<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Google-Ads-Target-based-bidding.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Google-Ads-Target-based-bidding.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Ads-Target-based-bidding-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Ads-Target-based-bidding-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Ads-Target-based-bidding-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google's upcoming bidding changes could alter campaign performance unless advertisers review and adjust goals.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Google-Ads-Target-based-bidding.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Google-Ads-Target-based-bidding.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Ads-Target-based-bidding-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Ads-Target-based-bidding-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Ads-Target-based-bidding-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Google is changing how target-based bid strategies behave when campaigns are constrained by budget, aiming to make performance more consistent with advertiser targets even as budgets fluctuate.</p>



<p>The update will take effect on August 17th, with a new Bid Target Adjustment Tool becoming available on July 6.</p>



<p><strong>What&#8217;s happening</strong>. Google says campaigns using target-based bidding strategies, such as Target CPA, will more closely align with their configured targets when budget limitations exist.</p>



<p>The company is also introducing a Bid Target Adjustment Tool that will allow advertisers to review and modify targets before the changes take effect.</p>



<p><strong>Why we care.</strong> Campaigns that have been outperforming their target CPA or ROAS goals may no longer continue doing so automatically after the update. Google&#8217;s changes are designed to make budget-constrained campaigns adhere more closely to their stated targets, which could alter performance and efficiency if targets haven&#8217;t been reviewed recently.</p>



<p>For example, a campaign using a Target CPA of $10 that is currently achieving a $5 CPA could see performance move closer to the $10 target unless the advertiser updates the target setting.</p>



<p>The new Bid Target Adjustment Tool gives advertisers a chance to proactively update bidding goals before the August rollout. For some advertisers, failing to adjust targets could mean paying more per conversion or seeing performance shift toward Google&#8217;s target rather than the campaign&#8217;s historical results.</p>



<p><strong>Why Google is making the change</strong>. According to Google, the update is intended to reduce volatility and create more predictable performance when advertisers increase, decrease or otherwise adjust campaign budgets.</p>



<p>The company says the new tool will help advertisers align bidding targets more closely with actual business objectives before enforcement begins.</p>



<p><strong>What advertisers should do</strong>. Google is encouraging advertisers to review campaigns that use target-based bidding strategies and evaluate whether existing targets still reflect desired outcomes.</p>



<p>Advertisers will receive notifications in their Google Ads accounts before the rollout and can use the Bid Target Adjustment Tool to identify campaigns that may be affected.</p>



<p><strong>Between the lines</strong>. The update could have significant implications for advertisers whose campaigns are consistently outperforming their targets. In some cases, maintaining current performance levels may require lowering target settings rather than leaving them unchanged.</p>



<p><strong>Bottom line:</strong> <a href="https://support.google.com/google-ads/answer/17061251">Google is tightening the relationship between target-based bidding goals and actual campaign performance</a>, making it more important for advertisers to regularly review and update bidding targets as business conditions change.</p>


Google is changing how target-based bid strategies behave when campaigns are constrained by budget, aiming to make performance more consistent with advertiser targets even as budgets fluctuate.
The update will take effect on August 17th, with a new Bid Target Adjustment Tool becoming available on July 6.
What’s happening. Google says campaigns using target-based bidding strategies, such as Target CPA, will more closely align with their configured targets when budget limitations exist.
The company is also introducing a Bid Target Adjustment Tool that will allow advertisers to review and modify targets before the changes take effect.
Why we care. Campaigns that have been outperforming their target CPA or ROAS goals may no longer continue doing so automatically after the update. Google’s changes are designed to make budget-constrained campaigns adhere more closely to their stated targets, which could alter performance and efficiency if targets haven’t been reviewed recently.
For example, a campaign using a Target CPA of $10 that is currently achieving a $5 CPA could see performance move closer to the $10 target unless the advertiser updates the target setting.
The new Bid Target Adjustment Tool gives advertisers a chance to proactively update bidding goals before the August rollout. For some advertisers, failing to adjust targets could mean paying more per conversion or seeing performance shift toward Google’s target rather than the campaign’s historical results.
Why Google is making the change. According to Google, the update is intended to reduce volatility and create more predictable performance when advertisers increase, decrease or otherwise adjust campaign budgets.
The company says the new tool will help advertisers align bidding targets more closely with actual business objectives before enforcement begins.
What advertisers should do. Google is encouraging advertisers to review campaigns that use target-based bidding strategies and evaluate whether existing targets still reflect desired outcomes.
Advertisers will receive notifications in their Google Ads accounts before the rollout and can use the Bid Target Adjustment Tool to identify campaigns that may be affected.
Between the lines. The update could have significant implications for advertisers whose campaigns are consistently outperforming their targets. In some cases, maintaining current performance levels may require lowering target settings rather than leaving them unchanged.
Bottom line: Google is tightening the relationship between target-based bidding goals and actual campaign performance, making it more important for advertisers to regularly review and update bidding targets as business conditions change.

Google's upcoming bidding changes could alter campaign performance unless advertisers review and adjust goals.

Google Ads updates target-based bidding for budget-limited campaigns

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/3-questions-that-reveal-your-real-search-performance.png" class="attachment-large size-large wp-post-image" alt="3 questions that reveal your real search performance" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/3-questions-that-reveal-your-real-search-performance.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/3-questions-that-reveal-your-real-search-performance-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/3-questions-that-reveal-your-real-search-performance-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/3-questions-that-reveal-your-real-search-performance-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Rankings and conversions show outcomes. The bigger challenge is understanding how buyers discover, evaluate, and choose brands.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/3-questions-that-reveal-your-real-search-performance.png" class="attachment-large size-large wp-post-image" alt="3 questions that reveal your real search performance" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/3-questions-that-reveal-your-real-search-performance.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/3-questions-that-reveal-your-real-search-performance-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/3-questions-that-reveal-your-real-search-performance-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/3-questions-that-reveal-your-real-search-performance-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Rankings, traffic, and conversions still matter. But they don&#8217;t tell you whether buyers can find you, understand you, and feel confident enough to choose you.</p>



<p>That&#8217;s a bigger challenge now that people move between search engines, AI assistants, social platforms, marketplaces, review sites, and private communities before making a decision.</p>



<p>Viewed through that lens, search performance comes down to three things: presence, interpretation, and momentum.</p>



<ul class="wp-block-list">
<li>Are you present where demand forms?</li>



<li>Are you being understood?</li>



<li>Is anything compounding?</li>
</ul>



<h2 class="wp-block-heading" id="h-1-are-you-present-where-demand-forms">1. Are you present where demand forms?</h2>



<p>Is your brand showing up in the places where demand starts, not just where it converts?</p>



<p>This goes far beyond rankings or impression share to ask whether the brand appears when people are exploring the category: </p>



<ul class="wp-block-list">
<li>Asking early questions.</li>



<li>Comparing options.</li>



<li>Reading reviews.</li>



<li>Checking marketplaces.</li>



<li>Watching creators.</li>



<li>Trying to understand the problem in their own words.</li>
</ul>



<p>If a brand only appears once someone already knows its name, it&#8217;s arriving late. It may look efficient because branded demand converts well. But commercially, it means the brand is depending on other forces to create demand before it turns up to harvest it.</p>



<p>A pattern we see repeatedly is brands mistaking weak presence for weak conversion. Across 196 brands we tracked over 12 months, the same shape kept appearing.</p>



<p>Branded search healthy, CPA respectable, but presence sitting in the bottom half of the competitive set. The brand was converting people who already knew it while missing the moments where the category was being explored by everyone else.</p>



<p>Travel illustrates this most clearly. It’s a category where presence is the dominant driver of market share because people often shop for holidays before they have a brand in mind. </p>



<p>If a travel brand is absent from those early discovery moments, it never enters the consideration set. CRO can&#8217;t fix that. Ask what share of category discovery moments you&#8217;re actually present in.</p>



<p>When branded conversion is strong but unbranded presence is weak, the growth opportunity sits upstream: </p>



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



<li>Marketplaces.</li>



<li>Creator content.</li>



<li>Social search.</li>



<li>Long-tail non-brand queries. </li>
</ul>



<p>That&#8217;s where the category is being decided.</p>



<p>If presence is weak, interpretation won&#8217;t save you. But presence alone isn&#8217;t enough — being found is only useful if what people find makes sense.</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0101ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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        Be the brand <span>AI recommends</span>.
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        See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
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      <span id="semrush-one-cta">See your AI visibility</span>
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<h2 class="wp-block-heading" id="h-2-are-you-being-understood">2. Are you being understood?</h2>



<p>When your brand does show up, is it being understood in a way that helps you get chosen?&nbsp;</p>



<p>People search to find something and reduce doubt. The language gives it away:&nbsp;</p>



<ul class="wp-block-list">
<li>“Is it worth it?”</li>



<li>“Best alternative to X.” </li>



<li>“What do real customers think?”</li>



<li>“For people like me.”</li>
</ul>



<p>It&#8217;s the buyer showing you the anxiety in the decision. Many brands answer these questions badly or too late. </p>



<p>The ad says one thing, the organic result says another, the reviews raise a concern, the landing page is generic, and the AI answer gives a technically accurate but underwhelming summary. The customer is left to connect the dots themselves.</p>



<p>AI makes this harder because the answer is increasingly compressed and increasingly unstable. Brands aren’t just fighting for a blue link anymore. They are fighting to be included, described, and trusted inside an answer that may look completely different next month.</p>



<p>In practice, that means AI search visibility should not be judged solely by traffic volume. </p>



<p>Publishers such as Reuters and The Guardian receive <a href="https://digiday.com/media/geo-hype-busted-experts-call-it-more-seo-than-new-discipline/" target="_blank" rel="noopener">less than 1%</a> of referral traffic from AI platforms despite being frequently cited, but The Washington Post found that visitors arriving from AI platforms converted to subscriptions at four to five times the rate of traditional search visitors. </p>



<p>The audience AI search delivers can be smaller and significantly more valuable. That only holds if the brand is being described accurately and compellingly enough to send the right people.</p>



<p>Our own research across categories adds a layer of nuance worth sitting with: LLM visibility isn’t what most brands think it is. LLM visibility correlates with market share at +0.19 on average. That&#8217;s weaker than many brands assume.&nbsp;</p>



<p>But the average hides a category split that matters. In fashion, the correlation is +0.58. In travel, +0.43. In finance and general retail, it inverts: −0.26 and −0.25. In those latter two categories, the brands appearing most often in AI answers are the ones losing share.</p>



<p>Reading the underlying signals, we see that AI systems are describing those brands in ways that don’t help them be chosen. The attributes being surfaced are wrong, outdated, or framed in terms of challenger comparisons that the established brand can’t win.</p>



<p>Audit your AI citations. Run the prompts your category buyers actually run, in the platforms they use, and read what is being said about you. </p>



<p>If the framing is wrong, the fix isn’t paid media. It&#8217;s the source signals AI systems pull from — editorial coverage, structured content, and the third-party comparisons in your category. That’s the work that changes how the answer reads next month, and the month after that.</p>



<p>Solving interpretation without presence means you&#8217;re explaining yourself to people who can&#8217;t find you. But solving both without momentum means you&#8217;re winning the same ground again every month.</p>



<div class="nl-inline-form border py-2 px-1 my-2">
	
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<hr class="wp-block-separator has-text-color has-cyan-bluish-gray-color has-css-opacity has-cyan-bluish-gray-background-color has-background"/>



<h2 class="wp-block-heading" id="h-3-is-anything-compounding">3. Is anything compounding?</h2>



<p>Is your brand becoming easier to find, trust, and choose over time — or does every sale still need to be bought?</p>



<p>Most measurement gets too short-term to answer this question honestly, and marketers know it. Search shows whether compounding is happening.&nbsp;</p>



<ul class="wp-block-list">
<li>Is branded search growing without heavy brand spend? </li>



<li>Is direct traffic strengthening? </li>



<li>Is organic content continuing to bring people in without fresh media spend? </li>



<li>Is review volume building? </li>
</ul>



<p>These are signs that the brand is accumulating memory, trust, and proof — that the system is starting to work harder without needing to be pushed every time.</p>



<p>The opposite is equally visible. Paid dependency increases. Organic demand softens. Branded search only moves when campaigns are live. People arrive, compare, but don&#8217;t choose. The brand is active, but nothing is building.</p>



<p>The first thing to look at when performance feels stuck is the gap between a brand&#8217;s discoverability and its actual market share. The size and direction of that gap is usually enough to diagnose which problem you are dealing with.</p>



<p>A brand whose demand rank outperforms its discoverability rank is running on borrowed time. The numbers look strong, but the upstream signals aren&#8217;t building. In our data, that gap tends to close within three months. The play is to reinvest now before the lag catches up.</p>



<p>A brand whose discoverability outperforms its demand rank has something building that hasn&#8217;t surfaced yet. The instinct is to keep optimizing the conversion layer. Usually, the right call is the opposite: hold, let the upstream work compound, then drill into where the gap actually lives.</p>



<ul class="wp-block-list">
<li>Weak presence with healthy momentum means the funnel is working, but the top of it is empty. Invest in category visibility, not conversion. </li>



<li>A strong presence with weak interpretation means visibility isn&#8217;t the problem. Fix how the brand is described in search, reviews, and AI answers before spending more on media. </li>



<li>Weak momentum with both presence and interpretation intact usually means proof is missing: reviews, share of voice, and word of mouth need building before acquisition spend pays back.</li>
</ul>



<p>Almost no brand sits cleanly in one bucket. But knowing which gap to fund and which to let run is usually enough to act on.</p>


<a href="https://www.semrush.com/ai-seo/overview?utm_campaign=ic_sel_0102ai&#038;utm_source=searchengineland.com&#038;utm_medium=overlay&#038;onboarding=off" target="_blank">
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       Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
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<h2 class="wp-block-heading" id="h-the-structural-problem-underneath">The structural problem underneath</h2>



<p>The challenge is knowing which of these three problems you&#8217;re dealing with before you start optimizing the wrong thing. That&#8217;s often harder than it sounds because brand and performance are managed by separate teams, with separate budgets, and separate definitions of success.</p>



<p>Customers don&#8217;t experience the brand in silos. They experience the search result, the review, the ad, the AI answer, the marketplace listing, the creator mention, and the thing their friend said in the group chat as one decision environment.</p>



<p>Presence feeds interpretation. Interpretation feeds momentum. Momentum reduces the cost of presence. Understanding where that loop breaks is often the fastest way to identify the constraint holding growth back.</p>

Rankings, traffic, and conversions still matter. But they don’t tell you whether buyers can find you, understand you, and feel confident enough to choose you.
That’s a bigger challenge now that people move between search engines, AI assistants, social platforms, marketplaces, review sites, and private communities before making a decision.
Viewed through that lens, search performance comes down to three things: presence, interpretation, and momentum.
Are you present where demand forms?
Are you being understood?
Is anything compounding?
1. Are you present where demand forms?
Is your brand showing up in the places where demand starts, not just where it converts?
This goes far beyond rankings or impression share to ask whether the brand appears when people are exploring the category: 
Asking early questions.
Comparing options.
Reading reviews.
Checking marketplaces.
Watching creators.
Trying to understand the problem in their own words.
If a brand only appears once someone already knows its name, it’s arriving late. It may look efficient because branded demand converts well. But commercially, it means the brand is depending on other forces to create demand before it turns up to harvest it.
A pattern we see repeatedly is brands mistaking weak presence for weak conversion. Across 196 brands we tracked over 12 months, the same shape kept appearing.
Branded search healthy, CPA respectable, but presence sitting in the bottom half of the competitive set. The brand was converting people who already knew it while missing the moments where the category was being explored by everyone else.
Travel illustrates this most clearly. It’s a category where presence is the dominant driver of market share because people often shop for holidays before they have a brand in mind. 
If a travel brand is absent from those early discovery moments, it never enters the consideration set. CRO can’t fix that. Ask what share of category discovery moments you’re actually present in.
When branded conversion is strong but unbranded presence is weak, the growth opportunity sits upstream: 
Review sites.
Marketplaces.
Creator content.
Social search.
Long-tail non-brand queries. 
That’s where the category is being decided.
If presence is weak, interpretation won’t save you. But presence alone isn’t enough — being found is only useful if what people find makes sense.
AI recommends.
      
See your AI visibility
    
2. Are you being understood?
When your brand does show up, is it being understood in a way that helps you get chosen? 
People search to find something and reduce doubt. The language gives it away: 
“Is it worth it?”
“Best alternative to X.” 
“What do real customers think?”
“For people like me.”
It’s the buyer showing you the anxiety in the decision. Many brands answer these questions badly or too late. 
The ad says one thing, the organic result says another, the reviews raise a concern, the landing page is generic, and the AI answer gives a technically accurate but underwhelming summary. The customer is left to connect the dots themselves.
AI makes this harder because the answer is increasingly compressed and increasingly unstable. Brands aren’t just fighting for a blue link anymore. They are fighting to be included, described, and trusted inside an answer that may look completely different next month.
In practice, that means AI search visibility should not be judged solely by traffic volume. 
Publishers such as Reuters and The Guardian receive less than 1% of referral traffic from AI platforms despite being frequently cited, but The Washington Post found that visitors arriving from AI platforms converted to subscriptions at four to five times the rate of traditional search visitors. 
The audience AI search delivers can be smaller and significantly more valuable. That only holds if the brand is being described accurately and compellingly enough to send the right people.
Our own research across categories adds a layer of nuance worth sitting with: LLM visibility isn’t what most brands think it is. LLM visibility correlates with market share at +0.19 on average. That’s weaker than many brands assume. 
But the average hides a category split that matters. In fashion, the correlation is +0.58. In travel, +0.43. In finance and general retail, it inverts: −0.26 and −0.25. In those latter two categories, the brands appearing most often in AI answers are the ones losing share.
Reading the underlying signals, we see that AI systems are describing those brands in ways that don’t help them be chosen. The attributes being surfaced are wrong, outdated, or framed in terms of challenger comparisons that the established brand can’t win.
Audit your AI citations. Run the prompts your category buyers actually run, in the platforms they use, and read what is being said about you. 
If the framing is wrong, the fix isn’t paid media. It’s the source signals AI systems pull from — editorial coverage, structured content, and the third-party comparisons in your category. That’s the work that changes how the answer reads next month, and the month after that.
Solving interpretation without presence means you’re explaining yourself to people who can’t find you. But solving both without momentum means you’re winning the same ground again every month.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
3. Is anything compounding?
Is your brand becoming easier to find, trust, and choose over time — or does every sale still need to be bought?
Most measurement gets too short-term to answer this question honestly, and marketers know it. Search shows whether compounding is happening. 
Is branded search growing without heavy brand spend? 
Is direct traffic strengthening? 
Is organic content continuing to bring people in without fresh media spend? 
Is review volume building? 
These are signs that the brand is accumulating memory, trust, and proof — that the system is starting to work harder without needing to be pushed every time.
The opposite is equally visible. Paid dependency increases. Organic demand softens. Branded search only moves when campaigns are live. People arrive, compare, but don’t choose. The brand is active, but nothing is building.
The first thing to look at when performance feels stuck is the gap between a brand’s discoverability and its actual market share. The size and direction of that gap is usually enough to diagnose which problem you are dealing with.
A brand whose demand rank outperforms its discoverability rank is running on borrowed time. The numbers look strong, but the upstream signals aren’t building. In our data, that gap tends to close within three months. The play is to reinvest now before the lag catches up.
A brand whose discoverability outperforms its demand rank has something building that hasn’t surfaced yet. The instinct is to keep optimizing the conversion layer. Usually, the right call is the opposite: hold, let the upstream work compound, then drill into where the gap actually lives.
Weak presence with healthy momentum means the funnel is working, but the top of it is empty. Invest in category visibility, not conversion. 
A strong presence with weak interpretation means visibility isn’t the problem. Fix how the brand is described in search, reviews, and AI answers before spending more on media. 
Weak momentum with both presence and interpretation intact usually means proof is missing: reviews, share of voice, and word of mouth need building before acquisition spend pays back.
Almost no brand sits cleanly in one bucket. But knowing which gap to fund and which to let run is usually enough to act on.
customers won’t either.
      
See your AI visibility
    
The structural problem underneath
The challenge is knowing which of these three problems you’re dealing with before you start optimizing the wrong thing. That’s often harder than it sounds because brand and performance are managed by separate teams, with separate budgets, and separate definitions of success.
Customers don’t experience the brand in silos. They experience the search result, the review, the ad, the AI answer, the marketplace listing, the creator mention, and the thing their friend said in the group chat as one decision environment.
Presence feeds interpretation. Interpretation feeds momentum. Momentum reduces the cost of presence. Understanding where that loop breaks is often the fastest way to identify the constraint holding growth back.

Rankings and conversions show outcomes. The bigger challenge is understanding how buyers discover, evaluate, and choose brands.

3 questions that reveal your real search performance

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/reddit-marketing-2.png" class="attachment-large size-large wp-post-image" alt="Reddit marketing" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/reddit-marketing-2.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/reddit-marketing-2-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/reddit-marketing-2-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/reddit-marketing-2-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Reddit's new features aim to help advertisers build more relevant campaigns using insights drawn directly from user discussions.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/reddit-marketing-2.png" class="attachment-large size-large wp-post-image" alt="Reddit marketing" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/reddit-marketing-2.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/reddit-marketing-2-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/reddit-marketing-2-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/reddit-marketing-2-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Reddit is rolling out a suite of new advertising products powered by its Community Intelligence engine, betting that authentic user conversations have become one of the most influential forces in consumer decision-making.</p>



<p id="h-what-s-happening"><strong>What&#8217;s happening</strong>. Reddit is introducing new ad products designed to help brands tap into the platform&#8217;s vast repository of consumer conversations, which now includes more than 25 billion posts and comments.</p>



<p>The company says the new tools will help advertisers create more relevant campaigns, accelerate purchase decisions and improve performance by leveraging insights derived directly from Reddit communities.</p>



<p><strong>Why we care. </strong>Reddit is giving brands new ways to turn authentic community conversations into ad creative, shopping experiences and performance signals. </p>



<p>As more consumers use Reddit to research and validate purchase decisions—including AI-generated recommendations—these tools could help brands reach high-intent audiences with messaging rooted in real user sentiment.</p>



<p id="h-new-creative-tools"><strong>New creative tools</strong>. Reddit is launching several new AI-powered creative features.</p>



<p><em>Free-form ad generator (Beta)</em>



<p>The tool automatically combines information from a brand&#8217;s website with relevant Reddit conversations to create ads inspired by Reddit&#8217;s long-form post format.</p>



<p>The goal is to help advertisers build campaigns that feel more native to the platform.</p>



<p><em>Tailored creative assets (Beta)</em>



<p>Using AI, Reddit identifies relevant communities and audience segments, then generates customized headlines and image variations designed to improve ad relevance.</p>



<p>The feature is currently available for Max Campaigns.</p>



<p><em>Redditor Highlights (GA)</em>



<p>Now generally available, the feature allows eligible brands to incorporate real Reddit conversations directly into their ads.</p>



<p>Ads can display summaries of community sentiment alongside related organic posts, helping brands showcase authentic customer perspectives.</p>



<p><strong>Shopping Listing Ads (Alpha)</strong>. Reddit is also entering the shopping ads space with a new ad type. The new format matches products from participating advertisers to relevant conversations and displays them in a carousel format.</p>



<p>The experience mirrors how users already compare products and seek recommendations within Reddit discussions.</p>



<p><strong>Performance gets an AI boost.</strong> Alongside the new ad formats, Reddit says it continues to invest heavily in machine learning across its advertising platform.</p>



<p>They highlighted early testing results from a new six-second engaged video view optimization goal, which delivered:</p>



<ul class="wp-block-list">
<li>A 130% increase in view-through rates.</li>



<li>A 71% improvement in video completion rates.</li>
</ul>



<p>Reddit says its combination of machine learning and Community Intelligence enables more contextual ad targeting and content understanding across the platform.</p>



<p id="h-bottom-line"><strong>Bottom line.</strong> <a href="https://redditinc.com/news/live-from-cannes-2026-real-conversations-are-driving-the-new-consumer-decision-journey">Reddit is turning its vast archive of user conversations into advertising products</a>, giving brands new ways to build campaigns, influence purchase decisions and improve performance using insights drawn directly from community discussions</p>


Reddit is rolling out a suite of new advertising products powered by its Community Intelligence engine, betting that authentic user conversations have become one of the most influential forces in consumer decision-making.
What’s happening. Reddit is introducing new ad products designed to help brands tap into the platform’s vast repository of consumer conversations, which now includes more than 25 billion posts and comments.
The company says the new tools will help advertisers create more relevant campaigns, accelerate purchase decisions and improve performance by leveraging insights derived directly from Reddit communities.
Why we care. Reddit is giving brands new ways to turn authentic community conversations into ad creative, shopping experiences and performance signals. 
As more consumers use Reddit to research and validate purchase decisions—including AI-generated recommendations—these tools could help brands reach high-intent audiences with messaging rooted in real user sentiment.
New creative tools. Reddit is launching several new AI-powered creative features.
Free-form ad generator (Beta)



The tool automatically combines information from a brand’s website with relevant Reddit conversations to create ads inspired by Reddit’s long-form post format.
The goal is to help advertisers build campaigns that feel more native to the platform.
Tailored creative assets (Beta)



Using AI, Reddit identifies relevant communities and audience segments, then generates customized headlines and image variations designed to improve ad relevance.
The feature is currently available for Max Campaigns.
Redditor Highlights (GA)



Now generally available, the feature allows eligible brands to incorporate real Reddit conversations directly into their ads.
Ads can display summaries of community sentiment alongside related organic posts, helping brands showcase authentic customer perspectives.
Shopping Listing Ads (Alpha). Reddit is also entering the shopping ads space with a new ad type. The new format matches products from participating advertisers to relevant conversations and displays them in a carousel format.
The experience mirrors how users already compare products and seek recommendations within Reddit discussions.
Performance gets an AI boost. Alongside the new ad formats, Reddit says it continues to invest heavily in machine learning across its advertising platform.
They highlighted early testing results from a new six-second engaged video view optimization goal, which delivered:
A 130% increase in view-through rates.
A 71% improvement in video completion rates.
Reddit says its combination of machine learning and Community Intelligence enables more contextual ad targeting and content understanding across the platform.
Bottom line. Reddit is turning its vast archive of user conversations into advertising products, giving brands new ways to build campaigns, influence purchase decisions and improve performance using insights drawn directly from community discussions

Reddit's new features aim to help advertisers build more relevant campaigns using insights drawn directly from user discussions.

Reddit unveils new AI-powered ad tools built on community conversations

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Googles-LLM-patent-suggests-a-new-goal-for-SEO-Teaching-AI-who-you-are.png" class="attachment-large size-large wp-post-image" alt="Google&#039;s LLM patent suggests a new goal for SEO: Teaching AI who you are" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Googles-LLM-patent-suggests-a-new-goal-for-SEO-Teaching-AI-who-you-are.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Googles-LLM-patent-suggests-a-new-goal-for-SEO-Teaching-AI-who-you-are-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Googles-LLM-patent-suggests-a-new-goal-for-SEO-Teaching-AI-who-you-are-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Googles-LLM-patent-suggests-a-new-goal-for-SEO-Teaching-AI-who-you-are-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google's patent suggests AI may build entity profiles from websites, reviews, and public information. Here's what that could mean for SEO.

Google's patent suggests AI may build entity profiles from websites, reviews, and public information. Here's what that could mean for SEO.

Google’s LLM patent suggests a new goal for SEO: Teaching AI who you are

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/3-ways-to-build-a-more-complete-SEO-ROI-model.png" class="attachment-large size-large wp-post-image" alt="3 ways to build a more complete SEO ROI model" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/3-ways-to-build-a-more-complete-SEO-ROI-model.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/3-ways-to-build-a-more-complete-SEO-ROI-model-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/3-ways-to-build-a-more-complete-SEO-ROI-model-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/3-ways-to-build-a-more-complete-SEO-ROI-model-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Account for defended traffic, assisted conversions, and cross-channel influence to better reflect search's contribution to revenue.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/3-ways-to-build-a-more-complete-SEO-ROI-model.png" class="attachment-large size-large wp-post-image" alt="3 ways to build a more complete SEO ROI model" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/3-ways-to-build-a-more-complete-SEO-ROI-model.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/3-ways-to-build-a-more-complete-SEO-ROI-model-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/3-ways-to-build-a-more-complete-SEO-ROI-model-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/3-ways-to-build-a-more-complete-SEO-ROI-model-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p><a href="https://searchengineland.com/seo-attribution-why-its-broken-what-you-can-do-456776">SEO attribution</a> has always been messy. Unlike paid search, organic search lacks the same tracking granularity, has a lag between the work and the results, and often depends on signals you can&#8217;t control, such as rankings.</p>



<p>To make matters worse, attribution is even more of a black box today, with AI-generated answers dominating SERPs and LLMs that don&#8217;t always link back to your site or pass referrer strings.</p>



<p>Businesses have never really cared about that complexity. They care about the return they&#8217;re getting from their marketing dollars.</p>



<p>The good news? </p>



<p>SEOs can still tell a compelling ROI story, but it takes more nuance, more data digging, and more math than ever before. This article walks through key considerations as you build your next SEO ROI story.</p>



<h2 class="wp-block-heading" id="h-the-historical-formula-for-calculating-roi">The historical formula for calculating ROI</h2>



<p>SEO ROI has traditionally been calculated using variations of the same formula:</p>



<ul class="wp-block-list">
<li>ROI = ((Incremental organic revenue − SEO costs) / SEO costs) x 100</li>
</ul>



<p>It&#8217;s clean, fits on a single slide for executives, and makes sense for a long time. Before the rise of generative AI, driving incremental traffic — and therefore revenue — was the north star for most SEO campaigns.</p>



<p>However, sharp increases in zero-click searches and major attribution gaps from LLMs have upended traditional models.</p>



<p>In many cases, organic traffic may be flat or even declining, even as overall visibility increases through impressions, AI Overview rankings, or prompt visibility.</p>



<p>Looking only at organic metrics and incremental gains tells only part of the story. To show SEO&#8217;s true value, we need to rethink the formula.</p>


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<p>Here are three ways to build a more complete SEO ROI model.</p>



<h2 class="wp-block-heading" id="h-1-take-credit-for-all-organic-revenue-not-just-incremental-gains">1. Take credit for all organic revenue, not just incremental gains</h2>



<p>With <a href="https://searchengineland.com/google-search-zero-click-study-2024-443869">60% of searches</a> ending without a click, and that number continuing to grow, a huge part of SEO&#8217;s value today is defensive. That means <a href="https://searchengineland.com/seo-defense-declining-organic-traffic-452569">maintaining and protecting traffic</a> that would otherwise erode due to various factors.</p>



<p>The formula above doesn&#8217;t account for that at all. For example, would you judge a goalkeeper&#8217;s performance by how many goals they&#8217;ve scored?</p>



<p>The same is increasingly true of SEO. Only counting what&#8217;s new erases everything you preserved.</p>



<p>In a flat or declining landscape, holding the line is a major win, yet it&#8217;s completely ignored when you focus only on incremental gains.</p>



<p>The starting point for this ROI story shouldn&#8217;t be incremental organic revenue alone. It should be all organic revenue. That&#8217;s the entire asset SEO is responsible for maintaining and defending.</p>



<p>This may be a tough sell for many website owners, but it&#8217;s the truth. And if you go this route, there&#8217;s one major caveat.</p>



<h3 class="wp-block-heading" id="h-segment-out-brand-vs-non-brand-clicks">Segment out brand vs. non-brand clicks</h3>



<p>Claiming all organic revenue is disingenuous if branded growth is driving most of the performance.</p>



<p>Branded traffic is influenced by many factors outside SEO&#8217;s control, including PR campaigns, paid media, product, word of mouth, and more. When someone Googles your website by name, SEO rarely created that demand. It simply captured it.</p>



<p>SEO&#8217;s real lever is non-branded search. Before taking credit for total organic revenue, you need to segment it accordingly.</p>



<p>Since this can&#8217;t be done cleanly in Google Analytics, start by pulling the branded-versus-non-branded split from Google Search Console. Then apply that split to your total organic revenue using a weighted model.</p>



<p>Here&#8217;s some real-world data, for example:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="752" height="452" src="https://searchengineland.com/wp-content/seloads/2026/06/Segment-out-brand-vs.-non-brand-clicks-Real-world-example.png" alt="Segment out brand vs. non-brand clicks - Real-world example" class="wp-image-480581"/></figure>
</div>


<p>Branded traffic accounts for about 70% of total clicks and is declining, while non-brand traffic accounts for roughly 30% and is growing. That split tells a value story on its own because non-branded growth offsetting branded decline is exactly what good SEO should produce. Before taking credit for total organic revenue, though, we need to create a blended weight.</p>



<p>Let&#8217;s say SEO gets 10% credit for branded traffic and 100% credit for non-branded traffic. Treat these as starting points and calibrate them for each client. The calculation would be:</p>



<ul class="wp-block-list">
<li>(70% brand x 10% weight) + (30% non-brand x 100% weight) = 7% + 30% = 37% blended attribution weight</li>
</ul>



<p>Apply that 37% weight to total organic revenue. If the site generates $100,000 in organic revenue per month, SEO gets credit for $37,000, not the full $100,000.</p>



<p>That&#8217;s likely far higher than the revenue you&#8217;d attribute to incremental gains alone. Because you&#8217;ve openly discounted credit you don&#8217;t deserve, the model is more defensible and shows stakeholders that you understand its limitations.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/the-dark-seo-funnel-why-traffic-no-longer-proves-seo-success-470334">The dark SEO funnel: Why traffic no longer proves SEO success</a></em></strong>



<h2 class="wp-block-heading" id="h-2-account-for-assisted-conversions-and-first-click-influence">2. Account for assisted conversions and first-click influence</h2>



<p>Last-click attribution buries SEO. That&#8217;s nothing new, but it&#8217;s even more relevant today.</p>



<p>Organic is often the first touchpoint in a user&#8217;s journey. Today, that might mean only an impression, with no measurable click at all.</p>



<p>Remove that influence, and SEO can look like a minor contributor to revenue it actually initiated.</p>



<p>SEO should take credit for the conversions it assists, even when another channel closes them. Here&#8217;s an example:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="800" height="530" src="https://searchengineland.com/wp-content/seloads/2026/06/Account-for-assisted-conversions-and-first-click-influence.png" alt="Account for assisted conversions and first-click influence" class="wp-image-480582" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Account-for-assisted-conversions-and-first-click-influence.png 800w, https://searchengineland.com/wp-content/seloads/2026/06/Account-for-assisted-conversions-and-first-click-influence-768x509.png 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>
</div>


<p>Organic dominates all three stages of touchpoint credit, but a last-click-only model rewards only the final click.</p>



<p>The catch is that GA4 doesn&#8217;t surface a clean assisted-conversion value the way Universal Analytics did. Precisely calculating assisted conversions requires exporting path data to BigQuery and deriving a true fractional value for each channel.</p>



<p>However, data-driven attribution in GA4 provides a defensible shortcut. Google already assigns each channel fractional credit based on its influence on conversions. We can use organic&#8217;s early- and mid-touch credit as a proxy for the assist value that last-click attribution ignores.</p>



<ul class="wp-block-list">
<li>1,345.69 (early) + 687.34 (mid) = 2,033.03 in conversion credit</li>
</ul>



<p>From there, multiply by the value of a conversion, using $100 as an illustration.</p>



<ul class="wp-block-list">
<li>2,033.03 x $100 (conversion value) = $203,303</li>
</ul>



<p>The same brand-versus-non-brand logic technically applies to assists. Since GA4 doesn&#8217;t cleanly split assist credit by query type, we left out late-touch credit. That&#8217;s where branded behavior tends to concentrate, and excluding it removes much of the credit that would otherwise be discounted.</p>



<p>Even as a directional number, the data proves the point: Organic is providing real value. Relying on last-click attribution alone leaves that ROI out of your story.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/roi-driven-seo-enterprise-cmo-guide-471465">Stop paying for traffic: The enterprise CMO’s guide to ROI-driven SEO</a></em></strong>



<h2 class="wp-block-heading" id="h-3-measure-seo-content-impact-across-other-channels">3. Measure SEO content impact across other channels</h2>



<p>SEO-led content doesn&#8217;t stay within the organic channel. The same research, briefs, and articles your team produces can be repurposed across multiple channels:</p>



<ul class="wp-block-list">
<li><a href="https://searchengineland.com/seo-paid-media-need-each-other-ai-search-461485">Paid search runs ads on SEO-created landing pages</a> and copy.</li>



<li>Social channels run campaigns and posts built on organic content.</li>



<li>Email runs drip campaigns fueled by blog content and resources.</li>
</ul>



<p>Looking only at organic revenue ignores all the downstream value generated by SEO efforts.</p>



<p>I recently looked at a client where we&#8217;d just started publishing new articles and refreshing existing ones. After only one month, we were already seeing some of that content being used and generating conversions in other channels.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="988" height="268" src="https://searchengineland.com/wp-content/seloads/2026/06/Measure-SEO-content-impact-across-other-channels.png" alt="Measure SEO content impact across other channels" class="wp-image-480583" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Measure-SEO-content-impact-across-other-channels.png 988w, https://searchengineland.com/wp-content/seloads/2026/06/Measure-SEO-content-impact-across-other-channels-768x208.png 768w" sizes="(max-width: 988px) 100vw, 988px" /></figure>
</div>


<p>Sure, 29 calls and five qualified leads aren&#8217;t a huge number. But it will grow over time, and those are conversions SEO shouldn&#8217;t ignore.</p>



<p>Similar to assisted conversions, drilling down to an actual dollar amount requires some fancy math, but it&#8217;s possible.</p>



<p>Start by:</p>



<ul class="wp-block-list">
<li>Finding and cataloging which SEO-led pages are used across different channels.</li>



<li>Calculating the percentage of conversions generated by those SEO-led pages.</li>



<li>Applying that percentage to the total conversion value of each channel.</li>
</ul>



<p>In practice, it could look something like this:</p>



<ul class="wp-block-list">
<li>500 conversions (paid search) x $100 (conversion value) x 5% (percentage of conversions from SEO-led pages) = $2,500 in downstream value</li>
</ul>



<p>Even if the numbers feel small, this is another way SEO can rightfully claim revenue and help justify the overall cost of the campaign.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/first-touch-analytics-matters-seo-467840">What 4 AI search experiments reveal about attribution and buying decisions</a></em></strong>


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<h2 class="wp-block-heading" id="h-the-dos-and-don-ts-of-seo-roi">The dos and don’ts of SEO ROI</h2>



<p>SEO can and should take credit for value beyond incremental organic revenue. Your exact methodology may differ, so work with your most data-savvy team members to get it right. The general concepts are what matter:</p>



<ul class="wp-block-list">
<li>Take credit for all organic performance, but don&#8217;t take credit for every branded click as if SEO created that demand.</li>



<li>Look at assisted conversions and other attribution models. Don&#8217;t evaluate SEO within the organic silo alone.</li>



<li>Take credit when SEO content is used by other channels. Don&#8217;t ignore the downstream impact it can have.</li>



<li>Get creative when solving the ROI puzzle. Don&#8217;t let an outdated formula undersell your work.</li>
</ul>



<p>The classic ROI formula isn&#8217;t wrong. It&#8217;s incomplete.&nbsp;</p>



<p>As search evolves, the way we measure ROI should evolve with it.</p>


SEO attribution has always been messy. Unlike paid search, organic search lacks the same tracking granularity, has a lag between the work and the results, and often depends on signals you can’t control, such as rankings.
To make matters worse, attribution is even more of a black box today, with AI-generated answers dominating SERPs and LLMs that don’t always link back to your site or pass referrer strings.
Businesses have never really cared about that complexity. They care about the return they’re getting from their marketing dollars.
The good news? 
SEOs can still tell a compelling ROI story, but it takes more nuance, more data digging, and more math than ever before. This article walks through key considerations as you build your next SEO ROI story.
The historical formula for calculating ROI
SEO ROI has traditionally been calculated using variations of the same formula:
ROI = ((Incremental organic revenue − SEO costs) / SEO costs) x 100
It’s clean, fits on a single slide for executives, and makes sense for a long time. Before the rise of generative AI, driving incremental traffic — and therefore revenue — was the north star for most SEO campaigns.
However, sharp increases in zero-click searches and major attribution gaps from LLMs have upended traditional models.
In many cases, organic traffic may be flat or even declining, even as overall visibility increases through impressions, AI Overview rankings, or prompt visibility.
Looking only at organic metrics and incremental gains tells only part of the story. To show SEO’s true value, we need to rethink the formula.
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Here are three ways to build a more complete SEO ROI model.
1. Take credit for all organic revenue, not just incremental gains
With 60% of searches ending without a click, and that number continuing to grow, a huge part of SEO’s value today is defensive. That means maintaining and protecting traffic that would otherwise erode due to various factors.
The formula above doesn’t account for that at all. For example, would you judge a goalkeeper’s performance by how many goals they’ve scored?
The same is increasingly true of SEO. Only counting what’s new erases everything you preserved.
In a flat or declining landscape, holding the line is a major win, yet it’s completely ignored when you focus only on incremental gains.
The starting point for this ROI story shouldn’t be incremental organic revenue alone. It should be all organic revenue. That’s the entire asset SEO is responsible for maintaining and defending.
This may be a tough sell for many website owners, but it’s the truth. And if you go this route, there’s one major caveat.
Segment out brand vs. non-brand clicks
Claiming all organic revenue is disingenuous if branded growth is driving most of the performance.
Branded traffic is influenced by many factors outside SEO’s control, including PR campaigns, paid media, product, word of mouth, and more. When someone Googles your website by name, SEO rarely created that demand. It simply captured it.
SEO’s real lever is non-branded search. Before taking credit for total organic revenue, you need to segment it accordingly.
Since this can’t be done cleanly in Google Analytics, start by pulling the branded-versus-non-branded split from Google Search Console. Then apply that split to your total organic revenue using a weighted model.
Here’s some real-world data, for example:




Branded traffic accounts for about 70% of total clicks and is declining, while non-brand traffic accounts for roughly 30% and is growing. That split tells a value story on its own because non-branded growth offsetting branded decline is exactly what good SEO should produce. Before taking credit for total organic revenue, though, we need to create a blended weight.
Let’s say SEO gets 10% credit for branded traffic and 100% credit for non-branded traffic. Treat these as starting points and calibrate them for each client. The calculation would be:
(70% brand x 10% weight) + (30% non-brand x 100% weight) = 7% + 30% = 37% blended attribution weight
Apply that 37% weight to total organic revenue. If the site generates $100,000 in organic revenue per month, SEO gets credit for $37,000, not the full $100,000.
That’s likely far higher than the revenue you’d attribute to incremental gains alone. Because you’ve openly discounted credit you don’t deserve, the model is more defensible and shows stakeholders that you understand its limitations.
Dig deeper: The dark SEO funnel: Why traffic no longer proves SEO success



2. Account for assisted conversions and first-click influence
Last-click attribution buries SEO. That’s nothing new, but it’s even more relevant today.
Organic is often the first touchpoint in a user’s journey. Today, that might mean only an impression, with no measurable click at all.
Remove that influence, and SEO can look like a minor contributor to revenue it actually initiated.
SEO should take credit for the conversions it assists, even when another channel closes them. Here’s an example:




Organic dominates all three stages of touchpoint credit, but a last-click-only model rewards only the final click.
The catch is that GA4 doesn’t surface a clean assisted-conversion value the way Universal Analytics did. Precisely calculating assisted conversions requires exporting path data to BigQuery and deriving a true fractional value for each channel.
However, data-driven attribution in GA4 provides a defensible shortcut. Google already assigns each channel fractional credit based on its influence on conversions. We can use organic’s early- and mid-touch credit as a proxy for the assist value that last-click attribution ignores.
1,345.69 (early) + 687.34 (mid) = 2,033.03 in conversion credit
From there, multiply by the value of a conversion, using $100 as an illustration.
2,033.03 x $100 (conversion value) = $203,303
The same brand-versus-non-brand logic technically applies to assists. Since GA4 doesn’t cleanly split assist credit by query type, we left out late-touch credit. That’s where branded behavior tends to concentrate, and excluding it removes much of the credit that would otherwise be discounted.
Even as a directional number, the data proves the point: Organic is providing real value. Relying on last-click attribution alone leaves that ROI out of your story.
Dig deeper: Stop paying for traffic: The enterprise CMO’s guide to ROI-driven SEO



3. Measure SEO content impact across other channels
SEO-led content doesn’t stay within the organic channel. The same research, briefs, and articles your team produces can be repurposed across multiple channels:
Paid search runs ads on SEO-created landing pages and copy.
Social channels run campaigns and posts built on organic content.
Email runs drip campaigns fueled by blog content and resources.
Looking only at organic revenue ignores all the downstream value generated by SEO efforts.
I recently looked at a client where we’d just started publishing new articles and refreshing existing ones. After only one month, we were already seeing some of that content being used and generating conversions in other channels.




Sure, 29 calls and five qualified leads aren’t a huge number. But it will grow over time, and those are conversions SEO shouldn’t ignore.
Similar to assisted conversions, drilling down to an actual dollar amount requires some fancy math, but it’s possible.
Start by:
Finding and cataloging which SEO-led pages are used across different channels.
Calculating the percentage of conversions generated by those SEO-led pages.
Applying that percentage to the total conversion value of each channel.
In practice, it could look something like this:
500 conversions (paid search) x $100 (conversion value) x 5% (percentage of conversions from SEO-led pages) = $2,500 in downstream value
Even if the numbers feel small, this is another way SEO can rightfully claim revenue and help justify the overall cost of the campaign.
Dig deeper: What 4 AI search experiments reveal about attribution and buying decisions



  
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The dos and don’ts of SEO ROI
SEO can and should take credit for value beyond incremental organic revenue. Your exact methodology may differ, so work with your most data-savvy team members to get it right. The general concepts are what matter:
Take credit for all organic performance, but don’t take credit for every branded click as if SEO created that demand.
Look at assisted conversions and other attribution models. Don’t evaluate SEO within the organic silo alone.
Take credit when SEO content is used by other channels. Don’t ignore the downstream impact it can have.
Get creative when solving the ROI puzzle. Don’t let an outdated formula undersell your work.
The classic ROI formula isn’t wrong. It’s incomplete. 
As search evolves, the way we measure ROI should evolve with it.

Account for defended traffic, assisted conversions, and cross-channel influence to better reflect search's contribution to revenue.

3 ways to build a more complete SEO ROI model

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Why-continuous-learning-is-now-part-of-search-performance.png" class="attachment-large size-large wp-post-image" alt="Why continuous learning is now part of search performance" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Why-continuous-learning-is-now-part-of-search-performance.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Why-continuous-learning-is-now-part-of-search-performance-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Why-continuous-learning-is-now-part-of-search-performance-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Why-continuous-learning-is-now-part-of-search-performance-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>As AI takes on more execution, you need stronger skills in interpretation, prioritization, and performance analysis.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Why-continuous-learning-is-now-part-of-search-performance.png" class="attachment-large size-large wp-post-image" alt="Why continuous learning is now part of search performance" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Why-continuous-learning-is-now-part-of-search-performance.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Why-continuous-learning-is-now-part-of-search-performance-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Why-continuous-learning-is-now-part-of-search-performance-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Why-continuous-learning-is-now-part-of-search-performance-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Platform changes, AI-driven SERPs, and shifting measurement models are forcing search and performance marketers to rethink their skills more frequently.</p>



<p>What worked six months ago may not work today, and the gap between current best practices and outdated knowledge keeps widening.</p>



<p>That&#8217;s why continuous learning now directly affects <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> performance. The organizations that adapt fastest don&#8217;t treat learning as a separate activity. They build it into how they test, share knowledge, and make decisions.</p>



<h2 class="wp-block-heading" id="h-why-search-and-performance-marketing-skills-expire-quickly">Why search and performance marketing skills expire quickly</h2>



<p>Search skills have a shorter shelf life than most people realize. I&#8217;ve sat in meetings where approaches that were solid 18 months ago were actively working against performance.</p>



<p>Platform updates, automation changes, and shifts in user behavior can turn effective tactics into outdated ones faster than most expect. Without ongoing learning, it&#8217;s easy to fall behind current best practices.</p>



<p>Misinterpreting data, overrelying on automation, or using outdated SEO methods can all weaken results. To keep pace, you need to adapt to changes driven by AI Overviews, evolving SERP features, and increasing <a href="https://searchengineland.com/guide/zero-click-searches">zero-click experiences</a>.</p>


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<h2 class="wp-block-heading" id="h-ai-has-made-learning-more-important">AI has made learning more important</h2>



<p>AI reduces execution time, but it increases the need to validate outputs, particularly in reporting and prioritization. As automation becomes more capable, the value shifts from execution to interpretation, prioritization, and decision-making.</p>



<p>If you rely on AI outputs without validation, you risk inaccurate reporting, weak content decisions, and poor prioritization. Prioritizing decisions over activity shows up in trade-offs, validation of automated outputs, cross-channel performance interpretation, and commercial decision-making.</p>



<p>As AI adoption outpaces structured training, gaps between tool use and real capability become more visible. The challenge isn&#8217;t operating tools efficiently. It&#8217;s turning outputs into decisions.</p>



<p>In this environment, learning is less about mastering tools and more about applying sound judgment. Most people aren&#8217;t limited by access to learning. They&#8217;re limited by the assumption that what they already know is still good enough.</p>



<h2 class="wp-block-heading" id="h-skill-decay-and-the-rise-of-systems-thinking">Skill decay and the rise of systems thinking</h2>



<p>One of the biggest mistakes I see is assuming knowledge stays relevant longer than it does. Skills can become outdated surprisingly quickly when platforms, reporting, and user behavior are changing at once.</p>



<p>As platforms evolve and delivery pressure increases, gaps form between what the job requires and what people know. Those gaps become especially visible during platform updates, reporting changes, and shifts in search behavior. They&#8217;re also more likely when knowledge sits with individuals instead of documented systems.</p>



<p>That&#8217;s why systems thinking matters more than isolated tool knowledge.</p>



<p>High-performing organizations focus on how disciplines connect:</p>



<ul class="wp-block-list">
<li>SEO, paid media, analytics, and content operate as one system.</li>



<li>Technical work is tied to commercial impact.</li>



<li>Prioritization is driven by outcomes, not activity volume.</li>



<li>Platform updates are interpreted at the system level, not the task level.</li>
</ul>



<p>You also need to learn across adjacent disciplines because performance issues rarely sit within a single channel.</p>



<p>Tools such as Semrush, Ahrefs, Screaming Frog, and Sitebulb remain important, but they don&#8217;t prevent skill decay on their own. The key difference is how well you interpret what the tools show.</p>



<p>If you learned SEO primarily through legacy keyword tactics, adapting to entity-based search, AI Overviews, and changing SERP layouts becomes much harder once learning stops.</p>



<p>To reduce knowledge loss, build simple reinforcement habits: review campaign performance regularly, share platform updates internally, and document what tests reveal so learning carries forward instead of staying with one person.</p>



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<h2 class="wp-block-heading" id="h-what-continuous-learning-looks-like-in-practice">What continuous learning looks like in practice</h2>



<p>Staying current requires more than consuming information. You need processes that turn new insights into better decisions.</p>



<h3 class="wp-block-heading" id="h-build-depth-in-core-seo-tools">Build depth in core SEO tools</h3>



<p>SEO tools are often used for basic tasks despite far broader functionality. Tools such as Semrush, Ahrefs, Screaming Frog, and Sitebulb are typically used for only a fraction of their capability.</p>



<p>I&#8217;ve often found that investing time in deeper product knowledge delivers faster gains than adding another tool to the stack.</p>



<p>That deeper knowledge shows up in audits that take half the time, diagnoses that don&#8217;t rely on a third party, and analysis that moves things forward rather than restating what the tool already surfaced.</p>



<h3 class="wp-block-heading" id="h-use-certifications-to-build-cross-channel-understanding-nbsp">Use certifications to build cross-channel understanding&nbsp;</h3>



<p>Some of the most effective people I&#8217;ve worked with understand far more than SEO. They understand how paid media, analytics, and measurement fit together, which makes collaboration and prioritization much easier.</p>



<p>Training across Google Ads helps you understand how paid and organic search interact, along with bidding behaviors, visibility dynamics, and data structures across channels.</p>



<p>This broader view supports better decision-making and reduces siloed thinking.</p>



<p>Google Skillshop certifications are also useful for building broader platform knowledge, particularly across Google Ads and Google Analytics.</p>



<h3 class="wp-block-heading" id="h-turning-conference-insights-into-something-usable">Turning conference insights into something usable</h3>



<p>Industry events create value when the learning continues after you leave.</p>



<p>At our agency, insights from conferences are shared directly in our Teams channel alongside publicly available slide decks, so everyone benefits regardless of who attended. Anything worth exploring gets tested in live environments rather than filed away.</p>



<p>That loop — share, test, reflect — is what turns conference insights into meaningful changes in how you work.</p>



<h3 class="wp-block-heading" id="h-combine-learning-with-experimentation">Combine learning with experimentation</h3>



<p>As part of our internal testing process, anything worth exploring gets tried on our own site first. We monitor it over weeks or months, depending on what we&#8217;re testing, before any recommendation touches a client account.</p>



<p>If the results are positive, it goes onto the client roadmap. If something is already well supported by industry evidence, we&#8217;ll move faster and factor it into client work sooner.</p>



<p>That approach grounds recommendations in either our own evidence or a strong body of industry data.</p>



<h3 class="wp-block-heading" id="h-measure-the-impact-of-learning-nbsp">Measure the impact of learning&nbsp;</h3>



<p>The clearest signs of progress tend to be operational.&nbsp;</p>



<ul class="wp-block-list">
<li>Onboarding takes less time when knowledge is documented and shared consistently.&nbsp;</li>



<li>Reporting becomes more reliable when you understand what you&#8217;re measuring and why.&nbsp;</li>



<li>Prioritization improves when you have enough context to make confident decisions rather than defaulting to activity.</li>
</ul>



<p>When these habits are working, you&#8217;ll notice it in the quality of conversations, decisions, and outcomes.</p>


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<h2 class="wp-block-heading" id="h-continuous-learning-is-now-part-of-performance">Continuous learning is now part of performance</h2>



<p>AI is accelerating the pace of change in search. Skills evolve faster, and success depends increasingly on judgment, adaptation, and decision-making.</p>



<p>If you&#8217;re falling behind, it&#8217;s rarely because you lack tools or data. More often, it&#8217;s because you&#8217;re relying on knowledge that no longer reflects the current landscape.</p>



<p>The best people in search don&#8217;t assume yesterday&#8217;s knowledge still applies. They stay curious, keep learning, and adapt as the landscape changes.</p>

Platform changes, AI-driven SERPs, and shifting measurement models are forcing search and performance marketers to rethink their skills more frequently.
What worked six months ago may not work today, and the gap between current best practices and outdated knowledge keeps widening.
That’s why continuous learning now directly affects SEO performance. The organizations that adapt fastest don’t treat learning as a separate activity. They build it into how they test, share knowledge, and make decisions.
Why search and performance marketing skills expire quickly
Search skills have a shorter shelf life than most people realize. I’ve sat in meetings where approaches that were solid 18 months ago were actively working against performance.
Platform updates, automation changes, and shifts in user behavior can turn effective tactics into outdated ones faster than most expect. Without ongoing learning, it’s easy to fall behind current best practices.
Misinterpreting data, overrelying on automation, or using outdated SEO methods can all weaken results. To keep pace, you need to adapt to changes driven by AI Overviews, evolving SERP features, and increasing zero-click experiences.
AI recommends.
      
See your AI visibility
    
AI has made learning more important
AI reduces execution time, but it increases the need to validate outputs, particularly in reporting and prioritization. As automation becomes more capable, the value shifts from execution to interpretation, prioritization, and decision-making.
If you rely on AI outputs without validation, you risk inaccurate reporting, weak content decisions, and poor prioritization. Prioritizing decisions over activity shows up in trade-offs, validation of automated outputs, cross-channel performance interpretation, and commercial decision-making.
As AI adoption outpaces structured training, gaps between tool use and real capability become more visible. The challenge isn’t operating tools efficiently. It’s turning outputs into decisions.
In this environment, learning is less about mastering tools and more about applying sound judgment. Most people aren’t limited by access to learning. They’re limited by the assumption that what they already know is still good enough.
Skill decay and the rise of systems thinking
One of the biggest mistakes I see is assuming knowledge stays relevant longer than it does. Skills can become outdated surprisingly quickly when platforms, reporting, and user behavior are changing at once.
As platforms evolve and delivery pressure increases, gaps form between what the job requires and what people know. Those gaps become especially visible during platform updates, reporting changes, and shifts in search behavior. They’re also more likely when knowledge sits with individuals instead of documented systems.
That’s why systems thinking matters more than isolated tool knowledge.
High-performing organizations focus on how disciplines connect:
SEO, paid media, analytics, and content operate as one system.
Technical work is tied to commercial impact.
Prioritization is driven by outcomes, not activity volume.
Platform updates are interpreted at the system level, not the task level.
You also need to learn across adjacent disciplines because performance issues rarely sit within a single channel.
Tools such as Semrush, Ahrefs, Screaming Frog, and Sitebulb remain important, but they don’t prevent skill decay on their own. The key difference is how well you interpret what the tools show.
If you learned SEO primarily through legacy keyword tactics, adapting to entity-based search, AI Overviews, and changing SERP layouts becomes much harder once learning stops.
To reduce knowledge loss, build simple reinforcement habits: review campaign performance regularly, share platform updates internally, and document what tests reveal so learning carries forward instead of staying with one person.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
What continuous learning looks like in practice
Staying current requires more than consuming information. You need processes that turn new insights into better decisions.
Build depth in core SEO tools
SEO tools are often used for basic tasks despite far broader functionality. Tools such as Semrush, Ahrefs, Screaming Frog, and Sitebulb are typically used for only a fraction of their capability.
I’ve often found that investing time in deeper product knowledge delivers faster gains than adding another tool to the stack.
That deeper knowledge shows up in audits that take half the time, diagnoses that don’t rely on a third party, and analysis that moves things forward rather than restating what the tool already surfaced.
Use certifications to build cross-channel understanding 
Some of the most effective people I’ve worked with understand far more than SEO. They understand how paid media, analytics, and measurement fit together, which makes collaboration and prioritization much easier.
Training across Google Ads helps you understand how paid and organic search interact, along with bidding behaviors, visibility dynamics, and data structures across channels.
This broader view supports better decision-making and reduces siloed thinking.
Google Skillshop certifications are also useful for building broader platform knowledge, particularly across Google Ads and Google Analytics.
Turning conference insights into something usable
Industry events create value when the learning continues after you leave.
At our agency, insights from conferences are shared directly in our Teams channel alongside publicly available slide decks, so everyone benefits regardless of who attended. Anything worth exploring gets tested in live environments rather than filed away.
That loop — share, test, reflect — is what turns conference insights into meaningful changes in how you work.
Combine learning with experimentation
As part of our internal testing process, anything worth exploring gets tried on our own site first. We monitor it over weeks or months, depending on what we’re testing, before any recommendation touches a client account.
If the results are positive, it goes onto the client roadmap. If something is already well supported by industry evidence, we’ll move faster and factor it into client work sooner.
That approach grounds recommendations in either our own evidence or a strong body of industry data.
Measure the impact of learning 
The clearest signs of progress tend to be operational. 
Onboarding takes less time when knowledge is documented and shared consistently. 
Reporting becomes more reliable when you understand what you’re measuring and why. 
Prioritization improves when you have enough context to make confident decisions rather than defaulting to activity.
When these habits are working, you’ll notice it in the quality of conversations, decisions, and outcomes.
customers won’t either.
      
See your AI visibility
    
Continuous learning is now part of performance
AI is accelerating the pace of change in search. Skills evolve faster, and success depends increasingly on judgment, adaptation, and decision-making.
If you’re falling behind, it’s rarely because you lack tools or data. More often, it’s because you’re relying on knowledge that no longer reflects the current landscape.
The best people in search don’t assume yesterday’s knowledge still applies. They stay curious, keep learning, and adapt as the landscape changes.

As AI takes on more execution, you need stronger skills in interpretation, prioritization, and performance analysis.

Why continuous learning is now part of search performance

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Citation-Labs-20260622.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Citation-Labs-20260622.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Citation-Labs-20260622-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/Citation-Labs-20260622-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Citation-Labs-20260622-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>To get cited in AI answers, stop writing for keywords. Map buyer-role evidence gaps, find the missing gatekeeper, and build the evidence.


In May 2025, Google brought 25 of us into a closed-door room at I/O to talk about the post-click SERP. The instruction we left with was short: create non-commoditized content.



Here’s the uncomfortable part:
For 15+ years, we never commoditized content. We commoditized the sale. We built billions of pages that took a messy human problem, targeted whatever keyword the person compressed it into, and answered with some version of “buying now is the best choice.”
We did it well for our brands, and we built a sales-first web.
Oops.



AI search is the bill for that cognitive debt finally coming due. For years, we skipped past the buyer’s real thinking and answered “buy now” instead of their actual questions. 
Paying that debt off is a link building problem. Not just hyperlinks, but the links between sources, roles, risks, and decisions.
A co-citation gap analysis maps those links, showing which sources AI search trusts for each buyer role and where your content is missing from the decision.
In this piece, we’ll show you how to run it: map the sources AI search reads and cites, find the role your content doesn’t support yet, and build the asset that closes the gap.
Moving from anchor text to anchor context
We’ve been running co-citation analysis on the link graph for 15 years at Citation Labs. 
In 2011, I published a six-step co-citation method for link builders: find the pages that curate a topic, count which sources they cite together, and reverse-engineer what made those pages worth citing.
What’s changed now is the unit of work: from focusing on anchor text to anchor context.




Anchor text told a search engine what a page was about.
Anchor context tells the model why that evidence belongs in a specific answer, for a specific role, at a specific point in the decision.
The work moves from describing the page to supporting the decision.
Instead of asking which pages mention a topic together, you’re asking which sources an AI assistant trusts when each buyer role asks about the same decision, and which role your content fails to support. 
That missing decision support is the co-citation gap.
How to run a co-citation gap analysis by hand
A co-citation gap analysis counts what AI search reads and cites across same-phase, same-problem prompts for different buyer roles.
The overlaps and absences show which decisions your content doesn’t support yet.
You don’t need software for this, just one buyer decision, the committee around it, a set of prompts, an AI tool that shows its work, and a spreadsheet.
View embedded content



1. Map the committee, and write down each role’s fear
List everyone who has to say yes before the thing you sell gets bought — the real deciders, not the org chart. 
For example, for a funded biotech that’s choosing a logo, the committee is the CEO, in-house counsel, ops, and marketing.



Next to each, write what that person is afraid of. That fear is the question they’ll bring to an assistant, so write it in first person:
CEO: Do we look like a serious, fundable company?
Counsel: Is this name or mark going to get us sued or forced to rebrand?
Ops: Will this survive production (from a favicon to signage to a 96-well plate label)?
Marketing: Will this perform (get recognized, stand out cold to investors and recruits)?
Four roles, four fears, one decision.
Note: Solo purchases work the same way. The “committee” is one buyer’s competing concerns. The unit isn’t the committee, it’s the choice point.
2. Write one prompt per role
Create one controlled prompt per role, all based on the same buying decision. Keep the scenario fixed and change only the role. That makes the role’s domain of practice the variable.
Follow these five rules:
One shared scenario. Keep the situation fixed; change only who’s asking.
First person, in the role’s voice. Model their words, worries, and perspective.
Put the role in a specific situation. Generic prompts get generic answers. Specific situations trigger retrieval, and retrieval is the data.
Bundle the role’s real concerns. Real deciders carry a cluster, not one tidy question.
Name no brands. Brand names pollute the citation set.
Here’s the CEO prompt from our “make a logo” example: 
“I’m the founder-CEO of a biotech that closed a Series A, briefing a design firm on a new logo. I want a point of view to show them, not a blank page. What do credible, well-funded biotech brands share visually? Which startups nailed a post-raise rebrand? Which botched it? What do investors and pharma partners read into a young brand? What should a non-designer like me use to rough out a few options?”



Then add one kill-switch prompt per role: “What’s the one thing here that, if we get it wrong, we can’t undo? What would make me say no?” 
This surfaces the hard veto role.
Run more than one phrasing. The signal is what repeats, not what appears once.
3. Capture what the assistant searches, reads, and cites
Use an assistant that exposes its sources. For each role’s run, open the activity or sources panel and copy three fields into your spreadsheet:
Sub-queries generated
Pages read
Pages cited
Tag each read page by type as you go: 
Forum/hub (Reddit, aggregators, etc.)
Primary/official (a regulator, a standards body, etc.)
Vendor (services and solutions)
The mix shows what kind of evidence each role trusts. 
In our example, nearly a third of everything read was Reddit, and over 40% was high-volume hubs.
Your pages will sort into three states:
If it’s read and cited, it was consulted and used.
If it’s read but not cited, it was consulted but dropped. That’s a content problem.
If it’s not read, it was never consulted. That’s a discoverability problem.
The dropped-but-read pages are where the cheapest wins hide.
4. Build the citation matrix
Now, turn your sort list into a matrix — this is the co-citation analysis.
Make one row per unique cited URL, one column per role, and one count column showing how many roles cited it.






  Note: One row per cited URL





Here’s what the matrix looked like in our “make a logo” example:
Sort by count. You’ll see which sources are shared, which are role-exclusive, and which roles have almost no overlap.
5. Find the role with the veto power
With the matrix sorted, look for three structures. 
The shared core is everything cited by 2+ roles. If it’s nearly empty, the committee is disjointed (you serve it seat by seat). If it’s substantial, the committee is convergent (you win the commons and the gatekeeper).
An isolated decider is a decisive role whose cited sources are mostly role-exclusive. An empty edge is two must-agree roles that share nothing.
Then set priority: whose “no” is final, and whose sources overlap least with everyone else’s?
That’s your veto × isolate seat. Build content there first.
Capture this in a spreadsheet you can update and rerun:




6. Add the phase axis
Run the analysis again, but move the same decision forward in time. Keep each role the same while changing the scenario:
Choosing → Rolling Out → Getting Value and Renewing
The cited set shifts at every stage while the owner of the answer hands off as the buyer moves.
Note where you’re present in one stage but missing in others. 
Those gaps are asset targets.
7. Create a content and outreach plan
Your matrix gives you a prioritized content strategy:
Gatekeeper: The veto-isolate seat, decisive and unserved.
Empty edges: Where two must-agree roles share nothing.
Shared core: Sources cited by multiple roles.
Phase gaps: Stages where your brand appears, then disappears, by role.
The new work is placement, and the model already told you where. The sub-queries show the domains it trusts for each role.
In our example, the CEO run ran site:businesswire.com, site:fiercebiotech.com, and site:prnewswire.com; the ops run ran site:developers.google.com/search/docs



Nearly every role appended “official” to chase primary sources. 
That’s your placement and outreach list — written by the AI assistant. Earn your evidence on the surfaces it already uses when answering that role.
And remember the unit you’re building in: anchor context, not anchor text. 
The asset’s job isn’t to show a popular option. It shows: who this helps, what it solves, when it fits, and why it belongs — for this role, at this choice point.
8. Measure the impact
Lock your prompt set and rerun it in a few weeks. (We’ve seen results in less than 3 weeks with our clients.)
Rebuild the cited column and compare it to the baseline. Look for three movements: your brand appears where it didn’t before, placed sources enter the gatekeeper’s answer, or an empty edge starts to fill.
It’s tedious by hand, but the loop is always the same: baseline, build, re-run, compare. 
The cited set is the scoreboard.
If you’d like a faster way to run this, reach out to Citation Labs. We’ll share our prompt stack with you.



How to turn the matrix into decisions
Once the matrix is sorted, review the shape of the citation set.
If a few sources are cited by most of the committee, it’s convergent. There’s shared ground to win. If the top is nearly bare, it’s disjointed. Every role is in its own world, and generic “bottom of funnel” content won’t carry the decision.
In our make-a-logo example, exactly one source was cited by multiple roles: Canva’s brand-kit page. At the read level, Reddit, Wikipedia, and arXiv showed up across roles, but almost none of that survived into what got cited. 
Now find the seat that shares almost nothing with anyone. 
That’s the gap. 



In our example run, it was Counsel: 14 cited sources, none shared with another role, all from legal, regulatory, and trademark sources.



Lowest competition on the map. Highest leverage.
You may also find an empty edge: two roles that both have to say yes but cite nothing in common. Their criteria collide with no content in between. Each empty edge is a bridge asset waiting to be built.
Don’t be surprised by who the gatekeeper is. In the committees we’ve mapped so far, the veto-isolate has consistently been compliance, security, or legal. The org chart underweights them, but the citation map doesn’t. It shows the seat that can stop the decision and has the least content support. 
That’s where you build first.
Then check the phase re-run. When you move the committee from choosing to rolling out to getting value, the citation set shifts. Most brands focus on “choosing” and ignore everything after.
Also, the decision doesn’t end at the sale. It runs through rollout, adoption, renewal, and the next internal justification.
The move that pays is to drag the late “no” upstream, so the veto lands as a redirect rather than a demolition.
For our logo committee, “gatekeeper first” became a Founder’s Preliminary Trademark Clearance Brief: a one-page brief the founder fills out before Counsel reviews a name or mark. It captures proposed assets, commercial context, preliminary checks, and specific questions for Counsel.
Watch what that single page does:




It gives the CEO something Counsel can review before the work goes too far. The veto surfaces before money gets spent. And both sides avoid the silent standoff: “Why do they keep blocking this?” versus “FFS, did they even check?”
The brief makes them check (in a form that Counsel can act on) before either of them gets angry.
That’s anchor text becoming anchor context: “here’s exactly what this decider needs, at the moment they need it, in the form that lets them say yes.”
This is the work for link builders
For 15 years, we built links at Citation Labs.
The good ones were about putting the right evidence in front of the right person at the moment they had to make a decision.
AI search didn’t end that work. It clarified it.
Build more than hyperlinks. Build the links between choice points: the places where you reduce effort, uncertainty, and the risk of being misread.
Start with the seat that can say no and that no one is writing for.
That’s the gatekeeper. That’s the gap. 
That’s the work.

To get cited in AI answers, stop writing for keywords. Map buyer-role evidence gaps, find the missing gatekeeper, and build the evidence.

How to win the gatekeeper’s ‘Yes’ with stakeholder co-citation gap analysis by Citation Labs

Google is automatically giving eligible advertisers new audience-building capabilities powered by conversion data.


<p>Google Ads is automatically enabling conversion-based customer lists for eligible advertisers starting, with data processing scheduled to begin on Aug. 18.</p>



<p>The update applies to advertisers already using both Enhanced Conversions and Customer Match but who have not yet activated conversion-based customer lists.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="912" height="1024" src="https://searchengineland.com/wp-content/seloads/2026/06/Customer-based-list.jpeg" alt="" class="wp-image-480695" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Customer-based-list.jpeg 912w, https://searchengineland.com/wp-content/seloads/2026/06/Customer-based-list-768x862.jpeg 768w" sizes="(max-width: 912px) 100vw, 912px" /></figure>



<p><strong>Why we care. </strong>As privacy changes continue to reshape digital advertising, Google is increasingly encouraging advertisers to rely on first-party data. Conversion-based customer lists provide another way to build audiences using customer data already collected through conversions.</p>



<p>The feature could help advertisers create more relevant audience segments and improve campaign performance without requiring additional implementation work.</p>



<p><strong>The details</strong>. Eligible advertisers do not need to take any action. Beginning Aug. 18, Google will start processing data and automatically make conversion-based customer lists available within affected accounts.</p>



<p>Advertisers can then choose whether to attach those audiences to campaigns and ad groups as part of their targeting strategy.</p>



<p><strong>The catch</strong>. Advertisers who do not want the feature enabled can opt out before Aug. 18 by disabling conversion-based customer lists within their account settings.</p>



<p>After that date, Google will begin processing data and generating the lists automatically.</p>



<p><strong>First spotted.</strong> This update was spotted by JXT Group Founder Menachem Ani, who shared the comms he recevied about it on <a href="https://x.com/MenachemAni/status/2067316618716147876">X</a>.</p>


Google Ads is automatically enabling conversion-based customer lists for eligible advertisers starting, with data processing scheduled to begin on Aug. 18.
The update applies to advertisers already using both Enhanced Conversions and Customer Match but who have not yet activated conversion-based customer lists.




Why we care. As privacy changes continue to reshape digital advertising, Google is increasingly encouraging advertisers to rely on first-party data. Conversion-based customer lists provide another way to build audiences using customer data already collected through conversions.
The feature could help advertisers create more relevant audience segments and improve campaign performance without requiring additional implementation work.
The details. Eligible advertisers do not need to take any action. Beginning Aug. 18, Google will start processing data and automatically make conversion-based customer lists available within affected accounts.
Advertisers can then choose whether to attach those audiences to campaigns and ad groups as part of their targeting strategy.
The catch. Advertisers who do not want the feature enabled can opt out before Aug. 18 by disabling conversion-based customer lists within their account settings.
After that date, Google will begin processing data and generating the lists automatically.
First spotted. This update was spotted by JXT Group Founder Menachem Ani, who shared the comms he recevied about it on X.

Google Ads automatically enrols advertisers in conversion-based customer lists

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads.png" class="attachment-large size-large wp-post-image" alt="6 mistakes that hurt ecommerce campaigns on Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google's update will make it easier for advertisers to distinguish between target-based and volume-based bidding strategies.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads.png" class="attachment-large size-large wp-post-image" alt="6 mistakes that hurt ecommerce campaigns on Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/6-mistakes-that-hurt-ecommerce-campaigns-on-Google-Ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google Ads is changing how Smart Bidding strategies are labeled, separating target-based bidding strategies from volume-based bidding strategies.</p>



<p>Starting this month, &#8220;Maximize conversions with a Target CPA&#8221; will once again be called Target CPA, while &#8220;Maximize conversion value with a Target ROAS&#8221; will return to Target ROAS.</p>



<p id="h-why-it-matters"><strong>Why we care.</strong> The change is designed to make it clearer whether a campaign is optimising for maximum volume or attempting to hit a specific performance target.</p>



<p id="h-the-details"><strong>The details.</strong>



<ul class="wp-block-list">
<li><strong>Maximize Conversions</strong> remains available for advertisers focused on driving as many conversions as possible within budget.</li>



<li><strong>Maximize Conversion Value</strong> remains available for advertisers focused on generating the highest conversion value possible within budget.</li>
</ul>



<p id="h-what-isn-t-changing"><strong>What isn&#8217;t changing. </strong>The update is purely organisational.</p>



<p>Google says there are:</p>



<ul class="wp-block-list">
<li>No changes to bidding behaviour</li>



<li>No changes to campaign performance</li>



<li>No changes required from advertisers</li>
</ul>



<p>Campaigns will continue to bid exactly as they do today.</p>



<p id="h-for-api-users"><strong>For API users.</strong> Google is also aligning the interface more closely with how bidding strategies are represented in the Google Ads API.</p>



<p>Developers should review integrations, reporting tools, and campaign creation workflows to ensure they correctly recognise standalone TARGET_CPA and TARGET_ROAS strategy types.</p>



<p>Google is encouraging API users to monitor future updates related to:</p>



<ul class="wp-block-list">
<li>The BiddingStrategyType enum</li>



<li>Standalone TargetCpa and TargetRoas messages</li>



<li>Optional target settings within MaximizeConversions and MaximizeConversionValue</li>



<li></li>
</ul>



<p id="h-bottom-line"><strong>Bottom line</strong>. Nothing changes in how Smart Bidding works, but advertisers may find campaign setup and reporting easier to understand as <a href="https://ads-developers.googleblog.com/2026/06/updates-to-smart-bidding-strategy.html">Google restores Target CPA and Target ROAS as clearly defined standalone strategy names</a>.</p>


Google Ads is changing how Smart Bidding strategies are labeled, separating target-based bidding strategies from volume-based bidding strategies.
Starting this month, “Maximize conversions with a Target CPA” will once again be called Target CPA, while “Maximize conversion value with a Target ROAS” will return to Target ROAS.
Why we care. The change is designed to make it clearer whether a campaign is optimising for maximum volume or attempting to hit a specific performance target.
The details.




Maximize Conversions remains available for advertisers focused on driving as many conversions as possible within budget.
Maximize Conversion Value remains available for advertisers focused on generating the highest conversion value possible within budget.
What isn’t changing. The update is purely organisational.
Google says there are:
No changes to bidding behaviour
No changes to campaign performance
No changes required from advertisers
Campaigns will continue to bid exactly as they do today.
For API users. Google is also aligning the interface more closely with how bidding strategies are represented in the Google Ads API.
Developers should review integrations, reporting tools, and campaign creation workflows to ensure they correctly recognise standalone TARGET_CPA and TARGET_ROAS strategy types.
Google is encouraging API users to monitor future updates related to:
The BiddingStrategyType enum
Standalone TargetCpa and TargetRoas messages
Optional target settings within MaximizeConversions and MaximizeConversionValue





Bottom line. Nothing changes in how Smart Bidding works, but advertisers may find campaign setup and reporting easier to understand as Google restores Target CPA and Target ROAS as clearly defined standalone strategy names.

Google's update will make it easier for advertisers to distinguish between target-based and volume-based bidding strategies.

Google Ads brings back Target CPA and Target ROAS naming

<div><img width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/06/PLTP-Image-2.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/PLTP-Image-2.jpg 1600w, https://searchengineland.com/wp-content/seloads/2026/06/PLTP-Image-2-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/PLTP-Image-2-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/06/PLTP-Image-2-1200x675.jpg 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></div>Laura I. Abreu shares how one wrong client taught her lasting lessons about trusting her instincts and setting expectations.

<div><img width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/06/PLTP-Image-2.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/PLTP-Image-2.jpg 1600w, https://searchengineland.com/wp-content/seloads/2026/06/PLTP-Image-2-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/06/PLTP-Image-2-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/06/PLTP-Image-2-1200x675.jpg 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></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/jPO8wOU39SE?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>







<p>One of the hardest lessons in PPC has nothing to do with bidding strategies, keywords, or campaign structure. It&#8217;s knowing when to walk away from a client.</p>



<p>On a recent episode of PPC Live The Podcast, performance marketing strategist Laura Abreu shared how taking on the wrong client early in her career became one of her most valuable professional lessons.</p>



<h2 class="wp-block-heading" id="h-when-your-gut-is-telling-you-something">When your gut is telling you something</h2>



<p>Laura&#8217;s first client was launching an ecommerce store selling beauty products from well-known brands. On the surface, it seemed like a great opportunity, but something felt off.</p>



<p>The products were available elsewhere at the same price, giving customers little reason to buy from an unknown retailer. Despite her concerns, Laura ignored her instincts and accepted the project anyway.</p>



<h2 class="wp-block-heading" id="h-great-marketing-can-t-fix-a-weak-business-model">Great marketing can&#8217;t fix a weak business model</h2>



<p>The team tried everything. Search campaigns, Meta ads, seasonal offers, product bundles, PR activity, and customer testimonials.</p>



<p>After three months of testing and optimisation, they hadn&#8217;t generated a single sale. The issue wasn&#8217;t the marketing. The business simply hadn&#8217;t established a compelling reason for customers to choose them over established competitors.</p>



<h2 class="wp-block-heading" id="h-the-importance-of-market-validation">The importance of market validation</h2>



<p>Many business owners believe hiring a marketer will automatically create growth. In reality, marketing amplifies demand—it doesn&#8217;t create it.</p>



<p>Today, Laura asks prospective clients whether they&#8217;ve tested the market, generated sales, and gathered customer feedback before investing in advertising. If the foundations aren&#8217;t there, paid media won&#8217;t solve the problem.</p>



<h2 class="wp-block-heading" id="h-pretty-creative-doesn-t-equal-performance">Pretty creative doesn&#8217;t equal performance</h2>



<p>One of the biggest mistakes marketers make is judging creative based on personal preference rather than data.</p>



<p>The team invested heavily in creating beautiful visuals, but attractive creative alone wasn&#8217;t enough to drive sales. Customers don&#8217;t buy because an ad looks good; they buy because the offer resonates with their needs.</p>



<h2 class="wp-block-heading" id="h-the-emotional-cost-of-a-bad-client">The emotional cost of a bad client</h2>



<p>The failed project affected Laura far beyond the campaign results. As many marketers do, she tied her self-worth to the outcome.</p>



<p>The experience damaged her confidence so much that she stopped taking PPC clients for a period of time. Looking back, she realised she was carrying responsibility for a business problem that advertising could never have fixed.</p>



<h2 class="wp-block-heading" id="h-why-expectations-matter">Why expectations matter</h2>



<p>One lesson Laura now applies with every client is setting expectations early and clearly.</p>



<p>Rather than promising immediate growth, she positions advertising as a way to test assumptions, validate demand, and uncover opportunities. This creates more honest conversations and avoids unrealistic expectations from the outset.</p>



<h2 class="wp-block-heading" id="h-why-laura-doesn-t-work-with-friends-or-family">Why Laura doesn&#8217;t work with friends or family</h2>



<p>Perhaps the strongest lesson from the experience is a rule she follows to this day: she doesn&#8217;t work with friends or family.</p>



<p>Maintaining professional distance allows her to stay objective, make decisions based on data, and avoid the emotional complications that can arise when personal relationships and business become intertwined.</p>



<h2 class="wp-block-heading" id="h-reputation-is-more-valuable-than-revenue">Reputation is more valuable than revenue</h2>



<p>When campaigns don&#8217;t go as planned, Laura believes honesty is non-negotiable.</p>



<p>Whether that means admitting mistakes, offering additional support, or refunding fees where appropriate, protecting your reputation is more important than protecting your ego. In an industry built on referrals, trust is everything.</p>



<h2 class="wp-block-heading" id="h-common-mistakes-laura-sees-in-ppc-accounts">Common mistakes Laura sees in PPC accounts</h2>



<p>Having audited accounts across multiple markets, Laura says one of the biggest mistakes marketers make is treating campaigns as &#8220;set and forget&#8221; assets. She often finds underperforming creatives left running for months, ad copy that hasn&#8217;t been refreshed, and winning ads that aren&#8217;t being scaled effectively.</p>



<p>She also sees businesses creating unnecessary friction in lead generation campaigns. Long-form copy, overly complex forms, and sending users to external landing pages instead of testing native lead forms can all reduce conversion rates. In her experience, simpler journeys often deliver better results.</p>



<h2 class="wp-block-heading">How Laura thinks marketers should use AI</h2>



<p>Laura sees AI as a powerful tool for automating repetitive tasks rather than replacing marketers. She recommends using it to monitor performance, automate alerts, and streamline workflows so practitioners can spend more time on strategy and client communication.</p>



<p id="h-">At the same time, she warns against relying blindly on AI-generated outputs. Poor-quality ad descriptions and generic messaging can hurt performance, so human oversight remains essential. The marketers who succeed will be those who combine AI efficiency with strong strategic thinking.</p>


Watch this video on YouTube








One of the hardest lessons in PPC has nothing to do with bidding strategies, keywords, or campaign structure. It’s knowing when to walk away from a client.
On a recent episode of PPC Live The Podcast, performance marketing strategist Laura Abreu shared how taking on the wrong client early in her career became one of her most valuable professional lessons.
When your gut is telling you something
Laura’s first client was launching an ecommerce store selling beauty products from well-known brands. On the surface, it seemed like a great opportunity, but something felt off.
The products were available elsewhere at the same price, giving customers little reason to buy from an unknown retailer. Despite her concerns, Laura ignored her instincts and accepted the project anyway.
Great marketing can’t fix a weak business model
The team tried everything. Search campaigns, Meta ads, seasonal offers, product bundles, PR activity, and customer testimonials.
After three months of testing and optimisation, they hadn’t generated a single sale. The issue wasn’t the marketing. The business simply hadn’t established a compelling reason for customers to choose them over established competitors.
The importance of market validation
Many business owners believe hiring a marketer will automatically create growth. In reality, marketing amplifies demand—it doesn’t create it.
Today, Laura asks prospective clients whether they’ve tested the market, generated sales, and gathered customer feedback before investing in advertising. If the foundations aren’t there, paid media won’t solve the problem.
Pretty creative doesn’t equal performance
One of the biggest mistakes marketers make is judging creative based on personal preference rather than data.
The team invested heavily in creating beautiful visuals, but attractive creative alone wasn’t enough to drive sales. Customers don’t buy because an ad looks good; they buy because the offer resonates with their needs.
The emotional cost of a bad client
The failed project affected Laura far beyond the campaign results. As many marketers do, she tied her self-worth to the outcome.
The experience damaged her confidence so much that she stopped taking PPC clients for a period of time. Looking back, she realised she was carrying responsibility for a business problem that advertising could never have fixed.
Why expectations matter
One lesson Laura now applies with every client is setting expectations early and clearly.
Rather than promising immediate growth, she positions advertising as a way to test assumptions, validate demand, and uncover opportunities. This creates more honest conversations and avoids unrealistic expectations from the outset.
Why Laura doesn’t work with friends or family
Perhaps the strongest lesson from the experience is a rule she follows to this day: she doesn’t work with friends or family.
Maintaining professional distance allows her to stay objective, make decisions based on data, and avoid the emotional complications that can arise when personal relationships and business become intertwined.
Reputation is more valuable than revenue
When campaigns don’t go as planned, Laura believes honesty is non-negotiable.
Whether that means admitting mistakes, offering additional support, or refunding fees where appropriate, protecting your reputation is more important than protecting your ego. In an industry built on referrals, trust is everything.
Common mistakes Laura sees in PPC accounts
Having audited accounts across multiple markets, Laura says one of the biggest mistakes marketers make is treating campaigns as “set and forget” assets. She often finds underperforming creatives left running for months, ad copy that hasn’t been refreshed, and winning ads that aren’t being scaled effectively.
She also sees businesses creating unnecessary friction in lead generation campaigns. Long-form copy, overly complex forms, and sending users to external landing pages instead of testing native lead forms can all reduce conversion rates. In her experience, simpler journeys often deliver better results.
How Laura thinks marketers should use AI
Laura sees AI as a powerful tool for automating repetitive tasks rather than replacing marketers. She recommends using it to monitor performance, automate alerts, and streamline workflows so practitioners can spend more time on strategy and client communication.
At the same time, she warns against relying blindly on AI-generated outputs. Poor-quality ad descriptions and generic messaging can hurt performance, so human oversight remains essential. The marketers who succeed will be those who combine AI efficiency with strong strategic thinking.

Laura I. Abreu shares how one wrong client taught her lasting lessons about trusting her instincts and setting expectations.

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Read the Latest Trends and Headlines in Digital Marketing.

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

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

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Digital Marketing