoughts on outdated tactics: Subdomains, parameters, and the rise of &#8220;&#8221;bad&#8221;&#8221; SEO advice on TikTok. </li>



<li>The impact of AI Overviews (AIO) on publisher traffic and searcher behavior. </li>



<li>Is SEO ending? Why AI is an evolution of search, not its demise. </li>



<li>Frustrations with Search Console data: The lack of metrics for Featured Snippets and AI Overviews. </li>



<li>How Vanessa uses AI (Claude) today for structural tasks while maintaining human expertise. </li>



<li>Proudest moment: Institutionalizing a culture at Google that listens to and supports site owners. </li>



<li>The long-term impact of Search Engine Land and the Search Engine Roundtable on the industry.&#8221;</li>
</ul>



<p>You can learn more about <a href="https://www.vanessafox.com/about/">Vanessa Fox on her site over here</a>.</p>


Vanessa Fox was the individual who was instrumental in what we call Google Search Console today. I sat down with Vanessa Fox for a one-on-one interview to discuss the early days of Google, how Search Console came about and the early days of the industry, through what the industry is now.
We spoke about what it was like to work at Google in the early days, how XML Sitemaps turned into Webmaster Tools, which turned into Search Console, what it was like working with Matt Cutts. We also spoke about how she sold her Google stock options way too soon and then how she left Google and found herself for a period of time writing at Search Engine Land, this site.
Vanessa spoke about the early days of SEO misconceptions, her Panda SEO audits and recoveries, how AI is changing search and SEO, and much more.
Here is the interview:

Watch this video on YouTube




Here is an outline of what we spoke about:
The early days of XML sitemaps and the beginnings of Google Search Console. 
Vanessa’s professional background in UX and technical writing before joining Google. 
Joining Google: The Kirkland office culture and working with 200 employees worldwide. 
Collaborating with Matt Cutts and using help center data to educate site owners. 
A “sad story” about selling Google stock options too early due to a past experience at AOL. 
Leaving Google in 2007 and joining Search Engine Land to provide a unique technical perspective. 
Debunking early SEO misconceptions: The reality of the Google spam team vs. “”sneaky”” tactics. 
Investigating Super Bowl search trends and the disconnect between brands and searchers. 
Deep dive into the Panda algorithm: Analyzing sitewide quality over individual pages. 
Thoughts on outdated tactics: Subdomains, parameters, and the rise of “”bad”” SEO advice on TikTok. 
The impact of AI Overviews (AIO) on publisher traffic and searcher behavior. 
Is SEO ending? Why AI is an evolution of search, not its demise. 
Frustrations with Search Console data: The lack of metrics for Featured Snippets and AI Overviews. 
How Vanessa uses AI (Claude) today for structural tasks while maintaining human expertise. 
Proudest moment: Institutionalizing a culture at Google that listens to and supports site owners. 
The long-term impact of Search Engine Land and the Search Engine Roundtable on the industry.”
You can learn more about Vanessa Fox on her site over here.

From the super early days of Google through using AI today for SEO - we covered a lot in this interview.

Vanessa Fox on the birth of Google Search Console

<div><img width="1658" height="949" src="https://searchengineland.com/wp-content/seloads/2026/05/ChatGPT-Image-May-28-2026-05_18_13-PM.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/ChatGPT-Image-May-28-2026-05_18_13-PM.png 1658w, https://searchengineland.com/wp-content/seloads/2026/05/ChatGPT-Image-May-28-2026-05_18_13-PM-768x440.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/ChatGPT-Image-May-28-2026-05_18_13-PM-1536x879.png 1536w" sizes="(max-width: 1658px) 100vw, 1658px" /></div>Google’s new AI targeting mode is designed to help brands reach entirely new brand-unaware audiences earlier in the discovery journey.

<div><img width="1658" height="949" src="https://searchengineland.com/wp-content/seloads/2026/05/ChatGPT-Image-May-28-2026-05_18_13-PM.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/ChatGPT-Image-May-28-2026-05_18_13-PM.png 1658w, https://searchengineland.com/wp-content/seloads/2026/05/ChatGPT-Image-May-28-2026-05_18_13-PM-768x440.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/ChatGPT-Image-May-28-2026-05_18_13-PM-1536x879.png 1536w" sizes="(max-width: 1658px) 100vw, 1658px" /></div>
<p>Google is introducing a new “prospects” targeting mode designed to help advertisers reach consumers who have never interacted with their brand before.</p>



<p><strong>What’s happening.</strong> The company is expanding its New Customer Acquisition tools with a new “new prospects” mode launching this year.</p>



<p>Unlike traditional new customer targeting, which focuses on users who haven’t purchased before, the new mode is designed to reach people completely unfamiliar with a brand.</p>



<p>Google says the system automatically excludes users who have:</p>



<ul class="wp-block-list">
<li>purchased previously,</li>



<li>searched for brand terms,</li>



<li>visited a website or app,</li>



<li>or engaged with brand content across Google and YouTube.</li>
</ul>



<p>The goal is to focus ad spend entirely on “cold” audiences still in the discovery phase.</p>



<p><strong>Why we care. </strong>Google is giving brands more control over how aggressively they pursue incremental customer growth instead of repeatedly targeting existing audiences. The new “prospects” mode could help advertisers reach entirely new users earlier in the buying journey while improving acquisition efficiency through AI-powered audience exclusions and automation.</p>



<p><strong>The bigger picture.</strong> Google is increasingly positioning AI-driven audience targeting as a way for advertisers to balance growth with efficiency.</p>



<p>The company says advertisers using New Customer Acquisition Value Mode — which assigns higher value to new buyers while still reaching existing customers — saw a 9% improvement in ROAS when valuing new customers at twice average order value.</p>



<p><strong>Between the lines.</strong> As AI-driven targeting expands, platforms are increasingly using behavioral signals, first-party data and automated exclusions to identify users earlier in the purchase journey.</p>



<p><strong>What to watch. </strong><a href="https://business.google.com/us/accelerate/announcements/new-customer-acquisition-modes/">The effectiveness of “new prospects” </a>mode may depend heavily on Google’s ability to accurately identify brand-unaware users while balancing reach, efficiency and privacy restrictions tied to audience tracking.</p>


Google is introducing a new “prospects” targeting mode designed to help advertisers reach consumers who have never interacted with their brand before.
What’s happening. The company is expanding its New Customer Acquisition tools with a new “new prospects” mode launching this year.
Unlike traditional new customer targeting, which focuses on users who haven’t purchased before, the new mode is designed to reach people completely unfamiliar with a brand.
Google says the system automatically excludes users who have:
purchased previously,
searched for brand terms,
visited a website or app,
or engaged with brand content across Google and YouTube.
The goal is to focus ad spend entirely on “cold” audiences still in the discovery phase.
Why we care. Google is giving brands more control over how aggressively they pursue incremental customer growth instead of repeatedly targeting existing audiences. The new “prospects” mode could help advertisers reach entirely new users earlier in the buying journey while improving acquisition efficiency through AI-powered audience exclusions and automation.
The bigger picture. Google is increasingly positioning AI-driven audience targeting as a way for advertisers to balance growth with efficiency.
The company says advertisers using New Customer Acquisition Value Mode — which assigns higher value to new buyers while still reaching existing customers — saw a 9% improvement in ROAS when valuing new customers at twice average order value.
Between the lines. As AI-driven targeting expands, platforms are increasingly using behavioral signals, first-party data and automated exclusions to identify users earlier in the purchase journey.
What to watch. The effectiveness of “new prospects” mode may depend heavily on Google’s ability to accurately identify brand-unaware users while balancing reach, efficiency and privacy restrictions tied to audience tracking.

Google’s new AI targeting mode is designed to help brands reach entirely new brand-unaware audiences earlier in the discovery journey.

Google expands customer acquisition targeting with “new prospects” mode

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-persuasive-content-taps-into-human-psychology.png" class="attachment-large size-large wp-post-image" alt="How persuasive content taps into human psychology" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-persuasive-content-taps-into-human-psychology.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-persuasive-content-taps-into-human-psychology-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-persuasive-content-taps-into-human-psychology-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-persuasive-content-taps-into-human-psychology-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>TikTok Shop creators understand the psychology that drives action. Here’s how to apply the same persuasion principles to written content.

SEO content is built to rank. Conversion isn’t always the priority when you’re focused on everything else on your SEO checklist.
But as AI Overviews and declining click-through rates make visibility harder to earn, it’s worth thinking about whether your content is built to inspire action once people see it.
TikTok Shop creators have become especially effective at this. The top performers aren’t succeeding because they have massive followings. They’re succeeding because they understand persuasion, consumer psychology, and how to drive action at scale.
You can apply the same principles to written content.
The TikTok Shop conversion formula
In January, I became a TikTok Shop affiliate to better understand what was working on the platform. The more I studied top-performing creators, the more I realized their success wasn’t random.
They were following a formula rooted in consumer psychology. It wasn’t about massive followings or celebrity. On my own videos, 99% of views don’t come from followers.
The creators generating hundreds of thousands of dollars in sales tend to use the same patterns repeatedly, because they understand the psychology that drives action.
The formula usually comes down to four things: visual hooks, psychology levers, storytelling, and relentless testing. Applied to written content, the same structure can drive results.

  
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The foundation: People buy emotionally, then justify rationally
People aren’t rationally evaluating your features and pricing. They’re responding to emotional triggers and rationalizing the decision afterward.
It’s no longer enough to have the right words on your page. They need to connect with the motivations driving someone to act.
The TikTok Shop formula works because it’s built on this principle. Every element in a high-converting video is designed to speak to a natural human desire.
The creators doing this well aren’t necessarily marketing experts. They’re consistently applying consumer psychology.
The 8 human desires in sales psychology
Sales psychology is built around eight natural human desires that motivate behavior:
Care and protection of loved ones.
Survival, enjoyment of life, and life extension.
Enjoyment of food and beverage.
Freedom from fear, pain, and danger.
Sexual companionship.
Comfortable living conditions.
Being superior, winning, and keeping up.
Social approval.
These desires drive human behavior across cultures and contexts. Part of our job as marketers is identifying which ones matter to our audience and weaving them into the content people are already searching for.
Here’s what each desire means and what it might look like in your content.
1. Care and protection of loved ones
The instinct to protect family is one of the strongest levers in consumer psychology. People are more motivated to act when it helps protect the people they love.
What it sounds like in your content:




Insurance: “When something difficult happens, having the right protections in place means your family doesn’t have to think about finances during an already difficult time.”
Home security: “A break-in doesn’t just threaten your belongings. It can shatter your family’s sense of safety. The right security system helps protect both.”
Home repairs: “Fixing a small roof leak today can help protect the home your family depends on tomorrow.”
The shift is subtle but significant. You’re not just describing a product or service. You’re speaking to the person behind the purchase decision and helping them understand the impact of not using your product or service on their family.
2. Survival, enjoyment of life, and life extension
This lever taps into people’s natural desire to protect their health, preserve their quality of life, and make the most of the time they have. It works by showing how a product or decision can help them feel better, stay active, avoid decline, or enjoy life more fully.
What it sounds like in your content:




Supplements: “The right nutrients can help you maintain the energy to keep chasing after your kids, hiking on weekends, or simply feeling like yourself again.”
Outdoor gear: “Reliable gear helps you spend less time worrying about the trail ahead and more time enjoying the experience around you.”
Travel: “Life moves fast. Taking the trip you’ve been putting off for years gives you the chance to experience more of the world while you can.”
3. Enjoyment of food and beverage
People don’t just eat and drink to survive. Food is tied to enjoyment, comfort, celebration, and connection. This lever highlights the emotions and experiences associated with eating and drinking.
What it sounds like in your content:




Meal kit delivery: “Stop treating dinner like a task. Make it the part of your day you actually look forward to with the people who matter most.”
Travel: “From family-owned cafés to local dishes you’ll be talking about long after your trip ends, experience Italy through your taste buds with our food tour.”
Restaurant technology: “When the kitchen runs smoothly, the food gets to the table the way the chef intended.”
4. Freedom from fear, pain, and danger
People are more motivated to avoid pain than pursue gain. This lever applies to almost every industry.
What it sounds like in your content:




Cybersecurity: “One vulnerability could cost you more than your data. Businesses lose [X] every year to attacks that were entirely preventable.”
Domain registration: “Forgetting to renew your domain can mean losing your website, business email, and the online presence you’ve spent years building.”
Financial planning: “The biggest retirement risk isn’t market volatility. It’s reaching retirement and realizing you haven’t saved enough to maintain the lifestyle you worked so hard to build.”
5. Sexual companionship
We’ve all heard the phrase “sex sells” because it taps into a natural human desire. This lever highlights how a product, service, or action can increase confidence, attractiveness, connection, desirability, intimacy, or relationship satisfaction.
What it sounds like in your content:




Fitness and wellness: “When you feel stronger, healthier, and more confident in your own skin, others begin to notice.”
Skincare: “There’s something powerful about leaving the house without makeup, filters, or hesitation.”
Fashion and retail: “The right fit isn’t about impressing everyone. It’s about catching yourself in the mirror and liking what you see.”
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6. Comfortable living conditions
People naturally want life to feel easier, more comfortable, and less stressful. This lever highlights how a product improves someone’s environment, routines, convenience, peace, or quality of life.
What it sounds like in your content:




Smart home technology: “Walk into a home that’s already the perfect temperature, the lights are on, and everything is ready for you to relax.”
Headphones: “Whether it’s a noisy commute or a busy household, sometimes comfort means having a quiet moment that’s entirely your own.”
Meal delivery service: “Dinner shouldn’t feel like another task on your to-do list. Enjoy fresh meals without the planning, shopping, or cleanup.”
7. Being superior, winning, and keeping up
People want to feel capable, successful, respected, and ahead of the curve. This lever highlights how a product helps them perform better, achieve more, stand out, or avoid falling behind.
What it sounds like in your content:




SEO agencies or tools: “While competitors are still trying to understand AI search results, you’ll already know where you stand and how to improve visibility.”
Online education: “The skills that are optional today could become the qualifications employers expect tomorrow.”
Fitness: “Most people quit after a few weeks. Consistent training puts you among the small percentage who actually follow through.”
8. Social approval
This is one of the easiest levers to use. It can be as simple as highlighting customer volume, units sold, customer quotes, reviews, or Trustpilot widgets on your site.
You don’t have to tell people your brand has social approval. You need to show it.
In this context, the goal is to highlight your brand’s credibility to customers and Google, not necessarily to convince people that your product gives them social approval.
A bonus layer: Learned desires
Beyond the eight core desires, there are learned desires that can complement the deeper psychological levers. These include:
Convenience (“this takes 10 minutes, not an hour”).
Curiosity (“here’s what most [audience] gets wrong about this”).
Financial gain (“here’s what this is actually costing you right now”).
How this framework improved our results
My team has applied this framework to blog content over the past year. We’re not doing anything dramatic. We’re being more deliberate about which levers we use, for which audience, and where they appear in the article.
While other factors certainly contributed, psychology-based content optimization became a major focus for my junior team. Since July 2025, that focus has driven a 136% increase in total blog visits and a 286% increase in participation order volume from our blog.
Average ranking position across roughly 600 articles has moved from the bottom of page two to the top of page one. No link building. No major technical overhaul. Just really strong content.
The article converting best for us right now, “I bought a domain, now what?”, combines multiple levers throughout the piece. We use:
Authority layering through SME quotes.
Pain-avoidance framing around domain expiration.
Social approval positioning around email credibility.
Building a psychology-driven content strategy
This is easier said than done, and it takes time and patience to implement. But this human layer will become increasingly important for conversion as views and clicks continue declining.
The traffic you do get will likely be more qualified. The question becomes how to increase the likelihood that people take action once they arrive.
Here are three ways to start optimizing your content for conversion.
Understand the person behind the search
Knowing your audience’s demographics isn’t enough. You need to understand:
Their environment.
What they do and don’t have access to.
What influences their decisions beyond your product category.
A small business owner in a large city like New York has different foundational needs than one in a market like West Virginia, where starting a business is less common.
Map levers and intended actions to the journey 
Think of this as user journey mapping 2.0. For each page, persona, and journey stage, identify which levers make the most sense and what action you want someone to take. Not every page is designed to sell.
Ask yourself:
What is this person afraid of at this stage?
What do they want to feel after reading this?
What action do they need to take?
Start optimizing
This should become an ongoing part of your content strategy, so start where you can, even if it’s just one page.
Apply one or two levers throughout the page and measure what happens. When something works, reverse-engineer the combination:
Which desire was involved?
Which persona?
Which stage of the journey?
Then expand those insights across related content once you’ve identified a combination that works.
How to make these levers feel natural
A lot of this is the art side of marketing, which means there’s room for interpretation and experimentation. 
Use data: You don’t need proprietary research. The Census Bureau, Bureau of Labor Statistics, industry reports, and competitor review platforms are full of data that can make abstract concepts feel tangible. The goal is to use a few well-placed statistics to make a point land.
Name your audience: Users are adding more context to long-tail searches, so reflect those audiences in your content. If you want to reach small business owners, say “small business owners” in your content. If you’re speaking to CMOs, say that. You can address multiple audiences throughout a piece. Just make sure the storytelling still feels cohesive.
Don’t overdo it: Just like too much salt can ruin a meal, overly hyped content can feel inauthentic and turn people away. Keep it simple. Look for one or two strong opportunities to apply these tactics and use them deliberately.

  
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To make this more practical, I’ve built two resources you can use in your process.
A custom GPT for blog-level analysis: Paste in an existing article and ask it to identify which psychology levers are present, which are missing, and where they could be added.
A user journey mapping template: A prebuilt template for mapping personas, desired actions, psychology levers, and content by funnel stage. Copy it and adapt it to your site.
The human layer behind content performance
Technical optimization will continue to play a major role in content visibility. But as visibility becomes harder to earn, the content that performs best will be the content that motivates people to act.
Content that understands the person behind the query — including their fears, motivations, and decisions — will drive stronger business outcomes for SEO and content teams.
TikTok creators and UGC affiliates have already figured this out. They understand how to create persuasive content at scale by tapping into human psychology. You can apply the same thinking to written content.

TikTok Shop creators understand the psychology that drives action. Here’s how to apply the same persuasion principles to written content.

How persuasive content taps into human psychology

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/5-places-to-find-FAQ-content-that-improves-AI-visibility.png" class="attachment-large size-large wp-post-image" alt="5 places to find FAQ content that improves AI visibility" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/5-places-to-find-FAQ-content-that-improves-AI-visibility.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/5-places-to-find-FAQ-content-that-improves-AI-visibility-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/5-places-to-find-FAQ-content-that-improves-AI-visibility-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/5-places-to-find-FAQ-content-that-improves-AI-visibility-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Use GSC, Reddit, People Also Ask data, customer insights, and AI prompt trends to uncover the questions that drive search visibility.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/5-places-to-find-FAQ-content-that-improves-AI-visibility.png" class="attachment-large size-large wp-post-image" alt="5 places to find FAQ content that improves AI visibility" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/5-places-to-find-FAQ-content-that-improves-AI-visibility.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/5-places-to-find-FAQ-content-that-improves-AI-visibility-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/5-places-to-find-FAQ-content-that-improves-AI-visibility-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/5-places-to-find-FAQ-content-that-improves-AI-visibility-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Frequently asked questions (FAQs) used to sit quietly on support pages and product hubs. Now they influence visibility across AI Overviews, People Also Ask results, and AI search engines that prioritize direct answers to user questions.</p>



<p>More than <a href="https://www.semrush.com/blog/ai-overviews-study/">80% of AI Overview queries</a> are informational, and 82% have average monthly search volumes under 1,000, Semrush research found. That means longer-tail, lower-volume queries are increasingly driving AI visibility opportunities.</p>



<p>As search behavior becomes more conversational, the quality of your FAQ strategy increasingly depends on the quality of the questions behind it.</p>



<p>The problem is that many brands still rely on the same limited sources for FAQ ideation, even as search behavior evolves. The strongest FAQ opportunities often come from the places where customers are already asking questions naturally — across search, support channels, communities, and AI platforms.</p>



<p>Here are five sources to help you find and prioritize better FAQ content opportunities.</p>



<h2 class="wp-block-heading" id="h-1-google-search-console-data-nbsp">1. Google Search Console data&nbsp;</h2>



<p>To some people, this may seem obvious. But it’s also easily overlooked.</p>



<p>Before you begin ideating new FAQ content, audit what’s already gaining traction. <a href="https://searchengineland.com/google-search-console-seo-guide-443942">Google Search Console</a> (GSC) is the perfect place to do that. It’s an underutilized FAQ research tool because many SEOs filter for their highest-impression or highest-clicked queries, not their highest-intent ones.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="630" height="357" src="https://searchengineland.com/wp-content/seloads/2026/05/Google-Search-Console-regex-to-mine-question-based-queries.png" alt="Google Search Console regex to mine question based queries" class="wp-image-478796"/></figure>
</div>


<p>To zero in on intent queries, start by filtering your query data for question-based search patterns using this regex:</p>



<p><code>^(who|what|where|when|why|how|which|whose|whom|is|are|was|were|do|does|did|can|could|will|would|should|has|have|had)\b</code>



<p>Then, cross-reference average position against CTR. For queries ranking from the middle of page one through the bottom of page two with low CTR, you’ve likely found a candidate for dedicated FAQ content.</p>



<p>If you rank too low, you may not be relevant enough. If you already rank highly, you probably don’t want to disrupt what’s working. Positions 4-20 can be a sweet spot, although it’s always worth looking outside that range just in case.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="630" height="394" src="https://searchengineland.com/wp-content/seloads/2026/05/Google-Search-Console-regex-to-mine-long-tail-queries-1.png" alt="Google Search Console regex to mine long tail queries" class="wp-image-478800"/></figure>
</div>


<p>To expand from there, use this regex to filter for long-tail queries with eight or more words:</p>



<p><code>^(\S+\s+){8,}\S+$ </code>



<p>If eight-plus-word queries don’t produce enough results, try lowering the threshold to five to seven words. This can be another GSC gold mine for uncovering FAQ opportunities from existing traffic.</p>



<p>It can also turn up a ton of great queries worth tracking in your <a href="https://searchengineland.com/choose-best-ai-visibility-tool-454457">AI visibility software</a>.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/how-to-use-google-search-console-to-unlock-easy-seo-wins-445389">How to use Google Search Console to unlock easy SEO wins</a></em></strong>



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<h2 class="wp-block-heading" id="h-2-people-also-ask-data">2. People Also Ask data</h2>



<p>The <a href="https://searchengineland.com/guide/people-also-ask">People Also Ask SERP feature</a> is a valuable signal for understanding how your audience’s questions relate, cluster, and evolve. It’s also one of the few publicly available windows into Google’s understanding of search intent relationships.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="630" height="249" src="https://searchengineland.com/wp-content/seloads/2026/05/People-also-ask-example-for-query-google-marketing-live-updates.png" alt="People also ask example for query “google marketing live updates”" class="wp-image-478801"/></figure>
</div>


<p>Some PAA questions may warrant an entire page, depending on the topic and the value you can provide by going deeper. But they’re also a strong way to identify related FAQs that can strengthen pages you already have.</p>



<p>Tools like AnswerThePublic and AlsoAsked surface these branching question trees at scale, helping you map how topics are explored across search behavior.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="718" height="430" src="https://searchengineland.com/wp-content/seloads/2026/05/AnswerThePublic-data-for-query-search-everywhere-optimization.png" alt="AnswerThePublic data for query “search everywhere optimization”" class="wp-image-478802"/></figure>
</div>


<p>There’s still value in doing manual SERP research, too. Search your target topics on Google and expand PAA results across five to 10 priority terms. Look for recurring questions that are relevant to your customers.</p>



<p>Those recurring questions are high-signal candidates that reflect broad informational intent. They’re also more likely to earn AI citations because they represent genuine, repeated user demand.</p>



<p>Exploding Topics doesn’t specifically surface PAA queries, but it’s another valuable research tool for spotting topics that are gaining search interest before they peak. That gives you an opportunity to build FAQ content ahead of demand, anticipating the questions your audience may soon start asking more frequently.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1985" height="989" src="https://searchengineland.com/wp-content/seloads/2026/05/Sample-exploding-topics-data.png" alt="Exploding Topics - Indie publisher and influence engineering" class="wp-image-478803" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Sample-exploding-topics-data.png 1985w, https://searchengineland.com/wp-content/seloads/2026/05/Sample-exploding-topics-data-768x383.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Sample-exploding-topics-data-1536x765.png 1536w" sizes="(max-width: 1985px) 100vw, 1985px" /></figure>



<h2 class="wp-block-heading" id="h-3-customer-facing-teams-and-internal-data">3. Customer-facing teams and internal data</h2>



<p>The richest source of FAQ ideation is your own data.</p>



<p>Your customer service team fields real questions from real people every single day. Those questions reveal genuine confusion, hesitation, what gets people excited, what pushes them toward conversion, and the specific language your audience uses.</p>



<p>That matters even more now because AI language models are trained to understand natural, conversational language. Yet somehow, this is one of the data sources we rarely receive when working with agency clients.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="624" height="348" src="https://searchengineland.com/wp-content/seloads/2026/05/Sample-of-dummy-sales-call-data-analysis-via-Claude-1.png" alt="Sample data sales call analysis by Claude" class="wp-image-478805"/></figure>
</div>


<p>Getting this kind of direct customer insight isn’t difficult. It can be as simple as creating a communication channel between teams using tools you already have:</p>



<ul class="wp-block-list">
<li>Build a Google Doc where customer service teams can drop in questions.</li>



<li>Create a Slack or Teams channel dedicated to customer questions and concerns.</li>



<li>Periodically ask customer service teams for the top or newest questions they’ve received.</li>



<li>If calls are recorded, and your company’s AI policy allows it, use AI to analyze those calls and surface recurring themes and questions.</li>
</ul>



<p>Internal site search data is also valuable and similarly underused. If your site has a search function, the queries people type into it are direct signals of intent that weren’t fully satisfied by your existing content, either because the information was difficult to find or didn’t exist.</p>



<p>Pull your site search queries monthly and filter for question-based phrasing or longer queries. These are especially useful for FAQ content on product, service, and support pages where questions tend to be highly specific.</p>



<p>Together, these internal sources can produce FAQ content that performs well at the bottom of the funnel, where specificity and accuracy can significantly impact search visibility, user experience, and conversions.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/how-to-use-first-party-data-to-find-high-impact-content-ideas-472500">How to use first-party data to find high-impact content ideas</a></em></strong>



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<h2 class="wp-block-heading" id="h-4-reddit-nbsp">4. Reddit&nbsp;</h2>



<p>Unlike keyword tools that aggregate search behavior, <a href="https://searchengineland.com/reddit-seo-453406">Reddit</a> shows you exactly how people phrase their questions, feedback, and opinions.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="624" height="397" src="https://searchengineland.com/wp-content/seloads/2026/05/RedditAnswers-data-for-query-whats-the-best-AI-search-visibility-tool-for-a-small-business.png" alt="RedditAnswers data for query “what's the best AI search visibility tool for a small business”" class="wp-image-478806"/></figure>
</div>


<p>To start your research, search topics related to your products or services on RedditAnswers or Google to identify the subreddits where your audience gathers. Then search within those communities using your target keywords.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="826" height="384" src="https://searchengineland.com/wp-content/seloads/2026/05/Reddit-thread-surfaced-on-Google-for-best-SEO-tools-for-a-small-business.png" alt="Reddit thread surfaced on Google for “best SEO tools for a small business”" class="wp-image-478807" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Reddit-thread-surfaced-on-Google-for-best-SEO-tools-for-a-small-business.png 826w, https://searchengineland.com/wp-content/seloads/2026/05/Reddit-thread-surfaced-on-Google-for-best-SEO-tools-for-a-small-business-768x357.png 768w" sizes="(max-width: 826px) 100vw, 826px" /></figure>
</div>


<p>Sort results by <em>Best</em>, <em>Top</em>, and <em>New</em>, and pay close attention to the questions people ask in those threads. You’ll likely uncover topics relevant to your audience that you can incorporate into your content strategy.</p>



<p>Reddit threads are also valuable because they often surface follow-up questions after an initial answer. Those discussions reveal the “OK, but what about …” layer of audience curiosity that PAA data and keyword tools often miss.</p>



<p>Building FAQ content that addresses those secondary questions can improve topical depth and strengthen the topical authority signals that AI models tend to favor.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/a-smarter-reddit-strategy-for-organic-and-ai-search-visibility-459369">A smarter Reddit strategy for organic and AI search visibility</a></em></strong>



<h2 class="wp-block-heading" id="h-5-ai-prompt-volumes">5. AI prompt volumes</h2>



<p>One of the newest signals for understanding what questions your audience is asking comes from prompt volume data within AI platforms themselves.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="624" height="244" src="https://searchengineland.com/wp-content/seloads/2026/05/Estimated-monthly-prompt-volume-data-from-Writesonic-AI-visibility-tool.png" alt="Writesonic AI visibility tool, &quot;Sample prompt volume data from Writesonic AI visibility tool&quot;" class="wp-image-478809"/></figure>
</div>


<p>The datasets are still imperfect, much like traditional search data. But AI visibility tools like Writesonic and Profound do surface aggregated data on the prompts users enter into AI tools. That gives you insight into questions people may be asking before they turn to traditional search.</p>



<p>Because AI searches tend to be longer, more conversational, and more specific than traditional Google searches, prompt volume data can uncover FAQ opportunities that keyword research tools may miss.</p>



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<h2 class="wp-block-heading" id="h-faqs-are-an-ongoing-process">FAQs are an ongoing process</h2>



<p>It’s important to remember that FAQ creation isn’t a one-time activity. The questions your audience asks will continue to evolve as your products change, competitors evolve, and the search environment shifts.</p>



<p>For example, a company that sells phone cases will need updated FAQs each time Apple announces a new iPhone. A SaaS brand will need to revisit FAQ content with every product update. A tax software brand has clear triggers for new FAQ content, such as new tax laws or updated forms. A plumber, by contrast, operates in a more established field and may not need to update FAQ content as frequently.</p>



<p>Find a cadence that makes sense for your business, and revisit your research and content regularly. The brands winning search visibility aren’t the ones that created strategic FAQ content once. They’re the ones that keep showing up with the right answers as customer questions evolve.</p>

Frequently asked questions (FAQs) used to sit quietly on support pages and product hubs. Now they influence visibility across AI Overviews, People Also Ask results, and AI search engines that prioritize direct answers to user questions.
More than 80% of AI Overview queries are informational, and 82% have average monthly search volumes under 1,000, Semrush research found. That means longer-tail, lower-volume queries are increasingly driving AI visibility opportunities.
As search behavior becomes more conversational, the quality of your FAQ strategy increasingly depends on the quality of the questions behind it.
The problem is that many brands still rely on the same limited sources for FAQ ideation, even as search behavior evolves. The strongest FAQ opportunities often come from the places where customers are already asking questions naturally — across search, support channels, communities, and AI platforms.
Here are five sources to help you find and prioritize better FAQ content opportunities.
1. Google Search Console data 
To some people, this may seem obvious. But it’s also easily overlooked.
Before you begin ideating new FAQ content, audit what’s already gaining traction. Google Search Console (GSC) is the perfect place to do that. It’s an underutilized FAQ research tool because many SEOs filter for their highest-impression or highest-clicked queries, not their highest-intent ones.




To zero in on intent queries, start by filtering your query data for question-based search patterns using this regex:
^(who|what|where|when|why|how|which|whose|whom|is|are|was|were|do|does|did|can|could|will|would|should|has|have|had)\b



Then, cross-reference average position against CTR. For queries ranking from the middle of page one through the bottom of page two with low CTR, you’ve likely found a candidate for dedicated FAQ content.
If you rank too low, you may not be relevant enough. If you already rank highly, you probably don’t want to disrupt what’s working. Positions 4-20 can be a sweet spot, although it’s always worth looking outside that range just in case.




To expand from there, use this regex to filter for long-tail queries with eight or more words:
^(\S+\s+){8,}\S+$ 



If eight-plus-word queries don’t produce enough results, try lowering the threshold to five to seven words. This can be another GSC gold mine for uncovering FAQ opportunities from existing traffic.
It can also turn up a ton of great queries worth tracking in your AI visibility software.
Dig deeper: How to use Google Search Console to unlock easy SEO wins




  
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2. People Also Ask data
The People Also Ask SERP feature is a valuable signal for understanding how your audience’s questions relate, cluster, and evolve. It’s also one of the few publicly available windows into Google’s understanding of search intent relationships.




Some PAA questions may warrant an entire page, depending on the topic and the value you can provide by going deeper. But they’re also a strong way to identify related FAQs that can strengthen pages you already have.
Tools like AnswerThePublic and AlsoAsked surface these branching question trees at scale, helping you map how topics are explored across search behavior.




There’s still value in doing manual SERP research, too. Search your target topics on Google and expand PAA results across five to 10 priority terms. Look for recurring questions that are relevant to your customers.
Those recurring questions are high-signal candidates that reflect broad informational intent. They’re also more likely to earn AI citations because they represent genuine, repeated user demand.
Exploding Topics doesn’t specifically surface PAA queries, but it’s another valuable research tool for spotting topics that are gaining search interest before they peak. That gives you an opportunity to build FAQ content ahead of demand, anticipating the questions your audience may soon start asking more frequently.




3. Customer-facing teams and internal data
The richest source of FAQ ideation is your own data.
Your customer service team fields real questions from real people every single day. Those questions reveal genuine confusion, hesitation, what gets people excited, what pushes them toward conversion, and the specific language your audience uses.
That matters even more now because AI language models are trained to understand natural, conversational language. Yet somehow, this is one of the data sources we rarely receive when working with agency clients.




Getting this kind of direct customer insight isn’t difficult. It can be as simple as creating a communication channel between teams using tools you already have:
Build a Google Doc where customer service teams can drop in questions.
Create a Slack or Teams channel dedicated to customer questions and concerns.
Periodically ask customer service teams for the top or newest questions they’ve received.
If calls are recorded, and your company’s AI policy allows it, use AI to analyze those calls and surface recurring themes and questions.
Internal site search data is also valuable and similarly underused. If your site has a search function, the queries people type into it are direct signals of intent that weren’t fully satisfied by your existing content, either because the information was difficult to find or didn’t exist.
Pull your site search queries monthly and filter for question-based phrasing or longer queries. These are especially useful for FAQ content on product, service, and support pages where questions tend to be highly specific.
Together, these internal sources can produce FAQ content that performs well at the bottom of the funnel, where specificity and accuracy can significantly impact search visibility, user experience, and conversions.
Dig deeper: How to use first-party data to find high-impact content ideas




	
	
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4. Reddit 
Unlike keyword tools that aggregate search behavior, Reddit shows you exactly how people phrase their questions, feedback, and opinions.




To start your research, search topics related to your products or services on RedditAnswers or Google to identify the subreddits where your audience gathers. Then search within those communities using your target keywords.




Sort results by Best, Top, and New, and pay close attention to the questions people ask in those threads. You’ll likely uncover topics relevant to your audience that you can incorporate into your content strategy.
Reddit threads are also valuable because they often surface follow-up questions after an initial answer. Those discussions reveal the “OK, but what about …” layer of audience curiosity that PAA data and keyword tools often miss.
Building FAQ content that addresses those secondary questions can improve topical depth and strengthen the topical authority signals that AI models tend to favor.
Dig deeper: A smarter Reddit strategy for organic and AI search visibility



5. AI prompt volumes
One of the newest signals for understanding what questions your audience is asking comes from prompt volume data within AI platforms themselves.




The datasets are still imperfect, much like traditional search data. But AI visibility tools like Writesonic and Profound do surface aggregated data on the prompts users enter into AI tools. That gives you insight into questions people may be asking before they turn to traditional search.
Because AI searches tend to be longer, more conversational, and more specific than traditional Google searches, prompt volume data can uncover FAQ opportunities that keyword research tools may miss.
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FAQs are an ongoing process
It’s important to remember that FAQ creation isn’t a one-time activity. The questions your audience asks will continue to evolve as your products change, competitors evolve, and the search environment shifts.
For example, a company that sells phone cases will need updated FAQs each time Apple announces a new iPhone. A SaaS brand will need to revisit FAQ content with every product update. A tax software brand has clear triggers for new FAQ content, such as new tax laws or updated forms. A plumber, by contrast, operates in a more established field and may not need to update FAQ content as frequently.
Find a cadence that makes sense for your business, and revisit your research and content regularly. The brands winning search visibility aren’t the ones that created strategic FAQ content once. They’re the ones that keep showing up with the right answers as customer questions evolve.

Use GSC, Reddit, People Also Ask data, customer insights, and AI prompt trends to uncover the questions that drive search visibility.

5 places to find FAQ content that improves AI visibility

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Brand-depth-determines-what-AI-systems-recommend.png" class="attachment-large size-large wp-post-image" alt="Brand depth determines what AI systems recommend" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Brand-depth-determines-what-AI-systems-recommend.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Brand-depth-determines-what-AI-systems-recommend-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Brand-depth-determines-what-AI-systems-recommend-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Brand-depth-determines-what-AI-systems-recommend-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Citations only show the outcome. The real advantage comes from building a brand AI systems consistently retrieve, recognize, and recommend.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Brand-depth-determines-what-AI-systems-recommend.png" class="attachment-large size-large wp-post-image" alt="Brand depth determines what AI systems recommend" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Brand-depth-determines-what-AI-systems-recommend.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Brand-depth-determines-what-AI-systems-recommend-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Brand-depth-determines-what-AI-systems-recommend-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Brand-depth-determines-what-AI-systems-recommend-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Getting cited in AI answers is becoming a common visibility metric. But citations alone don’t explain why certain brands consistently appear in ChatGPT, Google AI Mode, Perplexity, and other AI search systems.</p>



<p>Citations reflect visibility outcomes, not the underlying systems that produce them. AI platforms prioritize brands with strong semantic presence across training data, reviews, media coverage, search systems, and interconnected web entities.</p>



<p>That’s why <a href="https://searchengineland.com/what-is-generative-engine-optimization-geo-444418">GEO</a> is really two visibility challenges happening at once: building long-term brand weight inside AI systems while also creating content that survives modern retrieval pipelines.</p>



<p>AI recommendations are shaped during both retrieval and synthesis. Brand depth is what increases your odds in both systems.</p>



<h2 class="wp-block-heading" id="h-geo-means-playing-two-games-at-once">GEO means playing two games at once</h2>



<p>Each layer influences visibility differently.</p>



<h3 class="wp-block-heading" id="h-game-1-parametric-weight">Game 1: Parametric weight</h3>



<p>Brands act as coordinates in an LLM’s embedding space, defined by the density and consistency of signals in training data. </p>



<p>This parametric weight is built slowly over months and years through consistent presence across the web. If messaging is inconsistent, the brand’s vector becomes fuzzy, reducing recall and confidence.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1049" height="525" src="https://searchengineland.com/wp-content/seloads/2026/05/Low-entity-depth-vs-high-entity-depth.png" alt="Low entity depth vs high entity depth" class="wp-image-478884" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Low-entity-depth-vs-high-entity-depth.png 1049w, https://searchengineland.com/wp-content/seloads/2026/05/Low-entity-depth-vs-high-entity-depth-768x384.png 768w" sizes="(max-width: 1049px) 100vw, 1049px" /></figure>
</div>


<p>A brand with little parametric weight is functional, forgettable, and interchangeable. You can’t easily alter what a model has already internalized during training, so most efforts are directed toward future training cycles. </p>



<p>Focusing exclusively on citations for months means neglecting the structural foundation that eventually makes those citations unavoidable.</p>



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<h3 class="wp-block-heading" id="h-game-2-retrieval-survival">Game 2: Retrieval survival</h3>



<p>When a system like Google AI Mode or ChatGPT Search fires its retrieval pipeline, does your content make it through?</p>



<p><a href="https://www.airops.com/report/the-influence-of-offsite-signals-in-ai-search" target="_blank" rel="noopener">About 85% of brand mentions</a> in AI search come from external domains, not the brand’s own site. Every major AI search system starts with retrieval, but each handles it differently:</p>



<ul class="wp-block-list">
<li>Perplexity retrieves, ranks, and embeds citations into the context window before the LLM generates a single token. The model synthesizes answers from retrieved evidence rather than directly from training data.</li>



<li>Google AI Mode decomposes a single query into 8-12 parallel subqueries across the live web, Google’s Knowledge Graph, and specialized data sources before synthesizing a response. Google calls this query fan-out.</li>



<li>ChatGPT search expands a query into five or six semantic variations, retrieves 35 to 42 candidate URLs, disqualifies 83% before extraction, and synthesizes three to five citations in the final response. Retrieval is typically skipped only for nonfactual prompts, such as creative writing or basic math.</li>
</ul>



<p>In fan-out systems, you compete across 8-12 parallel subqueries simultaneously.</p>



<h3 class="wp-block-heading" id="h-citations-are-receipts">Citations are receipts</h3>



<p><a href="https://www.semrush.com/blog/most-cited-domains-ai/">Only 6% to 27%</a> of frequently mentioned brands are also top-cited sources. Models can know a brand without citing it.</p>



<p>Citation frequency tracks output presence, not the retrieval and synthesis decisions that surfaced the brand in the first place. Optimizing for citations focuses on the receipt rather than the underlying driver.</p>



<p>Brand depth, built through density, consistency, and cross-source coverage, is what makes a brand the statistically low-risk answer before a citation is ever generated.</p>



<h2 class="wp-block-heading" id="h-brand-depth-how-human-brains-and-llms-default-to-the-familiar">Brand depth: How human brains and LLMs default to the familiar</h2>



<p>The human brain operates similarly to LLMs. We manage a massive volume of daily decisions by relying on mental frameworks and heuristics that have been built over time.</p>



<p>This idea is rooted in <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC7509909/" target="_blank" rel="noopener">predictive processing theory</a>, which describes the brain as a forecasting engine that uses past information to minimize errors.</p>



<p>LLMs and human cognition handle ambiguity in similar ways: Both prioritize information that is most densely established within their respective systems.</p>



<figure class="wp-block-table"><table><tbody><tr><td><strong>Brand element</strong></td><td><strong>Human brain</strong></td><td><strong>LLM</strong></td></tr><tr><td><strong>Memory and recall</strong></td><td>Episodic and emotional, triggered by sensory cues.</td><td>Statistical frequency and co-occurrence density in training data. High frequency increases recall.</td></tr><tr><td><strong>Brand identity</strong></td><td>Sensory and visual: logo, typography, and packaging.</td><td>Semantic proximity: adjectives, reviews, and articles associated with the brand name. A coordinate in embedding space.</td></tr><tr><td><strong>Building trust</strong></td><td>Social proof, word-of-mouth, and personal trial.</td><td>Parametric authority: training data weighted toward high-authority sources.</td></tr><tr><td><strong>Handling mistakes</strong></td><td>Forgiveness through empathy. An apology can repair the relationship.</td><td>Data permanence: models consolidate patterns, not intent. Negative signal floods persist until newer data outweighs them.</td></tr><tr><td><strong>The recommendation</strong></td><td>Impulsive and bias-driven: scarcity, FOMO, and halo effect.</td><td>Synthesis-weighted: shaped by what’s most densely represented in parametric memory and retrieved sources simultaneously.</td></tr></tbody></table></figure>



<h2 class="wp-block-heading" id="h-getting-technical-about-branding-with-brand-depth">Getting technical about branding with brand depth</h2>



<p>AI models and Google’s Knowledge Graph learn from many of the same trusted websites. AI models learn by identifying which words frequently appear together, while Google uses that same information to build a network of connected facts.</p>



<p>Google’s systems specifically evaluate entity salience, entity coherence, and inter-entity relationship density.</p>



<h3 class="wp-block-heading" id="h-entity-salience">Entity salience</h3>



<p>How prominent and distinct your brand is within a specific topic cluster. Entity salience influences citation probability.</p>



<ul class="wp-block-list">
<li>Google asks: How prominent is this brand within a topic cluster?&nbsp;</li>



<li>LLMs ask a similar question at inference time: Which entities have enough statistical weight to surface when a topic is queried?&nbsp;</li>
</ul>



<p>Low salience means you’re retrievable only through exact branded queries. High salience means you appear when the topic comes up, not just when your name is searched.</p>



<p>Google evaluates salience through systems like <a href="https://hexdocs.pm/google_api_content_warehouse/0.4.0/GoogleApi.ContentWarehouse.V1.Model.RepositoryWebrefLatentEntity.html" target="_blank" rel="noopener">RepositoryWebrefLatentEntities</a>, which maps the latent entities a brand co-occurs with, and <a href="https://hexdocs.pm/google_api_content_warehouse/0.4.0/GoogleApi.ContentWarehouse.V1.Model.ResearchScienceSearchSourceUrlDocjoinInfoWebrefEntityInfo.html#module-attributes" target="_blank" rel="noopener">RepositoryWebrefKGCollection</a>.</p>



<h3 class="wp-block-heading" id="h-entity-coherence">Entity coherence</h3>



<p>The consistency of your brand&#8217;s identity across all retrieved contexts.</p>



<p>Inconsistent naming, conflicting positioning, and contradictory dates signal that an entity is unreliable. LLMs trained on that same corpus learn a fragmented, low-confidence representation.</p>



<p>The model fills gaps created by entity incoherence, leading to brand drift, where the model’s version of your brand slowly diverges from reality because the training signal was never stable enough to anchor it.</p>



<h3 class="wp-block-heading" id="h-inter-entity-relationship-density">Inter-entity relationship density</h3>



<p>The strength and number of connections between your brand and other authoritative entities, including products, concepts, and proofs.</p>



<p>Inter-entity relationship density influences associative retrieval paths.</p>



<p>In agentic systems like Deep Research, AI Mode, and Perplexity Pro, each reasoning step is a retrieval event. Relationship density determines whether your brand survives hop two and hop three.</p>



<p>A brand that only exists at the center of its own graph disappears the moment the query moves one step sideways. <a href="https://hexdocs.pm/google_api_content_warehouse/0.4.0/GoogleApi.ContentWarehouse.V1.Model.RepositoryWebrefGlobalLinkInfo.html" target="_blank" rel="noopener">GlobalLinkInfo</a> and <a href="https://hexdocs.pm/google_api_content_warehouse/0.4.0/GoogleApi.ContentWarehouse.V1.Model.RepositoryWebrefLatentEntity.html#t:t/0" target="_blank" rel="noopener">LatentEntity</a> in Google&#8217;s Content Warehouse map these inter-entity edges.</p>



<h2 class="wp-block-heading" id="h-the-rag-layer-is-where-site-quality-becomes-a-gate">The RAG layer is where site quality becomes a gate</h2>



<p>Mark Williams-Cook <a href="https://searchengineland.com/google-exploit-scoring-classifications-449333">documented a site quality score in December 2024</a>. The score uses a 0-to-1 scale, and sites scoring below roughly 0.4 are not retrieved as candidates, regardless of optimization efforts.</p>



<p>That matters because retrieval eligibility influences which entities and sources repeatedly enter AI systems in the first place. Brand integrity becomes an infrastructure problem. You can’t optimize your way into LLM citations if you haven’t first built the entity coherence and relationship density that make your brand consistently retrievable.</p>



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<h2 class="wp-block-heading" id="h-why-ai-systems-repeatedly-surface-black-honey">Why AI systems repeatedly surface Black Honey</h2>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="867" height="521" src="https://searchengineland.com/wp-content/seloads/2026/05/Cliniques-Black-Honey-lipstick-short-videos.png" alt="Clinique’s Black Honey lipstick short videos" class="wp-image-478886" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Cliniques-Black-Honey-lipstick-short-videos.png 867w, https://searchengineland.com/wp-content/seloads/2026/05/Cliniques-Black-Honey-lipstick-short-videos-768x462.png 768w" sizes="(max-width: 867px) 100vw, 867px" /></figure>
</div>


<p>The more co-occurrences you have, the higher your mutual information score, and the more often you appear in answers.</p>



<p>Clinique’s Black Honey lipstick is a good example of how this works in practice because of its strong entity depth:</p>



<ul class="wp-block-list">
<li><strong>Concept:</strong> Co-occurs with “universally flattering” and “my lips but better” (MLBB) value propositions.</li>



<li><strong>Trend:</strong> Co-occurs with “TikTok virality,” driven by high velocity in 2021.</li>



<li><strong>Competitors and dupes: </strong>Co-occurs with “e.l.f. Black Cherry dupe,” reinforcing benchmark status.</li>



<li><strong>Proof: </strong>Co-occurs with “Liv Tyler” and “Arwen,” creating a cultural anchor.</li>



<li><strong>History:</strong> Co-occurs with “1971,” reinforcing longevity.</li>
</ul>



<p>Because of this density, AI systems repeatedly surface Black Honey when answering questions about universally flattering lipstick.</p>



<ul class="wp-block-list">
<li><strong>High recall:</strong> AI models are more likely to recall and mention Clinique Black Honey across a wide range of relevant queries, including “best universally flattering lipsticks,” “viral makeup trends,” and “iconic ’90s beauty.”</li>



<li><strong>High authority:</strong> The depth of co-occurrence, including historical evidence, cultural context, and product variants, provides AI models with sufficient information to generate detailed, authoritative, and multifaceted answers.&nbsp;</li>
</ul>



<h2 class="wp-block-heading" id="h-building-for-retrieval-recall-and-recommendation">Building for retrieval, recall, and recommendation</h2>



<p>Preference is what survives. Build for the layer that determines synthesis weight and for what happens inside the retrieval funnel.</p>



<p>When your brand is specific, consistent, and densely connected across topical clusters, it becomes easier for AI systems to retrieve, synthesize, and recommend.</p>



<h3 class="wp-block-heading" id="h-focus-on-what-survives-the-retrieval-funnel">Focus on what survives the retrieval funnel</h3>



<p>Specific, data-rich, hard-to-reproduce content gets retrieved and cited. Academic literature refers to this as adaptive retrieval.&nbsp;</p>



<p>Generic, predictable content gets skipped because the model can generate it on its own.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Low entropy gets ignored</strong></td><td><strong>High entropy gets cited</strong></td></tr><tr><td>“Our coffee is smooth and delicious.”</td><td>“The Gesha variety from Hacienda La Esmeralda in Boquete, Panama. Grown at 1,700 meters. Water at 94 C. Brew ratio 1:16.”</td></tr></tbody></table></figure>



<p>The second version anchors named entities, including a variety, an organization, a location, and quantitative values. These are details the model <a href="https://arxiv.org/abs/2311.09735">can’t plausibly generate without a source</a>.</p>



<p><strong>Actionable tip:</strong> Add high-density assets, including company history, team bios, and ISO certifications, designed to serve as grounding data for retrieval-augmented generation (RAG) systems.</p>



<h3 class="wp-block-heading" id="h-build-ai-navigation-maps">Build AI navigation maps</h3>



<p>Your website functions like a knowledge graph. AI systems use internal links to build a semantic map of your domain.</p>



<p>Embed links that define logical relationships between entities and create clear paths for crawlers to follow. Structure links around the user’s decision journey, which often mirrors AI retrieval paths:</p>



<ul class="wp-block-list">
<li>Topic → Subtopic (broad context)</li>



<li>Subtopic → Product (specific solution)</li>



<li>Product → Review (social proof)</li>



<li>Review → Return policy (trust signal)</li>



<li>Return policy → Organization (entity credibility)</li>
</ul>



<h3 class="wp-block-heading" id="h-avoid-orphan-pages-nbsp">Avoid orphan pages&nbsp;</h3>



<p>Pages with no meaningful incoming anchors are likely demoted in processing. They don’t accumulate siteAuthority or <a href="https://searchengineland.com/unpacking-googles-massive-search-documentation-leak-442716#:~:text=quiz%20(rough%20analogy).-,NavBoost,-I%E2%80%99ll%20discuss%20this">NavBoost</a> signals.</p>



<p>The fix is to give these pages strategic internal links that connect them to the graph, or delete them. If a page isn’t worth linking to, is it worth human or bot attention?</p>



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<h2 class="wp-block-heading" id="h-visibility-starts-before-the-citation">Visibility starts before the citation</h2>



<p>Citation frequency studies are symptom trackers, not diagnostic tools. They can tell you that certain brands appear more often. They can’t reliably explain whether that visibility comes from training data, RAG retrieval, entity salience, or category dominance.</p>



<p>Build the thing that causes citations, not the thing that imitates them.</p>

Getting cited in AI answers is becoming a common visibility metric. But citations alone don’t explain why certain brands consistently appear in ChatGPT, Google AI Mode, Perplexity, and other AI search systems.
Citations reflect visibility outcomes, not the underlying systems that produce them. AI platforms prioritize brands with strong semantic presence across training data, reviews, media coverage, search systems, and interconnected web entities.
That’s why GEO is really two visibility challenges happening at once: building long-term brand weight inside AI systems while also creating content that survives modern retrieval pipelines.
AI recommendations are shaped during both retrieval and synthesis. Brand depth is what increases your odds in both systems.
GEO means playing two games at once
Each layer influences visibility differently.
Game 1: Parametric weight
Brands act as coordinates in an LLM’s embedding space, defined by the density and consistency of signals in training data. 
This parametric weight is built slowly over months and years through consistent presence across the web. If messaging is inconsistent, the brand’s vector becomes fuzzy, reducing recall and confidence.




A brand with little parametric weight is functional, forgettable, and interchangeable. You can’t easily alter what a model has already internalized during training, so most efforts are directed toward future training cycles. 
Focusing exclusively on citations for months means neglecting the structural foundation that eventually makes those citations unavoidable.

  
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Game 2: Retrieval survival
When a system like Google AI Mode or ChatGPT Search fires its retrieval pipeline, does your content make it through?
About 85% of brand mentions in AI search come from external domains, not the brand’s own site. Every major AI search system starts with retrieval, but each handles it differently:
Perplexity retrieves, ranks, and embeds citations into the context window before the LLM generates a single token. The model synthesizes answers from retrieved evidence rather than directly from training data.
Google AI Mode decomposes a single query into 8-12 parallel subqueries across the live web, Google’s Knowledge Graph, and specialized data sources before synthesizing a response. Google calls this query fan-out.
ChatGPT search expands a query into five or six semantic variations, retrieves 35 to 42 candidate URLs, disqualifies 83% before extraction, and synthesizes three to five citations in the final response. Retrieval is typically skipped only for nonfactual prompts, such as creative writing or basic math.
In fan-out systems, you compete across 8-12 parallel subqueries simultaneously.
Citations are receipts
Only 6% to 27% of frequently mentioned brands are also top-cited sources. Models can know a brand without citing it.
Citation frequency tracks output presence, not the retrieval and synthesis decisions that surfaced the brand in the first place. Optimizing for citations focuses on the receipt rather than the underlying driver.
Brand depth, built through density, consistency, and cross-source coverage, is what makes a brand the statistically low-risk answer before a citation is ever generated.
Brand depth: How human brains and LLMs default to the familiar
The human brain operates similarly to LLMs. We manage a massive volume of daily decisions by relying on mental frameworks and heuristics that have been built over time.
This idea is rooted in predictive processing theory, which describes the brain as a forecasting engine that uses past information to minimize errors.
LLMs and human cognition handle ambiguity in similar ways: Both prioritize information that is most densely established within their respective systems.

Brand elementHuman brainLLM
Memory and recallEpisodic and emotional, triggered by sensory cues.Statistical frequency and co-occurrence density in training data. High frequency increases recall.
Brand identitySensory and visual: logo, typography, and packaging.Semantic proximity: adjectives, reviews, and articles associated with the brand name. A coordinate in embedding space.
Building trustSocial proof, word-of-mouth, and personal trial.Parametric authority: training data weighted toward high-authority sources.
Handling mistakesForgiveness through empathy. An apology can repair the relationship.Data permanence: models consolidate patterns, not intent. Negative signal floods persist until newer data outweighs them.
The recommendationImpulsive and bias-driven: scarcity, FOMO, and halo effect.Synthesis-weighted: shaped by what’s most densely represented in parametric memory and retrieved sources simultaneously.





Getting technical about branding with brand depth
AI models and Google’s Knowledge Graph learn from many of the same trusted websites. AI models learn by identifying which words frequently appear together, while Google uses that same information to build a network of connected facts.
Google’s systems specifically evaluate entity salience, entity coherence, and inter-entity relationship density.
Entity salience
How prominent and distinct your brand is within a specific topic cluster. Entity salience influences citation probability.
Google asks: How prominent is this brand within a topic cluster? 
LLMs ask a similar question at inference time: Which entities have enough statistical weight to surface when a topic is queried? 
Low salience means you’re retrievable only through exact branded queries. High salience means you appear when the topic comes up, not just when your name is searched.
Google evaluates salience through systems like RepositoryWebrefLatentEntities, which maps the latent entities a brand co-occurs with, and RepositoryWebrefKGCollection.
Entity coherence
The consistency of your brand’s identity across all retrieved contexts.
Inconsistent naming, conflicting positioning, and contradictory dates signal that an entity is unreliable. LLMs trained on that same corpus learn a fragmented, low-confidence representation.
The model fills gaps created by entity incoherence, leading to brand drift, where the model’s version of your brand slowly diverges from reality because the training signal was never stable enough to anchor it.
Inter-entity relationship density
The strength and number of connections between your brand and other authoritative entities, including products, concepts, and proofs.
Inter-entity relationship density influences associative retrieval paths.
In agentic systems like Deep Research, AI Mode, and Perplexity Pro, each reasoning step is a retrieval event. Relationship density determines whether your brand survives hop two and hop three.
A brand that only exists at the center of its own graph disappears the moment the query moves one step sideways. GlobalLinkInfo and LatentEntity in Google’s Content Warehouse map these inter-entity edges.
The RAG layer is where site quality becomes a gate
Mark Williams-Cook documented a site quality score in December 2024. The score uses a 0-to-1 scale, and sites scoring below roughly 0.4 are not retrieved as candidates, regardless of optimization efforts.
That matters because retrieval eligibility influences which entities and sources repeatedly enter AI systems in the first place. Brand integrity becomes an infrastructure problem. You can’t optimize your way into LLM citations if you haven’t first built the entity coherence and relationship density that make your brand consistently retrievable.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Why AI systems repeatedly surface Black Honey




The more co-occurrences you have, the higher your mutual information score, and the more often you appear in answers.
Clinique’s Black Honey lipstick is a good example of how this works in practice because of its strong entity depth:
Concept: Co-occurs with “universally flattering” and “my lips but better” (MLBB) value propositions.
Trend: Co-occurs with “TikTok virality,” driven by high velocity in 2021.
Competitors and dupes: Co-occurs with “e.l.f. Black Cherry dupe,” reinforcing benchmark status.
Proof: Co-occurs with “Liv Tyler” and “Arwen,” creating a cultural anchor.
History: Co-occurs with “1971,” reinforcing longevity.
Because of this density, AI systems repeatedly surface Black Honey when answering questions about universally flattering lipstick.
High recall: AI models are more likely to recall and mention Clinique Black Honey across a wide range of relevant queries, including “best universally flattering lipsticks,” “viral makeup trends,” and “iconic ’90s beauty.”
High authority: The depth of co-occurrence, including historical evidence, cultural context, and product variants, provides AI models with sufficient information to generate detailed, authoritative, and multifaceted answers. 
Building for retrieval, recall, and recommendation
Preference is what survives. Build for the layer that determines synthesis weight and for what happens inside the retrieval funnel.
When your brand is specific, consistent, and densely connected across topical clusters, it becomes easier for AI systems to retrieve, synthesize, and recommend.
Focus on what survives the retrieval funnel
Specific, data-rich, hard-to-reproduce content gets retrieved and cited. Academic literature refers to this as adaptive retrieval. 
Generic, predictable content gets skipped because the model can generate it on its own.

Low entropy gets ignoredHigh entropy gets cited
“Our coffee is smooth and delicious.”“The Gesha variety from Hacienda La Esmeralda in Boquete, Panama. Grown at 1,700 meters. Water at 94 C. Brew ratio 1:16.”





The second version anchors named entities, including a variety, an organization, a location, and quantitative values. These are details the model can’t plausibly generate without a source.
Actionable tip: Add high-density assets, including company history, team bios, and ISO certifications, designed to serve as grounding data for retrieval-augmented generation (RAG) systems.
Build AI navigation maps
Your website functions like a knowledge graph. AI systems use internal links to build a semantic map of your domain.
Embed links that define logical relationships between entities and create clear paths for crawlers to follow. Structure links around the user’s decision journey, which often mirrors AI retrieval paths:
Topic → Subtopic (broad context)
Subtopic → Product (specific solution)
Product → Review (social proof)
Review → Return policy (trust signal)
Return policy → Organization (entity credibility)
Avoid orphan pages 
Pages with no meaningful incoming anchors are likely demoted in processing. They don’t accumulate siteAuthority or NavBoost signals.
The fix is to give these pages strategic internal links that connect them to the graph, or delete them. If a page isn’t worth linking to, is it worth human or bot attention?

  
complete picture of your search visibility.
      
Start Free Trial
    
Get started with
Visibility starts before the citation
Citation frequency studies are symptom trackers, not diagnostic tools. They can tell you that certain brands appear more often. They can’t reliably explain whether that visibility comes from training data, RAG retrieval, entity salience, or category dominance.
Build the thing that causes citations, not the thing that imitates them.

Citations only show the outcome. The real advantage comes from building a brand AI systems consistently retrieve, recognize, and recommend.

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