<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-to-run-prompt-level-SEO-experiments-for-AI-search.png" class="attachment-large size-large wp-post-image" alt="How to run prompt-level SEO experiments for AI search" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-to-run-prompt-level-SEO-experiments-for-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-run-prompt-level-SEO-experiments-for-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-run-prompt-level-SEO-experiments-for-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-run-prompt-level-SEO-experiments-for-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Learn how to isolate variables, measure prompt-response inclusion, and build repeatable frameworks for testing LLM visibility.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-to-run-prompt-level-SEO-experiments-for-AI-search.png" class="attachment-large size-large wp-post-image" alt="How to run prompt-level SEO experiments for AI search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-to-run-prompt-level-SEO-experiments-for-AI-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-run-prompt-level-SEO-experiments-for-AI-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-run-prompt-level-SEO-experiments-for-AI-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-run-prompt-level-SEO-experiments-for-AI-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>As LLMs continue to grow, optimizing brand visibility in AI-generated responses is becoming increasingly important. Consumers are turning to these models for answers, recommendations, recipes, vacations, and nearly everything else imaginable.</p>



<p>But what happens if your brand isn’t included in those responses? Can you influence the outcome? And what are some proven ways to improve your brand’s inclusion and visibility?</p>



<p>That’s where structured experimentation comes in. Prompt-level SEO requires more than assumptions or one-off wins. It requires repeatable testing frameworks that help isolate what actually influences LLM responses.</p>



<h2 class="wp-block-heading" id="h-build-prompt-level-seo-tests-with-a-hypothesis-framework">Build prompt-level SEO tests with a hypothesis framework</h2>



<p>There are countless recommendations on how to improve your LLM presence. Experimentation is key to discovering what works for your industry and brand.</p>



<p>Hypothesis-driven testing is the way we structure these tests for our brands. It breaks things down in a structured way that can be replicated across tests and situations.</p>



<p>This framework creates a common approach to testing and helps you quickly understand the test and its outputs. The structure consists of three main pieces: if, then, because.</p>



<ul class="wp-block-list">
<li><strong>If:</strong> This part provides the hypothesis: what is the test action?
<ul class="wp-block-list">
<li>“If we include more detailed product specifications in our content.”</li>
</ul>
</li>



<li><strong>Then:</strong> What will happen once the “if” section is completed? The outcome.
<ul class="wp-block-list">
<li>“Then we’ll see our brand get included in more product-specific prompts.”</li>
</ul>
</li>



<li><strong>Because:</strong> This is why you believe this will occur. What is the theory behind this test?
<ul class="wp-block-list">
<li>“Because LLMs value detailed and specific information in their prompt responses.”</li>
</ul>
</li>
</ul>



<p>This framework requires some basic fundamentals that ensure you’re thinking through the test. It also allows you to go back later and validate whether you have tested these specific elements in the past and what the premises, theories, and outcomes were.&nbsp;</p>



<p>This helps because, as things change, the test elements may still be valid simply because the world shifts — changing the “because” section.</p>



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<h2 class="wp-block-heading" id="h-key-considerations-before-running-prompt-level-seo-tests">Key considerations before running prompt-level SEO tests</h2>



<p>Before we get to the recommendations for testing best practices, here are some considerations when running these tests:</p>



<ul class="wp-block-list">
<li><strong>Model updates:</strong> These models are updated constantly. As some models move from 4.1 to 4.2, it’s time to revisit those results. How did the model change the inputs and outputs?</li>



<li><strong>Prompt drift:</strong> Have you ever run the exact same prompt twice in a day or on consecutive days? Often, the results change. Therefore, running the prompt more than once and on consecutive days to evaluate the outcome is important to get a true baseline. This is no different from personalized search results. Brands get comfortable with the variance, but some averages surface and become the benchmark. Prompt testing works much the same way.</li>
</ul>



<p>Now that you have the framework of the test, let’s think about the core elements of tests that can be used in prompt-specific testing.</p>



<h2 class="wp-block-heading" id="h-how-to-isolate-variables-a-methodological-approach">How to isolate variables: A methodological approach</h2>



<p>Designing a reliable prompt-level SEO experiment requires isolating a single causal variable. This is crucial for confidently attributing changes in LLM response inclusion or position to a specific action.</p>



<h3 class="wp-block-heading" id="h-1-content-changes">1. Content changes</h3>



<p>When testing content modifications, the variable must be surgical. A common pitfall is changing too much at once (e.g., updating a product description and the page&#8217;s schema).</p>



<ul class="wp-block-list">
<li><strong>Best practice — The single-paragraph swap: </strong>Focus on modifying a single, targeted piece of text on the page, such as a product description, FAQ answer, or a specific feature bullet point.</li>



<li><strong>Methodology:</strong> For true isolation, implement A/B testing with a control page containing the original content and a test page containing the modified content. The prompt should be designed to target the specific information you changed. Measure the brand&#8217;s inclusion rate and position-in-response over a defined period (e.g., seven days – keep in mind these models are moving at a variety of speeds. This work, much like SEO, isn’t a microwave, but more like an oven).</li>
</ul>



<h3 class="wp-block-heading" id="h-2-structured-data">2. Structured data</h3>



<p>Structured data (schema) provides explicit signals to both search engines and LLM ingestion layers. Testing this requires treating the schema update as the only change to the page.</p>



<ul class="wp-block-list">
<li><strong>Variable isolation:</strong> Test adding new properties (e.g., brand, model, and offer details) without altering the visible HTML text. This isolates the impact of the machine-readable layer.</li>



<li><strong>Specific experiment — FAQ schema:</strong> A highly effective experiment is adding FAQ schema to pages that already have Q&amp;A sections in their HTML, isolating the effect of the explicit schema markup on LLM ingestion. Our work with brands has demonstrated that adding FAQ schema to pages with Q&amp;A sections makes those sections easier for LLMs to ingest.</li>
</ul>



<h3 class="wp-block-heading" id="h-3-before-and-after-prompt-testing">3. Before-and-after prompt testing</h3>



<p>This process involves establishing a stringent baseline, making the change, and then repeating the prompt query. This is an essential control method in lieu of true A/B testing on the LLM itself.</p>



<p><strong>Protocol</strong>



<ul class="wp-block-list">
<li><strong>Phase 1 (baseline):</strong> Execute a set of 5-10 target prompts daily for seven consecutive days to establish a true average of inclusion and position-in-response, accounting for prompt drift.
<ul class="wp-block-list">
<li><strong>Action:</strong> Deploy the isolated change (e.g., content or schema update).</li>
</ul>
</li>



<li><strong>Phase 2 (measurement):</strong> Re-run the exact same set of prompts daily for the next seven days.
<ul class="wp-block-list">
<li><strong>Analysis:</strong> Compare the average inclusion rate and position of Phase 1 versus Phase 2. This method is central to initial presence score analyses, such as using three buckets of 25 keywords and prompts for a total of 75 queries.</li>
</ul>
</li>
</ul>



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<h2 class="wp-block-heading" id="h-encouraging-reproducible-experiments">Encouraging reproducible experiments</h2>



<p>With the speed of model evolution and the lack of detailed model insights, it’s difficult to ensure reproducibility of results. However, the goal is to move beyond simple “it worked once” findings to build a durable methodology.</p>



<h3 class="wp-block-heading" id="h-mandatory-frameworks">Mandatory frameworks</h3>



<p>Ensure every test is documented using the “if, then, because” hypothesis structure. This archives the premise, action, and expected outcome, allowing future teams to quickly validate whether a test remains relevant as LLMs evolve.</p>



<h3 class="wp-block-heading" id="h-technical-integrity">Technical integrity</h3>



<ul class="wp-block-list">
<li><strong>Version control:</strong> Document the specific model and version used for testing (e.g., “Gemini 4.1.2”). This allows for easy comparison when a model update occurs.</li>



<li><strong>Prompt libraries:</strong> Maintain an organized, time-stamped repository of the exact prompt queries used for baseline and measurement phases. This repository should track inclusion rate, position-in-response, and sentiment/framing for each query.</li>
</ul>



<h3 class="wp-block-heading" id="h-infrastructure-consistency">Infrastructure consistency </h3>



<p>Define the testing environment (e.g., clear browser cache, no login state) and, where possible, use APIs or synthetic testing platforms to remove the impact of personalization and location bias, which is analogous to controlling for personalized search results in traditional SEO.</p>



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<h2 class="wp-block-heading" id="h-moving-beyond-one-off-wins-in-ai-search">Moving beyond one-off wins in AI search</h2>



<p>The key to prompt-level SEO is rigorous methodology. By adopting a hypothesis-driven approach, surgically isolating variables (content, entities, schema), and establishing strict before-and-after testing protocols, you can confidently move past speculation. </p>



<p>The path to influencing LLM responses is paved with controlled, documented, and reproducible experiments.</p>

As LLMs continue to grow, optimizing brand visibility in AI-generated responses is becoming increasingly important. Consumers are turning to these models for answers, recommendations, recipes, vacations, and nearly everything else imaginable.
But what happens if your brand isn’t included in those responses? Can you influence the outcome? And what are some proven ways to improve your brand’s inclusion and visibility?
That’s where structured experimentation comes in. Prompt-level SEO requires more than assumptions or one-off wins. It requires repeatable testing frameworks that help isolate what actually influences LLM responses.
Build prompt-level SEO tests with a hypothesis framework
There are countless recommendations on how to improve your LLM presence. Experimentation is key to discovering what works for your industry and brand.
Hypothesis-driven testing is the way we structure these tests for our brands. It breaks things down in a structured way that can be replicated across tests and situations.
This framework creates a common approach to testing and helps you quickly understand the test and its outputs. The structure consists of three main pieces: if, then, because.
If: This part provides the hypothesis: what is the test action?

“If we include more detailed product specifications in our content.”
Then: What will happen once the “if” section is completed? The outcome.

“Then we’ll see our brand get included in more product-specific prompts.”
Because: This is why you believe this will occur. What is the theory behind this test?

“Because LLMs value detailed and specific information in their prompt responses.”
This framework requires some basic fundamentals that ensure you’re thinking through the test. It also allows you to go back later and validate whether you have tested these specific elements in the past and what the premises, theories, and outcomes were. 
This helps because, as things change, the test elements may still be valid simply because the world shifts — changing the “because” section.

  
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Start Free Trial
    
Get started with
Key considerations before running prompt-level SEO tests
Before we get to the recommendations for testing best practices, here are some considerations when running these tests:
Model updates: These models are updated constantly. As some models move from 4.1 to 4.2, it’s time to revisit those results. How did the model change the inputs and outputs?
Prompt drift: Have you ever run the exact same prompt twice in a day or on consecutive days? Often, the results change. Therefore, running the prompt more than once and on consecutive days to evaluate the outcome is important to get a true baseline. This is no different from personalized search results. Brands get comfortable with the variance, but some averages surface and become the benchmark. Prompt testing works much the same way.
Now that you have the framework of the test, let’s think about the core elements of tests that can be used in prompt-specific testing.
How to isolate variables: A methodological approach
Designing a reliable prompt-level SEO experiment requires isolating a single causal variable. This is crucial for confidently attributing changes in LLM response inclusion or position to a specific action.
1. Content changes
When testing content modifications, the variable must be surgical. A common pitfall is changing too much at once (e.g., updating a product description and the page’s schema).
Best practice — The single-paragraph swap: Focus on modifying a single, targeted piece of text on the page, such as a product description, FAQ answer, or a specific feature bullet point.
Methodology: For true isolation, implement A/B testing with a control page containing the original content and a test page containing the modified content. The prompt should be designed to target the specific information you changed. Measure the brand’s inclusion rate and position-in-response over a defined period (e.g., seven days – keep in mind these models are moving at a variety of speeds. This work, much like SEO, isn’t a microwave, but more like an oven).
2. Structured data
Structured data (schema) provides explicit signals to both search engines and LLM ingestion layers. Testing this requires treating the schema update as the only change to the page.
Variable isolation: Test adding new properties (e.g., brand, model, and offer details) without altering the visible HTML text. This isolates the impact of the machine-readable layer.
Specific experiment — FAQ schema: A highly effective experiment is adding FAQ schema to pages that already have Q&A sections in their HTML, isolating the effect of the explicit schema markup on LLM ingestion. Our work with brands has demonstrated that adding FAQ schema to pages with Q&A sections makes those sections easier for LLMs to ingest.
3. Before-and-after prompt testing
This process involves establishing a stringent baseline, making the change, and then repeating the prompt query. This is an essential control method in lieu of true A/B testing on the LLM itself.
Protocol




Phase 1 (baseline): Execute a set of 5-10 target prompts daily for seven consecutive days to establish a true average of inclusion and position-in-response, accounting for prompt drift.

Action: Deploy the isolated change (e.g., content or schema update).
Phase 2 (measurement): Re-run the exact same set of prompts daily for the next seven days.

Analysis: Compare the average inclusion rate and position of Phase 1 versus Phase 2. This method is central to initial presence score analyses, such as using three buckets of 25 keywords and prompts for a total of 75 queries.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Encouraging reproducible experiments
With the speed of model evolution and the lack of detailed model insights, it’s difficult to ensure reproducibility of results. However, the goal is to move beyond simple “it worked once” findings to build a durable methodology.
Mandatory frameworks
Ensure every test is documented using the “if, then, because” hypothesis structure. This archives the premise, action, and expected outcome, allowing future teams to quickly validate whether a test remains relevant as LLMs evolve.
Technical integrity
Version control: Document the specific model and version used for testing (e.g., “Gemini 4.1.2”). This allows for easy comparison when a model update occurs.
Prompt libraries: Maintain an organized, time-stamped repository of the exact prompt queries used for baseline and measurement phases. This repository should track inclusion rate, position-in-response, and sentiment/framing for each query.
Infrastructure consistency 
Define the testing environment (e.g., clear browser cache, no login state) and, where possible, use APIs or synthetic testing platforms to remove the impact of personalization and location bias, which is analogous to controlling for personalized search results in traditional SEO.

  
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Get started with
Moving beyond one-off wins in AI search
The key to prompt-level SEO is rigorous methodology. By adopting a hypothesis-driven approach, surgically isolating variables (content, entities, schema), and establishing strict before-and-after testing protocols, you can confidently move past speculation. 
The path to influencing LLM responses is paved with controlled, documented, and reproducible experiments.

Learn how to isolate variables, measure prompt-response inclusion, and build repeatable frameworks for testing LLM visibility.

How to run prompt-level SEO experiments for AI search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/SEOs-new-goal-in-2026-Recognition-not-rankings.png" class="attachment-large size-large wp-post-image" alt="SEO’s new goal in 2026: Recognition, not rankings" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/SEOs-new-goal-in-2026-Recognition-not-rankings.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/SEOs-new-goal-in-2026-Recognition-not-rankings-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/SEOs-new-goal-in-2026-Recognition-not-rankings-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/SEOs-new-goal-in-2026-Recognition-not-rankings-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Visibility today depends on authority, citations, entity clarity, and brand presence across the broader web — not just SERP position.


For the best part of two decades, we had a clear and accepted mandate: Get your brand to the top of the search results page. The problem was understood, the success metrics were agreed upon, and a supporting ensemble of tools, talent, and tactics was built around solving it. 
Rankings were the scoreboard. Position 1 meant visibility. Traffic followed, and a brand’s value seemed to follow it.
It’s this core premise that is now under serious renegotiation with the search landscape changing more in the past 18 months than in the previous 10 years combined: 
AI Overviews are absorbing queries that previously generated clicks. 
AI/LLM platforms are becoming the first stop for research and decision-making. 
Zero-click is no longer a niche concern. It’s increasingly becoming the default.
What’s required now isn’t a new set of tactics. It’s a fundamental change in mindset. This is the SEO problem of 2026. Let me show you why recognition is your new goal and how to earn it.
The world changed faster than we did
SEO has always been a discipline that chases the algorithm.
We reverse-engineered signals, built strategies around them, and then scrambled to adapt when they shifted. Yes, there has always been the argument that if you cater your content to humans, you typically perform well. 
That said, there have been obvious shifts in the types of content that resonate with the algorithm and those that don’t, dictated by changes to the Google algorithm at specific times.  
It was never a perfect or complete system; anyone who worked through (or has since learned about) the Panda and Penguin years will tell you the algorithm was always a shifting target. But the fundamentals remained stable. Aim to rank well, get found, win.
The shift we’re living through now isn’t a Google core update. Instead, we’re experiencing a structural change in how information is surfaced, interacted with, and ultimately trusted. 

  
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AI has fundamentally transformed what searchers see
There’s a mental model baked into traditional SEO: If you’re at the top of the SERP, you’re visible. That model was accurate for a long time. But it isn’t now.
AI and LLM platforms — whether Google’s own generative features or external tools like ChatGPT, Perplexity, or Claude — don’t crawl the SERP and pick from the top results. They build understanding from training data, citation patterns, entity relationships in knowledge graphs, and signals about who is genuinely considered authoritative on a given topic. 
A high-ranking page can be largely invisible to these systems if the brand behind it hasn’t established recognition and preference (a.k.a., the quality of being known, cited, and trusted beyond its own domain).
Dig deeper: Entity-first SEO: How to align content with Google’s Knowledge Graph



Ranking no longer equals visibility
If your instinct is to treat it like another algorithm update, to find the new signals, maybe even game the new system, you are missing how dramatically the search landscape has shifted.
Think about it this way:
A brand can rank No. 1 for vital trophy keywords.
Their domain authority is strong.
Their technical SEO is clean, meeting best practices.
Their content team publishes weekly.
Their link profile is healthy.
By every traditional metric, this brand would be seen as winning. And yet, when their potential customers ask an AI or LLM platform which brand solutions to consider in their category, this brand doesn’t come up.
When Google’s AI Overview summarizes the landscape, it cites three competitors. When a journalist writes a roundup and asks an LLM to help research it, this brand is invisible.
They rank. Yet it’s as if they don’t exist — because ranking well doesn’t solve for recognition.
Even if the dashboards still report rankings and the tools still track positions one through ten, optimizing for a metric that’s losing its meaning is no longer a viable strategy.
User behavior is also changing
A growing share of search journeys now end before a user ever clicks a result, because they get the information they need without having to click through. 
AI Overviews takes the majority of the headlines for this, but there has also been a huge shift in the SERP towards featured snippet expansions. This is further amplified by the adoption of  LLM-powered assistants that surface direct answers outside the traditional search environment.
Meanwhile, queries are increasingly conversational, with more and more users asking AI tools questions the way they’d ask a knowledgeable colleague or trusted friend, and they’re expecting thorough, contextualized, and personalized answers rather than a list of blue links.
In this world, the question your SEO strategy needs to answer is no longer “how do I rank?”, it’s “Is my brand the preferred option in the conversation?” 
And these are absolutely different questions that require different answers.
How AI ‘chooses’ brands to recognize
Think about how an AI model decides what to say when someone asks, “What’s the best CRM for a small B2B team?” It doesn’t run a Google search and summarize the top result. It draws on patterns it sees throughout the knowledge at its disposal:
Training data.
Industry publications.
Reviews.
Expert commentary.
Forum discussions.
Solution comparisons.
The brands that appear in that answer are the ones that have accumulated recognition across the broader landscape, not just the one that ranks. 
This is becoming an invisible tax on brands that have focused exclusively on rankings. They may dominate the SERP today. But in the AI-mediated version of that same query, they’re absent.
“Recognition” doesn’t have to be a vague brand concept. It has specific, measurable components. Let’s break them down.
Brand awareness across the search universe
This is the most basic layer. Does your brand name appear, in context, across the search universe?
Not just on your own domain, but in industry publications, analyst reports, user reviews, forum discussions, podcast transcripts, and news coverage. You must also consider where audiences are spending time, because they are developing brand awareness on social-search destinations, too.
AI and LLM platforms are increasingly trained on and drawing from the wider internet when answering questions. Certain domains are massively outperforming others in terms of citations from these platforms, Semrush found. 
If your brand is only present on your own website, you’re harder to find and aren’t in the platforms’ go-to sources.
Topical authority 
This goes beyond keyword rankings. Topical authority means that when a given subject area comes up, your brand is consistently associated with it — not just by Google’s algorithms, but by writers, analysts, content creators, and communities. 
It’s the difference between a site that covers a topic and a brand that owns the conversation in people’s minds who discuss it.
The signal here isn’t domain authority. It’s authority, trust, and relevance (a.k.a., preference). You are asking, “Does our brand appear alongside the recognized leaders in our space?” and “When people discuss an essential topic, are we in the conversation?”
Dig deeper: Why topical authority isn’t enough for AI search 



Entity clarity
This is the most technical layer and the one most often overlooked. An “entity” in SEO terms is a clearly defined, consistently described “thing.” This could be: 
Your company.
Your product.
Key voice or person. 
Key topic or conversation.
Put simply, it’s something that knowledge systems can reliably identify and categorize.
If your brand’s description varies across your site, your Wikipedia page (if you have one), your Google Business Profile, your Crunchbase entry, and your LinkedIn page, you create ambiguity for every system. 
This is as confusing for your human audience as it is for the AI/LLM layer trying to understand who you are and what you do.
Entity clarity means having a canonical, consistent answer to the questions:
What is this company?
What does it do?
Who does it serve?
How is it different?
Brands with strong entity clarity get pulled into knowledge graphs. They get cited. They get recognized.
Dig deeper: From links to brand signals: The new SEO authority model




	
	
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6 things to get you started on the path to recognition
True recognition cannot be built overnight. Instead, your focus is on engineering discovery that develops recognition over time. With that in mind, here are six ways to begin the process:
1. Audit your entity presence
Go and look at how your brand is described in the places that matter: 
Google’s Knowledge Panel. 
Wikipedia (if applicable). 
Wikidata.
Social media conversations.
Key person/business LinkedIn profiles.
Your own “About” page. 
You should be asking if the messaging here is consistent. If your homepage describes you as “an AI-powered B2B sales platform” while the content you discuss and share on your YouTube says “CRM software for startups,” you have an entity problem. 
2. Fix the inconsistencies
Write a canonical description of your company — one clear, accurate, jargon-free paragraph — and work to get it reflected everywhere. Then mold the content format to the needs of the various platforms you want to show up on.
Alongside this, decide which conversations are most important to your brand and consistently look to own these topics. This is part engineering discovery, but it’s also developing your entity and the topics that contribute to that.
Dig deeper: Why entity authority is the foundation of AI search visibility



3. Create citable assets
There’s a difference between content that ranks on a SERP and content that gets cited.
Ranking content is optimized around keywords, and too often, content has become homogenized in trying to meet the expectations of an algorithm so that you can rank.
Citable content, on the other hand, is original, specific, and useful enough that other people (and AI/LLM platforms) want to reference it. Citable content is strong enough that your audience feels like they miss an integral part of a conversation by not featuring or citing the asset or source. 
Think original research and surveys, clear and ownable frameworks or methodologies, definitions that don’t yet exist clearly in your space, and data that journalists, analysts, creators, and bloggers actually want to quote or build upon.
If the only content on your site are search-optimized blog posts, ask yourself: 
Is there anything here that a writer at a key niche publication or a researcher at a relevant public body would want to cite? 
Is there anything that a content creator would want to build upon or explore further? 
If the answer is no, that’s the gap to close.
4. Build off-site recognition deliberately
This isn’t about traditional link building. It’s about building presence in the right conversations, be that industry publications, podcasts, analyst briefings, conference talks, social content, or community forums.
Every time your brand name appears in a meaningful context outside your own domain, you’re building the recognition signal that AI and LLMs draw on and that resonates with humans in the journey.
Prioritize quality of context over volume. A single, substantive mention in a respected publication is worth more than fifty low-quality directory listings.
5. Optimize for clarity and intent
A keyword is a moment. Intent is a journey. Traditional SEO has trained us to think in snapshots: a user types a query, we rank for it, we win. 
But a real buying journey in 2026 looks nothing like that. It might start with a conversational AI query, move through a Reddit thread, surface a YouTube comparison, hit a review platform, and only then arrive at a branded search. The keyword at any single point is almost beside the point.
What matters is whether your brand shows up meaningfully across the full arc of that journey — not just at the moment someone is ready to convert.
Start by mapping intent honestly. 
What is someone actually trying to understand when they enter your space? 
What does the journey from problem-aware to solution-decided look like for your customer? 
Then audit where your brand is present, absent, or ambiguous across it.
The second part is clarity. As search becomes more conversational and AI-mediated, the brands that get surfaced are those that clearly communicate what they do, who they serve, and why they’re the right choice — consistently across every touchpoint. 
Vague positioning might survive a keyword-match algorithm. It won’t survive a language model deciding whether your brand is the right answer to a specific human question.
Be specific and consistent. Make sure your description holds up whether someone finds you on your own site, in a third-party review, or in an AI-generated summary.
Dig deeper: If you can’t say what problem your brand solves, AI won’t either



6. Start measuring recognition
Your current reporting probably tracks keyword rankings, organic traffic, and backlinks. I would argue that this should continue, but there should be a shift in the importance of these metrics versus the following signal:
[Brand] search volume: Are more people searching directly for you?
[Brand] + [Intent or Keyword]: Are more people associating you with specific topics?
Unlinked mentions: Is your brand name appearing in content that doesn’t link to you?
You can then use the following alongside these and begin to further understand if your brand is being recognized:
Increase in referral traffic.
Increase in direct traffic.
Increase in quality of traffic (measured in longer sessions, per user increase in pages viewed, purchases earlier in the journey).
This will then allow you to look towards the most important SEO metric there should ever be: revenue. Especially if you can assess and report on the development of average order value (AOV) and lifetime value (LTV) or the specific values of the pages that have seen higher traffic because of an increase in unlinked mentions and/or brand searches.
When you begin to think about these considerations, the most important shift isn’t adding new metrics to your dashboard. It’s changing what you treat as the primary signal.
Branded search volume, specifically branded search paired with intent, is one of the clearest indicators of genuine preference in the user journey and also the competitive landscape. 
Someone searching for you by name, combined with a buying signal, isn’t discovering you. They’ve already decided you’re worth considering. That’s recognition doing its job.
The goal is to grow that signal deliberately, and then make sure that when someone arrives with that intent, you meet it head on.
A branded intent search that lands on a generic homepage is a wasted moment. These users are telling you exactly what they need. Your job as an SEO in 2026 is to have already built the page, the answer, the experience that closes the gap.
The supporting metrics — unlinked mentions, referral traffic, direct traffic, AOV, LTV — all tell you whether recognition is compounding into something commercially meaningful. 
And that’s ultimately the conversation that needs to happen in every boardroom and strategy session: Recognition isn’t a brand vanity play, it’s a revenue strategy.
Rankings as the primary focus have gotten us so far. Recognition, with a view and monitoring mindset on the signals identified here, is what takes us, the SEO’s role and importance to brands further than ever before.

  
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Here’s the uncomfortable truth about the recognition-first approach: It’s slower.
You can’t optimize your way to being well-known in the same way you can optimize your way to a ranking — and I think it’s what’s most intimidating to SEOs.
Recognition compounds over time, developed through consistent presence, genuine authoritativeness, relevance, and the slow accumulation of trustworthiness. But that’s also what makes it durable. 
Rankings fluctuate with every algorithm update, and the value of a No. 1 ranking is seemingly shrinking with every update due to the continued and increasing number of SERP features and AI/LLM integrations into the SERP.
Recognition, though, once established, is much harder to displace. To own AI-mediated search in the coming years, spend this period building something that AI systems — and the increasing number of humans utilizing them — genuinely recognize as authoritative.
The No. 1 ranking is a vanity metric if it ends up below the fold, stuck under a SERP of AI/LLM integrations and SERP features — ultimately ensuring nobody knows who you are.
Start building recognition. Your appearance in those top-of-page SERP features and AI/LLM integrations will follow.

Visibility today depends on authority, citations, entity clarity, and brand presence across the broader web — not just SERP position.

SEO’s new goal in 2026: Recognition, not rankings

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Why-intent-alignment-matters-more-than-perfect-technical-SEO.png" class="attachment-large size-large wp-post-image" alt="Why intent alignment matters more than perfect technical SEO" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Why-intent-alignment-matters-more-than-perfect-technical-SEO.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Why-intent-alignment-matters-more-than-perfect-technical-SEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Why-intent-alignment-matters-more-than-perfect-technical-SEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Why-intent-alignment-matters-more-than-perfect-technical-SEO-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Technical fixes alone won’t resolve weak relevance signals. Here’s how to identify and correct intent mismatches across your site.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Why-intent-alignment-matters-more-than-perfect-technical-SEO.png" class="attachment-large size-large wp-post-image" alt="Why intent alignment matters more than perfect technical SEO" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Why-intent-alignment-matters-more-than-perfect-technical-SEO.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Why-intent-alignment-matters-more-than-perfect-technical-SEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Why-intent-alignment-matters-more-than-perfect-technical-SEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Why-intent-alignment-matters-more-than-perfect-technical-SEO-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Improving <a href="https://searchengineland.com/guide/what-is-technical-seo">technical SEO</a> on your site may not be enough to move the needle these days.&nbsp;</p>



<p>Once a site reaches technical parity with its competitors — the point at which a proper infrastructure no longer gives you an advantage — Google shifts its ranking criteria toward relevance. And relevance is determined by aligning with search intent.&nbsp;</p>



<p>Let’s talk about how to make your site more relevant.</p>



<h2 class="wp-block-heading" id="h-why-an-intent-mismatch-may-be-suppressing-your-site-s-performance">Why an intent mismatch may be suppressing your site’s performance</h2>



<p>An intent mismatch occurs when the copy on a page doesn’t match what the user is expecting to find on it. This happens when pages aren’t relevant to a topic or have mismatched signals.</p>



<p>This generates poor behavior signals — users click through from a SERP, see that the page doesn’t answer their need, and leave. Google interprets these signals as evidence that the page doesn’t satisfy the query.&nbsp;</p>



<p>This can lead to a decline in rankings, which means fewer users see the page, which means the behavioral signals worsen. It’s a feedback loop that technical SEO alone can’t resolve.</p>



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<h2 class="wp-block-heading" id="h-technical-seo-improvements-may-no-longer-make-a-difference">Technical SEO improvements may no longer make a difference</h2>



<p>In the early stages of implementing an SEO strategy, the needle can move quickly. If a site is operating below the technical baseline needed for Google to properly evaluate it, applying simple fixes — such as fixed crawl errors, resolved duplicate content issues, improved page speed, and adding schema — can produce big gains.</p>



<p>However, after these changes, your site’s technical foundations are now comparable to those of your main competitors — you hit a ceiling. Now, Google isn’t ranking pages based on which ones it can access the easiest, but on those that best satisfy the user’s query.&nbsp;</p>



<p>Your technical infrastructure, or lack thereof, no longer disadvantages you, but now the rules of the ranking game have changed.</p>



<p>This is where intent alignment becomes the primary lever for improvement.&nbsp;</p>



<h2 class="wp-block-heading" id="h-signals-that-reinforce-search-intent">Signals that reinforce search intent</h2>



<p>Elements that have an impact on a page’s intent, and how Google decides whether the intent matches the page, include:&nbsp;</p>



<ul class="wp-block-list">
<li>Click-through rate.</li>



<li>Engagement signals.</li>



<li>Core Web Vitals.</li>



<li>Schema type.</li>



<li>Internal linking anchor texts.</li>



<li>URL structure.</li>
</ul>



<h3 class="wp-block-heading" id="h-click-through-rate-ctr">Click-through rate (CTR)</h3>



<p><a href="https://searchengineland.com/guide/clickthrough-rate-ctr">Click-through rate</a> can be determined by your title tag, meta description, URL structure, and schema. It is also measured against intent.&nbsp;</p>



<p>For example, if your title tag is optimized for a keyword but doesn’t match the user’s query, your CTR will drop. Google treats a low CTR as a relevance signal and adjusts rankings accordingly.</p>



<h3 class="wp-block-heading" id="h-engagement-rate">Engagement rate</h3>



<p>Time-on-page, scroll depth, and interaction rates can suffer when intent doesn’t align with a page.&nbsp;</p>



<p>If a user is searching to purchase something but lands on a how-to guide, they may exit that page within seconds. The same can be said of a user looking for an emergency plumber who lands on a page without a phone number.&nbsp;</p>



<p>Engagement signals feed directly into how Google evaluates a page’s usefulness for a given query.</p>



<h3 class="wp-block-heading" id="h-core-web-vitals-cwv">Core Web Vitals (CWV)</h3>



<p>The three <a href="https://searchengineland.com/guide/core-web-vitals">Core Web Vitals</a> — Largest Contentful Paint (LCP), Interaction to Next Paint (INP), and Cumulative Layout Shift (CLS) — determine page loading speed.</p>



<p>A transactional page that loads slowly suffers more than a slow-loading informational article. With the transactional page, the user is ready to buy and their patience is minimal, whereas a reader in research mode can tolerate a longer wait.&nbsp;</p>



<p>CWV thresholds matter everywhere, but their impact on conversion and bounce behavior is greater on high-intent pages.&nbsp;</p>



<h3 class="wp-block-heading" id="h-schema-type">Schema type</h3>



<p><a href="https://searchengineland.com/guide/what-is-schema-markup">Schema markup</a> tells Google explicitly what type of content is on a page. Generally:</p>



<ul class="wp-block-list">
<li>Article/HowTo is informational.</li>



<li>Product is transactional.</li>



<li>FAQ is informational and commercial.</li>



<li>Local business/event is navigational.</li>
</ul>



<p>When schema type contradicts the content on a page, Google gets a conflicting signal, resulting in a traffic drop.</p>



<h3 class="wp-block-heading" id="h-internal-linking-anchor-texts">Internal linking anchor texts</h3>



<p>The <a href="https://searchengineland.com/guide/anchor-text">anchor text</a> of internal links tells Google about the page that’s being linked to, including its intent.&nbsp;</p>



<p>If a transactional landing page receives internal links with informational anchor text — “learn more about X,” rather than “get a quote for X” or “buy X” —&nbsp; the intent signal Google receives about that page’s purpose gets diluted.</p>



<h3 class="wp-block-heading" id="h-url-structure">URL structure</h3>



<p>Google uses URL patterns to infer page type.&nbsp;</p>



<p>For example, URLs sitting under /blog/ are treated with informational bias. A product or service page buried under a blog path fights against that structural expectation, regardless of its content, and it may not rank well.&nbsp;</p>



<h3 class="wp-block-heading" id="h-cannibalization-and-canonicalization">Cannibalization and canonicalization</h3>



<p>If your site has multiple pages targeting the same keyword but with different intents, neither is likely to rank well. They compete against each other and dilute the signal Google receives.&nbsp;</p>



<p>To fix, use <a href="https://searchengineland.com/canonicalization-seo-448161">canonical tags</a> to clearly signal which page is the preferred one for a given keyword, consolidate or redirect competing pages where appropriate, and ensure your internal linking reinforces the canonical choice.</p>



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<h2 class="wp-block-heading" id="h-how-to-fix-intent-misalignment">How to fix intent misalignment</h2>



<p>Here’s an example of a common intent mismatch and some steps to audit your content and fix it.&nbsp;</p>



<h3 class="wp-block-heading" id="h-what-an-intent-mismatch-looks-like">What an intent mismatch looks like</h3>



<p>For example, if a user searches for “financial analysis software,” they’re looking to buy software. The keyword phrase is highly transactional.&nbsp;</p>



<p>But if your site targets this keyword phrase for an informational blog post that explains how a person can complete a financial analysis report themselves, this creates a mismatch.</p>



<p>The user is looking for a product that does the analysis for them, which means they want to compare features, understand pricing, see integrations, or book a demo.&nbsp;</p>



<p>The keyword phrase should be applied to a dedicated product or landing page that clearly outlines functionality, benefits, use cases, and pricing. This would align more with the user’s needs, resulting in more inquiries, leads, and conversions.</p>



<h3 class="wp-block-heading" id="h-identify-the-intent-of-your-pages">Identify the intent of your pages</h3>



<p>To fix intent mismatches, to start, compile a list of the top performing keywords that best describe your business and manually check the Google rankings for each.</p>



<p>This initial research will tell you exactly what type of page and copy you should have for these keywords. For example:</p>



<ul class="wp-block-list">
<li>Knowledge panels, AI Overviews, and People Also Ask boxes usually appear for informational searches.</li>



<li>Paid results usually suggest commercial intent.</li>



<li>Shopping feeds suggest a transactional keyword.</li>
</ul>



<p>Next, add the keywords to a spreadsheet and add a column for intent. Work down the list, adding whether you think the page is informational, commercial, transactional, or navigational.&nbsp;</p>



<p>You can then create another column that states the type of page that will rank well:&nbsp;</p>



<ul class="wp-block-list">
<li><strong>Informational:</strong> Blog or resource content.</li>



<li><strong>Commercial: </strong>Service or landing pages.</li>



<li><strong>Transactional:</strong> Collection, category, or product pages.</li>



<li><strong>Navigational:</strong> Brand, specific service, or specific location pages.</li>
</ul>



<h3 class="wp-block-heading" id="h-see-what-your-competitors-are-doing">See what your competitors are doing</h3>



<p>Research your competitors’ pages for the keywords you’re targeting. Analyze and note what they have that your pages don’t have.</p>



<p>They may have:</p>



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



<li>Comparisons.</li>



<li>Calculators.</li>



<li>Tools.</li>



<li>FAQs.</li>



<li>Reviews.</li>



<li>Step-by-steps.</li>



<li>Images.</li>



<li>Videos.</li>



<li>And more. </li>
</ul>



<p>Consider how to improve your own pages to match theirs.&nbsp;</p>



<h3 class="wp-block-heading" id="h-measure-your-page-s-performance-based-on-intent-metrics">Measure your page’s performance based on intent metrics</h3>



<p>Once you’ve made changes to your pages, track their performance to see whether they helped. Look at:</p>



<ul class="wp-block-list">
<li>Clicks and impressions for intent-aligned keywords.</li>



<li>Rankings for core target queries.</li>



<li>Time on page.</li>



<li>Conversion rates, particularly those of previously underperforming pages.</li>
</ul>



<h2 class="wp-block-heading" id="h-technical-seo-still-plays-a-decisive-role">Technical SEO still plays a decisive role</h2>



<p>Technical SEO is still important, especially for complex, enterprise-scale sites. Here are some ways that technical SEO work can still move the needle significantly, in ways that content optimization alone can’t.</p>



<h3 class="wp-block-heading" id="h-crawl-budget-management">Crawl budget management</h3>



<p>An ecommerce site with thousands of URLs can have its <a href="https://searchengineland.com/guide/crawl-budget">crawl budget</a> consumed by low-value pages before its allotment reaches high-intent category and product pages that you want to rank.&nbsp;</p>



<p>Cleaning up low-value pages is purely technical work and will ensure your crawl budget goes toward pages that count.&nbsp;</p>



<h3 class="wp-block-heading" id="h-international-site-architecture">International site architecture</h3>



<p>Technical SEO is crucial when handling international sites that contain pages in multiple languages.&nbsp;</p>



<p>A keyword that’s purely informational in one market may be transactional in another, reflecting different buyer behaviors and levels of market maturity. <a href="https://searchengineland.com/guide/what-is-hreflang">Hreflang</a> implementation, regional subdomain or subdirectory structures, and URL strategies all affect whether the right page, with the right intent, reaches the right audience.</p>



<h3 class="wp-block-heading" id="h-log-file-analysis">Log file analysis</h3>



<p>A <a href="https://searchengineland.com/log-file-analysis-ai-crawlers-search-visibility-474428">log file analysis</a> will reveal which pages Google is successfully crawling and how frequently they are. For sites with intent alignment problems, Google often spends a disproportionate amount of attention crawling low-value or misaligned pages, while high-intent pages are visited infrequently.&nbsp;</p>



<p>For small sites with a clean structure and limited number of URLs, technical SEO can reach parity quickly, so the need to shift to intent alignment happens sooner. For large, complex sites, technical and intent work often need to happen in parallel.</p>



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<h2 class="wp-block-heading" id="h-technical-seo-and-intent-need-to-work-together">Technical SEO and intent need to work together</h2>



<p>Technical SEO is still important today — think of it as a foundation that the rest of the site sits on. Pages that can’t be crawled, indexed, or rendered correctly will be unable to rank, regardless of how well their content matches user intent.</p>



<p>Think of intent alignment as the ceiling — it’s what determines how high a technically sound page can rank, and whether it converts the traffic it earns.&nbsp;</p>



<p>Every page on a site should have a clearly defined intent, expressed in the right format, with the right content type. And they should also be supported by technical signals, be it schema, URL structure, relevant anchor text, etc., so that the page’s intent is constantly reinforced.&nbsp;</p>

Improving technical SEO on your site may not be enough to move the needle these days. 
Once a site reaches technical parity with its competitors — the point at which a proper infrastructure no longer gives you an advantage — Google shifts its ranking criteria toward relevance. And relevance is determined by aligning with search intent. 
Let’s talk about how to make your site more relevant.
Why an intent mismatch may be suppressing your site’s performance
An intent mismatch occurs when the copy on a page doesn’t match what the user is expecting to find on it. This happens when pages aren’t relevant to a topic or have mismatched signals.
This generates poor behavior signals — users click through from a SERP, see that the page doesn’t answer their need, and leave. Google interprets these signals as evidence that the page doesn’t satisfy the query. 
This can lead to a decline in rankings, which means fewer users see the page, which means the behavioral signals worsen. It’s a feedback loop that technical SEO alone can’t resolve.
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Technical SEO improvements may no longer make a difference
In the early stages of implementing an SEO strategy, the needle can move quickly. If a site is operating below the technical baseline needed for Google to properly evaluate it, applying simple fixes — such as fixed crawl errors, resolved duplicate content issues, improved page speed, and adding schema — can produce big gains.
However, after these changes, your site’s technical foundations are now comparable to those of your main competitors — you hit a ceiling. Now, Google isn’t ranking pages based on which ones it can access the easiest, but on those that best satisfy the user’s query. 
Your technical infrastructure, or lack thereof, no longer disadvantages you, but now the rules of the ranking game have changed.
This is where intent alignment becomes the primary lever for improvement. 
Signals that reinforce search intent
Elements that have an impact on a page’s intent, and how Google decides whether the intent matches the page, include: 
Click-through rate.
Engagement signals.
Core Web Vitals.
Schema type.
Internal linking anchor texts.
URL structure.
Click-through rate (CTR)
Click-through rate can be determined by your title tag, meta description, URL structure, and schema. It is also measured against intent. 
For example, if your title tag is optimized for a keyword but doesn’t match the user’s query, your CTR will drop. Google treats a low CTR as a relevance signal and adjusts rankings accordingly.
Engagement rate
Time-on-page, scroll depth, and interaction rates can suffer when intent doesn’t align with a page. 
If a user is searching to purchase something but lands on a how-to guide, they may exit that page within seconds. The same can be said of a user looking for an emergency plumber who lands on a page without a phone number. 
Engagement signals feed directly into how Google evaluates a page’s usefulness for a given query.
Core Web Vitals (CWV)
The three Core Web Vitals — Largest Contentful Paint (LCP), Interaction to Next Paint (INP), and Cumulative Layout Shift (CLS) — determine page loading speed.
A transactional page that loads slowly suffers more than a slow-loading informational article. With the transactional page, the user is ready to buy and their patience is minimal, whereas a reader in research mode can tolerate a longer wait. 
CWV thresholds matter everywhere, but their impact on conversion and bounce behavior is greater on high-intent pages. 
Schema type
Schema markup tells Google explicitly what type of content is on a page. Generally:
Article/HowTo is informational.
Product is transactional.
FAQ is informational and commercial.
Local business/event is navigational.
When schema type contradicts the content on a page, Google gets a conflicting signal, resulting in a traffic drop.
Internal linking anchor texts
The anchor text of internal links tells Google about the page that’s being linked to, including its intent. 
If a transactional landing page receives internal links with informational anchor text — “learn more about X,” rather than “get a quote for X” or “buy X” —  the intent signal Google receives about that page’s purpose gets diluted.
URL structure
Google uses URL patterns to infer page type. 
For example, URLs sitting under /blog/ are treated with informational bias. A product or service page buried under a blog path fights against that structural expectation, regardless of its content, and it may not rank well. 
Cannibalization and canonicalization
If your site has multiple pages targeting the same keyword but with different intents, neither is likely to rank well. They compete against each other and dilute the signal Google receives. 
To fix, use canonical tags to clearly signal which page is the preferred one for a given keyword, consolidate or redirect competing pages where appropriate, and ensure your internal linking reinforces the canonical choice.
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How to fix intent misalignment
Here’s an example of a common intent mismatch and some steps to audit your content and fix it. 
What an intent mismatch looks like
For example, if a user searches for “financial analysis software,” they’re looking to buy software. The keyword phrase is highly transactional. 
But if your site targets this keyword phrase for an informational blog post that explains how a person can complete a financial analysis report themselves, this creates a mismatch.
The user is looking for a product that does the analysis for them, which means they want to compare features, understand pricing, see integrations, or book a demo. 
The keyword phrase should be applied to a dedicated product or landing page that clearly outlines functionality, benefits, use cases, and pricing. This would align more with the user’s needs, resulting in more inquiries, leads, and conversions.
Identify the intent of your pages
To fix intent mismatches, to start, compile a list of the top performing keywords that best describe your business and manually check the Google rankings for each.
This initial research will tell you exactly what type of page and copy you should have for these keywords. For example:
Knowledge panels, AI Overviews, and People Also Ask boxes usually appear for informational searches.
Paid results usually suggest commercial intent.
Shopping feeds suggest a transactional keyword.
Next, add the keywords to a spreadsheet and add a column for intent. Work down the list, adding whether you think the page is informational, commercial, transactional, or navigational. 
You can then create another column that states the type of page that will rank well: 
Informational: Blog or resource content.
Commercial: Service or landing pages.
Transactional: Collection, category, or product pages.
Navigational: Brand, specific service, or specific location pages.
See what your competitors are doing
Research your competitors’ pages for the keywords you’re targeting. Analyze and note what they have that your pages don’t have.
They may have:
Tables.
Comparisons.
Calculators.
Tools.
FAQs.
Reviews.
Step-by-steps.
Images.
Videos.
And more. 
Consider how to improve your own pages to match theirs. 
Measure your page’s performance based on intent metrics
Once you’ve made changes to your pages, track their performance to see whether they helped. Look at:
Clicks and impressions for intent-aligned keywords.
Rankings for core target queries.
Time on page.
Conversion rates, particularly those of previously underperforming pages.
Technical SEO still plays a decisive role
Technical SEO is still important, especially for complex, enterprise-scale sites. Here are some ways that technical SEO work can still move the needle significantly, in ways that content optimization alone can’t.
Crawl budget management
An ecommerce site with thousands of URLs can have its crawl budget consumed by low-value pages before its allotment reaches high-intent category and product pages that you want to rank. 
Cleaning up low-value pages is purely technical work and will ensure your crawl budget goes toward pages that count. 
International site architecture
Technical SEO is crucial when handling international sites that contain pages in multiple languages. 
A keyword that’s purely informational in one market may be transactional in another, reflecting different buyer behaviors and levels of market maturity. Hreflang implementation, regional subdomain or subdirectory structures, and URL strategies all affect whether the right page, with the right intent, reaches the right audience.
Log file analysis
A log file analysis will reveal which pages Google is successfully crawling and how frequently they are. For sites with intent alignment problems, Google often spends a disproportionate amount of attention crawling low-value or misaligned pages, while high-intent pages are visited infrequently. 
For small sites with a clean structure and limited number of URLs, technical SEO can reach parity quickly, so the need to shift to intent alignment happens sooner. For large, complex sites, technical and intent work often need to happen in parallel.
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Technical SEO and intent need to work together
Technical SEO is still important today — think of it as a foundation that the rest of the site sits on. Pages that can’t be crawled, indexed, or rendered correctly will be unable to rank, regardless of how well their content matches user intent.
Think of intent alignment as the ceiling — it’s what determines how high a technically sound page can rank, and whether it converts the traffic it earns. 
Every page on a site should have a clearly defined intent, expressed in the right format, with the right content type. And they should also be supported by technical signals, be it schema, URL structure, relevant anchor text, etc., so that the page’s intent is constantly reinforced.

Technical fixes alone won’t resolve weak relevance signals. Here’s how to identify and correct intent mismatches across your site.

Why intent alignment matters more than perfect technical SEO

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-800x450.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-550x310.jpg 550w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Advertisers can now create more tailored metrics using full conversion data, aligning reporting with business goals.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-800x450.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920-550x310.jpg 550w, https://searchengineland.com/wp-content/seloads/2019/07/Microsoft_Ads_1920.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Microsoft Advertising is giving advertisers more flexibility in reporting, with custom columns now supporting all conversion metrics — a move aimed at deeper, more tailored campaign analysis.</p>



<p><strong>What’s happening</strong>. According to Microsoft&#8217;s product liaison Navah Hopkins, advertisers can now build custom metrics using the full range of conversion data available in the platform.</p>



<p>This includes both all conversions and primary conversions, allowing marketers to align reporting more closely with their specific goals.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1196" height="1058" src="https://searchengineland.com/wp-content/seloads/2026/05/Microsoft-custom-columns.jpeg" alt="" class="wp-image-476846" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Microsoft-custom-columns.jpeg 1196w, https://searchengineland.com/wp-content/seloads/2026/05/Microsoft-custom-columns-768x679.jpeg 768w" sizes="(max-width: 1196px) 100vw, 1196px" /></figure>



<p><strong>Why we care</strong>. Standard reporting often doesn’t reflect how businesses actually measure success. By expanding custom columns, Microsoft is enabling advertisers to create metrics that better reflect their own performance definitions — whether that’s based on lead quality, revenue or blended conversion actions.</p>



<p>This is especially useful for advertisers managing multiple conversion types or complex funnels.</p>



<p><strong>More control over performance metrics</strong>. Advertisers can now create custom columns using ratios and combinations of metrics that matter most to them — such as cost per qualified lead, blended CPA or conversion rate based on primary goals.</p>



<p>Revenue and ROAS calculations will also reflect the values set at the conversion goal level, giving more accurate insights tied to business outcomes.</p>



<p><strong>Between the lines</strong>. This update signals a shift toward more flexible, advertiser-defined measurement — rather than relying solely on platform-standard metrics.</p>



<p>It also reflects ongoing demand for better reporting customisation as campaigns become more automated and complex.</p>



<p><strong>What to watch:</strong>



<ul class="wp-block-list">
<li>How advertisers use custom metrics to guide optimisation decisions</li>



<li>Whether this leads to more consistent reporting across teams and stakeholders</li>



<li>If similar flexibility expands across other areas of the platform</li>
</ul>



<p><strong>Bottom line</strong>.  <a href="https://www.linkedin.com/feed/update/urn:li:activity:7458131053925490689/">Microsoft is giving advertisers more control over how they measure success</a> — turning custom columns into a more powerful tool for campaign analysis.</p>


Microsoft Advertising is giving advertisers more flexibility in reporting, with custom columns now supporting all conversion metrics — a move aimed at deeper, more tailored campaign analysis.
What’s happening. According to Microsoft’s product liaison Navah Hopkins, advertisers can now build custom metrics using the full range of conversion data available in the platform.
This includes both all conversions and primary conversions, allowing marketers to align reporting more closely with their specific goals.




Why we care. Standard reporting often doesn’t reflect how businesses actually measure success. By expanding custom columns, Microsoft is enabling advertisers to create metrics that better reflect their own performance definitions — whether that’s based on lead quality, revenue or blended conversion actions.
This is especially useful for advertisers managing multiple conversion types or complex funnels.
More control over performance metrics. Advertisers can now create custom columns using ratios and combinations of metrics that matter most to them — such as cost per qualified lead, blended CPA or conversion rate based on primary goals.
Revenue and ROAS calculations will also reflect the values set at the conversion goal level, giving more accurate insights tied to business outcomes.
Between the lines. This update signals a shift toward more flexible, advertiser-defined measurement — rather than relying solely on platform-standard metrics.
It also reflects ongoing demand for better reporting customisation as campaigns become more automated and complex.
What to watch:




How advertisers use custom metrics to guide optimisation decisions
Whether this leads to more consistent reporting across teams and stakeholders
If similar flexibility expands across other areas of the platform
Bottom line.  Microsoft is giving advertisers more control over how they measure success — turning custom columns into a more powerful tool for campaign analysis.

Advertisers can now create more tailored metrics using full conversion data, aligning reporting with business goals.

Microsoft Ads expands custom columns to include all conversion metrics

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg" class="attachment-large size-large wp-post-image" alt="In Google Ads automation, everything is a signal in 2026" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Advertisers are questioning AI Max’s lack of granular landing page control compared to DSA, as Google confirms some gaps still exist.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg" class="attachment-large size-large wp-post-image" alt="In Google Ads automation, everything is a signal in 2026" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/In-Google-Ads-automation-everything-is-a-signal-in-2026-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Advertisers are starting to push back on gaps in AI Max capabilities — particularly around landing page control — as Google continues its shift away from legacy Dynamic Search Ads (DSA).</p>



<p><strong>What’s happening</strong>. In a LinkedIn exchange, digital marketing expert Gabriele Benedetti raised concerns about AI Max lacking the same level of URL-based targeting controls that DSA campaigns offered.</p>



<p>His point: DSA allowed advertisers to structure campaigns around website architecture — using categories, URL paths and page rules to guide where traffic lands. That level of control, he argued, is not yet fully replicated in AI Max.</p>



<p><strong>Why we care. </strong>For many advertisers — especially those managing large or structured websites — aligning campaign structure with site architecture is key to performance. Losing granular control over landing destinations could impact relevance, user experience and ultimately conversion rates.</p>



<p>This highlights a broader tension in Google Ads today: automation vs control.</p>



<p><strong>Google respon</strong>ds. Google Ads Liaison, Ginny Marvin responded, clarifying that AI Max does support several URL-based controls, including:</p>



<ul class="wp-block-list">
<li>URL rules and combinations</li>



<li>Page feeds with custom labels</li>



<li>URL inclusions at ad group level and exclusions at campaign level</li>
</ul>



<p>However, she acknowledged that not all DSA targeting rules are currently supported — such as “page contains” conditions.</p>



<p><strong>Between the lines</strong>. Google is not removing control entirely — but it is reshaping how that control works. Instead of granular rule-building, advertisers are being pushed toward structured inputs like page feeds and labels that AI can interpret.</p>



<p><strong>Migration reality check</strong>. For advertisers moving from DSA to AI Max, existing URL rules will carry over — but with limitations. Unsupported rules will remain active as read-only, meaning they’ll continue to function but cannot be edited.</p>



<p>That’s a temporary bridge, not a long-term solution.</p>



<p><strong>What’s next</strong>. Google says it plans to expand controls further, including bringing content and title-based exclusions to the account level later this year.</p>



<p>This would complement AI Max’s existing “inventory-aware” features, which already exclude out-of-stock items automatically.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1044" height="420" src="https://searchengineland.com/wp-content/seloads/2026/05/AI-Max-update.png" alt="" class="wp-image-476832" srcset="https://searchengineland.com/wp-content/seloads/2026/05/AI-Max-update.png 1044w, https://searchengineland.com/wp-content/seloads/2026/05/AI-Max-update-768x309.png 768w" sizes="(max-width: 1044px) 100vw, 1044px" /></figure>



<p><strong>Bottom line</strong>. AI Max is evolving, but it’s not yet a full replacement for DSA when it comes to granular control — and advertisers are making that clear.</p>



<p><strong>Dig deeper. </strong><a href="https://www.linkedin.com/posts/gabriele-benedetti-0ab02815_googleads-dsa-search-share-7455567056537780224-cej-?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAAAJTV04Bw5M-1qqJtnImkpsEOFw9bHvfSSs">Full discussion on LinkedIn</a>. </p>


Advertisers are starting to push back on gaps in AI Max capabilities — particularly around landing page control — as Google continues its shift away from legacy Dynamic Search Ads (DSA).
What’s happening. In a LinkedIn exchange, digital marketing expert Gabriele Benedetti raised concerns about AI Max lacking the same level of URL-based targeting controls that DSA campaigns offered.
His point: DSA allowed advertisers to structure campaigns around website architecture — using categories, URL paths and page rules to guide where traffic lands. That level of control, he argued, is not yet fully replicated in AI Max.
Why we care. For many advertisers — especially those managing large or structured websites — aligning campaign structure with site architecture is key to performance. Losing granular control over landing destinations could impact relevance, user experience and ultimately conversion rates.
This highlights a broader tension in Google Ads today: automation vs control.
Google responds. Google Ads Liaison, Ginny Marvin responded, clarifying that AI Max does support several URL-based controls, including:
URL rules and combinations
Page feeds with custom labels
URL inclusions at ad group level and exclusions at campaign level
However, she acknowledged that not all DSA targeting rules are currently supported — such as “page contains” conditions.
Between the lines. Google is not removing control entirely — but it is reshaping how that control works. Instead of granular rule-building, advertisers are being pushed toward structured inputs like page feeds and labels that AI can interpret.
Migration reality check. For advertisers moving from DSA to AI Max, existing URL rules will carry over — but with limitations. Unsupported rules will remain active as read-only, meaning they’ll continue to function but cannot be edited.
That’s a temporary bridge, not a long-term solution.
What’s next. Google says it plans to expand controls further, including bringing content and title-based exclusions to the account level later this year.
This would complement AI Max’s existing “inventory-aware” features, which already exclude out-of-stock items automatically.




Bottom line. AI Max is evolving, but it’s not yet a full replacement for DSA when it comes to granular control — and advertisers are making that clear.
Dig deeper. Full discussion on LinkedIn.

Advertisers are questioning AI Max’s lack of granular landing page control compared to DSA, as Google confirms some gaps still exist.

AI Max vs DSA: Advertisers question control as Google responds

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>This is due to the new Google search penalty for back button hijacking.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/07/google-ai-button-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is dropping the back button trigger for AdSense vignette ads on June 15, 2026 due to the new Google search penalty for <a href="https://searchengineland.com/google-search-to-penalize-back-button-hijacking-schemes-474167">back button hijacking</a>. Google <a href="https://support.google.com/adsense/answer/17048667">wrote</a>, &#8220;Starting June 15, 2026, the browser back button will no longer trigger a vignette ad.&#8221;</p>



<p><strong>What is changing. </strong>Google explained that the back button trigger will no longer work after June 15th. The &#8220;change will apply automatically for all publishers who have opted in to &#8220;Allow additional triggers for vignette ads&#8221; and will take effect across all supported browsers (including Chrome, Edge, and Opera).&#8221; Google added.</p>



<p>A Google spokesperson told me these same updates will apply to Ad Manager as well.</p>



<p><strong>Why the change. </strong>Google explained that the Google Search team &#8220;recently introduced <a href="https://developers.google.com/search/blog/2026/04/back-button-hijacking" target="_blank" rel="noopener">a new policy against &#8220;back button hijacking&#8221;</a> — a practice where websites or scripts interfere with a user&#8217;s ability to navigate back to their previous page. To ensure our publishers remain compliant with these latest user experience and search quality guidelines, we are removing the trigger that shows a vignette ad when the user navigates backward from the suite of <a href="https://support.google.com/adsense/answer/16531962#additional-triggers">vignette ad triggers</a>.&#8221;</p>



<p>This comes after the <a href="https://www.seroundtable.com/google-adsense-vignette-ads-back-button-penalty-41238.html">search community called this out</a> to Google and Google is making the right change here.  Of course, some publishers will not be happy because that trigger may have earned them a lot of money.</p>



<p><strong>Why we care. </strong>If you currently have the allow additional triggers for vignette ads setting on with AdSense, keep in mind, one of the triggers, the back button trigger, will be disabled on June 15th. It may impact your earnings, but it will ensure that your site does not get penalized by the <a href="https://searchengineland.com/google-search-to-penalize-back-button-hijacking-schemes-474167">back button hijacking</a> penalty. </p>


Google is dropping the back button trigger for AdSense vignette ads on June 15, 2026 due to the new Google search penalty for back button hijacking. Google wrote, “Starting June 15, 2026, the browser back button will no longer trigger a vignette ad.”
What is changing. Google explained that the back button trigger will no longer work after June 15th. The “change will apply automatically for all publishers who have opted in to “Allow additional triggers for vignette ads” and will take effect across all supported browsers (including Chrome, Edge, and Opera).” Google added.
A Google spokesperson told me these same updates will apply to Ad Manager as well.
Why the change. Google explained that the Google Search team “recently introduced a new policy against “back button hijacking” — a practice where websites or scripts interfere with a user’s ability to navigate back to their previous page. To ensure our publishers remain compliant with these latest user experience and search quality guidelines, we are removing the trigger that shows a vignette ad when the user navigates backward from the suite of vignette ad triggers.”
This comes after the search community called this out to Google and Google is making the right change here.  Of course, some publishers will not be happy because that trigger may have earned them a lot of money.
Why we care. If you currently have the allow additional triggers for vignette ads setting on with AdSense, keep in mind, one of the triggers, the back button trigger, will be disabled on June 15th. It may impact your earnings, but it will ensure that your site does not get penalized by the back button hijacking penalty.

This is due to the new Google search penalty for back button hijacking.

Google AdSense removes browser back button trigger for vignette ads

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control.jpg" class="attachment-large size-large wp-post-image" alt="When Google’s AI bidding breaks – and how to take control" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google is aiming to help advertisers capture untapped demand and optimise spend in real time whilst reducing manual work.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control.jpg" class="attachment-large size-large wp-post-image" alt="When Google’s AI bidding breaks – and how to take control" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control.jpg 1920w, https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2025/12/When-Googles-AI-bidding-breaks-–-and-how-to-take-control-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is rolling out new AI-driven bidding and budgeting features across Search, Shopping and Performance Max — aimed at helping advertisers capture more demand without increasing manual effort.</p>



<p><strong>What’s happening</strong>. Google is expanding its automation stack with updates like Journey-aware Bidding, Smart Bidding Exploration and demand-led budget pacing. Together, these changes are designed to help campaigns respond more dynamically to shifting consumer behaviour.</p>



<p>The focus: letting AI identify and act on opportunities advertisers may not see themselves.</p>



<p><strong>Why we care.</strong> These updates aim to capture more conversions without increasing manual work, using AI to find new demand and optimise spend in real time. By improving how bids respond to full-funnel signals and how budgets adapt to peak demand, campaigns can become more efficient and less reliant on constant adjustments. </p>



<p>Ultimately, it’s about getting more value from the same budget while staying competitive in a fast-changing search landscape.</p>



<p><strong>Smarter bidding gets more context</strong>. Journey-aware Bidding (beta) allows advertisers to feed more of the customer journey into optimisation, including non-biddable conversions. This gives Google AI a fuller picture of what leads to actual sales — not just initial actions like form fills.</p>



<p>At the same time, Smart Bidding Exploration is expanding beyond Search. Already delivering an average 27% increase in unique converting users, it will soon roll out to Performance Max and Shopping campaigns, helping advertisers tap into less obvious, incremental queries.</p>



<p><strong>Budgets that follow demand</strong>. On the budgeting side, Google is building on its campaign total budgets feature, which allows advertisers to set spend across a defined period instead of relying on daily limits.</p>



<p>The next step is demand-led pacing — where AI automatically adjusts spend based on real-time demand, increasing budgets on high-opportunity days and pulling back during slower periods, without exceeding overall limits.</p>



<p>Advertisers using total budgets have already seen a reported 66% reduction in manual budget adjustments.</p>



<p><strong>Why this is a big deal</strong>. Budget management has historically been one of the most manual parts of campaign optimisation. By automating pacing, Google is reducing the need for constant monitoring while aiming to improve efficiency.</p>



<p><strong>What to watch:</strong>



<ul class="wp-block-list">
<li>How much control advertisers are willing to give up for automation</li>



<li>Whether incremental gains from exploration translate into profitable growth</li>



<li>How transparent these systems remain as they scale</li>
</ul>



<p><strong>Bottom line</strong>. <a href="https://blog.google/products/ads-commerce/bidding-budgeting-google-marketing-live-2026">Google is directing advertisers to AI to handle both bidding and budgeting</a> — shifting the advertiser role from manual optimisation to guiding inputs and trusting the system to find growth.</p>


Google is rolling out new AI-driven bidding and budgeting features across Search, Shopping and Performance Max — aimed at helping advertisers capture more demand without increasing manual effort.
What’s happening. Google is expanding its automation stack with updates like Journey-aware Bidding, Smart Bidding Exploration and demand-led budget pacing. Together, these changes are designed to help campaigns respond more dynamically to shifting consumer behaviour.
The focus: letting AI identify and act on opportunities advertisers may not see themselves.
Why we care. These updates aim to capture more conversions without increasing manual work, using AI to find new demand and optimise spend in real time. By improving how bids respond to full-funnel signals and how budgets adapt to peak demand, campaigns can become more efficient and less reliant on constant adjustments. 
Ultimately, it’s about getting more value from the same budget while staying competitive in a fast-changing search landscape.
Smarter bidding gets more context. Journey-aware Bidding (beta) allows advertisers to feed more of the customer journey into optimisation, including non-biddable conversions. This gives Google AI a fuller picture of what leads to actual sales — not just initial actions like form fills.
At the same time, Smart Bidding Exploration is expanding beyond Search. Already delivering an average 27% increase in unique converting users, it will soon roll out to Performance Max and Shopping campaigns, helping advertisers tap into less obvious, incremental queries.
Budgets that follow demand. On the budgeting side, Google is building on its campaign total budgets feature, which allows advertisers to set spend across a defined period instead of relying on daily limits.
The next step is demand-led pacing — where AI automatically adjusts spend based on real-time demand, increasing budgets on high-opportunity days and pulling back during slower periods, without exceeding overall limits.
Advertisers using total budgets have already seen a reported 66% reduction in manual budget adjustments.
Why this is a big deal. Budget management has historically been one of the most manual parts of campaign optimisation. By automating pacing, Google is reducing the need for constant monitoring while aiming to improve efficiency.
What to watch:




How much control advertisers are willing to give up for automation
Whether incremental gains from exploration translate into profitable growth
How transparent these systems remain as they scale
Bottom line. Google is directing advertisers to AI to handle both bidding and budgeting — shifting the advertiser role from manual optimisation to guiding inputs and trusting the system to find growth.

Google is aiming to help advertisers capture untapped demand and optimise spend in real time whilst reducing manual work.

Google adds AI-powered bidding and demand-led budgeting to Search and Shopping

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites.png" class="attachment-large size-large wp-post-image" alt="5 JavaScript SEO lessons from top ecommerce sites" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>See how Chewy, Harrods, Under Armour, and more brands handle rendering, navigation, structured data, and scripts without hurting SEO.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites.png" class="attachment-large size-large wp-post-image" alt="5 JavaScript SEO lessons from top ecommerce sites" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/5-JavaScript-SEO-lessons-from-top-ecommerce-sites-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>JavaScript SEO should be a solved problem by now. It isn&#8217;t.</p>



<p>Ecommerce sites keep hitting the same crawling, rendering, and indexing issues they were five years ago, now stacked on top of headless builds, AI-powered recommendations, and frameworks that can hide critical content from Google.</p>



<p>These top ecommerce players have figured out how to ship fast, modern JavaScript without sacrificing organic visibility. Here are five lessons worth stealing.</p>



<h2 class="wp-block-heading" id="h-1-chewy-uses-javascript-for-ux">1. Chewy uses JavaScript for UX</h2>



<p>Chewy is one of the largest online retailers of pet food and supplies in the U.S. They use Next.js, a React framework for building websites with built-in support for server rendering, static generation, and full-stack development features.</p>



<p>That means you can put important content in the initial HTML response <a href="https://searchengineland.com/no-javascript-fallbacks-474605">without relying on client-side JavaScript</a>.</p>



<p>Let&#8217;s look at a product page like the Benebone Wishbone Chew Toy.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1175" src="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-product-page-scaled.png" alt="Chewy product page" class="wp-image-476728" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-product-page-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-product-page-768x440.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-product-page-1536x881.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>Navigate to <em>View Page Source</em> and you&#8217;ll see the product title, description, pricing, reviews, Q&amp;A, and breadcrumb navigation all present in the initial HTML. Googlebot can access it on the first pass, <a href="https://developers.google.com/search/docs/crawling-indexing/javascript/javascript-seo-basics#how-googlebot-processes-javascript" target="_blank" rel="noopener">without waiting for rendering</a>.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="197" src="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-page-source-scaled.png" alt="Chewy page source" class="wp-image-476730" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-page-source-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-page-source-768x74.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-page-source-1536x148.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>That&#8217;s important because if a web crawler like Googlebot encounters issues rendering your page, the important content can still be parsed on the first crawl. With the rise of AI chatbots, some of which still <a href="https://vercel.com/i/how-ai-is-changing-seo" target="_blank" rel="noopener">don&#8217;t render JavaScript</a>, this has become even more important.</p>



<p>Not everything needs to be in the initial HTML, though. Without client-side JavaScript, the page would feel static and clunky.</p>



<p>Take the &#8220;Compare Similar Items&#8221; carousel. It&#8217;s loaded client-side, primarily there for shoppers. The internal links could offer some SEO benefit, but they&#8217;re not critical for indexing this page the way the title, description, and pricing are. </p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1092" src="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-similar-items-carousel-scaled.png" alt="Chewy similar items carousel" class="wp-image-476731" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Chewy-similar-items-carousel-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-similar-items-carousel-768x410.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Chewy-similar-items-carousel-1536x819.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>Chewy gets this balance right. The content that matters most for indexing is available on initial load. Client-side JavaScript enhances the experience rather than delivering the content that needs to be indexed.</p>



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<h2 class="wp-block-heading" id="h-2-myprotein-makes-navigation-crawlable">2. Myprotein makes navigation crawlable</h2>



<p>Myprotein sells supplements, nutrition products, and some fitness apparel.</p>



<p>Their site is built on Astro, a content-first framework using Islands Architecture to ship zero JavaScript by default while supporting components from React, Vue, or Svelte.</p>



<p>Myprotein&#8217;s navigation is the part worth studying. It&#8217;s an important SEO area for ecommerce sites, and they get it right.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="321" src="https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-scaled.png" alt="Myprotein navigation" class="wp-image-476732" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-768x120.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-1536x241.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>View the source on any Myprotein page and the navigation links (categories, dropdown items, and footer links) are all in the initial HTML response. Astro makes this possible through its <a href="https://docs.astro.build/en/concepts/islands/" target="_blank" rel="noopener">island architecture</a>.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="194" src="https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-html-scaled.png" alt="Myprotein source code" class="wp-image-476733" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-html-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-html-768x73.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Myprotein-nav-html-1536x146.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>The navigation ships as an interactive island, meaning Astro will hydrate it with JavaScript as soon as the browser is ready. But JavaScript makes the flyout menus interactive. It doesn&#8217;t create them.</p>



<p>These links are also proper <code>&lt;a&gt;</code> elements with href attributes, which is what crawlers like Googlebot need to <a href="https://developers.google.com/search/docs/crawling-indexing/links-crawlable" target="_blank" rel="noopener">discover and follow links</a>. Avoid using JavaScript click handlers to simulate navigation, such as:</p>



<p><code>&lt;div onclick="navigate(item.slug)"&gt;Clear Protein Drinks&lt;/div&gt;</code>



<p>A crawler won&#8217;t follow that. Use a standard anchor element instead:</p>



<p><code>&lt;a href="https://us.myprotein.com/c/nutrition/protein/clear-protein-drinks/"&gt;Clear Protein Drinks&lt;/a&gt;</code>



<p>Not every site gets this right. When navigation depends entirely on client-side rendering, there&#8217;s a window where it&#8217;s invisible or empty.</p>



<p>Googlebot <a href="https://developers.google.com/search/docs/crawling-indexing/javascript/javascript-seo-basics#how-googlebot-processes-javascript">processes JavaScript</a> in a separate rendering pass that can lag behind the initial crawl, which can mean delayed discovery of internal links critical for crawl efficiency and link equity distribution.</p>



<h2 class="wp-block-heading" id="h-3-harrods-embeds-structured-data-in-the-html">3. Harrods embeds structured data in the HTML</h2>



<p>Harrods is a luxury department store selling fashion, beauty, and homeware.</p>



<p>Their site is built on Nuxt, a Vue framework for building websites with built-in routing, server rendering, and static generation, plus an opinionated project structure.</p>



<p>Their structured data is delivered in the initial HTML response. View the source on any <a href="https://searchengineland.com/ecommerce-product-pages-ai-first-world-458629">product page</a> and you&#8217;ll find structured data inside a <code>&lt;script type="application/ld+json"&gt;</code> element. The Product schema includes the product name, images, description, brand, and an Offer with price, currency, availability, and seller.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="324" src="https://searchengineland.com/wp-content/seloads/2026/05/Harrods-Product-schema-scaled.png" alt="Harrods page source" class="wp-image-476734" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Harrods-Product-schema-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Harrods-Product-schema-768x121.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Harrods-Product-schema-1536x243.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>JSON-LD is the <a href="https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data#supported-formats" target="_blank" rel="noopener">format Google recommends</a> for structured data, and because it&#8217;s in the HTML response, Google can parse it on the first crawl pass without needing to render the page.</p>



<p>On JavaScript-powered sites, structured data can easily become a client-side dependency. If a framework fetches product data in the browser and generates JSON-LD from the response, that structured data only exists after JavaScript executes. The same is true for structured data injected <span>through <a href="https://developers.google.com/search/docs/appearance/structured-data/generate-structured-data-with-javascript#use-google-tag-manager" target="_blank">Google</a></span><a href="https://developers.google.com/search/docs/appearance/structured-data/generate-structured-data-with-javascript#use-google-tag-manager" target="_blank" rel="noopener"> Tag Manager</a>.</p>



<p>If markup is only added after the page loads, Google has to render the page to find it. <a href="https://developers.google.com/search/docs/appearance/structured-data/generate-structured-data-with-javascript">Google has noted</a> that dynamically generated Product markup can make Shopping crawls less frequent and less reliable, which matters when prices and availability change often.</p>



<p>By serving that structured data in the HTML directly, Harrods avoids this risk entirely.</p>



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<h2 class="wp-block-heading" id="h-4-under-armour-handles-faceted-navigation-with-javascript">4. Under Armour handles faceted navigation with JavaScript</h2>



<p>Under Armour is a global sportswear brand selling athletic apparel, footwear, and accessories. Their site is built on Next.js, the same React framework Chewy uses.</p>



<p>A good place to see their JavaScript SEO in action is on category pages, where filters need to feel fast and interactive for shoppers, and be crawler-friendly.</p>



<p>Let&#8217;s look at the men&#8217;s shoes category page. When you apply a filter, say, selecting size 10, the product grid updates instantly without a full page reload. That&#8217;s client-side JavaScript updating the grid.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="1708" src="https://searchengineland.com/wp-content/seloads/2026/05/Under-Armour-faceted-navigation-scaled.png" alt="Under Armour porduct page" class="wp-image-476735" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Under-Armour-faceted-navigation-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Under-Armour-faceted-navigation-768x640.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Under-Armour-faceted-navigation-1536x1281.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>But the URL updates too. After selecting the filter, the URL becomes:</p>



<ul class="wp-block-list">
<li><code>https://www.underarmour.com/en-us/c/mens/shoes/?prefn1=size&amp;prefv1=10</code></li>
</ul>



<p>A shopper can copy that URL, send it to a friend, or bookmark it, and land right back on the same filtered view.</p>



<p>Notice what the URL isn&#8217;t:</p>



<ul class="wp-block-list">
<li>Not a hash fragment (#size=10), which doesn&#8217;t get sent to the server and is ignored by Google.</li>



<li>Not a mess of bracketed query strings (?filters[0][size]=10).</li>



<li>Not a dynamic route artifact like /shoes/[category]/ leaking into the live URL.</li>
</ul>



<p>It&#8217;s a clean, readable query string with named parameters.</p>



<p>Under Armour is using the Next.js router to update the URL as filters change. Under the hood, it wraps the browser&#8217;s <a href="https://developer.mozilla.org/en-US/docs/Web/API/History_API" target="_blank" rel="noopener">History API</a> and uses the <a href="https://developer.mozilla.org/en-US/docs/Web/API/History/pushState" target="_blank" rel="noopener">pushState() method</a> to update the address bar without a reload.</p>



<p>When someone visits that same URL directly, the page loads with the filter already applied.</p>



<h2 class="wp-block-heading" id="h-5-manors-golf-loads-third-party-scripts">5. Manors Golf loads third-party scripts</h2>



<p>Manors Golf sells golf apparel. Their site runs on Hydrogen, Shopify&#8217;s React-based framework for headless storefronts.</p>



<p>Hydrogen defers its own application scripts automatically since they load as ES modules. However, third-party scripts are the developer&#8217;s responsibility. On an ecommerce site, that can be a long list: reviews, chat, personalization, pixels, recommendations, payment scripts.</p>



<p>That matters for SEO in two ways. Render-blocking scripts hurt Core Web Vitals, most directly Largest Contentful Paint (LCP). They also give Googlebot more work to render the page, so it may get processed less reliably.</p>



<p>An external script (<code>&lt;script src="..."></code>) without async or defer <a href="https://shopify.dev/docs/storefronts/themes/tools/theme-check/checks/parser-blocking-javascript" target="_blank" rel="noopener">blocks HTML parsing</a>. <a href="https://developer.mozilla.org/en-US/docs/Web/HTML/Reference/Elements/script" target="_blank" rel="noopener">Async</a> fetches in the background and runs when ready. Defer waits until parsing finishes.</p>



<p>Manors loads external scripts from 12 third-party domains, including Klaviyo, TikTok, Microsoft Clarity, and Gorgias.</p>



<p>A look at the <em>Elements</em> panel shows them all loading with async:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="2048" height="175" src="https://searchengineland.com/wp-content/seloads/2026/05/Manors-async-attribute-scaled.png" alt="Manors async attribute" class="wp-image-476736" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Manors-async-attribute-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Manors-async-attribute-768x66.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Manors-async-attribute-1536x131.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>
</div>


<p>By loading third-party scripts with async, Manors keeps them from blocking the initial render. That protects LCP and reduces the work Google’s Web Rendering Service (WRS) has to do.</p>



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<h2 class="wp-block-heading" id="h-the-balance-between-interactivity-and-crawlability">The balance between interactivity and crawlability</h2>



<p>The issue isn&#8217;t that you&#8217;re using JavaScript. It&#8217;s what you&#8217;re using it for. </p>



<p>Googlebot can process JavaScript, but it&#8217;s slower and less reliable than reading HTML. The more your core content, structure, and navigation depend on JavaScript, the more room there is for things to go wrong.</p>



<p>The sites in this article all use JavaScript to enhance the experience rather than deliver it. Do that, and you won&#8217;t have to choose between a good user experience and good SEO.</p>

JavaScript SEO should be a solved problem by now. It isn’t.
Ecommerce sites keep hitting the same crawling, rendering, and indexing issues they were five years ago, now stacked on top of headless builds, AI-powered recommendations, and frameworks that can hide critical content from Google.
These top ecommerce players have figured out how to ship fast, modern JavaScript without sacrificing organic visibility. Here are five lessons worth stealing.
1. Chewy uses JavaScript for UX
Chewy is one of the largest online retailers of pet food and supplies in the U.S. They use Next.js, a React framework for building websites with built-in support for server rendering, static generation, and full-stack development features.
That means you can put important content in the initial HTML response without relying on client-side JavaScript.
Let’s look at a product page like the Benebone Wishbone Chew Toy.




Navigate to View Page Source and you’ll see the product title, description, pricing, reviews, Q&A, and breadcrumb navigation all present in the initial HTML. Googlebot can access it on the first pass, without waiting for rendering.




That’s important because if a web crawler like Googlebot encounters issues rendering your page, the important content can still be parsed on the first crawl. With the rise of AI chatbots, some of which still don’t render JavaScript, this has become even more important.
Not everything needs to be in the initial HTML, though. Without client-side JavaScript, the page would feel static and clunky.
Take the “Compare Similar Items” carousel. It’s loaded client-side, primarily there for shoppers. The internal links could offer some SEO benefit, but they’re not critical for indexing this page the way the title, description, and pricing are. 




Chewy gets this balance right. The content that matters most for indexing is available on initial load. Client-side JavaScript enhances the experience rather than delivering the content that needs to be indexed.

  
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2. Myprotein makes navigation crawlable
Myprotein sells supplements, nutrition products, and some fitness apparel.
Their site is built on Astro, a content-first framework using Islands Architecture to ship zero JavaScript by default while supporting components from React, Vue, or Svelte.
Myprotein’s navigation is the part worth studying. It’s an important SEO area for ecommerce sites, and they get it right.




View the source on any Myprotein page and the navigation links (categories, dropdown items, and footer links) are all in the initial HTML response. Astro makes this possible through its island architecture.




The navigation ships as an interactive island, meaning Astro will hydrate it with JavaScript as soon as the browser is ready. But JavaScript makes the flyout menus interactive. It doesn’t create them.
These links are also proper <a> elements with href attributes, which is what crawlers like Googlebot need to discover and follow links. Avoid using JavaScript click handlers to simulate navigation, such as:
<div onclick="navigate(item.slug)">Clear Protein Drinks</div>



A crawler won’t follow that. Use a standard anchor element instead:
<a href="https://us.myprotein.com/c/nutrition/protein/clear-protein-drinks/">Clear Protein Drinks</a>



Not every site gets this right. When navigation depends entirely on client-side rendering, there’s a window where it’s invisible or empty.
Googlebot processes JavaScript in a separate rendering pass that can lag behind the initial crawl, which can mean delayed discovery of internal links critical for crawl efficiency and link equity distribution.
3. Harrods embeds structured data in the HTML
Harrods is a luxury department store selling fashion, beauty, and homeware.
Their site is built on Nuxt, a Vue framework for building websites with built-in routing, server rendering, and static generation, plus an opinionated project structure.
Their structured data is delivered in the initial HTML response. View the source on any product page and you’ll find structured data inside a <script type="application/ld+json"> element. The Product schema includes the product name, images, description, brand, and an Offer with price, currency, availability, and seller.




JSON-LD is the format Google recommends for structured data, and because it’s in the HTML response, Google can parse it on the first crawl pass without needing to render the page.
On JavaScript-powered sites, structured data can easily become a client-side dependency. If a framework fetches product data in the browser and generates JSON-LD from the response, that structured data only exists after JavaScript executes. The same is true for structured data injected through Google Tag Manager.
If markup is only added after the page loads, Google has to render the page to find it. Google has noted that dynamically generated Product markup can make Shopping crawls less frequent and less reliable, which matters when prices and availability change often.
By serving that structured data in the HTML directly, Harrods avoids this risk entirely.
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4. Under Armour handles faceted navigation with JavaScript
Under Armour is a global sportswear brand selling athletic apparel, footwear, and accessories. Their site is built on Next.js, the same React framework Chewy uses.
A good place to see their JavaScript SEO in action is on category pages, where filters need to feel fast and interactive for shoppers, and be crawler-friendly.
Let’s look at the men’s shoes category page. When you apply a filter, say, selecting size 10, the product grid updates instantly without a full page reload. That’s client-side JavaScript updating the grid.




But the URL updates too. After selecting the filter, the URL becomes:
https://www.underarmour.com/en-us/c/mens/shoes/?prefn1=size&prefv1=10
A shopper can copy that URL, send it to a friend, or bookmark it, and land right back on the same filtered view.
Notice what the URL isn’t:
Not a hash fragment (#size=10), which doesn’t get sent to the server and is ignored by Google.
Not a mess of bracketed query strings (?filters[0][size]=10).
Not a dynamic route artifact like /shoes/[category]/ leaking into the live URL.
It’s a clean, readable query string with named parameters.
Under Armour is using the Next.js router to update the URL as filters change. Under the hood, it wraps the browser’s History API and uses the pushState() method to update the address bar without a reload.
When someone visits that same URL directly, the page loads with the filter already applied.
5. Manors Golf loads third-party scripts
Manors Golf sells golf apparel. Their site runs on Hydrogen, Shopify’s React-based framework for headless storefronts.
Hydrogen defers its own application scripts automatically since they load as ES modules. However, third-party scripts are the developer’s responsibility. On an ecommerce site, that can be a long list: reviews, chat, personalization, pixels, recommendations, payment scripts.
That matters for SEO in two ways. Render-blocking scripts hurt Core Web Vitals, most directly Largest Contentful Paint (LCP). They also give Googlebot more work to render the page, so it may get processed less reliably.
An external script (<script src="...">) without async or defer blocks HTML parsing. Async fetches in the background and runs when ready. Defer waits until parsing finishes.
Manors loads external scripts from 12 third-party domains, including Klaviyo, TikTok, Microsoft Clarity, and Gorgias.
A look at the Elements panel shows them all loading with async:




By loading third-party scripts with async, Manors keeps them from blocking the initial render. That protects LCP and reduces the work Google’s Web Rendering Service (WRS) has to do.

  
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The balance between interactivity and crawlability
The issue isn’t that you’re using JavaScript. It’s what you’re using it for. 
Googlebot can process JavaScript, but it’s slower and less reliable than reading HTML. The more your core content, structure, and navigation depend on JavaScript, the more room there is for things to go wrong.
The sites in this article all use JavaScript to enhance the experience rather than deliver it. Do that, and you won’t have to choose between a good user experience and good SEO.

See how Chewy, Harrods, Under Armour, and more brands handle rendering, navigation, structured data, and scripts without hurting SEO.

5 JavaScript SEO lessons from top ecommerce sites

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/8-GEO-metrics-to-track-in-2026.png" class="attachment-large size-large wp-post-image" alt="8 GEO metrics to track in 2026" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/8-GEO-metrics-to-track-in-2026.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/8-GEO-metrics-to-track-in-2026-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/8-GEO-metrics-to-track-in-2026-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/8-GEO-metrics-to-track-in-2026-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI search is creating new visibility gaps for brands. These metrics help you track presence, influence, and inclusion across platforms.

Search visibility no longer starts and ends with rankings. AI-driven search has changed where discovery happens — across Google, ChatGPT, Perplexity, and beyond. 
Generative engine optimization (GEO) is how brands adapt, shaping how they’re retrieved and represented inside those systems.
Traditional SEO metrics miss a growing share of that visibility. Pages are now summarized, excerpted, and cited in environments where clicks are optional, and attribution is fragmented. When an AI-generated summary appears, users click traditional search results far less often — in one analysis, just 8% of the time.
That creates a measurement gap. Assessing this gap is where GEO metrics come in.
What visibility means in generative search
GEO focuses on whether AI systems can find, understand, and select your content when generating answers. In generative search, visibility is more than about being indexed or ranked. Your content must be used — cited, summarized, or incorporated — into AI responses.
GEO builds on SEO and AEO, shifting the focus from where content ranks to how clearly it can be interpreted and trusted in context.
In practice, that means optimizing for:
Extractability: Can this be easily summarized?
Credibility: Is this a trustworthy source to cite?
Relevance: Does this directly resolve the query?
That’s where GEO metrics become useful.

  
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8 core GEO metrics brands need to track in 2026
GEO performance shows up across a distinct set of signals that reflect presence, usage, and downstream impact.
1. AI citation frequency
AI citation frequency measures how often your brand, website, content, or experts are cited in AI-generated answers.
This is one of the clearest GEO metrics because it shows whether generative systems consider your content useful enough to reference.
Track citation frequency across:
Google AI Overviews.
Google AI Mode.
Perplexity.
ChatGPT search.
Gemini.
Copilot.
Claude, where source visibility is available.
Industry-specific AI tools and assistants.
Citation frequency should be tracked at the topic level, not only the domain level. A SaaS company, for example, may want to know whether it’s cited for “customer onboarding software,” “product adoption metrics,” and “best tools for reducing churn” separately.
The goal is repeatable citation across high-value topics.
2. Share of Model Voice (SOMV)
Share of Model Voice measures how often your brand appears in AI-generated answers compared with competitors.
Traditional share of voice tells you how visible a brand is across search, media, or advertising. Share of Model Voice applies that idea to AI responses.
A simple way to calculate it:
SOMV = Brand appearances across a prompt set ÷ Total answers generated for that prompt set
For example:
You analyze 100 relevant prompts.
Your brand appears in 28 of the resulting AI-generated answers.
Your Share of Model Voice is 28%.
This metric is especially useful for competitive categories because AI answers often compress the consideration set. A user doesn’t see 10 blue links. They may see three recommended vendors, two cited articles, or one synthesized answer.
That’s why relative presence matters more than absolute visibility.
3. Answer inclusion rate
Answer inclusion rate measures how often your owned content is used to generate an AI answer, regardless of whether the user clicks.
This differs from citation frequency. A brand may be mentioned without its content being cited. And a page may be used as supporting material even when the brand is not the central recommendation.
Track inclusion across informational, comparison, and decision-stage prompts.
For example, a B2B SaaS company in the SEO or analytics space might track prompts like:
Informational: “What is generative engine optimization?”
Exploratory: “How should brands measure AI search visibility?”
Comparison: “SEO vs GEO vs AEO”
Category-level: “Best GEO tools for B2B SaaS”
Decision-stage: “How do I evaluate GEO platforms?”
This metric helps identify which content formats are easiest for AI systems to retrieve and summarize. 
In many cases, clear definitions, comparison tables, statistics pages, glossaries, and answer-first explainers perform better than broad thought leadership pages because they’re easier to extract and reuse.
4. Entity recognition and authority
Entity recognition measures how well AI systems understand who your brand is, what it does, and what topics it should be associated with.
This matters because generative systems don’t only match keywords. They interpret entities, relationships, topical authority, and corroborating signals.
Strong entity recognition means AI systems can accurately connect your brand to:
Your company name.
Products and services.
Founders or executives.
Authors and subject-matter experts.
Industry categories.
Locations.
Use cases.
Awards, partnerships, and third-party mentions.
Knowledge graph data.
Structured data.
Google’s guidance for AI features emphasizes that the same fundamentals still apply: make content accessible, maintain a strong page experience, and use structured data to help systems interpret what’s on the page.
In practice, inconsistencies across these signals make it harder for AI systems to reliably connect your brand to the right topics.
5. Sentiment in AI responses
Sentiment measures how AI systems describe your brand.
Tracking mentions isn’t enough. Brands also need to know whether AI-generated responses frame them as credible, outdated, expensive, risky, innovative, niche, enterprise-grade, beginner-friendly, or anything else.
You can monitor:
Positive, neutral, and negative descriptions.
Recurring adjectives or claims.
Incorrect comparisons.
Outdated product details.
Missing differentiators.
Reputation issues.
Hallucinated features or limitations.
This is where GEO overlaps with PR and brand management. AI-generated answers can shape perception before the user ever reaches your site.
6. Prompt coverage
Prompt coverage measures how many relevant prompts surface your brand. This is the GEO version of keyword coverage, but prompts are more conversational, specific, and intent-rich.
A strong prompt set should include:
Informational prompts.
Comparison prompts.
“Best” and “top” prompts.
Problem-aware prompts.
Solution-aware prompts.
Buyer-stage prompts.
Role-specific prompts.
Use-case prompts.
Local or industry-specific prompts.
Follow-up prompts.
For a cybersecurity company, “best cybersecurity platforms” is only part of the picture. Relevant prompts also look like:
“How do mid-market companies reduce phishing risk?”
“What tools help security teams manage vendor risk?”
“Compare managed detection and response providers.”
“What should a CISO look for in an incident response partner?”
Prompt coverage shows whether your brand is visible across the way people actually ask AI systems for help.
7. Content retrieval success rate
Content retrieval success rate measures how often AI systems pull from your owned content when answering relevant prompts. This is where it gets technical.
If your content isn’t crawlable, structured, fresh, or easy to parse, it may struggle to appear in generative outputs, regardless of subject-matter strength.
You should evaluate:
Crawlability.
Indexability.
Internal linking.
Page speed.
Schema markup.
Clear headings.
Answer-first formatting.
Author attribution.
Publication and update dates.
Canonical handling.
Robots.txt and AI crawler access rules.
Content freshness.
Source clarity.
Gaps in any of these areas reduce the likelihood that your content is retrieved and used — even when it’s the best answer available.
8. Conversion influence after AI interaction
Conversion influence measures how visibility in AI-generated outputs contributes to downstream business outcomes. That connection isn’t always direct — and it’s rarely cleanly attributed.
A user may see your brand in an AI answer, search your name later, visit directly, ask a colleague, or convert through a paid retargeting path.
Still, brands should track directional signals:
AI referral traffic.
Assisted conversions.
Branded search lift.
Direct traffic changes.
Demo or lead quality from AI-referred sessions.
Returning visitors after AI visibility spikes.
Sales conversations mentioning ChatGPT, Perplexity, Gemini, or AI Overviews.
Pipeline influenced by AI-discovery queries.
AI search visitors convert at a 23x higher rate than traditional organic search visitors, even though AI traffic volume was much smaller, according to Ahrefs.
That’s the measurement nuance: AI search may drive fewer sessions, but the sessions that do occur can be higher-intent.
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Tools and methods for tracking GEO metrics
GEO measurement is still in its early stages, and no single platform captures the full picture. Most brands will need a mix of automated tools, manual audits, analytics configuration, and competitive testing.
Emerging GEO analytics platforms
A growing set of tools — from established SEO platforms to GEO-native products — now track how brands appear across AI-driven search experiences.
For example:
Semrush AI Toolkit surfaces visibility trends tied to AI-driven search.
SE Ranking AI Visibility Tracker monitors brand presence across AI-generated outputs.
Profound focuses on AI citation frequency, sentiment, and competitive visibility.
Peec AI tracks brand presence and representation across AI systems.
The category is still evolving, but early tools give brands a way to move from assumptions to actual visibility data.
Prompt testing frameworks
Manual prompt testing is still useful, especially when building a baseline. Create a controlled prompt set by topic, funnel stage, persona, and geography. 
Run those prompts consistently across the same AI platforms. Capture:
Whether your brand appears.
Which competitors appear.
Which sources are cited.
How your brand is described.
Whether the answer is accurate.
Whether your owned content is cited.
Whether the answer changes across repeated tests.
Because AI answers can vary, single-prompt testing isn’t enough. Track patterns over time.
Analytics and logs
Use GA4, server logs, CRM fields, and referral data to identify traffic and conversions from AI platforms — particularly shifts in direct, branded, and assisted conversions.
Track known AI referrers, including ChatGPT, Perplexity, Gemini, Copilot, Claude, and other AI tools, where possible. Treat this as directional rather than complete, because many AI-influenced journeys show up as direct, branded search, or otherwise unattributed traffic.
Search Console and traditional SEO tools
Search Console still matters, even as clicks decline.
Impressions show whether content is being surfaced, while query data highlights where AI Overviews are absorbing demand, where branded search is increasing, and where content may need restructuring for answer inclusion.
Traditional SEO tools remain useful for technical health, content gaps, backlinks, keyword demand, and competitive research. GEO measurement builds on that foundation, tracking how content is surfaced in AI search.
How to build a GEO measurement framework
Start with a baseline. Choose 5-10 core topics you want AI systems to associate with your brand. For each, map prompts across the user journey. Then build a dashboard across four categories — and assign each to a clear action:
Visibility: Where do we show up?




AI citation frequency.
Share of Model Voice.
Prompt coverage.
Answer inclusion rate.
Accuracy and reputation: How are we represented?




Sentiment in AI responses.
Message consistency.
Misinformation or hallucination rate.
Competitive framing.
Technical and content: Can our content be used?




Content retrieval success rate.
Schema coverage.
Crawlability.
Freshness.
Entity consistency.
Business impact: Does it drive outcomes?




AI referral traffic.
Assisted conversions.
Branded search lift.
Direct traffic movement.
Lead quality.
Pipeline influenced by AI discovery.
Review these metrics together, not in isolation. Use them to decide what to update, expand, or deprioritize. Finally, connect the framework to business goals.
A publisher may prioritize citations and source inclusion. A B2B SaaS company may focus on category prompts and comparison visibility. An ecommerce brand may look at product recommendations, review sentiment, and visibility across discovery surfaces.
There’s no universal GEO dashboard — only the one that helps your team decide what to do next.

  
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Turning GEO metrics into action
GEO metrics are only useful if they change what teams do next. Define the topics you want to be known for, track how those topics show up across AI systems, and use that data to decide what to update, expand, or deprioritize.
Treat visibility as a feedback loop. If your brand isn’t appearing, refine the content. If it’s appearing inconsistently, strengthen the signals around it. If it’s showing up but misrepresented, correct the source.
Over time, the advantage goes to teams that act on these signals consistently — not just the ones that track them.

AI search is creating new visibility gaps for brands. These metrics help you track presence, influence, and inclusion across platforms.

8 GEO metrics to track in 2026

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-to-use-Google-and-LLM-insights-to-improve-international-SEO.png" class="attachment-large size-large wp-post-image" alt="How to use Google and LLM insights to improve international SEO" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-to-use-Google-and-LLM-insights-to-improve-international-SEO.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-use-Google-and-LLM-insights-to-improve-international-SEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-use-Google-and-LLM-insights-to-improve-international-SEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-use-Google-and-LLM-insights-to-improve-international-SEO-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Turn Google and AI search signals into localized content structures that better match regional user behavior.

Turn Google and AI search signals into localized content structures that better match regional user behavior.

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