<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2.png 1920w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>If you’re on top of the latest techniques and what’s to come in SEO, PPC, and AI search, submit a pitch to present at SMX Next.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2.png 1920w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2025/07/SMXN2026-Speaker-D-v2-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p><strong>SMX Next returns online Nov. 18</strong>, focusing on navigating today’s complex search landscape and mastering the tactics that will define success in 2027 and beyond.</p>



<p>Search marketing isn&#8217;t just changing; it has evolved into an entirely new beast. </p>



<ul class="wp-block-list">
<li>SEO has shifted to mastering AI Overviews, search everywhere optimization, and consideration for autonomous AI agents that browse on behalf of users. Demonstrating trustworthiness, digital authority, and precise alignment with user intent is even more important now.</li>



<li>On the PPC side, generative AI and deep automation are driving unprecedented personalization, but marketers are facing new challenges: How do you maintain strategic control, preserve data privacy, and prevent waste?</li>
</ul>



<p>If you’re an enthusiastic search marketer who has a passion for sharing knowledge please consider submitting a session pitch for SMX Next. We’re looking for subject matter experts to share insights, strategies, and tactics to help SEO and PPC marketers thrive in 2027.</p>



<p>Whether you’ve been speaking for years or you are a practitioner ready to share something new you&#8217;ve developed, we welcome your voice. We are actively seeking new speakers with diverse points of view and real-world experience.</p>



<p><strong>The deadline for SMX Next pitches is Aug. 7.</strong>



<p>Here are a few tips for submitting a compelling session proposal:</p>



<ul class="wp-block-list">
<li><strong>Present an original idea:</strong> Focus on advanced, forward-thinking topics or unique frameworks not commonly covered at other search events.</li>



<li><strong>Focus on actionability:</strong> Include explicit details about what attendees will be able to do better, faster, or differently as a result of your session.</li>



<li><strong>Bring the data:</strong> Back up your theories with a case study or specific examples, explaining how they can be scaled across different types of organizations.</li>



<li><strong>Keep it focused:</strong> Be realistic about what you can present in 30 minutes. Going deep on a narrow or specialized topic provides much more value than a broad overview.</li>



<li><strong>Provide tangible takeaways:</strong> Ensure attendees walk away with a clear action plan or framework they can implement immediately.</li>
</ul>



<p>Jump over to this <a href="https://marketinglandevents.com/smx/speaking/">page</a> for more details on how to submit a session idea, or directly to this <a href="https://thirddoorevents.com/dashboard">page</a> to create your profile and submit a session pitch.</p>



<p>If you have questions, feel free to contact me directly at kathy.bushman@semrush.com. I’m looking forward to reading your proposals!</p>


SMX Next returns online Nov. 18, focusing on navigating today’s complex search landscape and mastering the tactics that will define success in 2027 and beyond.
Search marketing isn’t just changing; it has evolved into an entirely new beast. 
SEO has shifted to mastering AI Overviews, search everywhere optimization, and consideration for autonomous AI agents that browse on behalf of users. Demonstrating trustworthiness, digital authority, and precise alignment with user intent is even more important now.
On the PPC side, generative AI and deep automation are driving unprecedented personalization, but marketers are facing new challenges: How do you maintain strategic control, preserve data privacy, and prevent waste?
If you’re an enthusiastic search marketer who has a passion for sharing knowledge please consider submitting a session pitch for SMX Next. We’re looking for subject matter experts to share insights, strategies, and tactics to help SEO and PPC marketers thrive in 2027.
Whether you’ve been speaking for years or you are a practitioner ready to share something new you’ve developed, we welcome your voice. We are actively seeking new speakers with diverse points of view and real-world experience.
The deadline for SMX Next pitches is Aug. 7.



Here are a few tips for submitting a compelling session proposal:
Present an original idea: Focus on advanced, forward-thinking topics or unique frameworks not commonly covered at other search events.
Focus on actionability: Include explicit details about what attendees will be able to do better, faster, or differently as a result of your session.
Bring the data: Back up your theories with a case study or specific examples, explaining how they can be scaled across different types of organizations.
Keep it focused: Be realistic about what you can present in 30 minutes. Going deep on a narrow or specialized topic provides much more value than a broad overview.
Provide tangible takeaways: Ensure attendees walk away with a clear action plan or framework they can implement immediately.
Jump over to this page for more details on how to submit a session idea, or directly to this page to create your profile and submit a session pitch.
If you have questions, feel free to contact me directly at kathy.bushman@semrush.com. I’m looking forward to reading your proposals!

If you’re on top of the latest techniques and what’s to come in SEO, PPC, and AI search, submit a pitch to present at SMX Next.

Want to present at SMX Next? Now’s the time to submit a pitch!

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources.png" class="attachment-large size-large wp-post-image" alt="ChatGPT search sources" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Two analyses found ChatGPT’s hidden search systems can change its sources, making citation tracking harder than answers suggest.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources.png" class="attachment-large size-large wp-post-image" alt="ChatGPT search sources" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/chatgpt-search-sources-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Two new analyses found that ChatGPT’s cited sources changed as its search traffic shifted between hidden retrieval pipelines.</p>



<p><strong>The findings.</strong> Research from <a href="https://chrisgreenseo.substack.com/p/what-can-we-learn-from-chatgpt-search" target="_blank" rel="noopener">Chris Green</a> and <a href="https://suganthan.com/blog/how-chatgpt-picks-sources/" target="_blank" rel="noopener">Suganthan Mohanadasan</a> adds a new complication to AI visibility tracking: the final answer doesn’t reveal how ChatGPT selected its sources.</p>



<ul class="wp-block-list">
<li>Both researchers found internal source-selection labels, including Labrador, Bright, Oxylabs, and SERP. </li>



<li>Those labels sit behind the answer, not in the citation cards users see.</li>
</ul>



<p><strong>Hidden pipelines changed sources</strong>. Green tested 1,000 prompts up to 10 times each and captured 9,946 completed search runs. Most prompts stayed on one retrieval source. Labrador accounted for 88.1% of primary search sources in his dataset, followed by Bright at 9.9%, Oxylabs at 1.7%, and SERP at 0.3%.</p>



<ul class="wp-block-list">
<li>But 11.6% of prompts changed primary search source across repeated runs.</li>



<li>URL overlap dropped from 0.273 to 0.149 when the search source changed. Domain overlap fell from 0.265 to 0.155. Green calculated that as roughly 45% lower URL overlap and 42% lower domain overlap.</li>
</ul>


<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">
  <div>
    
    <div>
      
      <div id="semrush-one-headline" class="headline-responsive">
        Be the brand <span>AI recommends</span>.
      </div>
      <p id="semrush-one-subhead">
        See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
      </p>
    </div>
    <div>
      <span id="semrush-one-cta">See your AI visibility</span>
    </div>
    </div>
    </a>




<p><strong>The labels behind results</strong>. Mohanadasan inspected two days of raw ChatGPT network traffic from one logged-in Pro account and logged about 1,240 source records across a few dozen searches.</p>



<ul class="wp-block-list">
<li>He found a <code>result_source</code> field attached to web results, with four observed values: SERP, Labrador, Bright, and Oxylabs.</li>



<li>He described Labrador as including established publishers and reference sites, Bright as tied to Bright Data, Oxylabs as tied to Oxylabs, and SERP as an open-web baseline that appeared mostly in news-style results.</li>



<li>Green’s repeated-prompt test found Labrador dominated his dataset. Mohanadasan saw Bright play a larger role in his sample, especially for commercial, shopping, finance, weather, and local queries.</li>
</ul>



<p><strong>Some prompts skipped search</strong>. Mohanadasan also found that ChatGPT classified some queries before searching, using a <code>turn_use_case</code> field. Some prompts were filed as text and skipped web search entirely, even when they sounded current. In those cases, no page could be fetched, cited, or used as evidence.</p>



<ul class="wp-block-list">
<li>More complex “thinking” queries behaved differently. Mohanadasan found that ChatGPT could fan out into many searches, including <code>site:</code> probes, pricing checks, and searches for unnamed competitors.</li>



<li>That behavior changes which pages can enter the answer process because ChatGPT may search rewritten queries, direct site probes, or follow-up checks instead of the exact phrase a user typed.</li>
</ul>



<p><strong>Fetched was not cited</strong>. Mohanadasan separated three outcomes: fetched, cited and mentioned.</p>



<ul class="wp-block-list">
<li>A page can be fetched into ChatGPT’s context without being shown to users. It can be cited as the source behind a specific sentence. Or a brand can be mentioned without being the source of the claim.</li>



<li>In his small commercial-query sample, Reddit and YouTube were both fetched often, but Reddit was cited and YouTube was not. He attributed the gap to text availability: Reddit threads expose text, while YouTube search results often provide metadata rather than full video transcripts.</li>



<li>Vendor pages were cited for their own facts, such as prices and specs. Third-party pages were more likely to support broader recommendation claims.</li>
</ul>



<p><strong>Why we care</strong>. There’s no single ChatGPT visibility result to measure. Your page may never be considered if ChatGPT skips search, uses another retrieval source, or finds a clearer third-party page to support the claim.</p>



<p><strong>Readable pages were easier to use</strong>. Both analyses showed that ChatGPT’s source selection depended partly on what it could retrieve and read.</p>



<ul class="wp-block-list">
<li>Mohanadasan found cases where ChatGPT appeared to prefer official pricing pages, then fell back to third-party sources when prices were hidden behind JavaScript or otherwise hard to parse.</li>



<li>Green’s results showed that source routing changed which URLs and domains entered the answer set.</li>



<li>Plain HTML, crawlable facts, clear pricing and specs, strong third-party coverage, and text-heavy pages became more important when source selection depended on retrieval and readability.</li>
</ul>


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



Two new analyses found that ChatGPT’s cited sources changed as its search traffic shifted between hidden retrieval pipelines.
The findings. Research from Chris Green and Suganthan Mohanadasan adds a new complication to AI visibility tracking: the final answer doesn’t reveal how ChatGPT selected its sources.
Both researchers found internal source-selection labels, including Labrador, Bright, Oxylabs, and SERP. 
Those labels sit behind the answer, not in the citation cards users see.
Hidden pipelines changed sources. Green tested 1,000 prompts up to 10 times each and captured 9,946 completed search runs. Most prompts stayed on one retrieval source. Labrador accounted for 88.1% of primary search sources in his dataset, followed by Bright at 9.9%, Oxylabs at 1.7%, and SERP at 0.3%.
But 11.6% of prompts changed primary search source across repeated runs.
URL overlap dropped from 0.273 to 0.149 when the search source changed. Domain overlap fell from 0.265 to 0.155. Green calculated that as roughly 45% lower URL overlap and 42% lower domain overlap.
AI recommends.
      
See your AI visibility
    
The labels behind results. Mohanadasan inspected two days of raw ChatGPT network traffic from one logged-in Pro account and logged about 1,240 source records across a few dozen searches.
He found a result_source field attached to web results, with four observed values: SERP, Labrador, Bright, and Oxylabs.
He described Labrador as including established publishers and reference sites, Bright as tied to Bright Data, Oxylabs as tied to Oxylabs, and SERP as an open-web baseline that appeared mostly in news-style results.
Green’s repeated-prompt test found Labrador dominated his dataset. Mohanadasan saw Bright play a larger role in his sample, especially for commercial, shopping, finance, weather, and local queries.
Some prompts skipped search. Mohanadasan also found that ChatGPT classified some queries before searching, using a turn_use_case field. Some prompts were filed as text and skipped web search entirely, even when they sounded current. In those cases, no page could be fetched, cited, or used as evidence.
More complex “thinking” queries behaved differently. Mohanadasan found that ChatGPT could fan out into many searches, including site: probes, pricing checks, and searches for unnamed competitors.
That behavior changes which pages can enter the answer process because ChatGPT may search rewritten queries, direct site probes, or follow-up checks instead of the exact phrase a user typed.
Fetched was not cited. Mohanadasan separated three outcomes: fetched, cited and mentioned.
A page can be fetched into ChatGPT’s context without being shown to users. It can be cited as the source behind a specific sentence. Or a brand can be mentioned without being the source of the claim.
In his small commercial-query sample, Reddit and YouTube were both fetched often, but Reddit was cited and YouTube was not. He attributed the gap to text availability: Reddit threads expose text, while YouTube search results often provide metadata rather than full video transcripts.
Vendor pages were cited for their own facts, such as prices and specs. Third-party pages were more likely to support broader recommendation claims.
Why we care. There’s no single ChatGPT visibility result to measure. Your page may never be considered if ChatGPT skips search, uses another retrieval source, or finds a clearer third-party page to support the claim.
Readable pages were easier to use. Both analyses showed that ChatGPT’s source selection depended partly on what it could retrieve and read.
Mohanadasan found cases where ChatGPT appeared to prefer official pricing pages, then fell back to third-party sources when prices were hidden behind JavaScript or otherwise hard to parse.
Green’s results showed that source routing changed which URLs and domains entered the answer set.
Plain HTML, crawlable facts, clear pricing and specs, strong third-party coverage, and text-heavy pages became more important when source selection depended on retrieval and readability.
customers won’t either.
      
See your AI visibility

Two analyses found ChatGPT’s hidden search systems can change its sources, making citation tracking harder than answers suggest.

ChatGPT citations change when hidden search pipelines switch

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget.png" class="attachment-large size-large wp-post-image" alt="The Google Ads mistake that’s draining your budget" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Learn what Search Partners are, why they usually produce junk traffic, and how to see if you're spending money on these placements.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget.png" class="attachment-large size-large wp-post-image" alt="The Google Ads mistake that’s draining your budget" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/google-ads-mistake-draining-your-budget-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-9-16 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<p><a href="https://www.youtube.com/embed/gEP-g00gzdU?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>







<p>If you’re running Google Ads in 2026, you’ve probably heard of “Search Partners.” It’s often checked by default in your campaign settings, promising to extend your reach beyond Google Search. Sounds like an easy way to get more traffic, right?</p>



<p>Traffic, yes. Quality traffic? Not so much. </p>



<p>Most advertisers shouldn’t advertise on Search Partners. Read on to learn why and what you should do instead.</p>



<h2 class="wp-block-heading" id="h-what-are-google-search-partners">What are Google Search Partners?</h2>



<p>Google Search Partners are third-party sites with search results powered by Google. When someone searches on those sites, your ad can appear. This includes YouTube, along with a massive list of other sites, directories, and even parked domains.</p>



<p>As you might imagine — or may have experienced firsthand — the traffic from these partner sites is often poor. You may get plenty of clicks, and the cost per click (CPC) is often much cheaper than Google Search, but you usually get what you pay for. Real conversions or meaningful business value from these placements are rare.</p>



<p>If you’re using conversion-focused Smart Bidding, you’ll often see Search Partners spend naturally drop to zero over time. Your bidding algorithm figures out it isn’t finding conversions there, so it stops spending your budget on those sites.</p>



<h2 class="wp-block-heading" id="h-how-are-google-search-partners-different-from-the-google-display-network">How are Google Search Partners different from the Google Display Network?</h2>



<p>People often confuse Search Partners with the Display Network. Websites can be part of both, so let’s clarify the difference.</p>



<p>The <a href="https://searchengineland.com/google-display-network-explained-456796">Google Display Network</a> is a collection of millions of websites and apps that use AdSense. You can show ads to users while they browse content. The GDN is a placement option in Demand Gen, Video, where it’s called “Video Partners,” and PMax campaigns.</p>



<p>Search Partners are specifically for search-based intent. That’s why they’re only compatible with Search, Shopping, and Performance Max campaigns.</p>



<h2 class="wp-block-heading" id="h-how-to-audit-your-search-partner-performance">How to audit your Search Partner performance</h2>



<p>Don’t take my word for it. See what Search Partners are doing in your Google Ads campaigns.</p>



<h3 class="wp-block-heading" id="h-for-search-or-shopping-campaigns">For Search or Shopping campaigns</h3>



<p>In your Google Ads account, go to your campaign view and click <em>Segment</em>. Choose <em>Network (with search partners)</em>. This breaks performance into two rows: <em>Google Search</em> and <em>Search Partners.</em>



<p>You’ll typically see plenty of impressions and clicks from Search Partners, with lower CPCs than Google Search. Conversions? Only if you’re tracking something shallow or bot-susceptible, like a page view or easy form fill.</p>



<h3 class="wp-block-heading" id="h-for-performance-max-campaigns">For Performance Max campaigns</h3>



<p>Performance Max is different. Search Partners are required for this campaign type; you can’t opt out. However, you can still monitor activity through your Channel Performance report.</p>



<p>If you see high Search Partners spend in a PMax campaign, it often indicates an issue with your conversion tracking or bid strategy settings.</p>



<h3 class="wp-block-heading" id="h-search-partner-transparency-the-content-suitability-report">Search Partner transparency: The Content Suitability report</h3>



<p>For more transparency, check the Content Suitability report under <em>Insights and reports.</em> It shows the actual websites or YouTube channels where your ads appeared on the Search Partner network. Review that list, and you’ll likely agree that much of it is junk and spam.</p>



<p>In Google Ads, the answer to almost every question is “it depends.” This is one of the few exceptions.</p>



<p>If you’re building a new Search or Shopping campaign, leave Search Partners unchecked. Once the campaign performs well and you have solid conversion data, you can consider testing Search Partners for more volume. Until then, keep your ads on the main Google SERP.</p>



<p><em>This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it – all in a quick 3-minute read.</em>






Watch this video on YouTube








If you’re running Google Ads in 2026, you’ve probably heard of “Search Partners.” It’s often checked by default in your campaign settings, promising to extend your reach beyond Google Search. Sounds like an easy way to get more traffic, right?
Traffic, yes. Quality traffic? Not so much. 
Most advertisers shouldn’t advertise on Search Partners. Read on to learn why and what you should do instead.
What are Google Search Partners?
Google Search Partners are third-party sites with search results powered by Google. When someone searches on those sites, your ad can appear. This includes YouTube, along with a massive list of other sites, directories, and even parked domains.
As you might imagine — or may have experienced firsthand — the traffic from these partner sites is often poor. You may get plenty of clicks, and the cost per click (CPC) is often much cheaper than Google Search, but you usually get what you pay for. Real conversions or meaningful business value from these placements are rare.
If you’re using conversion-focused Smart Bidding, you’ll often see Search Partners spend naturally drop to zero over time. Your bidding algorithm figures out it isn’t finding conversions there, so it stops spending your budget on those sites.
How are Google Search Partners different from the Google Display Network?
People often confuse Search Partners with the Display Network. Websites can be part of both, so let’s clarify the difference.
The Google Display Network is a collection of millions of websites and apps that use AdSense. You can show ads to users while they browse content. The GDN is a placement option in Demand Gen, Video, where it’s called “Video Partners,” and PMax campaigns.
Search Partners are specifically for search-based intent. That’s why they’re only compatible with Search, Shopping, and Performance Max campaigns.
How to audit your Search Partner performance
Don’t take my word for it. See what Search Partners are doing in your Google Ads campaigns.
For Search or Shopping campaigns
In your Google Ads account, go to your campaign view and click Segment. Choose Network (with search partners). This breaks performance into two rows: Google Search and Search Partners.



You’ll typically see plenty of impressions and clicks from Search Partners, with lower CPCs than Google Search. Conversions? Only if you’re tracking something shallow or bot-susceptible, like a page view or easy form fill.
For Performance Max campaigns
Performance Max is different. Search Partners are required for this campaign type; you can’t opt out. However, you can still monitor activity through your Channel Performance report.
If you see high Search Partners spend in a PMax campaign, it often indicates an issue with your conversion tracking or bid strategy settings.
Search Partner transparency: The Content Suitability report
For more transparency, check the Content Suitability report under Insights and reports. It shows the actual websites or YouTube channels where your ads appeared on the Search Partner network. Review that list, and you’ll likely agree that much of it is junk and spam.
In Google Ads, the answer to almost every question is “it depends.” This is one of the few exceptions.
If you’re building a new Search or Shopping campaign, leave Search Partners unchecked. Once the campaign performs well and you have solid conversion data, you can consider testing Search Partners for more volume. Until then, keep your ads on the main Google SERP.
This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it – all in a quick 3-minute read.

Learn what Search Partners are, why they usually produce junk traffic, and how to see if you're spending money on these placements.

Why you should opt out of Google Search Partners

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image.png" class="attachment-large size-large wp-post-image" alt="Why most original data never gets cited - featured-image" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Primary research is rare, but it earns 3.3x more AI citations per page. Learn why benchmark studies outperform other original data.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image.png" class="attachment-large size-large wp-post-image" alt="Why most original data never gets cited - featured-image" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Why-most-original-data-never-gets-cited-featured-image-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Part 1 tackled those all-important <a href="https://www.growth-memo.com/p/topics-matter-for-third-party-authority" target="_blank" rel="noopener">third-party citation signals</a>, while Part 2 made the case for <a href="https://www.growth-memo.com/p/why-proprietary-data-is-your-most" target="_blank" rel="noopener">publishing original data</a>: It is the strongest single predictor of page originality, and the bar to earning visibility/authority via this play is low.</p>



<p>This memo has more ammo to back up your use of proprietary data in content creation.</p>



<p>Publishing the number is necessary. But it’s not always what gets cited. We pulled Gauge&#8217;s citation data to find out what AI actually rewards when it comes to publishing first-party data, and the answer is narrower and more useful than “original data wins.” (Although original data does, in fact, win.)</p>



<p>AI rewards one format almost to the exclusion of everything else: The benchmark that answers “which is best.”</p>



<p><a href="https://www.growth-memo.com/embed?transparent=1" rel="noopener noreferrer">View embedded content</a>



<h2 class="wp-block-heading" id="h-first-party-research-is-scarce-and-punches-above-its-weight">First-party research is scarce and punches above its weight</h2>



<p>We worked from Gauge&#8217;s cited-URL set: 301 live pages that AI systems cited (316 unique prompts across 7 verticals), carrying 1,075 citations between them.&nbsp;</p>



<p>After a full URL audit,<strong> only 8 of those 301 pages qualified as primary research</strong>, meaning the original source of the data and methodology are on the page… rather than a writeup of someone else&#8217;s numbers.</p>



<p>Eight pages out of 301 is 2.7% of the set. Those same 8 pages earned 90 of 1,075 citations, or 8.4% of citation volume. First-party research shows up rarely, then over-indexes 3x on citation share when it does.</p>



<p>The cleaner way to see it is density.&nbsp;</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1639" height="1000" src="https://searchengineland.com/wp-content/seloads/2026/07/image-23.png" alt="" class="wp-image-481678" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-23.png 1639w, https://searchengineland.com/wp-content/seloads/2026/07/image-23-768x469.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/image-23-1536x937.png 1536w" sizes="(max-width: 1639px) 100vw, 1639px" /></figure>
</div>


<p>Primary research averaged 11.3 citations per page. Everything else averaged 3.4. A primary-research page was 3.3x as citation-dense as a non-primary one.</p>



<p>Primary research compounds citations.</p>



<p>This is the same shape as the <a href="https://www.growth-memo.com/p/why-proprietary-data-is-your-most" target="_blank" rel="noopener">information gain finding discussed in Part 2</a>, viewed from the AI side instead of the classic 10 blue links side.&nbsp;</p>



<p>There, original data correlated with page originality more than any other trait. Here, original data correlates with citation density. Both point the same direction: The number only you can produce is the lever.&nbsp;</p>



<h2 class="wp-block-heading" id="h-original-research-wins-when-the-question-has-a-benchmark">Original research wins when the question has a benchmark</h2>



<p>Here’s where the “original data wins” filter gets sharper.</p>



<p>The 90 primary-research citations are not spread across the 8 pages evenly, and they are not spread across topics evenly.&nbsp;</p>



<p>75 of the 90 came from one cluster: cloud data warehouse benchmarks. <a href="https://www.fivetran.com/blog/warehouse-benchmark">Fivetran&#8217;s warehouse benchmark</a> alone took 44 citations, just under half of every primary-research citation in the set. (More on that below.)</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1639" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/image-24.png" alt="" class="wp-image-481679" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-24.png 1639w, https://searchengineland.com/wp-content/seloads/2026/07/image-24-768x506.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/image-24-1536x1012.png 1536w" sizes="(max-width: 1639px) 100vw, 1639px" /></figure>
</div>


<p>Reality: Strip the benchmark cluster out and first-party research barely registers in the citation set. The win is not “we published original data.”&nbsp;</p>



<p>The win is “we published a benchmark that answers a buying comparison,” and almost nobody builds one. (“Benchmark” meaning you measure a set of named things against each other on a specific yardstick, and publish the results as numbers.)</p>



<p>Original research is most effective when it is packaged in a way that directly answers commercial comparison queries.</p>



<p>This is what Google is after with non-commodity content: new and helpful information that is hard to get.</p>



<p>Primary-research citations clustered where the prompt asked AI to compare options on measurable specs: speed, cost, latency, yield, or performance.</p>



<p>That explains the warehouse benchmark spike. The “HR Tech / Compensation” label is noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1639" height="1040" src="https://searchengineland.com/wp-content/seloads/2026/07/image-25.png" alt="" class="wp-image-481681" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-25.png 1639w, https://searchengineland.com/wp-content/seloads/2026/07/image-25-768x487.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/image-25-1536x975.png 1536w" sizes="(max-width: 1639px) 100vw, 1639px" /></figure>
</div>


<p>Crypto / Solana shows the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need first-hand ecosystem data, not generic explainers.</p>



<p>The pattern disappears in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning, none of those topics produced a cited primary-research page.</p>



<h3 class="wp-block-heading" id="h-a-closer-look-at-the-content-that-held-44-of-the-citations">A closer look at the content that held 44 of the citations</h3>



<p><a href="https://www.fivetran.com/blog/warehouse-benchmark" target="_blank" rel="noopener">Fivetran&#8217;s warehouse benchmark</a> took 44 of this data set’s citations on its own, and Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations in the set. Why?</p>



<p>It’s a piece of content from 2022, but when you examine it, it’s easy to see why LLMs prefer it.&nbsp;</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="800" height="557" src="https://searchengineland.com/wp-content/seloads/2026/07/image-3.jpeg" alt="" class="wp-image-481680" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-3.jpeg 800w, https://searchengineland.com/wp-content/seloads/2026/07/image-3-768x535.jpeg 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>
</div>


<ol class="wp-block-list">
<li><strong>It answers a measurable comparison head-on.</strong> Named warehouses, BigQuery, Redshift, Snowflake, and Databricks, ranked on speed and cost. It’s entity-rich and not afraid to name all the major players.</li>



<li><strong>It runs on real first-party data.</strong> Fivetran tested against actual customer usage rather than synthetic assumptions, and called out that choice directly. </li>



<li><strong>It shows the method, step by step.</strong> Trust signals. Separate sections walk through what data they queried, what queries they ran, and how they configured and tuned each warehouse. A reader (or a model) can see exactly how the numbers were produced. </li>



<li><strong>The structure is built to be lifted.</strong> Descriptive headings (&#8220;Results,&#8221; &#8220;How much did performance improve?,&#8221; &#8220;Why are our results different from previous benchmarks?&#8221;) let AI map a question to the one passage that answers it.</li>



<li><strong>It links to its raw data and sources.</strong> The page footnotes its references, including the C-Store paper, and points to the underlying data, so every claim is verifiable. Not many brands put this much work into a data-backed content piece, let alone offer the full data set for transparency.</li>



<li><strong>It shows its seams.</strong> Dated correction notes from December 2022, named qualitative limits, and an honest &#8220;performance floor&#8221; caveat make the quantitative claims more credible… not less. They also note corrections.</li>



<li><strong>The URL never moved.</strong> A 2022 page is still collecting citations in 2026 because it stayed put at one canonical address.</li>
</ol>



<p>The data behind a page like this is easier to pull and analyze than it has ever been. What is not easy is everything around it: the clean method, the linked sources, the corrections, the navigable structure, the willingness to name what the numbers do not prove. That’s craft, and that’s the moat here.&nbsp;</p>



<p>This first-party data-focused piece isn’t a sloppy press release with half-assed pulled data. It took a lot of work, and it’s holding authority for 4 years out. The takeaway: AI does not reward “original data” by default. It rewards first-party research when the page provides a clear answer to a measurable comparison that signals depth of expertise and trust.</p>



<p>The open opportunity here is to publish a retrievable dataset for a buyer question where AI currently has no clean benchmark source. This maps onto the <a href="https://www.growth-memo.com/p/query-fan-out">unanswered-questions finding</a> from Part 2: the open door exists, and in these verticals nobody has walked through it with a real dataset.</p>



<h2 class="wp-block-heading" id="h-original-data-needs-a-citation-ready-package">Original data needs a citation-ready package</h2>



<p>Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the question.</p>



<p>That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists. The citation does not.</p>



<p>The pages that won in this dataset had both: original numbers and a clean citation shape. Stable URL. Clear method. Named comparison. Results that answered a buyer question directly.</p>



<ul class="wp-block-list">
<li>Who wins: Brands sitting on proprietary product, usage, or pricing data who package it into a comparison a buyer can act on, one that informs LLM outputs for recommendations.&nbsp;</li>



<li>Who loses: Brands publishing original numbers buried in narrative, on slow or unstable pages, with no comparison frame for AI to lift.</li>
</ul>



<p>A citation-ready research page has four parts:</p>



<ul class="wp-block-list">
<li><strong>Lead with the comparison result.</strong> The headline finding (“X is fastest, Y is cheapest at scale”) goes in the first 30% of the page. Result, then method, then nuance.</li>



<li><strong>Box the methodology.</strong> Sample, time window, what was measured, how. Attribution confidence is part of what makes a number citable. Make your methodology clear on the page.</li>



<li><strong>Explicitly frame it as a comparison if it is one.</strong> AI reaches for benchmarks on “which is best” prompts. A table that compares named options on named specs is the shape it lifts.</li>



<li><strong>Keep the URL stable. </strong>One canonical page, kept live, not migrated or renamed every redesign. The citation you earn this quarter only compounds if the page is still there next quarter. Of 365 cited URLs in this data set, 64 were dead, redirected, or otherwise broken, taking 203 citations down with them.</li>
</ul>



<p>This is the work behind a citable benchmark, and it is more involved than it looks.&nbsp;</p>



<p>HockeyStack documented its own version in a <a href="https://www.hockeystack.com/resources/playbook/how-to-launch-research-reports" target="_blank" rel="noopener">playbook on launching research reports</a>: they published 18 original reports built entirely on anonymized first-party customer data, the kind no competitor could replicate. </p>



<p>Their process names every step the Fivetran page demonstrates: list the data points you need, get a teammate to pull them with SQL, define and document the method so the numbers hold up to scrutiny, then structure the report around a real ICP question. They call methodology non-negotiable for a reason, noting that without it, someone will always dispute your data.&nbsp;</p>



<p>With AI analysis, the data is the easy part now. Building the content into something that is citable, demonstrates E-E-A-T, and is still earning visibility 4 years out for commercial queries is where the hard work lies.</p>



<p><strong>What sites are already trusted for your topic? </strong>When a benchmark you did not publish is taking the citations in your category, the <a href="https://www.growth-memo.com/i/202107502/premium-citation-source-mapper-tool-build-your-targeted-off-site-authority" target="_blank" rel="noopener">Citation Source Mapper</a> maps that trusted set into a ranked, pitchable target list. It is in the <a href="https://www.growth-memo.com/p/exclusive-growth-premium-resources" target="_blank" rel="noopener">premium library</a>.</p>



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


Part 1 tackled those all-important third-party citation signals, while Part 2 made the case for publishing original data: It is the strongest single predictor of page originality, and the bar to earning visibility/authority via this play is low.
This memo has more ammo to back up your use of proprietary data in content creation.
Publishing the number is necessary. But it’s not always what gets cited. We pulled Gauge’s citation data to find out what AI actually rewards when it comes to publishing first-party data, and the answer is narrower and more useful than “original data wins.” (Although original data does, in fact, win.)
AI rewards one format almost to the exclusion of everything else: The benchmark that answers “which is best.”
View embedded content



First-party research is scarce and punches above its weight
We worked from Gauge’s cited-URL set: 301 live pages that AI systems cited (316 unique prompts across 7 verticals), carrying 1,075 citations between them. 
After a full URL audit, only 8 of those 301 pages qualified as primary research, meaning the original source of the data and methodology are on the page… rather than a writeup of someone else’s numbers.
Eight pages out of 301 is 2.7% of the set. Those same 8 pages earned 90 of 1,075 citations, or 8.4% of citation volume. First-party research shows up rarely, then over-indexes 3x on citation share when it does.
The cleaner way to see it is density. 




Primary research averaged 11.3 citations per page. Everything else averaged 3.4. A primary-research page was 3.3x as citation-dense as a non-primary one.
Primary research compounds citations.
This is the same shape as the information gain finding discussed in Part 2, viewed from the AI side instead of the classic 10 blue links side. 
There, original data correlated with page originality more than any other trait. Here, original data correlates with citation density. Both point the same direction: The number only you can produce is the lever. 
Original research wins when the question has a benchmark
Here’s where the “original data wins” filter gets sharper.
The 90 primary-research citations are not spread across the 8 pages evenly, and they are not spread across topics evenly. 
75 of the 90 came from one cluster: cloud data warehouse benchmarks. Fivetran’s warehouse benchmark alone took 44 citations, just under half of every primary-research citation in the set. (More on that below.)




Reality: Strip the benchmark cluster out and first-party research barely registers in the citation set. The win is not “we published original data.” 
The win is “we published a benchmark that answers a buying comparison,” and almost nobody builds one. (“Benchmark” meaning you measure a set of named things against each other on a specific yardstick, and publish the results as numbers.)
Original research is most effective when it is packaged in a way that directly answers commercial comparison queries.
This is what Google is after with non-commodity content: new and helpful information that is hard to get.
Primary-research citations clustered where the prompt asked AI to compare options on measurable specs: speed, cost, latency, yield, or performance.
That explains the warehouse benchmark spike. The “HR Tech / Compensation” label is noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use.




Crypto / Solana shows the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need first-hand ecosystem data, not generic explainers.
The pattern disappears in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning, none of those topics produced a cited primary-research page.
A closer look at the content that held 44 of the citations
Fivetran’s warehouse benchmark took 44 of this data set’s citations on its own, and Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations in the set. Why?
It’s a piece of content from 2022, but when you examine it, it’s easy to see why LLMs prefer it. 





It answers a measurable comparison head-on. Named warehouses, BigQuery, Redshift, Snowflake, and Databricks, ranked on speed and cost. It’s entity-rich and not afraid to name all the major players.
It runs on real first-party data. Fivetran tested against actual customer usage rather than synthetic assumptions, and called out that choice directly. 
It shows the method, step by step. Trust signals. Separate sections walk through what data they queried, what queries they ran, and how they configured and tuned each warehouse. A reader (or a model) can see exactly how the numbers were produced. 
The structure is built to be lifted. Descriptive headings (“Results,” “How much did performance improve?,” “Why are our results different from previous benchmarks?”) let AI map a question to the one passage that answers it.
It links to its raw data and sources. The page footnotes its references, including the C-Store paper, and points to the underlying data, so every claim is verifiable. Not many brands put this much work into a data-backed content piece, let alone offer the full data set for transparency.
It shows its seams. Dated correction notes from December 2022, named qualitative limits, and an honest “performance floor” caveat make the quantitative claims more credible… not less. They also note corrections.
The URL never moved. A 2022 page is still collecting citations in 2026 because it stayed put at one canonical address.
The data behind a page like this is easier to pull and analyze than it has ever been. What is not easy is everything around it: the clean method, the linked sources, the corrections, the navigable structure, the willingness to name what the numbers do not prove. That’s craft, and that’s the moat here. 
This first-party data-focused piece isn’t a sloppy press release with half-assed pulled data. It took a lot of work, and it’s holding authority for 4 years out. The takeaway: AI does not reward “original data” by default. It rewards first-party research when the page provides a clear answer to a measurable comparison that signals depth of expertise and trust.
The open opportunity here is to publish a retrievable dataset for a buyer question where AI currently has no clean benchmark source. This maps onto the unanswered-questions finding from Part 2: the open door exists, and in these verticals nobody has walked through it with a real dataset.
Original data needs a citation-ready package
Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the question.
That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists. The citation does not.
The pages that won in this dataset had both: original numbers and a clean citation shape. Stable URL. Clear method. Named comparison. Results that answered a buyer question directly.
Who wins: Brands sitting on proprietary product, usage, or pricing data who package it into a comparison a buyer can act on, one that informs LLM outputs for recommendations. 
Who loses: Brands publishing original numbers buried in narrative, on slow or unstable pages, with no comparison frame for AI to lift.
A citation-ready research page has four parts:
Lead with the comparison result. The headline finding (“X is fastest, Y is cheapest at scale”) goes in the first 30% of the page. Result, then method, then nuance.
Box the methodology. Sample, time window, what was measured, how. Attribution confidence is part of what makes a number citable. Make your methodology clear on the page.
Explicitly frame it as a comparison if it is one. AI reaches for benchmarks on “which is best” prompts. A table that compares named options on named specs is the shape it lifts.
Keep the URL stable. One canonical page, kept live, not migrated or renamed every redesign. The citation you earn this quarter only compounds if the page is still there next quarter. Of 365 cited URLs in this data set, 64 were dead, redirected, or otherwise broken, taking 203 citations down with them.
This is the work behind a citable benchmark, and it is more involved than it looks. 
HockeyStack documented its own version in a playbook on launching research reports: they published 18 original reports built entirely on anonymized first-party customer data, the kind no competitor could replicate. 
Their process names every step the Fivetran page demonstrates: list the data points you need, get a teammate to pull them with SQL, define and document the method so the numbers hold up to scrutiny, then structure the report around a real ICP question. They call methodology non-negotiable for a reason, noting that without it, someone will always dispute your data. 
With AI analysis, the data is the easy part now. Building the content into something that is citable, demonstrates E-E-A-T, and is still earning visibility 4 years out for commercial queries is where the hard work lies.
What sites are already trusted for your topic? When a benchmark you did not publish is taking the citations in your category, the Citation Source Mapper maps that trusted set into a ranked, pitchable target list. It is in the premium library.
This post first appeared on the author’s website and is republished here with permission.

Primary research is rare, but it earns 3.3x more AI citations per page. Learn why benchmark studies outperform other original data.

Why most original data never gets cited

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool.png" class="attachment-large size-large wp-post-image" alt="5 questions to ask AI vendors before buying a tool" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Separate genuine solutions from AI hype by evaluating business value, data policies, customer proof, and implementation before you invest.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool.png" class="attachment-large size-large wp-post-image" alt="5 questions to ask AI vendors before buying a tool" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/5-questions-to-ask-AI-vendors-before-buying-a-tool-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>There are many ways to use AI in marketing, and it feels like for every smart initiative, 10 AI vendors have cropped up with a tool to address it.</p>



<p>At the beginning of this wave, I took more calls and answered more emails than I do these days. Over time, I realized I was asking vendors the same handful of questions to assess whether their tools were worth deploying.</p>



<p>If you&#8217;re in the same boat and overwhelmed with vendor outreach, here are five questions to help you figure out whether they&#8217;re worth your time, along with my rationale for asking them and what I&#8217;m looking to hear — or not hear.</p>



<h2 class="wp-block-heading" id="h-1-what-problem-does-your-tool-solve-nbsp">1. What problem does your tool solve?&nbsp;</h2>



<p>This question should help you understand the purpose of the tool and — crucially — whether the value it creates maps to real business outcomes.&nbsp;</p>



<p>If the vendor can&#8217;t clearly state the challenges or use cases the tool addresses, it wasn&#8217;t purpose-built to solve a real problem your team faces, whether you&#8217;re in-house or at an agency. Beware of vendors who try to dazzle you with feature-heavy language but can&#8217;t explain the business benefits those features deliver.</p>



<p>If a vendor identifies at least one existing team problem the tool solves and explains how it improves business outcomes, it&#8217;s a good idea to keep talking. A great follow-up question is to ask for a case study showing how the tool was used and the outcomes it delivered for an organization similar to yours in size and vertical.</p>



<p>Look for benefits such as &#8220;increases output&#8221; or &#8220;identifies gaps in tracking to speed up troubleshooting.&#8221; But don&#8217;t rush to invest in tools that promise to &#8220;save time&#8221; (even if they truly do) unless you have a plan for how you&#8217;ll use that extra time.</p>


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<h2 class="wp-block-heading" id="h-2-what-expertise-do-you-have-in-the-space-where-this-tool-solves-a-problem">2. What expertise do you have in the space where this tool solves a problem?</h2>



<p>The answer to this question should tell you whether the vendor built this tool <strong>for</strong> advertisers or just <strong>at</strong> advertisers. </p>



<p>Technical chops matter, but so does understanding how a media buyer actually spends their day. If the vendor doesn&#8217;t have personal experience in media buying, they need to explain how they researched the media-buying market and incorporated those insights into the tool.</p>



<p>If they have a shallow understanding or lack expertise, that&#8217;s a red flag. It&#8217;s fine if a rep doesn&#8217;t have that expertise directly, but someone on their team should, and you should have access to that person as soon as possible if you intend to keep talking.</p>



<p>If a vendor has a story or background that led them to identify a problem you can relate to firsthand and decide to build the solution themselves, that&#8217;s compelling. A founding mission that addresses your existing challenges is a solid foundation for a tool that can make a difference in your team&#8217;s performance.</p>



<h2 class="wp-block-heading" id="h-3-what-case-studies-real-use-cases-and-results-can-you-share">3. What case studies, real use cases, and results can you share?</h2>



<p>I touched on case studies a few paragraphs above, and they&#8217;re a must-have in a new, fast-developing industry. I&#8217;d be looking to understand whether the vendor has an appealing track record with customers like me or whether we&#8217;d be an early adopter.</p>



<p>If you fall into the latter camp, there are pros and cons, just as there are with any advertising beta you can access before your competitors. You may get a leg up by finding an important growth accelerator before your competitors do, you may spin your wheels trying to work through bugs, or you may find that the tool simply doesn&#8217;t deliver on its promises. </p>



<p>If you can&#8217;t trust a tool or there&#8217;s a risk you&#8217;ll need to provide detailed feedback to get it working, it might not be the best use of your time and money unless you believe what it could eventually deliver would be a game-changer.</p>



<p>If you&#8217;re clearly going to be an early adopter and the vendor isn&#8217;t willing to be flexible on contract terms that help mitigate risk, that&#8217;s a nonstarter. More established tools will likely deliver more value consistently, although they&#8217;ll have less wiggle room on pricing because of it. Newer tools that take a hard line on pricing and contract terms, however, likely won&#8217;t be good long-term partners.</p>



<p>For established vendors, you need to see specific, relevant case studies with real numbers from advertisers in a similar space, of a similar size, or with a similar use case. </p>



<p>If they&#8217;re early-stage companies, the best answer is honesty: &#8220;You&#8217;d be one of our first clients in this vertical. Here&#8217;s what we&#8217;ve seen elsewhere, and here&#8217;s what that partnership would look like.&#8221; That transparency is a green flag.</p>



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<h2 class="wp-block-heading" id="h-4-who-owns-my-data-and-how-is-it-being-used-to-train-models">4. Who owns my data, and how is it being used to train models?</h2>



<p>It&#8217;s interesting how easily people share data with AI and AI tools in the rush to find a competitive edge. This is something I&#8217;d strongly caution potential buyers to consider before signing anything.</p>



<p>Watch for any answer that suggests your data is used to train shared or third-party models without your explicit consent. Another red flag is vague or deflecting answers or terms of service that contradict or muddy what the salesperson tells you verbally.</p>



<p>You own your data, full stop. </p>



<p>The vendor should be able to clearly explain its data-handling practices, including where your data is stored, how long it&#8217;s retained, whether it&#8217;s used for model training (and, if so, only to refine your own instance), and what happens to your data if you stop using the tool. This needs to be in the contract, not just a verbal assurance. If it&#8217;s not there, insist that it be included before you sign.</p>



<h2 class="wp-block-heading" id="h-5-what-does-implementation-actually-look-like-and-what-does-success-require-from-our-team">5. What does implementation actually look like, and what does success require from our team?</h2>



<p>Before you commit money, you need to understand the real cost of adopting this tool. That cost includes more than the price. It&#8217;s the time, the internal lift (including integration, training, and QA), and any potential disruption to your existing martech stack.</p>



<p>If this requires resources your team doesn&#8217;t have, or if you realistically can&#8217;t dedicate the time to make good use of the tool, it&#8217;s not worth the investment just yet. Many marketers could avoid wasted martech spend if they asked this question and seriously considered the answer.</p>



<p>No tool should be one-size-fits-all, but easy implementation and intuitive design are crucial to getting your team to adopt and stick with it.</p>


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<h2 class="wp-block-heading" id="h-don-t-let-ai-hype-rush-your-decision">Don&#8217;t let AI hype rush your decision</h2>



<p>I know firsthand that a lot of these tools sound too good to be true — and often, they are. You need to balance growth ambition and curiosity with a bit of caution. </p>



<p>Remember that we&#8217;re still in the early stages of AI adoption. If a tool seems too expensive or hard to onboard, or the contract is more rigid than it should be given the tool&#8217;s track record, a more attractive solution will likely emerge in the next few months.</p>



<p>When in doubt, ask for a free trial. Assuming it won&#8217;t create too much work for your team to integrate the tool, that could be the right next step in determining whether you&#8217;ve found your next competitive advantage.</p>

There are many ways to use AI in marketing, and it feels like for every smart initiative, 10 AI vendors have cropped up with a tool to address it.
At the beginning of this wave, I took more calls and answered more emails than I do these days. Over time, I realized I was asking vendors the same handful of questions to assess whether their tools were worth deploying.
If you’re in the same boat and overwhelmed with vendor outreach, here are five questions to help you figure out whether they’re worth your time, along with my rationale for asking them and what I’m looking to hear — or not hear.
1. What problem does your tool solve? 
This question should help you understand the purpose of the tool and — crucially — whether the value it creates maps to real business outcomes. 
If the vendor can’t clearly state the challenges or use cases the tool addresses, it wasn’t purpose-built to solve a real problem your team faces, whether you’re in-house or at an agency. Beware of vendors who try to dazzle you with feature-heavy language but can’t explain the business benefits those features deliver.
If a vendor identifies at least one existing team problem the tool solves and explains how it improves business outcomes, it’s a good idea to keep talking. A great follow-up question is to ask for a case study showing how the tool was used and the outcomes it delivered for an organization similar to yours in size and vertical.
Look for benefits such as “increases output” or “identifies gaps in tracking to speed up troubleshooting.” But don’t rush to invest in tools that promise to “save time” (even if they truly do) unless you have a plan for how you’ll use that extra time.
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2. What expertise do you have in the space where this tool solves a problem?
The answer to this question should tell you whether the vendor built this tool for advertisers or just at advertisers. 
Technical chops matter, but so does understanding how a media buyer actually spends their day. If the vendor doesn’t have personal experience in media buying, they need to explain how they researched the media-buying market and incorporated those insights into the tool.
If they have a shallow understanding or lack expertise, that’s a red flag. It’s fine if a rep doesn’t have that expertise directly, but someone on their team should, and you should have access to that person as soon as possible if you intend to keep talking.
If a vendor has a story or background that led them to identify a problem you can relate to firsthand and decide to build the solution themselves, that’s compelling. A founding mission that addresses your existing challenges is a solid foundation for a tool that can make a difference in your team’s performance.
3. What case studies, real use cases, and results can you share?
I touched on case studies a few paragraphs above, and they’re a must-have in a new, fast-developing industry. I’d be looking to understand whether the vendor has an appealing track record with customers like me or whether we’d be an early adopter.
If you fall into the latter camp, there are pros and cons, just as there are with any advertising beta you can access before your competitors. You may get a leg up by finding an important growth accelerator before your competitors do, you may spin your wheels trying to work through bugs, or you may find that the tool simply doesn’t deliver on its promises. 
If you can’t trust a tool or there’s a risk you’ll need to provide detailed feedback to get it working, it might not be the best use of your time and money unless you believe what it could eventually deliver would be a game-changer.
If you’re clearly going to be an early adopter and the vendor isn’t willing to be flexible on contract terms that help mitigate risk, that’s a nonstarter. More established tools will likely deliver more value consistently, although they’ll have less wiggle room on pricing because of it. Newer tools that take a hard line on pricing and contract terms, however, likely won’t be good long-term partners.
For established vendors, you need to see specific, relevant case studies with real numbers from advertisers in a similar space, of a similar size, or with a similar use case. 
If they’re early-stage companies, the best answer is honesty: “You’d be one of our first clients in this vertical. Here’s what we’ve seen elsewhere, and here’s what that partnership would look like.” That transparency is a green flag.
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4. Who owns my data, and how is it being used to train models?
It’s interesting how easily people share data with AI and AI tools in the rush to find a competitive edge. This is something I’d strongly caution potential buyers to consider before signing anything.
Watch for any answer that suggests your data is used to train shared or third-party models without your explicit consent. Another red flag is vague or deflecting answers or terms of service that contradict or muddy what the salesperson tells you verbally.
You own your data, full stop. 
The vendor should be able to clearly explain its data-handling practices, including where your data is stored, how long it’s retained, whether it’s used for model training (and, if so, only to refine your own instance), and what happens to your data if you stop using the tool. This needs to be in the contract, not just a verbal assurance. If it’s not there, insist that it be included before you sign.
5. What does implementation actually look like, and what does success require from our team?
Before you commit money, you need to understand the real cost of adopting this tool. That cost includes more than the price. It’s the time, the internal lift (including integration, training, and QA), and any potential disruption to your existing martech stack.
If this requires resources your team doesn’t have, or if you realistically can’t dedicate the time to make good use of the tool, it’s not worth the investment just yet. Many marketers could avoid wasted martech spend if they asked this question and seriously considered the answer.
No tool should be one-size-fits-all, but easy implementation and intuitive design are crucial to getting your team to adopt and stick with it.
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Don’t let AI hype rush your decision
I know firsthand that a lot of these tools sound too good to be true — and often, they are. You need to balance growth ambition and curiosity with a bit of caution. 
Remember that we’re still in the early stages of AI adoption. If a tool seems too expensive or hard to onboard, or the contract is more rigid than it should be given the tool’s track record, a more attractive solution will likely emerge in the next few months.
When in doubt, ask for a free trial. Assuming it won’t create too much work for your team to integrate the tool, that could be the right next step in determining whether you’ve found your next competitive advantage.

Separate genuine solutions from AI hype by evaluating business value, data policies, customer proof, and implementation before you invest.

5 questions to ask AI vendors before buying a tool

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/How-to-make-your-SEO-strategy-more-commercially-aware.png" class="attachment-large size-large wp-post-image" alt="How to make your SEO strategy more commercially aware" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/How-to-make-your-SEO-strategy-more-commercially-aware.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-make-your-SEO-strategy-more-commercially-aware-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-make-your-SEO-strategy-more-commercially-aware-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-make-your-SEO-strategy-more-commercially-aware-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Prioritize SEO using the metrics that matter most to the business, including revenue, profitability, and return on investment.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/How-to-make-your-SEO-strategy-more-commercially-aware.png" class="attachment-large size-large wp-post-image" alt="How to make your SEO strategy more commercially aware" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/How-to-make-your-SEO-strategy-more-commercially-aware.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-make-your-SEO-strategy-more-commercially-aware-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-make-your-SEO-strategy-more-commercially-aware-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-make-your-SEO-strategy-more-commercially-aware-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>The <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> industry has become extraordinarily good at improving its technical craft, making strides in crawl architecture, Core Web Vitals, content frameworks, entity optimization, and link acquisition at scale, to name a few.</p>



<p>What has lagged behind is its ability to connect that craft to the financial realities of the businesses it serves. SEO has trouble speaking the language that gets budgets approved and strategies prioritized.</p>



<p>Getting more funding and a better seat at the table requires a shift in how SEO teams define what they&#8217;re trying to achieve. </p>



<p>Let&#8217;s look at how SEO can become more commercially aware.</p>



<h2 class="wp-block-heading" id="h-why-paid-search-gets-more-funding">Why paid search gets more funding</h2>



<p>Paid search often structures its goals around commercial inputs and outputs: Spend goes in, revenue comes out, and the difference between the two determines whether investment in the channel increases or decreases. Every campaign exists within a financial framework.</p>



<p>Even if paid search is expensive or inefficient, leadership can clearly see its goals and numbers and make resource decisions accordingly.</p>



<p>SEO teams often present rankings as an end in themselves rather than a means to an end. They report traffic figures without connecting them to transactions and highlight technical improvements that may be meaningful from an SEO perspective but translate poorly to financial goals.</p>



<p>When organic search doesn&#8217;t get enough funding, it&#8217;s often attributed to leadership not understanding SEO. A more honest explanation is that SEO hasn&#8217;t clearly made its commercial case. Leadership needs to see organic search measured in sales, margins, and channel ROI.</p>


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<h2 class="wp-block-heading" id="h-what-commercial-awareness-requires">What commercial awareness requires</h2>



<p>SEO teams usually need to change the questions they ask before planning any work.</p>



<p>Instead of asking which topics have the highest search volume, ask which categories and product lines carry the highest margins, then evaluate the demand landscape within them.</p>



<p>Instead of asking where to create content, ask which existing pages would generate meaningful revenue if they ranked better, then work backward from there.</p>



<p>Instead of measuring success in organic sessions, measure it in organic profit, which requires knowing what the channel costs and what it returns.</p>



<h3 class="wp-block-heading" id="h-financial-metrics-for-commercial-awareness">Financial metrics for commercial awareness</h3>



<p>These are the performance metrics I use when running organic search as an acquisition channel:</p>



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



<li>Organic revenue.</li>



<li>Organic profit.</li>



<li>Average order value from organic traffic.</li>



<li>Average margin per organic sale.</li>



<li>Channel ROI.</li>
</ul>



<p>These aren&#8217;t exotic or difficult to calculate. They simply require connecting analytics data to backend transactional data, which most organizations can do with a modest investment in reporting infrastructure.</p>



<p>One metric I keep coming back to is organic profit per sale, calculated as organic profit divided by organic sales.</p>



<p>This metric can transform an organic channel into a customer acquisition channel with a measurable cost-per-outcome. It&#8217;s a concrete metric you can benchmark against other channels.</p>



<p>Breaking this metric down by category, subcategory, and page lets you make strategy decisions based on commercial data, with SEO execution layered on top.</p>



<h2 class="wp-block-heading" id="h-focus-on-value-side-metrics">Focus on value-side metrics</h2>



<p>Most SEO strategies rely primarily on demand-side metrics:</p>



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



<li>Keyword difficulty.</li>



<li>Current ranking positions.</li>



<li>Traffic estimates.</li>
</ul>



<p>These are necessary inputs, but they represent only half of the information you need to make good commercial decisions.</p>



<p>Layer value-side metrics on top of your demand data, including:</p>



<ul class="wp-block-list">
<li>Categories with strong margins.</li>



<li>Pages that drive high transaction values.</li>



<li>Customer segments that remain profitable over time.</li>
</ul>



<p>From a revenue and profit perspective, categories with modest search volume can outperform higher-traffic segments when they have stronger margins or higher average order values.</p>



<h2 class="wp-block-heading" id="h-seo-tactics-that-move-the-commercial-needle">SEO tactics that move the commercial needle</h2>



<p>With a commercially aware approach, strategic decisions, including those about informational content and authority building, are evaluated against commercial outcomes rather than traffic projections.</p>



<p>Informational content, topical authority, and brand visibility still matter, and a channel that only pursues transactional queries will eventually hit a ceiling.</p>



<p>Here are some tactics for taking a more commercially focused approach to SEO.</p>



<h3 class="wp-block-heading" id="h-score-opportunities-on-demand-and-business-value-together">Score opportunities on demand and business value together</h3>



<p>Apply a second filter that accounts for business value alongside search demand.</p>



<p>I look at margin potential, average sale value by category, and current organic performance relative to where it needs to be, alongside search demand.</p>



<p>High-priority work sits at the intersection of meaningful demand and a strong commercial signal, which is often a different set of pages than traditional keyword prioritization surfaces.</p>



<h3 class="wp-block-heading" id="h-update-commercial-pages-rather-than-create-new-content">Update commercial pages rather than create new content</h3>



<p>Commercial pages naturally decay over time as competitors improve their content, SERPs evolve, and freshness signals fade. This decay translates directly into lost revenue from pages that were previously performing well.</p>



<p>To update commercial pages:</p>



<ul class="wp-block-list">
<li>Use comprehensive keyword and competitor research to identify gaps in your content.</li>



<li>Restructure information into formats that search engines and AI interfaces can easily extract. Tables are particularly valuable here.</li>



<li>Use a large language model to review first drafts, stress-testing the content against what competitors are offering.</li>



<li>Strengthen internal linking to these pages.</li>
</ul>



<h3 class="wp-block-heading" id="h-increase-internal-linking">Increase internal linking</h3>



<p>Linking internally from strong informational assets and high-authority pages to commercial pages with high revenue and margin potential creates business value.</p>



<p>I spend a significant amount of time internally linking to commercial page clusters, particularly where there&#8217;s a gap between the authority of supporting content and the rankings of the connected commercial pages.</p>



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<h3 class="wp-block-heading" id="h-borrow-conversion-intelligence-from-paid-search">Borrow conversion intelligence from paid search</h3>



<p>SEO typically can&#8217;t see which specific keywords drive conversions. We often have page-level conversion data, but the queries that generate visits and purchases remain invisible.</p>



<p>The best workaround I&#8217;ve found is to pull recent PPC campaign data, generally from the last 30 to 90 days, and adjust for seasonality, to identify keyword patterns that generate sales and high-value customers in paid search.</p>



<p>These insights can inform which organic landing pages to prioritize, which commercial content to update, and where conversion optimization efforts are most likely to pay off.</p>



<h3 class="wp-block-heading" id="h-recover-transactional-positions-just-outside-page-1">Recover transactional positions just outside Page 1</h3>



<p>A meaningful group of transactional keywords often sits in positions 10 through 20 of the SERP. These are commercial-intent terms where you&#8217;re already in the conversation but not yet positioned to convert traffic at a meaningful rate.</p>



<p>Identify these opportunities by filtering for commercial intent and business potential, then apply targeted improvements such as content updates, internal linking, and relevant authority building.</p>



<h3 class="wp-block-heading" id="h-build-digital-pr-campaigns-with-commercial-architecture">Build digital PR campaigns with commercial architecture</h3>



<p>Digital PR campaigns that exist solely to acquire links rarely produce meaningful commercial impact. Instead, build a linking environment you can direct with deliberate commercial architecture:</p>



<ul class="wp-block-list">
<li>Focus on topics that are thematically relevant to the product categories you care about.</li>



<li>Create an on-site content asset that serves as the campaign&#8217;s destination and links back to relevant commercial pages.</li>



<li>Build the asset with internal links to the commercial page clusters it&#8217;s designed to support.</li>
</ul>



<h3 class="wp-block-heading" id="h-treat-branded-search-protection-as-a-profit-issue">Treat branded search protection as a profit issue</h3>



<p>When affiliates rank for discount and voucher terms and capture that traffic, you&#8217;re effectively paying a commission on customers who were already in your funnel and likely would have converted directly.</p>



<p>The fix is straightforward: Improve on-site pages that target branded intent, strengthen internal signals, monitor branded click share, and enforce affiliate program terms on branded bidding.</p>



<p>You&#8217;ll improve margins alongside revenue by eliminating acquisition costs on conversions that should have been organic in the first place.</p>



<h3 class="wp-block-heading" id="h-choose-an-attribution-model">Choose an attribution model</h3>



<p>Choosing an attribution model can be challenging because organic sessions can appear as direct traffic, GA4 and backend platforms may report different numbers, and multi-touch journeys often resist clean channel assignment.</p>



<p>These issues aren&#8217;t unique to organic search. As AI-mediated search continues to complicate organic referral paths, attribution will become even more challenging.</p>



<p>Choose an attribution model your organization can agree on, be transparent about its limitations, and focus on growing the revenue attributed to organic search under that model.</p>



<p>When leadership consistently sees how organic search contributes meaningful and growing revenue, attribution nuances become less important.</p>



<h3 class="wp-block-heading" id="h-think-of-your-budget-as-a-lever-not-a-constraint">Think of your budget as a lever, not a constraint</h3>



<p>An SEO budget should be viewed as a variable you can adjust based on commercial KPIs.</p>



<p>Here&#8217;s a simple model: SEO profit equals the business margin generated from organic search minus the cost of running the channel.</p>



<p>When the priority is revenue growth, invest more aggressively in link acquisition, digital PR, and content production to expand visibility and capture incremental demand.</p>



<p>When the priority is channel profitability, perhaps at a point in the business cycle when margin preservation matters more than top-line growth, you can reduce spending to improve short-term profit, provided you&#8217;re clear about the competitive risk of sustaining those reductions for too long.</p>



<h2 class="wp-block-heading" id="h-how-to-secure-internal-alignment">How to secure internal alignment</h2>



<p>Commercial SEO requires cross-functional cooperation. Here are some talking points to help secure internal alignment.</p>



<h3 class="wp-block-heading" id="h-speak-the-language-of-decision-makers">Speak the language of decision-makers</h3>



<p>Commercial and finance leaders care about growth, margins, and competitive position. Frame SEO in those terms, with revenue and margin projections tied to specific strategic initiatives.</p>



<h3 class="wp-block-heading" id="h-generate-proof-before-asking-for-investment-at-scale">Generate proof before asking for investment at scale</h3>



<p>SEO takes time to produce results. To earn buy-in, run a contained test first and use the results to make the case for additional investment. For example, update a group of commercial pages, complete a targeted internal linking project, or launch a branded search protection initiative.</p>



<h3 class="wp-block-heading" id="h-use-competitive-visibility-strategically">Use competitive visibility strategically</h3>



<p>Show leadership where competitors outrank you for high-value commercial terms, and quantify how that translates into lost market share and revenue. Concrete numbers can be persuasive.</p>



<h3 class="wp-block-heading" id="h-build-cross-functional-relationships-to-execute-faster">Build cross-functional relationships to execute faster</h3>



<p>When SEO is positioned as part of an integrated commercial growth engine with shared data flows and coordinated prioritization workflows, it becomes easier to get things done. SEO naturally touches paid search, content, product, and PR, so treat those teams as allies rather than separate workstreams.</p>


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<h2 class="wp-block-heading" id="h-prioritize-commercial-awareness-in-your-seo-strategy">Prioritize commercial awareness in your SEO strategy</h2>



<p>SEO&#8217;s technical sophistication has advanced tremendously. What&#8217;s lagged behind is its ability to speak the language of commercial leaders and connect its work to the outcomes that influence budgets and priorities.</p>



<p>Hold SEO to the same standards of commercial accountability as other marketing investments. Doing so allows organic search to become a cost-effective driver of growth and profitability.</p>



<p>Commercial awareness requires only a willingness to redefine what success means and the discipline to organize your strategy around that definition.</p>

The SEO industry has become extraordinarily good at improving its technical craft, making strides in crawl architecture, Core Web Vitals, content frameworks, entity optimization, and link acquisition at scale, to name a few.
What has lagged behind is its ability to connect that craft to the financial realities of the businesses it serves. SEO has trouble speaking the language that gets budgets approved and strategies prioritized.
Getting more funding and a better seat at the table requires a shift in how SEO teams define what they’re trying to achieve. 
Let’s look at how SEO can become more commercially aware.
Why paid search gets more funding
Paid search often structures its goals around commercial inputs and outputs: Spend goes in, revenue comes out, and the difference between the two determines whether investment in the channel increases or decreases. Every campaign exists within a financial framework.
Even if paid search is expensive or inefficient, leadership can clearly see its goals and numbers and make resource decisions accordingly.
SEO teams often present rankings as an end in themselves rather than a means to an end. They report traffic figures without connecting them to transactions and highlight technical improvements that may be meaningful from an SEO perspective but translate poorly to financial goals.
When organic search doesn’t get enough funding, it’s often attributed to leadership not understanding SEO. A more honest explanation is that SEO hasn’t clearly made its commercial case. Leadership needs to see organic search measured in sales, margins, and channel ROI.
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What commercial awareness requires
SEO teams usually need to change the questions they ask before planning any work.
Instead of asking which topics have the highest search volume, ask which categories and product lines carry the highest margins, then evaluate the demand landscape within them.
Instead of asking where to create content, ask which existing pages would generate meaningful revenue if they ranked better, then work backward from there.
Instead of measuring success in organic sessions, measure it in organic profit, which requires knowing what the channel costs and what it returns.
Financial metrics for commercial awareness
These are the performance metrics I use when running organic search as an acquisition channel:
Organic sales.
Organic revenue.
Organic profit.
Average order value from organic traffic.
Average margin per organic sale.
Channel ROI.
These aren’t exotic or difficult to calculate. They simply require connecting analytics data to backend transactional data, which most organizations can do with a modest investment in reporting infrastructure.
One metric I keep coming back to is organic profit per sale, calculated as organic profit divided by organic sales.
This metric can transform an organic channel into a customer acquisition channel with a measurable cost-per-outcome. It’s a concrete metric you can benchmark against other channels.
Breaking this metric down by category, subcategory, and page lets you make strategy decisions based on commercial data, with SEO execution layered on top.
Focus on value-side metrics
Most SEO strategies rely primarily on demand-side metrics:
Search volume.
Keyword difficulty.
Current ranking positions.
Traffic estimates.
These are necessary inputs, but they represent only half of the information you need to make good commercial decisions.
Layer value-side metrics on top of your demand data, including:
Categories with strong margins.
Pages that drive high transaction values.
Customer segments that remain profitable over time.
From a revenue and profit perspective, categories with modest search volume can outperform higher-traffic segments when they have stronger margins or higher average order values.
SEO tactics that move the commercial needle
With a commercially aware approach, strategic decisions, including those about informational content and authority building, are evaluated against commercial outcomes rather than traffic projections.
Informational content, topical authority, and brand visibility still matter, and a channel that only pursues transactional queries will eventually hit a ceiling.
Here are some tactics for taking a more commercially focused approach to SEO.
Score opportunities on demand and business value together
Apply a second filter that accounts for business value alongside search demand.
I look at margin potential, average sale value by category, and current organic performance relative to where it needs to be, alongside search demand.
High-priority work sits at the intersection of meaningful demand and a strong commercial signal, which is often a different set of pages than traditional keyword prioritization surfaces.
Update commercial pages rather than create new content
Commercial pages naturally decay over time as competitors improve their content, SERPs evolve, and freshness signals fade. This decay translates directly into lost revenue from pages that were previously performing well.
To update commercial pages:
Use comprehensive keyword and competitor research to identify gaps in your content.
Restructure information into formats that search engines and AI interfaces can easily extract. Tables are particularly valuable here.
Use a large language model to review first drafts, stress-testing the content against what competitors are offering.
Strengthen internal linking to these pages.
Increase internal linking
Linking internally from strong informational assets and high-authority pages to commercial pages with high revenue and margin potential creates business value.
I spend a significant amount of time internally linking to commercial page clusters, particularly where there’s a gap between the authority of supporting content and the rankings of the connected commercial pages.
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Borrow conversion intelligence from paid search
SEO typically can’t see which specific keywords drive conversions. We often have page-level conversion data, but the queries that generate visits and purchases remain invisible.
The best workaround I’ve found is to pull recent PPC campaign data, generally from the last 30 to 90 days, and adjust for seasonality, to identify keyword patterns that generate sales and high-value customers in paid search.
These insights can inform which organic landing pages to prioritize, which commercial content to update, and where conversion optimization efforts are most likely to pay off.
Recover transactional positions just outside Page 1
A meaningful group of transactional keywords often sits in positions 10 through 20 of the SERP. These are commercial-intent terms where you’re already in the conversation but not yet positioned to convert traffic at a meaningful rate.
Identify these opportunities by filtering for commercial intent and business potential, then apply targeted improvements such as content updates, internal linking, and relevant authority building.
Build digital PR campaigns with commercial architecture
Digital PR campaigns that exist solely to acquire links rarely produce meaningful commercial impact. Instead, build a linking environment you can direct with deliberate commercial architecture:
Focus on topics that are thematically relevant to the product categories you care about.
Create an on-site content asset that serves as the campaign’s destination and links back to relevant commercial pages.
Build the asset with internal links to the commercial page clusters it’s designed to support.
Treat branded search protection as a profit issue
When affiliates rank for discount and voucher terms and capture that traffic, you’re effectively paying a commission on customers who were already in your funnel and likely would have converted directly.
The fix is straightforward: Improve on-site pages that target branded intent, strengthen internal signals, monitor branded click share, and enforce affiliate program terms on branded bidding.
You’ll improve margins alongside revenue by eliminating acquisition costs on conversions that should have been organic in the first place.
Choose an attribution model
Choosing an attribution model can be challenging because organic sessions can appear as direct traffic, GA4 and backend platforms may report different numbers, and multi-touch journeys often resist clean channel assignment.
These issues aren’t unique to organic search. As AI-mediated search continues to complicate organic referral paths, attribution will become even more challenging.
Choose an attribution model your organization can agree on, be transparent about its limitations, and focus on growing the revenue attributed to organic search under that model.
When leadership consistently sees how organic search contributes meaningful and growing revenue, attribution nuances become less important.
Think of your budget as a lever, not a constraint
An SEO budget should be viewed as a variable you can adjust based on commercial KPIs.
Here’s a simple model: SEO profit equals the business margin generated from organic search minus the cost of running the channel.
When the priority is revenue growth, invest more aggressively in link acquisition, digital PR, and content production to expand visibility and capture incremental demand.
When the priority is channel profitability, perhaps at a point in the business cycle when margin preservation matters more than top-line growth, you can reduce spending to improve short-term profit, provided you’re clear about the competitive risk of sustaining those reductions for too long.
How to secure internal alignment
Commercial SEO requires cross-functional cooperation. Here are some talking points to help secure internal alignment.
Speak the language of decision-makers
Commercial and finance leaders care about growth, margins, and competitive position. Frame SEO in those terms, with revenue and margin projections tied to specific strategic initiatives.
Generate proof before asking for investment at scale
SEO takes time to produce results. To earn buy-in, run a contained test first and use the results to make the case for additional investment. For example, update a group of commercial pages, complete a targeted internal linking project, or launch a branded search protection initiative.
Use competitive visibility strategically
Show leadership where competitors outrank you for high-value commercial terms, and quantify how that translates into lost market share and revenue. Concrete numbers can be persuasive.
Build cross-functional relationships to execute faster
When SEO is positioned as part of an integrated commercial growth engine with shared data flows and coordinated prioritization workflows, it becomes easier to get things done. SEO naturally touches paid search, content, product, and PR, so treat those teams as allies rather than separate workstreams.
before your competitors.
      
Start your free trial
    
Prioritize commercial awareness in your SEO strategy
SEO’s technical sophistication has advanced tremendously. What’s lagged behind is its ability to speak the language of commercial leaders and connect its work to the outcomes that influence budgets and priorities.
Hold SEO to the same standards of commercial accountability as other marketing investments. Doing so allows organic search to become a cost-effective driver of growth and profitability.
Commercial awareness requires only a willingness to redefine what success means and the discipline to organize your strategy around that definition.

Prioritize SEO using the metrics that matter most to the business, including revenue, profitability, and return on investment.

How to make your SEO strategy more commercially aware

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-an-AI-powered-content-gap-analysis-workflow.png" class="attachment-large size-large wp-post-image" alt="How to build an AI-powered content gap analysis workflow" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-an-AI-powered-content-gap-analysis-workflow.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-an-AI-powered-content-gap-analysis-workflow-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-an-AI-powered-content-gap-analysis-workflow-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/How-to-build-an-AI-powered-content-gap-analysis-workflow-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>See how to combine Semrush, Google Search Console, Google Analytics, and Claude to prioritize the content opportunities that matter most.


See how to combine Semrush, Google Search Console, Google Analytics, and Claude to prioritize the content opportunities that matter most.

How to build an AI-powered content gap analysis workflow

<div><img width="1232" height="928" src="https://searchengineland.com/wp-content/seloads/2026/06/Rokt-20260626.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Rokt-20260626.jpg 1232w, https://searchengineland.com/wp-content/seloads/2026/06/Rokt-20260626-768x578.jpg 768w" sizes="(max-width: 1232px) 100vw, 1232px" /></div>As performance pressures mount, the future of marketing success lies in transforming your data foundation into a strategic AI-powered engine for growth.

<div><img width="1232" height="928" src="https://searchengineland.com/wp-content/seloads/2026/06/Rokt-20260626.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Rokt-20260626.jpg 1232w, https://searchengineland.com/wp-content/seloads/2026/06/Rokt-20260626-768x578.jpg 768w" sizes="(max-width: 1232px) 100vw, 1232px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1232" height="928" src="https://searchengineland.com/wp-content/seloads/2026/06/Rokt-20260626.jpg" alt="" class="wp-image-480747" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Rokt-20260626.jpg 1232w, https://searchengineland.com/wp-content/seloads/2026/06/Rokt-20260626-768x578.jpg 768w" sizes="(max-width: 1232px) 100vw, 1232px" /></figure>
</div>


<p>Performance marketing is under more pressure than it&#8217;s been in a decade — budgets are flat or reduced, expectations are rising,, and AI is raising the bar on what “good” looks like.</p>



<p>For years, performance marketing has been built on a familiar playbook. When performance plateaus, add another vendor. When targeting weakens, buy another dataset. When activation becomes difficult, introduce another layer of technology. But as budget constraints tighten and expectations for immediate ROI rise, this constant expansion of the stack is becoming unsustainable.</p>



<p>The challenge facing enterprise marketers today is not a shortage of data. It’s an inability to operationalize the data they already have.</p>



<p>At the same time, AI is exposing a hard truth about modern marketing architecture. Most AI failures are not model failures. They are data failures. The most sophisticated agent, model, or automation workflow cannot compensate for fragmented customer profiles, disconnected activation systems, or stale audience definitions. Yet much of the conversation in the customer data platform (CDP) market remains focused on shipping more AI agents.</p>



<p>That misses the point.</p>



<p>The real question isn&#8217;t whether your platform has an AI agent. It&#8217;s whether your data foundation can support the leap from automating tasks to partnering on strategic outcomes.</p>



<p>For too long, the industry’s north star was self-service — a mandate to bypass engineering tickets and data science queues. But that was a solution for the last decade. It effectively turned the marketer into a manual operator of complex systems. The new bar isn&#8217;t just self-service; it’s self-directed performance at scale.</p>



<p>We are witnessing a fundamental shift in the marketer’s job-to-be-done: you are moving away from the operational heavy lifting of building and managing audiences toward the high-level strategy of setting outcomes. Instead of spending your day wrangling segments, you now define your goal — whether it&#8217;s maximizing customer lifetime value or reversing churn — and the system suggests the optimal audience definitions and activation pathways to achieve it. By bridging the gap between intelligent agents and a clean data foundation, you move from managing technology to orchestrating outcomes. This is the new blueprint for performance.</p>



<p>At <a href="https://www.mparticle.com/" target="_blank" rel="noopener sponsored nofollow">mParticle</a>, we describe our approach as a performance engine: a model in which the data foundation and activation layer operate as a single system. The goal is not simply to collect customer data, but to make it immediately usable for performance outcomes.</p>



<p>The Audience Agent is one expression of this. Marketers describe what they want in plain language — e.g.,high-value customers who haven&#8217;t repurchased in 60 days — and the agent proposes the underlying logic for the marketer to review and approve.&nbsp;</p>



<p>The shift isn&#8217;t automation; it&#8217;s a marketer-led workflow with an expert collaborator alongside. The longer you work with it, the better it understands your business — your data, your customers, the patterns that actually move your performance. That understanding is only as deep as the data foundation it draws on — and ours was built for this long before AI demanded it. The marketer leads. The agent elevates and expands. Together, they push the limit of what’s possible.</p>



<p>That philosophy is also reflected in capabilities such as Audience Expansion and Household Reach. Audience Expansion helps marketers identify additional high-potential users directly from their own first-party datasets — without relying on third-party lookalike audiences or external data sources — giving teams precise control over the balance between scale and quality.&nbsp;</p>



<p>Household Reach addresses one of digital marketing’s most persistent blind spots: the reality that purchasing decisions rarely happen in isolation. By bringing your first-party customer data and enriching it with trusted 3rd party signals, Household Reach lets marketers engage the full decision-making unit — not just the individual who happened to convert first.&nbsp;</p>



<p>The key distinction: marketers only need to bring their own first-party data. The householding solution does the rest, enabling more reach across the household without spending resources building additional audiences or manually configuring your campaigns.</p>



<p>What unites these approaches is a shift in mindset. Better performance should not require more vendors, more engineering resources, or more external data. It should come from extracting more value from the customer relationships brands already understand.</p>



<p>In an era of unprecedented performance pressure, the advantage will go to the marketers who stop looking for more vendors to solve their problems. Success won’t come from adding to the stack, but from using a solid data foundation to navigate these rising expectations and activating more of the data they already own.</p>






Performance marketing is under more pressure than it’s been in a decade — budgets are flat or reduced, expectations are rising,, and AI is raising the bar on what “good” looks like.
For years, performance marketing has been built on a familiar playbook. When performance plateaus, add another vendor. When targeting weakens, buy another dataset. When activation becomes difficult, introduce another layer of technology. But as budget constraints tighten and expectations for immediate ROI rise, this constant expansion of the stack is becoming unsustainable.
The challenge facing enterprise marketers today is not a shortage of data. It’s an inability to operationalize the data they already have.
At the same time, AI is exposing a hard truth about modern marketing architecture. Most AI failures are not model failures. They are data failures. The most sophisticated agent, model, or automation workflow cannot compensate for fragmented customer profiles, disconnected activation systems, or stale audience definitions. Yet much of the conversation in the customer data platform (CDP) market remains focused on shipping more AI agents.
That misses the point.
The real question isn’t whether your platform has an AI agent. It’s whether your data foundation can support the leap from automating tasks to partnering on strategic outcomes.
For too long, the industry’s north star was self-service — a mandate to bypass engineering tickets and data science queues. But that was a solution for the last decade. It effectively turned the marketer into a manual operator of complex systems. The new bar isn’t just self-service; it’s self-directed performance at scale.
We are witnessing a fundamental shift in the marketer’s job-to-be-done: you are moving away from the operational heavy lifting of building and managing audiences toward the high-level strategy of setting outcomes. Instead of spending your day wrangling segments, you now define your goal — whether it’s maximizing customer lifetime value or reversing churn — and the system suggests the optimal audience definitions and activation pathways to achieve it. By bridging the gap between intelligent agents and a clean data foundation, you move from managing technology to orchestrating outcomes. This is the new blueprint for performance.
At mParticle, we describe our approach as a performance engine: a model in which the data foundation and activation layer operate as a single system. The goal is not simply to collect customer data, but to make it immediately usable for performance outcomes.
The Audience Agent is one expression of this. Marketers describe what they want in plain language — e.g.,high-value customers who haven’t repurchased in 60 days — and the agent proposes the underlying logic for the marketer to review and approve. 
The shift isn’t automation; it’s a marketer-led workflow with an expert collaborator alongside. The longer you work with it, the better it understands your business — your data, your customers, the patterns that actually move your performance. That understanding is only as deep as the data foundation it draws on — and ours was built for this long before AI demanded it. The marketer leads. The agent elevates and expands. Together, they push the limit of what’s possible.
That philosophy is also reflected in capabilities such as Audience Expansion and Household Reach. Audience Expansion helps marketers identify additional high-potential users directly from their own first-party datasets — without relying on third-party lookalike audiences or external data sources — giving teams precise control over the balance between scale and quality. 
Household Reach addresses one of digital marketing’s most persistent blind spots: the reality that purchasing decisions rarely happen in isolation. By bringing your first-party customer data and enriching it with trusted 3rd party signals, Household Reach lets marketers engage the full decision-making unit — not just the individual who happened to convert first. 
The key distinction: marketers only need to bring their own first-party data. The householding solution does the rest, enabling more reach across the household without spending resources building additional audiences or manually configuring your campaigns.
What unites these approaches is a shift in mindset. Better performance should not require more vendors, more engineering resources, or more external data. It should come from extracting more value from the customer relationships brands already understand.
In an era of unprecedented performance pressure, the advantage will go to the marketers who stop looking for more vendors to solve their problems. Success won’t come from adding to the stack, but from using a solid data foundation to navigate these rising expectations and activating more of the data they already own.

As performance pressures mount, the future of marketing success lies in transforming your data foundation into a strategic AI-powered engine for growth.

The future of performance marketing isn’t more vendors. It’s making your stack work harder. by Rokt mParticle

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Hydration-and-SEO-How-it-works-and-why-it-matters.png" class="attachment-large size-large wp-post-image" alt="Hydration and SEO- How it works and why it matters" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Hydration-and-SEO-How-it-works-and-why-it-matters.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Hydration-and-SEO-How-it-works-and-why-it-matters-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Hydration-and-SEO-How-it-works-and-why-it-matters-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Hydration-and-SEO-How-it-works-and-why-it-matters-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>From server-rendered HTML to interactive pages, understand where hydration fits and when it can affect search visibility.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Hydration-and-SEO-How-it-works-and-why-it-matters.png" class="attachment-large size-large wp-post-image" alt="Hydration and SEO- How it works and why it matters" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Hydration-and-SEO-How-it-works-and-why-it-matters.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Hydration-and-SEO-How-it-works-and-why-it-matters-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Hydration-and-SEO-How-it-works-and-why-it-matters-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Hydration-and-SEO-How-it-works-and-why-it-matters-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>If your site runs on a framework like Next.js or Nuxt, hydration shapes how your pages become interactive, but it&#8217;s rarely explained in terms that matter to SEOs.</p>



<p>It&#8217;s more approachable than it sounds. Here&#8217;s what hydration is, how it works, where it affects SEO (and where it doesn&#8217;t), and how different frameworks handle it.</p>



<h2 class="wp-block-heading" id="h-what-is-hydration">What is hydration?</h2>



<p>Hydration is the process of JavaScript running in your browser &#8220;taking over&#8221; the static HTML built on the server, turning it into a page you can actually interact with.</p>



<p>Here&#8217;s the process:</p>



<ul class="wp-block-list">
<li>The server builds complete, fully formed HTML and sends it to your browser. You see the content right away, but it isn&#8217;t interactive. The buttons don&#8217;t work yet, and nothing responds to clicks.</li>



<li>Hydration happens when the page&#8217;s framework (Next.js, Nuxt, SvelteKit, and others) finishes loading. It walks over the existing HTML, attaches event listeners, and reconnects the visible markup with the logic that makes it work.</li>



<li>After hydration, the page behaves like a normal interactive app.</li>
</ul>



<p>Server-rendered HTML paints quickly, which is great for first impressions and often for Largest Contentful Paint (LCP). As the timeline below shows, traditional hydration means the page isn&#8217;t actually usable until hydration finishes.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="777" src="https://searchengineland.com/wp-content/seloads/2026/07/image-20.png" alt="" class="wp-image-481672" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-20.png 2048w, https://searchengineland.com/wp-content/seloads/2026/07/image-20-768x291.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/image-20-1536x583.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>

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<h2 class="wp-block-heading" id="h-hydration-adds-interactivity-not-content">Hydration adds interactivity, not content</h2>



<p>Hydration doesn&#8217;t add content to the page. The text, images, and layout already arrived from the server. It only adds behavior, wiring up the existing HTML so it can respond to you. Put simply, before hydration you can read the page, and after hydration you can use it.</p>



<p>You can see this side by side below. The only difference between the two pages is whether the button responds.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="771" src="https://searchengineland.com/wp-content/seloads/2026/07/image-21.png" alt="" class="wp-image-481673" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-21.png 2048w, https://searchengineland.com/wp-content/seloads/2026/07/image-21-768x289.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/image-21-1536x578.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>Don&#8217;t confuse hydration with the rendering pattern, which determines where and when the page is built. <a href="https://developer.mozilla.org/en-US/docs/Glossary/SSR" target="_blank" rel="noopener">Server-side rendering (SSR)</a>, <a href="https://developer.mozilla.org/en-US/docs/Glossary/SSG" target="_blank" rel="noopener">static site generation (SSG)</a>, and <a href="https://developer.mozilla.org/en-US/docs/Glossary/CSR" target="_blank" rel="noopener">client-side rendering (CSR)</a> each decide how much of the page arrives as finished HTML versus how much JavaScript builds later in the browser.</p>



<p>Because hydration runs on server-rendered (SSR) and static (SSG) pages, the content is already present in the initial HTML. Google can index that content from the initial HTML <a href="https://developers.google.com/search/docs/crawling-indexing/javascript/javascript-seo-basics#how-googlebot-processes-javascript" target="_blank" rel="noopener">instead of relying on the render step</a>, which is more reliable than a client-rendered blank shell.</p>



<h2 class="wp-block-heading" id="h-when-hydration-becomes-an-seo-problem">When hydration becomes an SEO problem</h2>



<p>Most of the time, hydration isn&#8217;t directly an SEO issue. It only becomes one when something breaks, <a href="https://nuxt.com/docs/4.x/guide/best-practices/hydration" target="_blank" rel="noopener">usually a mismatch</a>. This is when the server&#8217;s HTML and what the framework builds in the browser don&#8217;t agree.</p>



<p>A mismatch typically comes from one of a few sources:</p>



<ul class="wp-block-list">
<li>Content rendered from a browser-only API that the server can&#8217;t access, like <code>localStorage</code>.</li>



<li>A value that changes between the server and client, such as <code>new Date()</code>.</li>



<li>A third-party script or browser extension that alters the DOM before the framework hydrates it.</li>



<li>Invalid HTML that the browser rewrites in the background, producing a structure the framework didn&#8217;t expect.</li>
</ul>



<p>In these cases, hydration can&#8217;t reconcile the two versions, so the framework throws out the mismatched part and re-renders it. The exact process depends on the framework.</p>



<p>Here, a <code>&lt;time></code> value from new <code>Date()</code> renders differently on the server and in the browser, forcing a re-render.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="789" src="https://searchengineland.com/wp-content/seloads/2026/07/image-22.png" alt="" class="wp-image-481674" srcset="https://searchengineland.com/wp-content/seloads/2026/07/image-22.png 2048w, https://searchengineland.com/wp-content/seloads/2026/07/image-22-768x296.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/image-22-1536x592.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>That creates problems on three fronts. The re-render makes the page feel sluggish (INP) and shifts the layout (CLS). It can also leave the page outright broken because event listeners may fail to attach, causing buttons and forms to stop working.</p>



<p>In severe cases, because Google may read the raw server HTML before rendering the JavaScript, it can index the version that&#8217;s about to be discarded, storing content visitors never actually see.</p>



<p>Developers can resolve these issues by fixing the underlying causes of the mismatches. For example, they can use valid HTML so the browser doesn&#8217;t rewrite it behind the scenes.</p>



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<h2 class="wp-block-heading" id="h-how-to-spot-hydration-problems-on-a-live-site">How to spot hydration problems on a live site</h2>



<p>Hydration errors aren&#8217;t as explicit on a live site as they are during development. Start by checking the browser&#8217;s <a href="https://developer.chrome.com/docs/devtools/console/understand-messages" target="_blank" rel="noopener">Developer Tools console</a> for hydration or JavaScript warnings, then use these additional checks:</p>



<ul class="wp-block-list">
<li>Watch the page load for content that shifts, flickers, or stays unresponsive.</li>



<li>Run important templates through Google Search Console&#8217;s URL Inspection tool to see how the page is rendered.</li>



<li>Crawl in JavaScript-rendering mode (Screaming Frog, Sitebulb) to compare rendered output against raw HTML at scale.</li>
</ul>



<h2 class="wp-block-heading" id="h-how-frameworks-handle-hydration">How frameworks handle hydration</h2>



<p>Modern frameworks take different approaches to hydration, including ways to reduce or skip it, to balance performance, interactivity, and JavaScript execution.</p>



<p>The most common approaches are:</p>



<ul class="wp-block-list">
<li><strong>Full hydration:</strong> The whole page hydrates in one go. It sounds simple, but it ships the most JavaScript and puts the most work on the main thread.</li>



<li><strong>Partial hydration:</strong> Only the interactive bits (&#8220;islands&#8221;) hydrate. The static parts stay as plain HTML and never get touched. Astro&#8217;s <a href="https://docs.astro.build/en/concepts/islands/" target="_blank" rel="noopener">islands architecture</a> is built around this.</li>



<li><strong>Progressive hydration:</strong> The page hydrates in pieces, either as sections scroll into view or on a schedule, instead of all at once. Angular&#8217;s <a href="https://angular.dev/guide/incremental-hydration" target="_blank" rel="noopener">incremental hydration</a> works this way.</li>
</ul>



<p>Two newer approaches sidestep hydration:</p>



<ul class="wp-block-list">
<li><strong>React Server Components:</strong> Some components <a href="https://react.dev/reference/rsc/server-components" target="_blank" rel="noopener">render entirely on the server</a> and ship zero JavaScript, so there&#8217;s nothing to hydrate on the client.</li>



<li><strong>Resumability:</strong> It <a href="https://qwik.dev/docs/concepts/resumable/" target="_blank" rel="noopener">skips hydration completely</a>. The page picks up exactly where the server left off, with no components re-running on load. Qwik does this. It&#8217;s also the newest of these approaches and the least battle-tested.</li>
</ul>



<p>Here’s how they compare:</p>



<figure class="wp-block-table aligncenter"><table><thead><tr><th><strong>Technique</strong></th><th><strong>What hydrates</strong></th><th><strong>JavaScript shipped</strong></th><th><strong>Example</strong></th></tr></thead><tbody><tr><td>Full hydration</td><td>The entire page</td><td>Most</td><td>Next.js (Pages Router)</td></tr><tr><td>Partial hydration (islands)</td><td>Only interactive components</td><td>Less</td><td>Astro</td></tr><tr><td>Progressive hydration</td><td>The page, in pieces over time</td><td>Same total, spread out</td><td>Angular</td></tr><tr><td>React Server Components</td><td>Nothing (for server-only parts)</td><td>Less</td><td>Next.js (App Router)</td></tr><tr><td>Resumability</td><td>Nothing, hydration is skipped</td><td>Least</td><td>Qwik</td></tr></tbody></table></figure>



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



<p>Most of the time, hydration isn&#8217;t an SEO problem. It only becomes one when the server&#8217;s HTML and the browser&#8217;s rendered version disagree.</p>



<p>Newer frameworks leave less room for that to happen because each generation ships less JavaScript and does less work in the browser. Still, the mismatches that do surface matter, especially when search engines index a version of the page your visitors never see.</p>

If your site runs on a framework like Next.js or Nuxt, hydration shapes how your pages become interactive, but it’s rarely explained in terms that matter to SEOs.
It’s more approachable than it sounds. Here’s what hydration is, how it works, where it affects SEO (and where it doesn’t), and how different frameworks handle it.
What is hydration?
Hydration is the process of JavaScript running in your browser “taking over” the static HTML built on the server, turning it into a page you can actually interact with.
Here’s the process:
The server builds complete, fully formed HTML and sends it to your browser. You see the content right away, but it isn’t interactive. The buttons don’t work yet, and nothing responds to clicks.
Hydration happens when the page’s framework (Next.js, Nuxt, SvelteKit, and others) finishes loading. It walks over the existing HTML, attaches event listeners, and reconnects the visible markup with the logic that makes it work.
After hydration, the page behaves like a normal interactive app.
Server-rendered HTML paints quickly, which is great for first impressions and often for Largest Contentful Paint (LCP). As the timeline below shows, traditional hydration means the page isn’t actually usable until hydration finishes.




  
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Hydration adds interactivity, not content
Hydration doesn’t add content to the page. The text, images, and layout already arrived from the server. It only adds behavior, wiring up the existing HTML so it can respond to you. Put simply, before hydration you can read the page, and after hydration you can use it.
You can see this side by side below. The only difference between the two pages is whether the button responds.




Don’t confuse hydration with the rendering pattern, which determines where and when the page is built. Server-side rendering (SSR), static site generation (SSG), and client-side rendering (CSR) each decide how much of the page arrives as finished HTML versus how much JavaScript builds later in the browser.
Because hydration runs on server-rendered (SSR) and static (SSG) pages, the content is already present in the initial HTML. Google can index that content from the initial HTML instead of relying on the render step, which is more reliable than a client-rendered blank shell.
When hydration becomes an SEO problem
Most of the time, hydration isn’t directly an SEO issue. It only becomes one when something breaks, usually a mismatch. This is when the server’s HTML and what the framework builds in the browser don’t agree.
A mismatch typically comes from one of a few sources:
Content rendered from a browser-only API that the server can’t access, like localStorage.
A value that changes between the server and client, such as new Date().
A third-party script or browser extension that alters the DOM before the framework hydrates it.
Invalid HTML that the browser rewrites in the background, producing a structure the framework didn’t expect.
In these cases, hydration can’t reconcile the two versions, so the framework throws out the mismatched part and re-renders it. The exact process depends on the framework.
Here, a <time> value from new Date() renders differently on the server and in the browser, forcing a re-render.




That creates problems on three fronts. The re-render makes the page feel sluggish (INP) and shifts the layout (CLS). It can also leave the page outright broken because event listeners may fail to attach, causing buttons and forms to stop working.
In severe cases, because Google may read the raw server HTML before rendering the JavaScript, it can index the version that’s about to be discarded, storing content visitors never actually see.
Developers can resolve these issues by fixing the underlying causes of the mismatches. For example, they can use valid HTML so the browser doesn’t rewrite it behind the scenes.
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How to spot hydration problems on a live site
Hydration errors aren’t as explicit on a live site as they are during development. Start by checking the browser’s Developer Tools console for hydration or JavaScript warnings, then use these additional checks:
Watch the page load for content that shifts, flickers, or stays unresponsive.
Run important templates through Google Search Console’s URL Inspection tool to see how the page is rendered.
Crawl in JavaScript-rendering mode (Screaming Frog, Sitebulb) to compare rendered output against raw HTML at scale.
How frameworks handle hydration
Modern frameworks take different approaches to hydration, including ways to reduce or skip it, to balance performance, interactivity, and JavaScript execution.
The most common approaches are:
Full hydration: The whole page hydrates in one go. It sounds simple, but it ships the most JavaScript and puts the most work on the main thread.
Partial hydration: Only the interactive bits (“islands”) hydrate. The static parts stay as plain HTML and never get touched. Astro’s islands architecture is built around this.
Progressive hydration: The page hydrates in pieces, either as sections scroll into view or on a schedule, instead of all at once. Angular’s incremental hydration works this way.
Two newer approaches sidestep hydration:
React Server Components: Some components render entirely on the server and ship zero JavaScript, so there’s nothing to hydrate on the client.
Resumability: It skips hydration completely. The page picks up exactly where the server left off, with no components re-running on load. Qwik does this. It’s also the newest of these approaches and the least battle-tested.
Here’s how they compare:

TechniqueWhat hydratesJavaScript shippedExample

Full hydrationThe entire pageMostNext.js (Pages Router)
Partial hydration (islands)Only interactive componentsLessAstro
Progressive hydrationThe page, in pieces over timeSame total, spread outAngular
React Server ComponentsNothing (for server-only parts)LessNext.js (App Router)
ResumabilityNothing, hydration is skippedLeastQwik





What this means for your site
Most of the time, hydration isn’t an SEO problem. It only becomes one when the server’s HTML and the browser’s rendered version disagree.
Newer frameworks leave less room for that to happen because each generation ships less JavaScript and does less work in the browser. Still, the mismatches that do surface matter, especially when search engines index a version of the page your visitors never see.

From server-rendered HTML to interactive pages, understand where hydration fits and when it can affect search visibility.

Hydration and SEO: How it works and why it matters

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Used-or-cited-The-two-ways-brands-appear-in-AI-search.png" class="attachment-large size-large wp-post-image" alt="Used or cited: The two ways brands appear in AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Used-or-cited-The-two-ways-brands-appear-in-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Used-or-cited-The-two-ways-brands-appear-in-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Used-or-cited-The-two-ways-brands-appear-in-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Used-or-cited-The-two-ways-brands-appear-in-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Learn how AI engines use and cite brand information, how to measure both, and why traditional SEO still matters.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/07/Used-or-cited-The-two-ways-brands-appear-in-AI-search.png" class="attachment-large size-large wp-post-image" alt="Used or cited: The two ways brands appear in AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Used-or-cited-The-two-ways-brands-appear-in-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/07/Used-or-cited-The-two-ways-brands-appear-in-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/07/Used-or-cited-The-two-ways-brands-appear-in-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/07/Used-or-cited-The-two-ways-brands-appear-in-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Ranking within Google&#8217;s traditional search results provides diminishing returns. Ads, AI Overviews, and other search engine results page (SERP) features push organic links further down the page.</p>



<p>As the search landscape changes, how should brands adapt to ensure they&#8217;re represented in AI-powered responses?</p>



<p>The more you know about how AI engines use your brand&#8217;s information and when they cite it, the better you can use AI search to your advantage. With that knowledge, you can move beyond whether AI models know your brand and start developing your own AI visibility strategy.</p>



<h2 class="wp-block-heading" id="h-collapse-of-the-click-economy">Collapse of the click economy</h2>



<p>It&#8217;s important for most brands to understand AI search and begin developing an AI SEO strategy as quickly as possible. While a full transformation from organic to AI search appears to be years away, AI SEO may eventually replace traditional SEO.</p>



<p>Google is already leaning heavily on AI search. As CEO Sundar Pichai said in an <a href="https://www.theverge.com/tech/920815/google-alphabet-q1-2026-earnings-sundar-pichai" target="_blank" rel="noopener">April article from The Verge</a>:&nbsp;</p>



<ul class="wp-block-list">
<li>&#8220;Search had a strong quarter with AI experiences driving usage, queries at an all-time high, and 19% revenue growth.&#8221;</li>
</ul>



<p>At the same time, users are adapting to AI search features. When users encounter an AI-powered summary in search results, they click a blue link just <a href="https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/" target="_blank" rel="noopener">8% of the time</a>, a Pew Research study found. When they don&#8217;t encounter AI summaries, they click blue links 15% of the time.</p>



<p>Although AI search traffic is still limited, it tends to have a higher conversion rate than organic search traffic. AI traffic had a conversion rate of <a href="https://www.similarweb.com/blog/insights/ai-news/google-reddit-dominate-in-genai-referrals-to-tech-platforms/" target="_blank" rel="noopener">11.4%</a>, compared to 5.3% for organic search traffic, per a Similarweb study.</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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<h2 class="wp-block-heading" id="h-brand-presence-within-ai-engines-usage-vs-citation">Brand presence within AI engines: Usage vs. citation</h2>



<p>Brands can exist in AI systems in two distinct ways: usage and citation.</p>



<p>AI engines ingest information about your brand and use it when responding to search queries. This is somewhat similar to how Google traditionally indexes pages before ranking and serving them in search results.</p>



<p>When AI engines use your content, they may also mention your brand as an unlinked citation. This can drive discovery and may prompt users to search for and engage with your brand.</p>



<p>Citation occurs when an AI engine directly references your brand as a source of information. This may be a link to your web page, a link to your social profile, or a clickable phone link that lets users call you.</p>



<p>Within OpenAI, usage and citation rely on separate technical levers. As <a href="https://developers.openai.com/api/docs/bots" target="_blank" rel="noopener">OpenAI&#8217;s documentation explains</a>, there are four distinct user agents, with OAI-SearchBot and GPTBot deployed separately. Other AI systems have similar controls and measures that point to the same distinction.</p>



<h2 class="wp-block-heading" id="h-why-citations-are-only-part-of-the-ai-visibility-equation">Why citations are only part of the AI visibility equation</h2>



<p>AI engines often answer questions directly without necessarily citing web sources. This isn&#8217;t a new phenomenon. Before AI Overviews, Google tried something similar with featured snippets.</p>



<p>ChatGPT retrieves <a href="https://ahrefs.com/blog/why-chatgpt-cites-pages/" target="_blank" rel="noopener">almost the exact same number</a> of cited (~16.57) and uncited (~16.58) URLs to generate an average response, according to an Ahrefs study. Yet Reddit accounts for more than two-thirds (67.8%) of uncited URLs. As a result, comparing cited and uncited URLs is really a comparison between search results and Reddit API output.</p>



<p>This demonstrates that many AI systems are biased in the uncited information they provide to users. Certain platforms and websites are better than others at helping brands appear in AI answers. Brands that try to force themselves into AI models without understanding where those models source most of their information will be at a distinct disadvantage.</p>



<h2 class="wp-block-heading" id="h-how-to-improve-ai-usage-and-citation-for-your-brand">How to improve AI usage and citation for your brand</h2>



<p>Start by tracking your brand&#8217;s status and progress over time. Run a representative selection of prompts through an AI visibility platform and examine the citation sources. Where do they land, and what does that tell you?</p>



<p>There are many emerging AI citation tracking platforms to choose from. Established platforms like Semrush and Ahrefs have also integrated AI tracking features.</p>



<p>Scale your tracking and research efforts as much as possible. This can be difficult because AI prompt tracking often relies on API calls and is more expensive than traditional search ranking tracking.</p>



<p>As long as your sample is broadly representative, most tracking platforms will pull multiple responses and calculate some type of average. Although the volume of data is smaller, it&#8217;s usually quite rich.</p>



<p>Don&#8217;t forget to read AI and data vendor studies. They&#8217;re valuable sources of information because they show where AI engines pull information from.</p>



<p>Continual monitoring and adaptation are key. Over time, you can place your brand within the sources AI engines rely on most heavily.</p>



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<h2 class="wp-block-heading" id="h-should-you-bother-with-traditional-search-rankings">Should you bother with traditional search rankings?</h2>



<p>Yes, you should continue to pursue traditional search rankings, but not for the reasons you might think. The connection between organic ranking positions and performance has become much more nebulous.</p>



<p>However, Ahrefs research suggests a correlation between AI citations and Google ranking positions, at least for Google AI Overviews. A July 2025 study found that <a href="https://ahrefs.com/blog/search-rankings-ai-citations/" target="_blank" rel="noopener">76.1% of pages</a> cited in AI Overviews ranked in Google&#8217;s top 10 organic search results. For AI Overviews, which may become a dominant force in AI search over the coming years, traditional rankings still seem to matter.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full is-resized"><img decoding="async" width="885" height="1412" src="https://searchengineland.com/wp-content/seloads/2026/07/Ahrefs-Rankings-Citations-Comparison.png" alt="How AI Overview citations rank in the SERPs" class="wp-image-481666" srcset="https://searchengineland.com/wp-content/seloads/2026/07/Ahrefs-Rankings-Citations-Comparison.png 885w, https://searchengineland.com/wp-content/seloads/2026/07/Ahrefs-Rankings-Citations-Comparison-768x1225.png 768w" sizes="(max-width: 885px) 100vw, 885px" /></figure>
</div>


<p>AI engines <a href="https://www.semrush.com/blog/ai-citing-my-site-vs-third-party-sources/" target="_blank" rel="noopener">rarely cite generic content</a> that restates what other sources already say, an April study from Semrush found. Content that earns citations adds unique value.</p>



<p>This aligns with Google&#8217;s helpful content guidance, which encourages brands to publish original information. Producing content with a unique, trusted, and statistically grounded perspective can also help improve Google rankings. </p>



<p>Since many tactics for earning higher organic rankings can also earn AI citations, there&#8217;s no reason to abandon traditional SEO techniques and content strategies.</p>



<h2 class="wp-block-heading" id="h-the-growth-of-ai-visibility-and-the-fate-of-traditional-seo">The growth of AI visibility and the fate of traditional SEO</h2>



<p>Both usage and citation require continual tracking and analysis. To increase the likelihood that AI engines use your brand&#8217;s knowledge and content, get your brand into the sources each AI model relies on. To earn citations, stay crawlable, rank organically, and say something original.</p>



<p>Classic SEO still earns its keep because the techniques that win organic rankings often earn AI citations as well. Yet the returns are diminishing, and AI SEO may one day replace traditional SEO altogether. That&#8217;s still a long way off, so for now, keep ranking, start tracking, and pursue both.</p>

Ranking within Google’s traditional search results provides diminishing returns. Ads, AI Overviews, and other search engine results page (SERP) features push organic links further down the page.
As the search landscape changes, how should brands adapt to ensure they’re represented in AI-powered responses?
The more you know about how AI engines use your brand’s information and when they cite it, the better you can use AI search to your advantage. With that knowledge, you can move beyond whether AI models know your brand and start developing your own AI visibility strategy.
Collapse of the click economy
It’s important for most brands to understand AI search and begin developing an AI SEO strategy as quickly as possible. While a full transformation from organic to AI search appears to be years away, AI SEO may eventually replace traditional SEO.
Google is already leaning heavily on AI search. As CEO Sundar Pichai said in an April article from The Verge: 
“Search had a strong quarter with AI experiences driving usage, queries at an all-time high, and 19% revenue growth.”
At the same time, users are adapting to AI search features. When users encounter an AI-powered summary in search results, they click a blue link just 8% of the time, a Pew Research study found. When they don’t encounter AI summaries, they click blue links 15% of the time.
Although AI search traffic is still limited, it tends to have a higher conversion rate than organic search traffic. AI traffic had a conversion rate of 11.4%, compared to 5.3% for organic search traffic, per a Similarweb study.
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Brand presence within AI engines: Usage vs. citation
Brands can exist in AI systems in two distinct ways: usage and citation.
AI engines ingest information about your brand and use it when responding to search queries. This is somewhat similar to how Google traditionally indexes pages before ranking and serving them in search results.
When AI engines use your content, they may also mention your brand as an unlinked citation. This can drive discovery and may prompt users to search for and engage with your brand.
Citation occurs when an AI engine directly references your brand as a source of information. This may be a link to your web page, a link to your social profile, or a clickable phone link that lets users call you.
Within OpenAI, usage and citation rely on separate technical levers. As OpenAI’s documentation explains, there are four distinct user agents, with OAI-SearchBot and GPTBot deployed separately. Other AI systems have similar controls and measures that point to the same distinction.
Why citations are only part of the AI visibility equation
AI engines often answer questions directly without necessarily citing web sources. This isn’t a new phenomenon. Before AI Overviews, Google tried something similar with featured snippets.
ChatGPT retrieves almost the exact same number of cited (~16.57) and uncited (~16.58) URLs to generate an average response, according to an Ahrefs study. Yet Reddit accounts for more than two-thirds (67.8%) of uncited URLs. As a result, comparing cited and uncited URLs is really a comparison between search results and Reddit API output.
This demonstrates that many AI systems are biased in the uncited information they provide to users. Certain platforms and websites are better than others at helping brands appear in AI answers. Brands that try to force themselves into AI models without understanding where those models source most of their information will be at a distinct disadvantage.
How to improve AI usage and citation for your brand
Start by tracking your brand’s status and progress over time. Run a representative selection of prompts through an AI visibility platform and examine the citation sources. Where do they land, and what does that tell you?
There are many emerging AI citation tracking platforms to choose from. Established platforms like Semrush and Ahrefs have also integrated AI tracking features.
Scale your tracking and research efforts as much as possible. This can be difficult because AI prompt tracking often relies on API calls and is more expensive than traditional search ranking tracking.
As long as your sample is broadly representative, most tracking platforms will pull multiple responses and calculate some type of average. Although the volume of data is smaller, it’s usually quite rich.
Don’t forget to read AI and data vendor studies. They’re valuable sources of information because they show where AI engines pull information from.
Continual monitoring and adaptation are key. Over time, you can place your brand within the sources AI engines rely on most heavily.
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Should you bother with traditional search rankings?
Yes, you should continue to pursue traditional search rankings, but not for the reasons you might think. The connection between organic ranking positions and performance has become much more nebulous.
However, Ahrefs research suggests a correlation between AI citations and Google ranking positions, at least for Google AI Overviews. A July 2025 study found that 76.1% of pages cited in AI Overviews ranked in Google’s top 10 organic search results. For AI Overviews, which may become a dominant force in AI search over the coming years, traditional rankings still seem to matter.




AI engines rarely cite generic content that restates what other sources already say, an April study from Semrush found. Content that earns citations adds unique value.
This aligns with Google’s helpful content guidance, which encourages brands to publish original information. Producing content with a unique, trusted, and statistically grounded perspective can also help improve Google rankings. 
Since many tactics for earning higher organic rankings can also earn AI citations, there’s no reason to abandon traditional SEO techniques and content strategies.
The growth of AI visibility and the fate of traditional SEO
Both usage and citation require continual tracking and analysis. To increase the likelihood that AI engines use your brand’s knowledge and content, get your brand into the sources each AI model relies on. To earn citations, stay crawlable, rank organically, and say something original.
Classic SEO still earns its keep because the techniques that win organic rankings often earn AI citations as well. Yet the returns are diminishing, and AI SEO may one day replace traditional SEO altogether. That’s still a long way off, so for now, keep ranking, start tracking, and pursue both.

Learn how AI engines use and cite brand information, how to measure both, and why traditional SEO still matters.

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