<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/Google-instore-connection.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Google-instore-connection.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/Google-instore-connection-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Google-instore-connection-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Google-instore-connection-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google Ads is making it easier for multi-location businesses to reach nearby customers and measure the campaign in-store sales.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/Google-instore-connection.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Google-instore-connection.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/Google-instore-connection-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Google-instore-connection-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Google-instore-connection-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google is rolling out two new features designed to help multi-location retailers, restaurants and local service businesses reach nearby customers and connect their ad campaigns with in-store sales.</p>



<p class="wp-block-paragraph"><strong>Driving the news</strong>. Google Ads Liaison Ginny Marvin announced Local Customer Optimization for Performance Max store goals campaigns and a new way to bring store sales data into Google Ads through Data Manager.</p>



<p class="wp-block-paragraph">Both are aimed at reducing the work required to drive and measure offline sales ahead of the holiday rush.</p>



<p class="wp-block-paragraph"><strong>Why we care.</strong> These updates make it easier to connect digital advertising with actual store visits and sales. Multi-location businesses can prioritize spend toward nearby, in-market customers across Maps, Waze and local Search, while simpler CRM and Google Sheets connections make it easier for advertisers to feed offline sales data back into Google Ads for measurement and optimization.</p>



<p class="wp-block-paragraph"><strong>Zoom in</strong>. Local Customer Optimization is a new campaign-level toggle in Performance Max for store goals campaigns.</p>



<p class="wp-block-paragraph">When enabled, Google will prioritize budget toward reaching consumers who are actively in-market nearby across Google Maps, Waze and local Search.</p>



<p class="wp-block-paragraph">The feature is rolling out now.</p>



<p class="wp-block-paragraph"><strong>Meanwhile</strong>. Google is also simplifying how businesses can share their offline sales data with Google Ads.</p>



<p class="wp-block-paragraph">With Store Sales in Data Manager, advertisers will be able to connect their CRM or Google Sheets directly within Data Manager rather than relying on more technically demanding methods of sharing that data.</p>



<p class="wp-block-paragraph">Advertisers can then use the information to measure in-store sales and optimize campaigns toward their highest-potential walk-in customers.</p>



<p class="wp-block-paragraph">Google says the feature will begin rolling out in the coming weeks.</p>



<p class="wp-block-paragraph"><strong>The big picture</strong>. The two updates are designed to work across opposite ends of the customer journey.</p>



<p class="wp-block-paragraph">Local Customer Optimization uses real-time navigation and local intent signals to help businesses reach potential customers nearby, while Store Sales in Data Manager gives advertisers a simpler way to feed the resulting offline revenue data back into Google Ads.</p>



<p class="wp-block-paragraph">For businesses with physical locations, that creates a more direct connection between finding nearby customers online and measuring what happens when they walk through the door.</p>



<p class="wp-block-paragraph"><strong>What to watch:</strong> <a href="https://x.com/adsliaison/status/2097347237546229977">Local Customer Optimization</a> is rolling out now, while Store Sales in Data Manager is expected in the coming weeks.</p>


Google is rolling out two new features designed to help multi-location retailers, restaurants and local service businesses reach nearby customers and connect their ad campaigns with in-store sales.
Driving the news. Google Ads Liaison Ginny Marvin announced Local Customer Optimization for Performance Max store goals campaigns and a new way to bring store sales data into Google Ads through Data Manager.
Both are aimed at reducing the work required to drive and measure offline sales ahead of the holiday rush.
Why we care. These updates make it easier to connect digital advertising with actual store visits and sales. Multi-location businesses can prioritize spend toward nearby, in-market customers across Maps, Waze and local Search, while simpler CRM and Google Sheets connections make it easier for advertisers to feed offline sales data back into Google Ads for measurement and optimization.
Zoom in. Local Customer Optimization is a new campaign-level toggle in Performance Max for store goals campaigns.
When enabled, Google will prioritize budget toward reaching consumers who are actively in-market nearby across Google Maps, Waze and local Search.
The feature is rolling out now.
Meanwhile. Google is also simplifying how businesses can share their offline sales data with Google Ads.
With Store Sales in Data Manager, advertisers will be able to connect their CRM or Google Sheets directly within Data Manager rather than relying on more technically demanding methods of sharing that data.
Advertisers can then use the information to measure in-store sales and optimize campaigns toward their highest-potential walk-in customers.
Google says the feature will begin rolling out in the coming weeks.
The big picture. The two updates are designed to work across opposite ends of the customer journey.
Local Customer Optimization uses real-time navigation and local intent signals to help businesses reach potential customers nearby, while Store Sales in Data Manager gives advertisers a simpler way to feed the resulting offline revenue data back into Google Ads.
For businesses with physical locations, that creates a more direct connection between finding nearby customers online and measuring what happens when they walk through the door.
What to watch: Local Customer Optimization is rolling out now, while Store Sales in Data Manager is expected in the coming weeks.

Google Ads is making it easier for multi-location businesses to reach nearby customers and measure the campaign in-store sales.

Google Ads adds new tools to drive and measure in-store sales

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Inside-Google-Maps-72-ranking-signals-and-the-architecture-behind-local-search.png" class="attachment-large size-large wp-post-image" alt="Inside Google Maps- 72 ranking signals and the architecture behind local search" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Inside-Google-Maps-72-ranking-signals-and-the-architecture-behind-local-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Inside-Google-Maps-72-ranking-signals-and-the-architecture-behind-local-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Inside-Google-Maps-72-ranking-signals-and-the-architecture-behind-local-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Inside-Google-Maps-72-ranking-signals-and-the-architecture-behind-local-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Newly recovered Google data reveals how entities, web signals, geography, and semantic systems shape local visibility.

When a business appears in Google Maps, the listing you see is the end product of a much larger system.
Behind it sits a canonical geographic entity assembled from multiple data sources, connected to the Knowledge Graph and the web, scored by several ranking systems, filtered through geographic and semantic retrieval, personalized for the user, and finally passed to a rendering engine that decides what can actually appear on the map.
Our team recently obtained a binary exposing a non-public scope of Geostore, the system Google uses to represent geographic entities. We crossed it with Maps protocols, network traffic, the web index, mobile services, style tables, on-device components, and Google’s 2024 leak.
The recovered material includes:
72 Geostore ranking signals.
793 data source providers.
446 local search intent types.
50,998 Mapcore styles.
12,936 label styles.
10,936 searchable Geostore declarations.
The ranking signals will probably attract attention. But they’re only one layer.
The architecture around them tells a more important story about how Google understands places and what local SEO may become as Maps turns into a conversational product.
The first thing to understand: A listing isn’t the entity
A useful mental model starts with Geostore. Google represents geographic objects internally as Features. A Feature can be a business, building, road, city, station, area, transit element, or even a 3D object.
For an establishment, the object can contain identity, geometry, source information, websites, business-chain relationships, Knowledge Graph references, concepts, and ranking information.
The familiar Maps listing is assembled later. What a business owner edits in Google Business Profile isn’t necessarily what Google maintains internally as the entity.
Google builds a canonical representation of the place that can incorporate data from multiple sources, survive changes in geometry, and connect to other Google identifiers, including the Knowledge Graph machine ID (MID).
For local SEO, the entity is the more useful unit to consider. The listing is the interface. The entity sits underneath it.
more calls, visits, and sales.
      
Connect your business
    
Google combines data from 793 providers
One of the most revealing parts of Geostore is its provenance system. 
A business doesn’t simply have one source. Its name might come from one provider, its phone number from another, its category from another, and its geometry from somewhere else entirely.
The corpus exposes 793 source providers, along with mechanisms for provenance, priority, trust, and conflation.
Conflation is the process used when several sources describe the same object and disagree.
Geostore contains generic mechanisms that can pick one value, merge several values, or combine them. It also models trust levels ranging from blocked or untrusted sources to trusted and super-trusted ones.
This gives a different interpretation to a common local SEO problem: changing a field in Google Business Profile doesn’t guarantee that Google’s canonical representation immediately becomes that new value.
The edit becomes another piece of evidence entering a system that may already have competing evidence.
For businesses struggling with persistent incorrect attributes, duplicate information, or changes that repeatedly revert, this architecture helps explain why the problem can be harder than editing a listing.
Dig deeper: The local SEO gatekeeper: How Google defines your entity



The archive makes the leak much more useful
The binary itself gives us structures, field numbers, and complete enumerations. Google’s March 2024 documentation often gives us something different: prose explaining what those structures mean.
We published the two together. The resulting archive contains 10,936 Geostore declarations that can be searched by message name, package, field type, documentation text, tag number, status, and other properties.
In reverse-engineering work, an internal name can easily become a theory once it circulates through the SEO community. The archive lets you inspect the underlying evidence.
If a signal exists, you can find its declaration. If a field existed in the 2024 documentation, you can read the associated description. If a field has been stripped from the newer client scope, its protobuf tag still leaves a numbered hole.
The 2024 leak gave us many descriptions of Google’s systems. The newer binary gives us much more of their actual vocabulary.
Together, they provide a more useful picture than either source does on its own.
Oyster Rank contains 72 ranking signals
Geostore has its own ranking system. Internally, it’s called Oyster Rank. We recovered a complete visible enumeration of 72 signals, including:
Google reviews.
Web query volume.
Listing impressions.
Listing opens.
Direction requests.
Website clicks.
Chain membership.
Wikipedia signals.
Popularity.
Prominence.
Landmark information.
Road usage.
Out of 72 values, 25 are explicitly marked deprecated.
The important limitation is that we recovered the signal names, not their current weights.
The schema shows a pipeline in which raw observations are extracted, normalized, and mixed into the Feature’s rank. But the coefficients that would tell us how much each signal contributes are outside the scope we recovered.
So SIGNAL_GOOGLE_REVIEWS proves that reviews belong to the Oyster Rank vocabulary. It doesn’t prove that reviews currently carry a particular weight in a Maps search.
The 72 signals aren’t the Google Maps algorithm
This is probably the most important clarification for SEOs. Oyster Rank appears to characterize the importance of the entity inside Geostore. A user query still has to go through additional systems.
Maps must understand what the person means, identify a geographic context, generate candidates, evaluate semantic relevance, and serve a final result set.
A simplified pipeline looks more like:
Geostore entity -> query understanding -> semantic matching -> candidate generation -> geography and quality -> reranking -> results



There are additional complications. We also found a separate scorer running entirely offline on the device. It has eight signals across 13 tiers and is distinct from both Oyster Rank and server-side Places ranking.
There isn’t a single Maps ranking formula. Different scoring and retrieval systems operate at different stages.
Turning the 72 Oyster Rank signals into a checklist of 72 Google Maps ranking factors would miss most of the architecture.
Local search doesn’t have a fixed radius
We also tested the geographic layer directly. A common local SEO model imagines Google looking within a predefined radius around the user and ranking the businesses found inside it.
Our measurements show something more dynamic. Using the same origin in Paris, the geographic footprint changed considerably depending on the query.
A dense query, such as pharmacie produced a far smaller search area than a brand query, such as Carrefour.
The environment matters, too. The same pharmacy query expanded dramatically when run in a sparsely populated rural area.
Google appears to adapt the candidate space to both the query and what exists around the user.
We then removed geographic weighting from the same engine. Across 5,083 calls and 86,584 results, the median distance moved from 6.87 km with geography to more than 4,000 km without it.
More interestingly, the non-geographic order remained extremely stable.
This suggests geography is doing more than reordering the same list of candidates by distance. It changes what the retrieval system considers in the first place.
Distance is still fundamental in local SEO. But “I’m closer, I should rank higher” is an incomplete model.
Dig deeper: The proximity paradox: Beating local SEO’s distance bias



Maps and the web are connected through entities
The connection between Maps and classic web SEO may be one of the most consequential findings in the corpus.
Geostore Features can connect to the Knowledge Graph through a MID. On the web index side, documents can also carry MIDs.
Google has a layer called webref that associates documents with entities and stores information, including topicality, confidence, geographic metadata, and document-level scores.
The relationship also works at the document-ranking level. The recovered structures describe a relative ranking signal between different documents for the same entity, along with properties such as whether a page is an author page, publisher page, or reference page.
This creates a very different way of thinking about a store locator or location page. Its role may extend beyond ranking for queries such as “shoe shop Paris.” The document can become evidence about the underlying entity.
The SEO objective is then partly to make it easy for Google to establish:
Which entity the document describes.
How much of the document is actually about that entity.
How confident that association should be.
Whether the document is a useful reference for it.
Web SEO and local SEO are much less separate inside Google’s infrastructure than their interfaces suggest.
Google understands concepts, not just categories
The semantic layer goes considerably beyond the primary category visible on a listing. Google uses GConcepts, a shared conceptual vocabulary that can describe businesses, dishes, attributes, cuisines, service modes, and other concepts.
We followed a simple ramen query through several parts of the system. The search results themselves didn’t all belong to one category. Google connected the query with ramen restaurants, Japanese restaurants, Asian restaurants, and other related concepts.
Inside listings, the semantic representation goes deeper. Review topics, menu dishes, and other attributes can be represented as entities rather than plain strings.
For an AI system, this is extremely useful. Instead of rereading thousands of reviews every time someone asks whether a restaurant has long waits or good ramen, Google can work from structured themes, entities, and precomputed signals already attached to the place.
Semantic understanding becomes much more important when the interface starts answering complex questions.
Part of Google’s geographic intelligence lives on the phone
Not everything is calculated on Google’s servers. We found on-device structures associated with visits, place candidates, frequent places, trips, home and work, mobility patterns, and user location profiles.
One particularly interesting object is ChainAffinity, which suggests the system can model affinity toward a recurring retail chain.
There’s also the separate offline scorer mentioned earlier.
The exact evidence level differs between components. Some structures are explicitly named in the recovered schema, while parts of the persona layer can only be reconstructed from compiled structures.
But the broader architecture is clear: The phone itself participates in building geographic context.
That means personalization in Maps can combine server-side knowledge of the world with a local model of the user’s own geography.
Ranking still doesn’t guarantee map visibility
Search results are only one of the outputs of Maps. 
The visual map has another problem to solve: Thousands of potentially relevant entities cannot all receive labels simultaneously. That job belongs partly to Mapcore.
We recovered 50,998 Mapcore styles and 12,936 label styles. Label visibility can change with zoom and other rendering conditions.
A business can be eligible or highly ranked and still fail to appear as a visible name on the map. Search ranking and map visibility are separate optimization problems.
This distinction becomes especially important when people measure “Maps visibility” using screenshots or map grids. The visual surface includes a rendering decision after retrieval and ranking have already happened.
Then Google puts Gemini on top
The timing of the recovery is useful. Google is rapidly expanding Ask Maps and other AI-powered experiences, but much of the infrastructure required to answer complex questions was already present.
The system already has:
Canonical place entities.
Semantic concepts and attributes.
Reviews and extracted topics.
Knowledge Graph relationships.
Web evidence.
Geographic retrieval.
Behavioral signals.
Personal geographic context.
Listing composition.
Ranking systems.
Gemini adds a conversational interface over these layers. That changes what a local query can be.
“Best ramen near me” is relatively easy. “Where can six people eat near my hotel tonight, with one vegetarian, little waiting time, and good recent feedback about service?” requires a different kind of place representation.
Google needs to know what the restaurant is, what it serves, when it’s open, what people say about it, where it is, how it relates to the user’s route or context, and whether the available evidence is reliable enough to recommend it.
Maps has been building many of those ingredients for years. The AI layer gives Google a new way to use them.
Dig deeper: Google Ask Maps: How to optimize for visibility



What to optimize beyond the Business Profile
The most actionable conclusion from this research isn’t a new list of ranking factors.
Local SEO has traditionally concentrated heavily on optimizing the Google Business Profile: categories, reviews, photos, attributes, opening hours, and other listing fields.
Those remain important.
But Google’s architecture suggests a broader objective: improve the representation Google can build of the entity itself.
For a business or retail brand, I would increasingly ask:
What exactly is this place?
What does it offer?
Which brand or chain does it belong to?
Which concepts and attributes describe it?
Does its website describe the same entity clearly?
Which Web documents provide evidence about it?
Does Google see real demand for the brand?
What do reviews consistently say about specific aspects of the experience?
Which audiences and contexts could make this place relevant?
When should Google recommend it rather than another candidate?
The quality of the answer Google can produce depends on the completeness of that representation.
Semantic completeness may become the next local SEO battleground
Proximity, relevance, and prominence remain useful concepts.
AI adds another requirement. The system needs enough structured evidence to reason about a place.
A restaurant can have an optimized profile and hundreds of reviews, yet still be poorly represented for a specific question if Google can’t confidently connect the relevant attributes, concepts, web pages, and review themes to the entity.
A large retail brand has an additional problem.
Google models both chains and individual locations. We observed stores from the same brand in the same metropolitan area carrying different primary concepts, even though the chain model contains a canonical concept structure.
Consistency can’t be assumed simply because every location belongs to the same brand.
For multi-location SEO, the task extends across the brand entity, each local entity, the website, structured data, third-party sources, user-generated content, and the relationships between all of them.
That’s a much larger surface than a Business Profile.
Dig deeper: Multi-location SEO: How to structure geographic pages at scale



  
more local customers.
      
Start winning locally
    
The listing is only the surface
The 72 Oyster Rank signals are fascinating because they expose categories of information Google can use when estimating the importance of a place.
The deeper finding is the system around them:
Geostore builds a canonical geographic entity from competing sources.
The Knowledge Graph gives that entity semantic meaning.
Webref connects Web documents to it.
Search and Places interpret the query and retrieve candidates within a dynamic geographic space.
On-device systems contribute personal geographic context.
Mapcore controls what reaches the visual map.
Ask Maps and Gemini can finally reason over the resulting representation in natural language.
The Google Business Profile still matters. But Google’s architecture suggests looking beyond the listing itself and considering the representation Google has built of the business.
The key question is whether that representation is complete enough for Google to confidently recommend the business.
The full study includes the reconstructed architecture, experiments, and technical evidence. The companion archive exposes the 10,936 recovered Geostore declarations alongside the 2024 documentation so the underlying schemas, enums, fields, and signals can be inspected directly.

Newly recovered Google data reveals how entities, web signals, geography, and semantic systems shape local visibility.

Inside Google Maps: 72 ranking signals and the architecture behind local search

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Before-scaling-programmatic-SEO-prove-the-pattern-works.png" class="attachment-large size-large wp-post-image" alt="Before scaling programmatic SEO, prove the pattern works" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Before-scaling-programmatic-SEO-prove-the-pattern-works.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Before-scaling-programmatic-SEO-prove-the-pattern-works-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Before-scaling-programmatic-SEO-prove-the-pattern-works-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Before-scaling-programmatic-SEO-prove-the-pattern-works-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Your first 50 pages can tell you whether the next 5,000 are worth building. Here’s how to turn a pilot into a scaling decision.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Before-scaling-programmatic-SEO-prove-the-pattern-works.png" class="attachment-large size-large wp-post-image" alt="Before scaling programmatic SEO, prove the pattern works" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Before-scaling-programmatic-SEO-prove-the-pattern-works.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Before-scaling-programmatic-SEO-prove-the-pattern-works-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Before-scaling-programmatic-SEO-prove-the-pattern-works-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Before-scaling-programmatic-SEO-prove-the-pattern-works-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph"><a href="https://searchengineland.com/guide/programmatic-seo">Programmatic SEO</a> is attractive because one strong page pattern can unlock hundreds or even thousands of search opportunities. That same scale can also amplify a weak idea very quickly.</p>



<p class="wp-block-paragraph">A team might identify 5,000 potential pages around locations, integrations, or use cases and build a template that works technically. The pages get launched, but only a fraction get indexed. Others attract overlapping queries or fail to gain meaningful visibility. By that point, the site is already carrying thousands of URLs that need crawling, internal links, and maintenance.</p>



<p class="wp-block-paragraph">The most important decision comes before scaling. First, prove that the underlying page pattern works across a representative sample.</p>



<p class="wp-block-paragraph">That means testing whether Google consistently discovers the pages, whether they attract the intended query families, and whether the strongest performers share identifiable characteristics. The pilot should also show whether the traffic produces useful business behavior.</p>



<p class="wp-block-paragraph">The results of that pilot can then guide the decision to scale.</p>



<h2 id="h-validate-the-pattern-before-building-the-library" class="wp-block-heading">Validate the pattern before building the library</h2>



<p class="wp-block-paragraph">Before building hundreds or thousands of pages, test whether the pattern can produce genuinely useful pages across different variations.</p>



<p class="wp-block-paragraph">Start by defining what changes from one URL to the next. For an integration-led SaaS strategy, that could be the connected tool, supported workflows, setup steps, screenshots, and product limitations. For location pages, it could be local inventory, provider availability, pricing, or market-specific data.</p>



<p class="wp-block-paragraph">Then take a small sample of candidate pages and sketch them before production begins.</p>



<p class="wp-block-paragraph">For example, imagine a project management platform planning pages for integrations with Slack, Salesforce, and Microsoft Teams. A useful Slack page should explain the actions users can trigger in Slack, show relevant workflows, and include setup instructions specific to that integration. The Salesforce page should contain equally specific information about CRM data flows and supported use cases.</p>



<p class="wp-block-paragraph">If most of the proposed pages would repeat the same copy with only a few fields changing, the template isn&#8217;t ready to scale. A simple pre-build check is to compare 10 candidate pages and ask:</p>



<ul class="wp-block-list">
<li>What information changes materially?</li>



<li>What user problem changes?</li>



<li>What proof or screenshots change?</li>



<li>What product capability changes?</li>



<li>What action should the visitor take next?</li>
</ul>



<p class="wp-block-paragraph">If those candidate pages show enough meaningful variation, there&#8217;s now something worth testing in search. Publish a small, representative group and use its performance to evaluate the page model before expanding further.</p>


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<h2 id="h-launch-a-representative-pilot" class="wp-block-heading">Launch a representative pilot</h2>



<p class="wp-block-paragraph">The pilot should reflect the range of pages you eventually plan to create. Testing only the easiest or highest-volume cases can give you a false sense of confidence.</p>



<p class="wp-block-paragraph">There is no universal pilot size. The right number depends on how much the dataset varies and how many different page conditions need to be tested. The sample should be large enough to expose patterns without requiring the full library to be built first.</p>



<p class="wp-block-paragraph">If the full opportunity includes location pages, for example, the pilot should include a mix of large markets, mid-sized markets, and smaller locations. It should also include pages with strong underlying data and pages where the available information is thinner.</p>



<p class="wp-block-paragraph">The same principle applies to integration or use-case pages. Include both obvious, high-demand combinations and weaker edge cases. The goal is to see where the template holds up and where it starts to break.</p>



<p class="wp-block-paragraph">Before publishing the pilot, define what each page is expected to achieve. Record the primary query, the related query family, the page that could potentially compete with it, and the action you want visitors to take. That gives you a clear baseline for evaluation later.</p>



<p class="wp-block-paragraph">A useful pilot does more than prove that the best pages can work. It shows whether the model can survive across different conditions before you scale it.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/semantic-programmatic-seo-blueprint-476262">A blueprint for semantic programmatic SEO</a></em></strong>



<h2 id="h-use-four-validation-gates-before-scaling" class="wp-block-heading">Use four validation gates before scaling</h2>



<p class="wp-block-paragraph">Once the pilot pages have been live long enough to collect meaningful data, evaluate them against four separate questions. Looking at traffic alone can hide problems in the underlying template.</p>



<h3 id="h-1-can-google-consistently-discover-and-index-the-pages" class="wp-block-heading">1. Can Google consistently discover and index the pages?</h3>



<p class="wp-block-paragraph">Start with discovery and indexing. Check whether Googlebot is finding the pilot pages through the internal linking structure. Then review how many pages enter the index and whether they remain there over time.</p>



<p class="wp-block-paragraph">The useful insight comes from comparing successful and unsuccessful pages.</p>



<p class="wp-block-paragraph">Suppose 40 out of 50 location pages remain indexed. The 10 excluded pages all have limited local inventory, while the indexed pages contain more providers and richer location-specific information. That pattern suggests inventory depth may be one factor worth testing before the next batch is published.</p>



<p class="wp-block-paragraph">The same analysis can be applied to integration pages, product categories, or other programmatic patterns. Group the pages by characteristics such as data depth, inventory size, or internal-link depth, and look for differences in indexing.</p>



<h3 id="h-2-are-the-pages-attracting-the-queries-they-were-built-for" class="wp-block-heading">2. Are the pages attracting the queries they were built for?</h3>



<p class="wp-block-paragraph">Next, compare the query set you expected before launch with the queries each URL actually receives in Search Console.</p>



<p class="wp-block-paragraph">Imagine a SaaS company testing pages for:</p>



<ul class="wp-block-list">
<li>CRM for accountants.</li>



<li>CRM for real estate agents.</li>



<li>CRM for consultants.</li>
</ul>



<p class="wp-block-paragraph">If each page begins receiving impressions for a distinct set of industry-specific searches, the differentiation is starting to show up in search performance.</p>



<p class="wp-block-paragraph">A different pattern may appear if all three URLs mainly receive impressions for generic terms such as “CRM software” and frequently compete with the main CRM page. That would suggest the industry distinction hasn&#8217;t translated clearly into search visibility.</p>



<p class="wp-block-paragraph">Look beyond the primary keyword here. The supporting query set often gives a better picture of whether Google understands the purpose of each page.</p>



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<h3 id="h-3-which-page-characteristics-are-associated-with-better-performance" class="wp-block-heading">3. Which page characteristics are associated with better performance?</h3>



<p class="wp-block-paragraph">Aggregate results can hide why some pages succeed while others fail. Segment the pilot by characteristics that could influence performance, such as:</p>



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



<li>Amount of unique data.</li>



<li>Inventory or product availability.</li>



<li>Internal-link depth.</li>



<li>SERP competition.</li>



<li>Content completeness.</li>
</ul>



<p class="wp-block-paragraph">Then compare how those groups perform.</p>



<p class="wp-block-paragraph">For example, a company testing 60 integration pages may discover that pages with detailed setup instructions and several supported workflows gain impressions consistently. Pages for integrations with limited functionality may remain poorly indexed and attract little demand.</p>



<p class="wp-block-paragraph">That finding can change the rollout plan. The company now has evidence for which integration types are worth expanding first and which ones need a stronger page model.</p>



<p class="wp-block-paragraph">The same approach works for marketplaces. Location pages with 20 or more active providers may gain visibility, while pages with only a handful of listings struggle. The useful result is the relationship between page characteristics and performance.</p>



<h3 id="h-4-does-the-traffic-lead-to-useful-behavior" class="wp-block-heading">4. Does the traffic lead to useful behavior?</h3>



<p class="wp-block-paragraph">Finally, check what users do after reaching the pilot pages.</p>



<p class="wp-block-paragraph">The right signal depends on the business model. A SaaS integration page might be evaluated through product exploration, trial starts, or demo requests. An ecommerce category page may be judged through product views and purchases. A marketplace page could be measured through provider views, inquiries, or bookings.</p>



<p class="wp-block-paragraph">Direct conversions may take time, so also review how the pages contribute to the wider journey. A programmatic landing page may introduce a user who later converts through another page.</p>



<p class="wp-block-paragraph">At the end of the review, you should understand which pages remain indexed and what queries they attract. You should also have a clearer view of the characteristics shared by stronger pages and whether those visits contribute to useful business outcomes. Those answers determine what happens next.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/programmatic-seo-real-world-examples-insights-433490">Exploring programmatic SEO: Real-world examples and insights</a></em></strong>



<h2 id="h-decide-what-the-pilot-has-actually-proven" class="wp-block-heading">Decide what the pilot has actually proven</h2>



<p class="wp-block-paragraph">Once the pilot has been evaluated, the next move should depend on what the data shows. There are three practical outcomes.</p>



<h3 id="h-scale-the-pattern" class="wp-block-heading">Scale the pattern</h3>



<p class="wp-block-paragraph">Scale when the pilot shows the page model works beyond a few isolated winners. The pattern should hold across the types of pages you expect to create in the wider rollout.</p>



<p class="wp-block-paragraph">Look for a consistent pattern:</p>



<ul class="wp-block-list">
<li>Pages are discovered and indexed reliably.</li>



<li>Each URL attracts the query family it was designed for.</li>



<li>Stronger performance can be explained by repeatable page characteristics.</li>



<li>Users take actions that matter to the business.</li>
</ul>



<p class="wp-block-paragraph">For example, a SaaS company may find that integration pages with detailed setup instructions and at least three supported workflows consistently gain visibility. Those characteristics can then become requirements for the next batch of pages.</p>



<p class="wp-block-paragraph">Scale in stages rather than generating the entire library at once. This makes it easier to check whether performance holds as the number of URLs increases.</p>



<h3 id="h-improve-the-pattern-and-test-again" class="wp-block-heading">Improve the pattern and test again</h3>



<p class="wp-block-paragraph">A pilot may reveal that only part of the idea works.</p>



<p class="wp-block-paragraph">Suppose a marketplace tests 100 city pages. Pages with a large number of active providers begin ranking, while locations with limited supply struggle to stay indexed.</p>



<p class="wp-block-paragraph">The next rollout can focus on cities that meet the stronger conditions. Pages with less inventory can be improved or held back until the underlying marketplace has enough supply.</p>



<p class="wp-block-paragraph">This is where segmentation from the previous section becomes useful. It helps identify which part of the template needs to change before another batch is published.</p>



<h3 id="h-stop-the-rollout" class="wp-block-heading">Stop the rollout</h3>



<p class="wp-block-paragraph">Stopping after a pilot can be as useful as deciding to scale. It can prevent thousands of weak URLs from entering the site and show the team where the original model needs to change.</p>



<p class="wp-block-paragraph">The same applies when only part of the pilot succeeds. Scale the segments with repeatable evidence, refine the weaker ones, and test them again before expanding further.</p>



<p class="wp-block-paragraph">Before growing the library, the first batch should have taught you what the next thousand pages need to get right.</p>


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<h2 id="h-let-the-pilot-determine-what-you-scale" class="wp-block-heading">Let the pilot determine what you scale</h2>



<p class="wp-block-paragraph">Programmatic SEO works best when you scale a page pattern that has already shown it can perform across different conditions. A representative pilot gives you the evidence to decide which pages to expand, which patterns need improvement, and which ideas should stop before they become thousands of URLs.</p>



<p class="wp-block-paragraph">Use what you learn from the first batch to shape the next one. If the pattern holds, scale it in stages and continue checking whether performance holds as the library grows.</p>

Programmatic SEO is attractive because one strong page pattern can unlock hundreds or even thousands of search opportunities. That same scale can also amplify a weak idea very quickly.
A team might identify 5,000 potential pages around locations, integrations, or use cases and build a template that works technically. The pages get launched, but only a fraction get indexed. Others attract overlapping queries or fail to gain meaningful visibility. By that point, the site is already carrying thousands of URLs that need crawling, internal links, and maintenance.
The most important decision comes before scaling. First, prove that the underlying page pattern works across a representative sample.
That means testing whether Google consistently discovers the pages, whether they attract the intended query families, and whether the strongest performers share identifiable characteristics. The pilot should also show whether the traffic produces useful business behavior.
The results of that pilot can then guide the decision to scale.
Validate the pattern before building the library
Before building hundreds or thousands of pages, test whether the pattern can produce genuinely useful pages across different variations.
Start by defining what changes from one URL to the next. For an integration-led SaaS strategy, that could be the connected tool, supported workflows, setup steps, screenshots, and product limitations. For location pages, it could be local inventory, provider availability, pricing, or market-specific data.
Then take a small sample of candidate pages and sketch them before production begins.
For example, imagine a project management platform planning pages for integrations with Slack, Salesforce, and Microsoft Teams. A useful Slack page should explain the actions users can trigger in Slack, show relevant workflows, and include setup instructions specific to that integration. The Salesforce page should contain equally specific information about CRM data flows and supported use cases.
If most of the proposed pages would repeat the same copy with only a few fields changing, the template isn’t ready to scale. A simple pre-build check is to compare 10 candidate pages and ask:
What information changes materially?
What user problem changes?
What proof or screenshots change?
What product capability changes?
What action should the visitor take next?
If those candidate pages show enough meaningful variation, there’s now something worth testing in search. Publish a small, representative group and use its performance to evaluate the page model before expanding further.
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Launch a representative pilot
The pilot should reflect the range of pages you eventually plan to create. Testing only the easiest or highest-volume cases can give you a false sense of confidence.
There is no universal pilot size. The right number depends on how much the dataset varies and how many different page conditions need to be tested. The sample should be large enough to expose patterns without requiring the full library to be built first.
If the full opportunity includes location pages, for example, the pilot should include a mix of large markets, mid-sized markets, and smaller locations. It should also include pages with strong underlying data and pages where the available information is thinner.
The same principle applies to integration or use-case pages. Include both obvious, high-demand combinations and weaker edge cases. The goal is to see where the template holds up and where it starts to break.
Before publishing the pilot, define what each page is expected to achieve. Record the primary query, the related query family, the page that could potentially compete with it, and the action you want visitors to take. That gives you a clear baseline for evaluation later.
A useful pilot does more than prove that the best pages can work. It shows whether the model can survive across different conditions before you scale it.
Dig deeper: A blueprint for semantic programmatic SEO



Use four validation gates before scaling
Once the pilot pages have been live long enough to collect meaningful data, evaluate them against four separate questions. Looking at traffic alone can hide problems in the underlying template.
1. Can Google consistently discover and index the pages?
Start with discovery and indexing. Check whether Googlebot is finding the pilot pages through the internal linking structure. Then review how many pages enter the index and whether they remain there over time.
The useful insight comes from comparing successful and unsuccessful pages.
Suppose 40 out of 50 location pages remain indexed. The 10 excluded pages all have limited local inventory, while the indexed pages contain more providers and richer location-specific information. That pattern suggests inventory depth may be one factor worth testing before the next batch is published.
The same analysis can be applied to integration pages, product categories, or other programmatic patterns. Group the pages by characteristics such as data depth, inventory size, or internal-link depth, and look for differences in indexing.
2. Are the pages attracting the queries they were built for?
Next, compare the query set you expected before launch with the queries each URL actually receives in Search Console.
Imagine a SaaS company testing pages for:
CRM for accountants.
CRM for real estate agents.
CRM for consultants.
If each page begins receiving impressions for a distinct set of industry-specific searches, the differentiation is starting to show up in search performance.
A different pattern may appear if all three URLs mainly receive impressions for generic terms such as “CRM software” and frequently compete with the main CRM page. That would suggest the industry distinction hasn’t translated clearly into search visibility.
Look beyond the primary keyword here. The supporting query set often gives a better picture of whether Google understands the purpose of each page.
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3. Which page characteristics are associated with better performance?
Aggregate results can hide why some pages succeed while others fail. Segment the pilot by characteristics that could influence performance, such as:
Search demand.
Amount of unique data.
Inventory or product availability.
Internal-link depth.
SERP competition.
Content completeness.
Then compare how those groups perform.
For example, a company testing 60 integration pages may discover that pages with detailed setup instructions and several supported workflows gain impressions consistently. Pages for integrations with limited functionality may remain poorly indexed and attract little demand.
That finding can change the rollout plan. The company now has evidence for which integration types are worth expanding first and which ones need a stronger page model.
The same approach works for marketplaces. Location pages with 20 or more active providers may gain visibility, while pages with only a handful of listings struggle. The useful result is the relationship between page characteristics and performance.
4. Does the traffic lead to useful behavior?
Finally, check what users do after reaching the pilot pages.
The right signal depends on the business model. A SaaS integration page might be evaluated through product exploration, trial starts, or demo requests. An ecommerce category page may be judged through product views and purchases. A marketplace page could be measured through provider views, inquiries, or bookings.
Direct conversions may take time, so also review how the pages contribute to the wider journey. A programmatic landing page may introduce a user who later converts through another page.
At the end of the review, you should understand which pages remain indexed and what queries they attract. You should also have a clearer view of the characteristics shared by stronger pages and whether those visits contribute to useful business outcomes. Those answers determine what happens next.
Dig deeper: Exploring programmatic SEO: Real-world examples and insights



Decide what the pilot has actually proven
Once the pilot has been evaluated, the next move should depend on what the data shows. There are three practical outcomes.
Scale the pattern
Scale when the pilot shows the page model works beyond a few isolated winners. The pattern should hold across the types of pages you expect to create in the wider rollout.
Look for a consistent pattern:
Pages are discovered and indexed reliably.
Each URL attracts the query family it was designed for.
Stronger performance can be explained by repeatable page characteristics.
Users take actions that matter to the business.
For example, a SaaS company may find that integration pages with detailed setup instructions and at least three supported workflows consistently gain visibility. Those characteristics can then become requirements for the next batch of pages.
Scale in stages rather than generating the entire library at once. This makes it easier to check whether performance holds as the number of URLs increases.
Improve the pattern and test again
A pilot may reveal that only part of the idea works.
Suppose a marketplace tests 100 city pages. Pages with a large number of active providers begin ranking, while locations with limited supply struggle to stay indexed.
The next rollout can focus on cities that meet the stronger conditions. Pages with less inventory can be improved or held back until the underlying marketplace has enough supply.
This is where segmentation from the previous section becomes useful. It helps identify which part of the template needs to change before another batch is published.
Stop the rollout
Stopping after a pilot can be as useful as deciding to scale. It can prevent thousands of weak URLs from entering the site and show the team where the original model needs to change.
The same applies when only part of the pilot succeeds. Scale the segments with repeatable evidence, refine the weaker ones, and test them again before expanding further.
Before growing the library, the first batch should have taught you what the next thousand pages need to get right.
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Let the pilot determine what you scale
Programmatic SEO works best when you scale a page pattern that has already shown it can perform across different conditions. A representative pilot gives you the evidence to decide which pages to expand, which patterns need improvement, and which ideas should stop before they become thousands of URLs.
Use what you learn from the first batch to shape the next one. If the pattern holds, scale it in stages and continue checking whether performance holds as the library grows.

Your first 50 pages can tell you whether the next 5,000 are worth building. Here’s how to turn a pilot into a scaling decision.

Before scaling programmatic SEO, prove the pattern works

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/The-11x-ROAS-account-that-lost-money-on-every-order.png" class="attachment-large size-large wp-post-image" alt="The 11x ROAS account that lost money on every order" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/The-11x-ROAS-account-that-lost-money-on-every-order.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/The-11x-ROAS-account-that-lost-money-on-every-order-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/The-11x-ROAS-account-that-lost-money-on-every-order-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/The-11x-ROAS-account-that-lost-money-on-every-order-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>High ROAS can mask negative contribution, weak incrementality, and costly inventory decisions. Here's how to measure what really matters.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/The-11x-ROAS-account-that-lost-money-on-every-order.png" class="attachment-large size-large wp-post-image" alt="The 11x ROAS account that lost money on every order" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/The-11x-ROAS-account-that-lost-money-on-every-order.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/The-11x-ROAS-account-that-lost-money-on-every-order-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/The-11x-ROAS-account-that-lost-money-on-every-order-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/The-11x-ROAS-account-that-lost-money-on-every-order-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">An 11x return on ad spend (<a href="https://searchengineland.com/your-roas-looks-great-but-is-it-actually-driving-growth-474543">ROAS</a>) sounds like a win. It can represent an outstanding ecommerce business. It can also represent a business quietly destroying cash.</p>



<p class="wp-block-paragraph">The difference is context. It&#8217;s probably the most overused phrase in marketing, other than “it depends.” It&#8217;s also the one people ignore most often.</p>



<h2 id="h-when-11x-roas-isn-t-what-it-seems" class="wp-block-heading">When 11x ROAS isn&#8217;t what it seems</h2>



<p class="wp-block-paragraph">We took over an ecommerce account that was reporting an 11x blended ROAS. The previous agency had it on a slide. The founder had quoted it to investors. Somewhere in the building, a finance director was staring at a cash flow forecast and quietly losing his mind because he was the only person in the company who knew both numbers.</p>



<p class="wp-block-paragraph">The account was losing money on every order. Not the bad orders. The average order. After the costs ROAS has never heard of, the contribution per order was negative, and it had been negative for some time.</p>



<p class="wp-block-paragraph">The figures below are a composite. The account is real, the pattern is real, and the numbers have been adjusted enough that no client is identifiable. </p>



<p class="wp-block-paragraph">Every step of the math is shown so you can run it against your own account. I&#8217;d recommend you do. Some of you are about to have a bad afternoon.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/high-roas-campaigns-more-budget-479149">Why high-ROAS campaigns don’t always deserve more budget</a></em></strong>


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<h2 id="h-what-11x-actually-claims" class="wp-block-heading">What 11x actually claims</h2>



<p class="wp-block-paragraph">An 11x ROAS makes exactly one claim: ad spend was about 9% of reported conversion value. That&#8217;s it. That&#8217;s the entire confession. Everything else people read into it — the profitability, the health of the business — is inference.</p>



<p class="wp-block-paragraph">The trouble is what lives inside “reported conversion value” and what never makes it into “ad spend.”</p>



<p class="wp-block-paragraph">Here&#8217;s the stack on a £100 apparel order. Conversion value includes VAT (UK-based) and is booked before returns, which is how nearly every Shopify and GA4 setup reports it. Not because anyone decided that. Because nobody decided anything.</p>



<ul class="wp-block-list">
<li>Reported conversion value: £100.00</li>



<li>Returns at 28%, booked after the conversion and invisible to the platform: £72.00 kept</li>



<li>VAT removed: £60.00 net revenue</li>



<li>COGS at 63% of net, a markdown-heavy trading period: £22.20 left</li>



<li>Fulfillment, outbound shipping subsidy, return postage and handling: £11.20 left</li>



<li>Payment and platform fees: £8.70 left</li>



<li>Ad cost at 11x ROAS: -£0.39</li>
</ul>



<p class="wp-block-paragraph">£11 back for every pound in, yet the company loses £0.39 on every £100 order. The dashboard and the P&amp;L describe the same orders. Only one of them is under oath.</p>



<p class="wp-block-paragraph">The 28% return rate is unremarkable for apparel. A 63% COGS rate is what promo-heavy trading looks like, which is exactly when ROAS peaks because discounted stock converts beautifully. The account looked its best at the precise moment it was performing its worst, and nobody noticed because the metric they were watching was incapable of showing it.</p>



<h2 id="h-the-blend-was-doing-the-rest" class="wp-block-heading">The blend was doing the rest</h2>



<p class="wp-block-paragraph">That per-order math would be damning enough if the 11x were honest. It was blended.</p>



<p class="wp-block-paragraph">Brand campaigns ran at roughly 18x and took most of the budget. Nonbrand sat around 3x. The headline figure was a weighted average of demand the brand already owned and a modest amount of actual work.</p>



<p class="wp-block-paragraph">A decent share of those brand orders would have arrived through organic or direct anyway. People who type your brand name into Google have, broadly speaking, already decided to buy from you. </p>



<p class="wp-block-paragraph">The true incremental return was well below 11x before the contribution stack even got involved. The account was taking credit for sales it didn&#8217;t cause, at a margin it didn&#8217;t have. As business models go, it&#8217;s a strong one. It just wasn&#8217;t the client&#8217;s.</p>



<p class="wp-block-paragraph">None of this is exotic. It&#8217;s the default state of an ecommerce account that has spent a few years being optimized to platform-reported revenue. The reporting isn&#8217;t lying. It&#8217;s answering a very narrow question while everyone in the room treats it as the answer to a broad one.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/separating-brand-non-brand-campaigns-roas-484080">Why separating brand and non-brand campaigns improves ROAS</a></em></strong>



<h2 id="h-the-part-nobody-warns-you-about" class="wp-block-heading">The part nobody warns you about</h2>



<p class="wp-block-paragraph">The textbook correction is to rebuild conversion values around contribution and bid to profit instead of revenue. We did this across our client base years ago. It works. POAS over ROAS is close to consensus now among people who think about ecommerce <a href="https://searchengineland.com/guide/what-is-paid-search">PPC</a> properly.</p>



<p class="wp-block-paragraph">Here&#8217;s what we learned from living with profit bidding for longer than most, and it&#8217;s the part I haven&#8217;t seen written anywhere: lean too hard into efficiency and you strangle the business from a different direction entirely.</p>



<p class="wp-block-paragraph">A tight efficiency target tells <a href="https://searchengineland.com/your-guide-to-google-ads-smart-bidding-453765">Smart Bidding</a> to buy only the cheapest, most certain conversions. It obliges. Volume drops. For a service business, that trade might be acceptable. For a retailer holding stock, it&#8217;s a serious problem because stock is a depreciating asset that happens to sit in a warehouse rather than anywhere a marketer might look at it.</p>



<p class="wp-block-paragraph">A fashion brand pays for its season months before it sells it. Every week a unit sits unsold, its recovery value falls and the eventual markdown gets deeper. Whatever survives the season gets cleared below cost or carried at a cost. And next season&#8217;s buy is funded by this season&#8217;s cash. An account tuned for maximum efficiency sells fewer units and quietly converts working capital into boxes.</p>



<p class="wp-block-paragraph">Consider a SKU with eight weeks left in season: 1,000 units at an £18 unit cost and £45 RRP. At a tight efficiency target, it sells 350 units at a strong margin, leaving 650 to be cleared at 70% off after the season, mostly below cost. </p>



<p class="wp-block-paragraph">Relax the target to 4x, and it sells 850 units at a lower per-unit margin, leaving just 150 to clear. The second path produces a worse ROAS but more total contribution, while getting cash back into the business in time to fund next season. The efficient setting was the expensive one.</p>



<p class="wp-block-paragraph">So the account in the title managed to lose money twice. On every order it made, through the contribution stack. And on every order it didn&#8217;t make, because it was too expensive to pursue. The first loss shows up in the P&amp;L. The second doesn&#8217;t, because there&#8217;s no report for the sales that never happened.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/paid-media-roas-is-wrong-485699">Your paid media ROAS isn’t 5x. Or 2x.</a></em></strong>



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<h2 id="h-how-to-optimize-google-ads-for-profit-and-cash-flow" class="wp-block-heading">How to optimize Google Ads for profit and cash flow</h2>



<p class="wp-block-paragraph">The solution comes down to four changes in how you set targets and manage the account.</p>



<h3 id="h-rebuild-conversion-value-as-contribution" class="wp-block-heading">Rebuild conversion value as contribution</h3>



<p class="wp-block-paragraph">Net of VAT, expected returns by category, COGS, and fulfillment. Send that to Google as the value it bids against. Until the algorithm can see margin, every target you set is being negotiated in a fictional currency.</p>



<h3 id="h-separate-brand-before-judging-anything" class="wp-block-heading">Separate brand before judging anything</h3>



<p class="wp-block-paragraph">Brand harvest at 18x tells you nothing except that people like the brand. Grade nonbrand on its own numbers, ideally on new-customer contribution.</p>



<h3 id="h-determine-one-job-per-sku" class="wp-block-heading">Determine one job per SKU</h3>



<p class="wp-block-paragraph">An SKU can be run for profit, run for volume, or run to get cash back before the season closes. It can&#8217;t do two of those at once. The bidding strategies actively conflict. We reassign each SKU&#8217;s job weekly against the P&amp;L. It is deeply unglamorous, and it&#8217;s the single change that got our clients&#8217; ad accounts and finance teams speaking to each other again.</p>



<h3 id="h-decide-the-cash-question-out-loud" class="wp-block-heading">Decide the cash question out loud</h3>



<p class="wp-block-paragraph">For a stock-holding retailer, target setting is a weekly commercial call on where each SKU sits between margin, sell-through, and cash recovery. Sometimes the less profitable sale is the smarter move. That decision belongs to the business, not to a blended tROAS somebody set in January.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/building-high-roas-ecommerce-search-campaigns-in-google-shopping-and-amazon-ads-473378">Building high-ROAS ecommerce search campaigns in Google Shopping and Amazon Ads</a></em></strong>


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<h2 id="h-when-lower-roas-is-the-better-result" class="wp-block-heading">When lower ROAS is the better result</h2>



<p class="wp-block-paragraph">When we restructured the account, blended ROAS fell. We separated brand, rebuilt values around contribution, and set volume targets against sell-through. The reporting got uglier, but the business got healthier. Contribution per order turned positive and cash began arriving during the season rather than at the clearance sale.</p>



<p class="wp-block-paragraph">If your account is posting a number that would embarrass a hedge fund, find out what the number is actually measuring before it goes on a slide. Because an 11x on the wrong catalog is mostly a list of everything the account declined to sell.</p>

An 11x return on ad spend (ROAS) sounds like a win. It can represent an outstanding ecommerce business. It can also represent a business quietly destroying cash.
The difference is context. It’s probably the most overused phrase in marketing, other than “it depends.” It’s also the one people ignore most often.
When 11x ROAS isn’t what it seems
We took over an ecommerce account that was reporting an 11x blended ROAS. The previous agency had it on a slide. The founder had quoted it to investors. Somewhere in the building, a finance director was staring at a cash flow forecast and quietly losing his mind because he was the only person in the company who knew both numbers.
The account was losing money on every order. Not the bad orders. The average order. After the costs ROAS has never heard of, the contribution per order was negative, and it had been negative for some time.
The figures below are a composite. The account is real, the pattern is real, and the numbers have been adjusted enough that no client is identifiable. 
Every step of the math is shown so you can run it against your own account. I’d recommend you do. Some of you are about to have a bad afternoon.
Dig deeper: Why high-ROAS campaigns don’t always deserve more budget



  
your competitors win.
      
Analyze your competitors
    
What 11x actually claims
An 11x ROAS makes exactly one claim: ad spend was about 9% of reported conversion value. That’s it. That’s the entire confession. Everything else people read into it — the profitability, the health of the business — is inference.
The trouble is what lives inside “reported conversion value” and what never makes it into “ad spend.”
Here’s the stack on a £100 apparel order. Conversion value includes VAT (UK-based) and is booked before returns, which is how nearly every Shopify and GA4 setup reports it. Not because anyone decided that. Because nobody decided anything.
Reported conversion value: £100.00
Returns at 28%, booked after the conversion and invisible to the platform: £72.00 kept
VAT removed: £60.00 net revenue
COGS at 63% of net, a markdown-heavy trading period: £22.20 left
Fulfillment, outbound shipping subsidy, return postage and handling: £11.20 left
Payment and platform fees: £8.70 left
Ad cost at 11x ROAS: -£0.39
£11 back for every pound in, yet the company loses £0.39 on every £100 order. The dashboard and the P&L describe the same orders. Only one of them is under oath.
The 28% return rate is unremarkable for apparel. A 63% COGS rate is what promo-heavy trading looks like, which is exactly when ROAS peaks because discounted stock converts beautifully. The account looked its best at the precise moment it was performing its worst, and nobody noticed because the metric they were watching was incapable of showing it.
The blend was doing the rest
That per-order math would be damning enough if the 11x were honest. It was blended.
Brand campaigns ran at roughly 18x and took most of the budget. Nonbrand sat around 3x. The headline figure was a weighted average of demand the brand already owned and a modest amount of actual work.
A decent share of those brand orders would have arrived through organic or direct anyway. People who type your brand name into Google have, broadly speaking, already decided to buy from you. 
The true incremental return was well below 11x before the contribution stack even got involved. The account was taking credit for sales it didn’t cause, at a margin it didn’t have. As business models go, it’s a strong one. It just wasn’t the client’s.
None of this is exotic. It’s the default state of an ecommerce account that has spent a few years being optimized to platform-reported revenue. The reporting isn’t lying. It’s answering a very narrow question while everyone in the room treats it as the answer to a broad one.
Dig deeper: Why separating brand and non-brand campaigns improves ROAS



The part nobody warns you about
The textbook correction is to rebuild conversion values around contribution and bid to profit instead of revenue. We did this across our client base years ago. It works. POAS over ROAS is close to consensus now among people who think about ecommerce PPC properly.
Here’s what we learned from living with profit bidding for longer than most, and it’s the part I haven’t seen written anywhere: lean too hard into efficiency and you strangle the business from a different direction entirely.
A tight efficiency target tells Smart Bidding to buy only the cheapest, most certain conversions. It obliges. Volume drops. For a service business, that trade might be acceptable. For a retailer holding stock, it’s a serious problem because stock is a depreciating asset that happens to sit in a warehouse rather than anywhere a marketer might look at it.
A fashion brand pays for its season months before it sells it. Every week a unit sits unsold, its recovery value falls and the eventual markdown gets deeper. Whatever survives the season gets cleared below cost or carried at a cost. And next season’s buy is funded by this season’s cash. An account tuned for maximum efficiency sells fewer units and quietly converts working capital into boxes.
Consider a SKU with eight weeks left in season: 1,000 units at an £18 unit cost and £45 RRP. At a tight efficiency target, it sells 350 units at a strong margin, leaving 650 to be cleared at 70% off after the season, mostly below cost. 
Relax the target to 4x, and it sells 850 units at a lower per-unit margin, leaving just 150 to clear. The second path produces a worse ROAS but more total contribution, while getting cash back into the business in time to fund next season. The efficient setting was the expensive one.
So the account in the title managed to lose money twice. On every order it made, through the contribution stack. And on every order it didn’t make, because it was too expensive to pursue. The first loss shows up in the P&L. The second doesn’t, because there’s no report for the sales that never happened.
Dig deeper: Your paid media ROAS isn’t 5x. Or 2x.




	
	
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How to optimize Google Ads for profit and cash flow
The solution comes down to four changes in how you set targets and manage the account.
Rebuild conversion value as contribution
Net of VAT, expected returns by category, COGS, and fulfillment. Send that to Google as the value it bids against. Until the algorithm can see margin, every target you set is being negotiated in a fictional currency.
Separate brand before judging anything
Brand harvest at 18x tells you nothing except that people like the brand. Grade nonbrand on its own numbers, ideally on new-customer contribution.
Determine one job per SKU
An SKU can be run for profit, run for volume, or run to get cash back before the season closes. It can’t do two of those at once. The bidding strategies actively conflict. We reassign each SKU’s job weekly against the P&L. It is deeply unglamorous, and it’s the single change that got our clients’ ad accounts and finance teams speaking to each other again.
Decide the cash question out loud
For a stock-holding retailer, target setting is a weekly commercial call on where each SKU sits between margin, sell-through, and cash recovery. Sometimes the less profitable sale is the smarter move. That decision belongs to the business, not to a blended tROAS somebody set in January.
Dig deeper: Building high-ROAS ecommerce search campaigns in Google Shopping and Amazon Ads



  
a customer you lose.
      
See who’s stealing your traffic
    
When lower ROAS is the better result
When we restructured the account, blended ROAS fell. We separated brand, rebuilt values around contribution, and set volume targets against sell-through. The reporting got uglier, but the business got healthier. Contribution per order turned positive and cash began arriving during the season rather than at the clearance sale.
If your account is posting a number that would embarrass a hedge fund, find out what the number is actually measuring before it goes on a slide. Because an 11x on the wrong catalog is mostly a list of everything the account declined to sell.

High ROAS can mask negative contribution, weak incrementality, and costly inventory decisions. Here's how to measure what really matters.

The 11x ROAS account that lost money on every order

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/09/google-eu-broken-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/google-eu-broken-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/google-eu-broken-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/google-eu-broken-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google said "the changes mark the largest reduction in quality of service ‌at the world's most popular internet search engine in its 29-year search history."

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/09/google-eu-broken-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/google-eu-broken-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/google-eu-broken-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/google-eu-broken-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Google told Reuters that the launch of the most recent changes to the search results in the European Union under the DMA requirements led to the worst degradation of search quality in its history. &#8220;The changes mark the largest reduction in quality of service ‌at the world&#8217;s most popular internet search engine in its 29-year search history,&#8221; Google official&nbsp;<a href="https://www.reuters.com/world/google-warns-lower-quality-it-revamps-europe-search-results-avoid-eu-fines-2026-09-08/">told Reuters</a>.</p>



<p class="wp-block-paragraph"><strong>What Google said. </strong>Google told Reuters:</p>



<ul class="wp-block-list">
<li>&#8220;Google said it sees that in reality the ​changes favor price comparison sites, also known as vertical search services (VSS), linked to sectors including ​hotels, airlines and restaurants, such as Expedia or Booking.com.&#8221;</li>



<li>&#8220;They get more prominence in ⁠search results over companies in those sectors that are listed with just a link to their websites, ​telephone numbers and address.&#8221;</li>



<li>&#8220;The revamped search results will ​highlight one specialized search engine at the top of the page, followed by two others with fewer details while a ‌carousel ⁠of hotels, airlines and restaurants for example will sit below them with key features such as real-time prices stripped out. The rankings will be determined by Google&#8217;s algorithm.&#8221;</li>
</ul>



<p class="wp-block-paragraph"><strong>More details. </strong>Google has been going back and forth with the EU over the Digital Markets Act and <a href="https://searchengineland.com/google-proposes-new-search-display-to-avoid-eu-antitrust-fine-457969">proposing</a> <a href="https://searchengineland.com/googles-new-search-experiences-in-the-european-economic-area-437548">changes</a> to its search results for a <a href="https://searchengineland.com/google-changes-digital-markets-act-compliance-438158">while now</a>.</p>



<p class="wp-block-paragraph">Now that these changes are live, Google is saying these changes have reduced its quality of its search service.</p>



<p class="wp-block-paragraph">In 2024, Google <a href="https://blog.google/feed/sharing-data-on-our-dma-hotels-test/">tested the changes</a>, so it is no surprise Google is saying this now.</p>



<p class="wp-block-paragraph">Google also added a <a href="https://developers.google.com/search/docs/appearance/aggregator-features">new help document</a> today documenting regional differences in its search experience.</p>



<p class="wp-block-paragraph"><strong>Why we care. </strong>These changes only impact those searching in the European regions. But that is a big percentage of Google&#8217;s searchers. Of course, Google doesn&#8217;t want to make these changes, but Google has been forced by the European Commision. Does it really have a negative impact on search quality for the end user? </p>


Google told Reuters that the launch of the most recent changes to the search results in the European Union under the DMA requirements led to the worst degradation of search quality in its history. “The changes mark the largest reduction in quality of service ‌at the world’s most popular internet search engine in its 29-year search history,” Google official told Reuters.
What Google said. Google told Reuters:
“Google said it sees that in reality the ​changes favor price comparison sites, also known as vertical search services (VSS), linked to sectors including ​hotels, airlines and restaurants, such as Expedia or Booking.com.”
“They get more prominence in ⁠search results over companies in those sectors that are listed with just a link to their websites, ​telephone numbers and address.”
“The revamped search results will ​highlight one specialized search engine at the top of the page, followed by two others with fewer details while a ‌carousel ⁠of hotels, airlines and restaurants for example will sit below them with key features such as real-time prices stripped out. The rankings will be determined by Google’s algorithm.”
More details. Google has been going back and forth with the EU over the Digital Markets Act and proposing changes to its search results for a while now.
Now that these changes are live, Google is saying these changes have reduced its quality of its search service.
In 2024, Google tested the changes, so it is no surprise Google is saying this now.
Google also added a new help document today documenting regional differences in its search experience.
Why we care. These changes only impact those searching in the European regions. But that is a big percentage of Google’s searchers. Of course, Google doesn’t want to make these changes, but Google has been forced by the European Commision. Does it really have a negative impact on search quality for the end user?

Google said "the changes mark the largest reduction in quality of service ‌at the world's most popular internet search engine in its 29-year search history."

Google says DMA changes in EU resulted in worse degradation of search quality ever

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/How-to-build-an-AI-governance-framework-for-SEO.png" class="attachment-large size-large wp-post-image" alt="How to build an AI governance framework for SEO" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/How-to-build-an-AI-governance-framework-for-SEO.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-build-an-AI-governance-framework-for-SEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-build-an-AI-governance-framework-for-SEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-build-an-AI-governance-framework-for-SEO-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>From hallucinated metrics to exposed customer data, here’s how to spot common AI risks and put practical guardrails in place.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/How-to-build-an-AI-governance-framework-for-SEO.png" class="attachment-large size-large wp-post-image" alt="How to build an AI governance framework for SEO" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/How-to-build-an-AI-governance-framework-for-SEO.png 1920w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-build-an-AI-governance-framework-for-SEO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-build-an-AI-governance-framework-for-SEO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/How-to-build-an-AI-governance-framework-for-SEO-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p class="wp-block-paragraph">Somewhere between &#8220;just ask ChatGPT&#8221; and a full-blown incident response plan, most <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> teams are winging it.</p>



<p class="wp-block-paragraph">We&#8217;ve all watched someone paste a client&#8217;s entire GA4 export into a random AI tool &#8220;just to see what it says.&#8221; We&#8217;ve all seen an LLM confidently invent a Search Console metric that doesn&#8217;t exist. And we&#8217;ve all used the biggest, priciest model for a task a toddler with a calculator could&#8217;ve handled.</p>



<p class="wp-block-paragraph">I got tired of watching this happen, so I wrote an internal framework for how my teams use AI day to day. It wasn&#8217;t built for a trade publication. It was built to stop people from doing something stupid with customer data at 4 p.m. on a Friday. But adapting it for this audience made me realize how few SEO teams have practical guardrails for AI — and how badly they need them.</p>



<p class="wp-block-paragraph">So consider this your starter kit: Not legal advice or a compliance lecture, just practical guidance for using AI in SEO without creating an &#8220;AI incident waiting to happen.&#8221;</p>



<h2 id="h-the-mandate-ai-is-a-research-partner-not-your-replacement-judgment" class="wp-block-heading">The mandate: AI is a research partner, not your replacement judgment</h2>



<p class="wp-block-paragraph">The whole framework sits on one idea: AI should be a catalyst for human thinking, not a substitute for it. In SEO terms, that means the model can draft your content brief, cluster your keywords, or summarize a 40-page technical audit in nine seconds flat, but it doesn&#8217;t get to decide anything on your behalf. You still own the call.</p>



<p class="wp-block-paragraph">We built this around five pillars:&nbsp;</p>



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



<li>Accountability.</li>



<li>Security.</li>



<li>Fairness.</li>



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



<p class="wp-block-paragraph">They sound like a corporate poster. They&#8217;re not. Here&#8217;s what they actually mean when you&#8217;re the one doing the keyword research.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/guide/ai-governance-in-seo">AI Governance in SEO: Balancing automation &amp; oversight</a></em></strong>


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        See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
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      <span id="semrush-one-cta">See your AI visibility</span>
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<h3 id="h-accuracy-ai-is-very-confident-and-confidently-wrong" class="wp-block-heading">Accuracy: AI is very confident, and confidently wrong</h3>



<p class="wp-block-paragraph">This is the one every SEO already half-knows and still ignores under deadline. AI will happily hallucinate a search volume, misquote an <a href="https://searchengineland.com/library/platforms/google/google-algorithm-updates">algorithm update</a>, or invent a source that sounds exactly credible enough to slip past you. It&#8217;s designed to sound convincing even when it&#8217;s talking absolute nonsense.</p>



<p class="wp-block-paragraph">The fix isn&#8217;t complicated: Treat every AI output the way you&#8217;d treat a junior analyst&#8217;s first draft. Good instincts, needs checking, don&#8217;t publish it raw.</p>



<h3 id="h-accountability-if-you-hit-publish-it-s-yours" class="wp-block-heading">Accountability: if you hit publish, it&#8217;s yours</h3>



<p class="wp-block-paragraph">It doesn&#8217;t matter how much of the piece the model wrote. If your name, or your client&#8217;s brand, is on it, you own every claim in it. This matters more in SEO than in almost any other area of marketing, because so much of our output ends up as public, indexable, citable content.</p>



<p class="wp-block-paragraph">An AI-fabricated stat in a blog post doesn&#8217;t just embarrass you. It becomes the thing Google&#8217;s AI Overview quotes back to someone else next month.</p>



<h3 id="h-security-your-prompts-are-not-a-diary" class="wp-block-heading">Security: Your prompts are not a diary</h3>



<p class="wp-block-paragraph">This is the one that keeps me up at night as someone who&#8217;s had access to real customer and performance data. </p>



<p class="wp-block-paragraph">Never put customer data, employee data, or confidential business info into an AI tool that isn&#8217;t approved by your company or client. That includes the &#8220;quick&#8221; trial version of a shiny new tool someone found on LinkedIn last Tuesday.</p>



<p class="wp-block-paragraph">If you&#8217;re testing an unapproved tool, a few non-negotiables:</p>



<ul class="wp-block-list">
<li>No personally identifiable information (PII), customer data, or confidential data, full stop.</li>



<li>The provider shouldn&#8217;t be allowed to train on your data. Get that in writing.</li>



<li>Keep it time-bound. Trials end. Data gets deleted.</li>



<li>No third-party tools that take control of your browser and quietly hoover up everything you&#8217;re looking at.</li>
</ul>



<p class="wp-block-paragraph">If it feels like something you&#8217;d need to explain to your legal team afterward, don&#8217;t do it beforehand.</p>



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<hr class="wp-block-separator has-text-color has-cyan-bluish-gray-color has-css-opacity has-cyan-bluish-gray-background-color has-background"/>



<h3 id="h-sustainability-stop-using-a-sledgehammer-to-crack-a-nut" class="wp-block-heading">Sustainability: Stop using a sledgehammer to crack a nut</h3>



<p class="wp-block-paragraph">This is the pillar SEOs skip most, and it&#8217;s the one I actually think about the most now. Not every task needs your most powerful model. Save the heavy-duty reasoning models for the heavy-duty jobs: actual coding, complex data work, genuinely hard problems.</p>



<p class="wp-block-paragraph">For the everyday stuff — drafting a meta description, summarizing a competitor page, rewording an internal Slack message — the lightest available model does the job just fine, faster, and for a fraction of the compute cost.</p>



<p class="wp-block-paragraph">This isn&#8217;t just a cost play, though it is that too. Over-automating everything, even the trivial stuff, is how teams end up dependent on a tool for tasks they could&#8217;ve done themselves in 30 seconds. </p>



<p class="wp-block-paragraph">Use AI when it adds real value. Otherwise, you&#8217;re just adding a middleman.</p>



<h3 id="h-fairness-the-model-has-opinions-it-didn-t-tell-you-about" class="wp-block-heading">Fairness: The model has opinions it didn&#8217;t tell you about</h3>



<p class="wp-block-paragraph">AI models carry the biases of their training data, and in content and keyword work, that shows up in subtle ways: assumptions baked into tone, framing, or who a piece of content is &#8220;for.&#8221; Actively look for it. Model inclusive language use yourself, rather than assuming the AI will get there unprompted.</p>



<p class="wp-block-paragraph"><strong><em>Dig deeper: <a href="https://searchengineland.com/governance-maturity-seo-468602">Why governance maturity is a competitive advantage for SEO</a></em></strong>



<h2 id="h-the-bit-nobody-talks-about-governance-and-culture" class="wp-block-heading">The bit nobody talks about: Governance and culture</h2>



<p class="wp-block-paragraph">Here&#8217;s what I think most SEO teams get wrong when they try to formalize AI use: They write the rules and never build the culture around them. And the thing is, most people want to do better, so why not give them an opportunity to do so?</p>



<p class="wp-block-paragraph">What I&#8217;ve learned in nearly two years of building this framework with various brands is that things only work when you give it an open feedback living space: a Slack channel, for example, let&#8217;s say #gen-ai, where people share what they&#8217;ve built, what broke, and what worked. This way, everyone&#8217;s contributing, from software engineers to content writers.</p>



<p class="wp-block-paragraph"><strong>A true story from one of my clients: </strong>



<p class="wp-block-paragraph">The CEO of an energy company built a complaint-response generator one afternoon that proved to be a useful internal tool, and instead of it dying in their personal drive, it gets shared, reviewed, and potentially rolled out properly. Good code gets checked in for a security review before it goes anywhere near a broad audience or production. Incidents get reported immediately, not buried.</p>



<p class="wp-block-paragraph">None of that happens if &#8220;AI governance&#8221; is a PDF nobody reads after week one.</p>


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




<h2 id="h-steal-this-adapt-it-don-t-skip-it" class="wp-block-heading">Steal this, adapt it, don&#8217;t skip it</h2>



<p class="wp-block-paragraph">You don&#8217;t need my exact stack to build a version of this for your own SEO team or agency. What you need is:</p>



<ul class="wp-block-list">
<li>A short, plain-English set of principles everyone actually reads (five pillars, not 50).</li>



<li>A clear line on data — what can and can&#8217;t touch an AI tool.</li>



<li>Guidance on picking the right-sized tool for the task, not the flashiest one.</li>



<li>A living channel for sharing wins, failures, and near-misses.</li>



<li>An actual person or team to flag incidents to, so &#8220;oh no&#8221; doesn&#8217;t become &#8220;oh no, and nobody knew.&#8221;</li>
</ul>



<p class="wp-block-paragraph">AI isn&#8217;t going away, and neither is the temptation to move fast and skip the guardrails. The best time to put those guardrails in place is before you need them.</p>

Somewhere between “just ask ChatGPT” and a full-blown incident response plan, most SEO teams are winging it.
We’ve all watched someone paste a client’s entire GA4 export into a random AI tool “just to see what it says.” We’ve all seen an LLM confidently invent a Search Console metric that doesn’t exist. And we’ve all used the biggest, priciest model for a task a toddler with a calculator could’ve handled.
I got tired of watching this happen, so I wrote an internal framework for how my teams use AI day to day. It wasn’t built for a trade publication. It was built to stop people from doing something stupid with customer data at 4 p.m. on a Friday. But adapting it for this audience made me realize how few SEO teams have practical guardrails for AI — and how badly they need them.
So consider this your starter kit: Not legal advice or a compliance lecture, just practical guidance for using AI in SEO without creating an “AI incident waiting to happen.”
The mandate: AI is a research partner, not your replacement judgment
The whole framework sits on one idea: AI should be a catalyst for human thinking, not a substitute for it. In SEO terms, that means the model can draft your content brief, cluster your keywords, or summarize a 40-page technical audit in nine seconds flat, but it doesn’t get to decide anything on your behalf. You still own the call.
We built this around five pillars: 
Accuracy.
Accountability.
Security.
Fairness.
Sustainability. 
They sound like a corporate poster. They’re not. Here’s what they actually mean when you’re the one doing the keyword research.
Dig deeper: AI Governance in SEO: Balancing automation & oversight



  
AI recommends.
      
See your AI visibility
    
Accuracy: AI is very confident, and confidently wrong
This is the one every SEO already half-knows and still ignores under deadline. AI will happily hallucinate a search volume, misquote an algorithm update, or invent a source that sounds exactly credible enough to slip past you. It’s designed to sound convincing even when it’s talking absolute nonsense.
The fix isn’t complicated: Treat every AI output the way you’d treat a junior analyst’s first draft. Good instincts, needs checking, don’t publish it raw.
Accountability: if you hit publish, it’s yours
It doesn’t matter how much of the piece the model wrote. If your name, or your client’s brand, is on it, you own every claim in it. This matters more in SEO than in almost any other area of marketing, because so much of our output ends up as public, indexable, citable content.
An AI-fabricated stat in a blog post doesn’t just embarrass you. It becomes the thing Google’s AI Overview quotes back to someone else next month.
Security: Your prompts are not a diary
This is the one that keeps me up at night as someone who’s had access to real customer and performance data. 
Never put customer data, employee data, or confidential business info into an AI tool that isn’t approved by your company or client. That includes the “quick” trial version of a shiny new tool someone found on LinkedIn last Tuesday.
If you’re testing an unapproved tool, a few non-negotiables:
No personally identifiable information (PII), customer data, or confidential data, full stop.
The provider shouldn’t be allowed to train on your data. Get that in writing.
Keep it time-bound. Trials end. Data gets deleted.
No third-party tools that take control of your browser and quietly hoover up everything you’re looking at.
If it feels like something you’d need to explain to your legal team afterward, don’t do it beforehand.
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Sustainability: Stop using a sledgehammer to crack a nut
This is the pillar SEOs skip most, and it’s the one I actually think about the most now. Not every task needs your most powerful model. Save the heavy-duty reasoning models for the heavy-duty jobs: actual coding, complex data work, genuinely hard problems.
For the everyday stuff — drafting a meta description, summarizing a competitor page, rewording an internal Slack message — the lightest available model does the job just fine, faster, and for a fraction of the compute cost.
This isn’t just a cost play, though it is that too. Over-automating everything, even the trivial stuff, is how teams end up dependent on a tool for tasks they could’ve done themselves in 30 seconds. 
Use AI when it adds real value. Otherwise, you’re just adding a middleman.
Fairness: The model has opinions it didn’t tell you about
AI models carry the biases of their training data, and in content and keyword work, that shows up in subtle ways: assumptions baked into tone, framing, or who a piece of content is “for.” Actively look for it. Model inclusive language use yourself, rather than assuming the AI will get there unprompted.
Dig deeper: Why governance maturity is a competitive advantage for SEO



The bit nobody talks about: Governance and culture
Here’s what I think most SEO teams get wrong when they try to formalize AI use: They write the rules and never build the culture around them. And the thing is, most people want to do better, so why not give them an opportunity to do so?
What I’ve learned in nearly two years of building this framework with various brands is that things only work when you give it an open feedback living space: a Slack channel, for example, let’s say #gen-ai, where people share what they’ve built, what broke, and what worked. This way, everyone’s contributing, from software engineers to content writers.
A true story from one of my clients: 



The CEO of an energy company built a complaint-response generator one afternoon that proved to be a useful internal tool, and instead of it dying in their personal drive, it gets shared, reviewed, and potentially rolled out properly. Good code gets checked in for a security review before it goes anywhere near a broad audience or production. Incidents get reported immediately, not buried.
None of that happens if “AI governance” is a PDF nobody reads after week one.
customers won’t either.
      
See your AI visibility
    
Steal this, adapt it, don’t skip it
You don’t need my exact stack to build a version of this for your own SEO team or agency. What you need is:
A short, plain-English set of principles everyone actually reads (five pillars, not 50).
A clear line on data — what can and can’t touch an AI tool.
Guidance on picking the right-sized tool for the task, not the flashiest one.
A living channel for sharing wins, failures, and near-misses.
An actual person or team to flag incidents to, so “oh no” doesn’t become “oh no, and nobody knew.”
AI isn’t going away, and neither is the temptation to move fast and skip the guardrails. The best time to put those guardrails in place is before you need them.

From hallucinated metrics to exposed customer data, here’s how to spot common AI risks and put practical guardrails in place.

How to build an AI governance framework for SEO

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Research finds a measurable digital visibility gap between expanding and shrinking restaurant chains across search, reviews, social, and AI.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div><div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header.jpg" alt="" class="wp-image-486657" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-header-1200x675.jpg 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></figure>
</div>


<p class="wp-block-paragraph">You’ve seen the headlines this year. Culver’s, Texas Roadhouse, and Nothing Bundt Cakes are <a href="https://thefw.com/fastest-growing-chain-restaurants-new-locations/" target="_blank" rel="noopener sponsored nofollow">opening</a> hundreds of new locations. Wendy’s, Papa John’s, and Pizza Hut are <a href="https://finance.yahoo.com/economy/articles/chain-restaurants-closing-hundreds-locations-135201078.html?guccounter=2" target="_blank" rel="noopener sponsored nofollow">closing</a> hundreds. Jack in the Box, Red Robin, Denny’s, and Red Lobster have trimmed their footprints, too.</p>



<p class="wp-block-paragraph">The easy explanation is the economy, menu fatigue, or private equity. Those factors likely play a role.&nbsp;</p>



<p class="wp-block-paragraph">But when we analyzed <a href="https://www.soci.ai/insights/lvi/?utm_source=searchengineland&amp;utm_medium=syn" target="_blank" rel="noopener sponsored nofollow">Local Visibility Index (LVI) data</a> comparing eight expanding restaurant brands with eight contracting ones, we found something missing from most of that coverage: a consistent, measurable gap in how these brands show up online across every channel that drives local discovery: search, reviews, social, and AI.</p>



<p class="wp-block-paragraph">This isn’t a claim that a Yelp rating directly causes a store closure. It’s something more useful for marketers: a real-time signal.&nbsp;</p>



<p class="wp-block-paragraph">The same digital fundamentals reflected in a company’s local visibility data appear to move in step with the fundamentals driving its real estate decisions, often before the story reaches the trade press.</p>



<p class="wp-block-paragraph">The LVI score is <a href="https://www.soci.ai/?utm_source=searchengineland&amp;utm_medium=syn" data-type="link" data-id="https://www.soci.ai/insights/lvi/?utm_source=searchengineland&amp;utm_medium=syn" target="_blank" rel="noopener sponsored nofollow">SOCi’s</a> composite measure of a brand’s local digital visibility, combining performance across search rankings, review sentiment, social engagement, and AI recommendation rates into a single 0–100 score. </p>



<p class="wp-block-paragraph">In this analysis, the gap was clear before we broke it down by channel. Growing restaurant brands averaged an LVI score of 61.4, while shrinking brands averaged 46.6 — a 14.8-point gap that appeared consistently across every underlying channel.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="768" height="610" src="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-1a-1.png" alt="" class="wp-image-486709"/></figure>



<h2 id="h-the-ai-gap-is-the-widest-and-the-newest" class="wp-block-heading">The AI gap is the widest, and the newest</h2>



<p class="wp-block-paragraph">The most striking finding is also the most forward-looking. Expanding chains are recommended by ChatGPT in about 20% of tested queries, compared with roughly 3% for contracting chains — a 6–7x gap. Gemini and Perplexity show the same pattern, and expanding brands earn meaningfully higher AI-assigned star ratings across all three platforms.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="768" height="587" src="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-2a.png" alt="" class="wp-image-486698"/></figure>
</div>


<p class="wp-block-paragraph">AI is no longer a marginal discovery channel.&nbsp;</p>



<p class="wp-block-paragraph">As SOCi’s 2026 Local Visibility Index found, AI recommendations are highly selective. Only about 1–11% of brand locations are recommended across ChatGPT, Gemini, and Perplexity, compared with 35.9% that appear in Google’s traditional 3-Pack.&nbsp;</p>



<p class="wp-block-paragraph">Earning a recommendation requires a higher standard of data accuracy, reputation, and differentiated content. Expanding restaurant brands appear to clear that bar. Contracting brands largely don’t.</p>



<h1 id="h-search-and-reputation-tell-the-same-story" class="wp-block-heading">Search and reputation tell the same story</h1>



<p class="wp-block-paragraph">The AI gap doesn&#8217;t exist in isolation. It&#8217;s built on the same local search fundamentals that have long mattered:</p>



<ul class="wp-block-list">
<li><strong>Search visibility:</strong> Expanding chains appear in Google&#8217;s 3-Pack for 35.3% of tracked searches, compared with 14.4% for contracting chains. They also earn Yelp&#8217;s top organic ranking nearly twice as often (55% vs. 29.3%).</li>



<li><strong>Reputation:</strong> Expanding brands average a 4.39 Google rating and a 3.65 Yelp rating, compared with 3.82 and 2.49 for contracting brands. The Yelp gap alone exceeds a full star. Expanding brands also respond to 72.4% of Google reviews, versus 43.6%, and do so two to three times faster.</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="768" height="587" src="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-3a.png" alt="" class="wp-image-486699"/></figure>
</div>

<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="678" height="513" src="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-4a-1.png" alt="" class="wp-image-486711"/></figure>
</div>

<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="768" height="449" src="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-5a.png" alt="" class="wp-image-486703"/></figure>
</div>


<p class="wp-block-paragraph">Review response rates are worth pausing on because, unlike star ratings built over years, they&#8217;re a lever marketing teams can pull today. The gap is one of the clearest signs that expanding brands treat local reputation as an operational discipline, not just something they monitor.</p>



<h2 id="h-social-shows-the-biggest-multiple-of-all" class="wp-block-heading">Social shows the biggest multiple of all</h2>



<p class="wp-block-paragraph">Expanding brands earn a 3.45% local social engagement rate. Contracting brands average just 0.13% — a roughly 26x gap. Expanding brands also have about five times more local followers on average.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="768" height="536" src="https://searchengineland.com/wp-content/seloads/2026/09/Soci-20260908-6a.png" alt="" class="wp-image-486704"/></figure>



<p class="wp-block-paragraph">The takeaway isn&#8217;t &#8220;post more.&#8221; Volume alone doesn&#8217;t explain the gap.</p>



<p class="wp-block-paragraph">Expanding brands are building real local audiences with content that resonates in each market instead of pushing the same corporate post to every location page — a tactic often called waterfall posting. SOCi&#8217;s broader LVI research found localized content is a stronger driver of engagement.</p>



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



<p class="wp-block-paragraph">Digital visibility alone won&#8217;t turn around a struggling chain. This data doesn&#8217;t claim otherwise.&nbsp;</p>



<p class="wp-block-paragraph">But for multi-location marketers, the pattern among brands expanding in 2026 offers a useful gut check: they treat local search, reputation, social, and AI visibility as one connected system rather than four separate to-do lists.</p>



<ul class="wp-block-list">
<li><strong>Treat data accuracy as infrastructure, not a one-time cleanup.</strong> Every channel in this analysis depends on complete, consistent business data across Google, Yelp, Facebook, and your local pages. It&#8217;s also the first thing AI platforms check.</li>



<li><strong>Systematize review response. </strong>Response speed and coverage — not just star ratings — separated the two groups. It&#8217;s also one of the few levers a marketing team can move in weeks instead of years.</li>



<li><strong>Localize your social content. </strong>Five times the followers and 26 times the engagement don&#8217;t come from posting more corporate content. They come from creating content that&#8217;s relevant to local audiences.</li>



<li><strong>Check your AI visibility specifically.</strong> Strong search performance no longer guarantees AI recommendations. If you haven&#8217;t tested how your brand appears in ChatGPT, Gemini, and Perplexity, you may be missing one of the biggest blind spots this analysis uncovered.</li>
</ul>



<p class="wp-block-paragraph">Want to see how your brand’s local visibility compares to the brands in this analysis, and to your category overall? <a href="https://www.soci.ai/insights/lvi/?utm_source=searchengineland&amp;utm_medium=syn" data-type="link" data-id="https://www.soci.ai/insights/lvi/?utm_source=searchengineland&amp;utm_medium=syn" target="_blank" rel="noopener sponsored nofollow">Request a free LVI Brand Audit</a> to get your Search, Reputation, Social, and AI scores benchmarked against your industry.<br>


You’ve seen the headlines this year. Culver’s, Texas Roadhouse, and Nothing Bundt Cakes are opening hundreds of new locations. Wendy’s, Papa John’s, and Pizza Hut are closing hundreds. Jack in the Box, Red Robin, Denny’s, and Red Lobster have trimmed their footprints, too.
The easy explanation is the economy, menu fatigue, or private equity. Those factors likely play a role. 
But when we analyzed Local Visibility Index (LVI) data comparing eight expanding restaurant brands with eight contracting ones, we found something missing from most of that coverage: a consistent, measurable gap in how these brands show up online across every channel that drives local discovery: search, reviews, social, and AI.
This isn’t a claim that a Yelp rating directly causes a store closure. It’s something more useful for marketers: a real-time signal. 
The same digital fundamentals reflected in a company’s local visibility data appear to move in step with the fundamentals driving its real estate decisions, often before the story reaches the trade press.
The LVI score is SOCi’s composite measure of a brand’s local digital visibility, combining performance across search rankings, review sentiment, social engagement, and AI recommendation rates into a single 0–100 score. 
In this analysis, the gap was clear before we broke it down by channel. Growing restaurant brands averaged an LVI score of 61.4, while shrinking brands averaged 46.6 — a 14.8-point gap that appeared consistently across every underlying channel.




The AI gap is the widest, and the newest
The most striking finding is also the most forward-looking. Expanding chains are recommended by ChatGPT in about 20% of tested queries, compared with roughly 3% for contracting chains — a 6–7x gap. Gemini and Perplexity show the same pattern, and expanding brands earn meaningfully higher AI-assigned star ratings across all three platforms.




AI is no longer a marginal discovery channel. 
As SOCi’s 2026 Local Visibility Index found, AI recommendations are highly selective. Only about 1–11% of brand locations are recommended across ChatGPT, Gemini, and Perplexity, compared with 35.9% that appear in Google’s traditional 3-Pack. 
Earning a recommendation requires a higher standard of data accuracy, reputation, and differentiated content. Expanding restaurant brands appear to clear that bar. Contracting brands largely don’t.
Search and reputation tell the same story
The AI gap doesn’t exist in isolation. It’s built on the same local search fundamentals that have long mattered:
Search visibility: Expanding chains appear in Google’s 3-Pack for 35.3% of tracked searches, compared with 14.4% for contracting chains. They also earn Yelp’s top organic ranking nearly twice as often (55% vs. 29.3%).
Reputation: Expanding brands average a 4.39 Google rating and a 3.65 Yelp rating, compared with 3.82 and 2.49 for contracting brands. The Yelp gap alone exceeds a full star. Expanding brands also respond to 72.4% of Google reviews, versus 43.6%, and do so two to three times faster.












Review response rates are worth pausing on because, unlike star ratings built over years, they’re a lever marketing teams can pull today. The gap is one of the clearest signs that expanding brands treat local reputation as an operational discipline, not just something they monitor.
Social shows the biggest multiple of all
Expanding brands earn a 3.45% local social engagement rate. Contracting brands average just 0.13% — a roughly 26x gap. Expanding brands also have about five times more local followers on average.




The takeaway isn’t “post more.” Volume alone doesn’t explain the gap.
Expanding brands are building real local audiences with content that resonates in each market instead of pushing the same corporate post to every location page — a tactic often called waterfall posting. SOCi’s broader LVI research found localized content is a stronger driver of engagement.
What this means for your brand
Digital visibility alone won’t turn around a struggling chain. This data doesn’t claim otherwise. 
But for multi-location marketers, the pattern among brands expanding in 2026 offers a useful gut check: they treat local search, reputation, social, and AI visibility as one connected system rather than four separate to-do lists.
Treat data accuracy as infrastructure, not a one-time cleanup. Every channel in this analysis depends on complete, consistent business data across Google, Yelp, Facebook, and your local pages. It’s also the first thing AI platforms check.
Systematize review response. Response speed and coverage — not just star ratings — separated the two groups. It’s also one of the few levers a marketing team can move in weeks instead of years.
Localize your social content. Five times the followers and 26 times the engagement don’t come from posting more corporate content. They come from creating content that’s relevant to local audiences.
Check your AI visibility specifically. Strong search performance no longer guarantees AI recommendations. If you haven’t tested how your brand appears in ChatGPT, Gemini, and Perplexity, you may be missing one of the biggest blind spots this analysis uncovered.
Want to see how your brand’s local visibility compares to the brands in this analysis, and to your category overall? Request a free LVI Brand Audit to get your Search, Reputation, Social, and AI scores benchmarked against your industry.

Research finds a measurable digital visibility gap between expanding and shrinking restaurant chains across search, reviews, social, and AI.

Why growing restaurant chains win at local search and AI visibility by SOCi

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/Google-AIO.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Google-AIO.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/Google-AIO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Google-AIO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Google-AIO-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>AI Overviews may be reducing Shopping ad impressions while concentrating clicks among higher-intent searches.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/Google-AIO.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Google-AIO.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/Google-AIO-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Google-AIO-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Google-AIO-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph" id="h-shopping-ad-click-through-rates-are-rising-while-impressions-are-falling-according-to-an-analysis-by-mike-ryan-head-of-ecommerce-at-smarter-ecommerce-a-pattern-he-thinks-could-be-connected-to-how-google-serves-ai-overviews">Shopping ad click-through rates are rising while impressions are falling, according to an analysis by Mike Ryan, head of ecommerce at Smarter Ecommerce — a pattern he thinks could be connected to how Google serves AI Overviews.</p>



<p class="wp-block-paragraph"><strong>Why it matters.</strong> AI Overviews have created what Ryan calls the SEO &#8220;crocodile effect,&#8221; where publishers gain impressions but lose clicks. Shopping ads appear to be experiencing the opposite: fewer impressions, but higher CTRs.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="800" height="489" src="https://searchengineland.com/wp-content/seloads/2026/09/AI-overviews-crocodile-effect-1.jpeg" alt="" class="wp-image-487290" srcset="https://searchengineland.com/wp-content/seloads/2026/09/AI-overviews-crocodile-effect-1.jpeg 800w, https://searchengineland.com/wp-content/seloads/2026/09/AI-overviews-crocodile-effect-1-768x469.jpeg 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>



<p class="wp-block-paragraph"><strong>Why we care.</strong> Rising Shopping CTRs may not mean ads are performing better, they may simply be appearing less often. If AI Overviews are changing which queries receive Shopping ads, advertisers may need to look beyond CTR and pay closer attention to impression volume, clicks and overall traffic when judging performance.</p>



<p class="wp-block-paragraph"><strong>Driving the news</strong>. Ryan had been seeing Shopping CTRs climb in Smarter Ecommerce&#8217;s Market Observer benchmark data. After separate ecommerce data from Optmyzr showed CTR up 17% year over year — close to the roughly 20% increase he was seeing — he took a deeper look.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="405" height="328" src="https://searchengineland.com/wp-content/seloads/2026/09/Google-Shopping-CTR-1.png" alt="" class="wp-image-487299"/></figure>
</div>


<p class="wp-block-paragraph">Across thousands of Shopping and Performance Max campaigns spanning hundreds of advertiser accounts, Ryan found a consistent pattern: clicks were often relatively flat or slightly down, while impressions had fallen more noticeably.</p>



<p class="wp-block-paragraph">In other words, Shopping ads aren&#8217;t necessarily generating significantly more clicks. They&#8217;re being shown less often, which is pushing CTR higher.</p>



<p class="wp-block-paragraph"><strong>The intrigue</strong>. Ryan&#8217;s hypothesis is that AI Overviews could help explain why.</p>



<p class="wp-block-paragraph">He theorizes that Google may be more likely to serve AI Overviews for queries with a lower predicted likelihood of generating an ad click, while preserving Shopping ad placements for searches with stronger click propensity.</p>



<p class="wp-block-paragraph">If that&#8217;s happening, Google could reduce the overall number of Shopping ad impressions without reducing clicks at the same rate.</p>



<p class="wp-block-paragraph">That would allow two seemingly contradictory things to be true: AI Overviews could suppress some advertising opportunities while Google&#8217;s remaining ads continue to monetize effectively.</p>



<p class="wp-block-paragraph"><strong>Between the lines</strong>. Ryan describes the idea as &#8220;experience switching.&#8221;</p>



<p class="wp-block-paragraph">Depending on the query, Google already serves very different Shopping experiences in its main search results. Ryan also says that, anecdotally, he rarely sees Shopping ads and AI Overviews together on the same SERP — it&#8217;s typically one or the other.</p>



<p class="wp-block-paragraph">That raises the possibility that Google is becoming more selective about when each experience appears.</p>



<p class="wp-block-paragraph"><strong>Yes, but</strong>. Ryan is clear that the data doesn&#8217;t prove AI Overviews are causing the change.</p>



<p class="wp-block-paragraph">The timing could be coincidental, AI Overviews could be contributing through another mechanism, or something else entirely could be responsible for the decline in impressions.</p>



<p class="wp-block-paragraph">For now, it&#8217;s a hypothesis.</p>



<p class="wp-block-paragraph"><strong>The big picture</strong>. If Ryan&#8217;s theory is right, the current pattern could be temporary.</p>



<p class="wp-block-paragraph">He expects Google to move from more &#8220;either/or&#8221; search experiences toward &#8220;both/and&#8221; experiences as it develops AI-native advertising formats and puts more Shopping ads directly inside AI Overviews.</p>



<p class="wp-block-paragraph"><strong>What to watch</strong>. For advertisers, the key signal isn&#8217;t simply that Shopping CTR is rising. It&#8217;s why it&#8217;s rising.</p>



<p class="wp-block-paragraph">If higher CTR is being driven partly by Google showing Shopping ads less frequently and on more commercially valuable queries, traditional CTR benchmarks could start telling a very different story about campaign performance.</p>



<p class="wp-block-paragraph"><strong>Dig deeper.</strong> <a href="https://www.linkedin.com/posts/mikeryanretail_heres-an-odd-one-ever-since-aios-launched-share-7501641705696436225-4IPi/">Mike Ryan shares dreaded reverse crocodile effect of AI Overviews</a>


Shopping ad click-through rates are rising while impressions are falling, according to an analysis by Mike Ryan, head of ecommerce at Smarter Ecommerce — a pattern he thinks could be connected to how Google serves AI Overviews.
Why it matters. AI Overviews have created what Ryan calls the SEO “crocodile effect,” where publishers gain impressions but lose clicks. Shopping ads appear to be experiencing the opposite: fewer impressions, but higher CTRs.




Why we care. Rising Shopping CTRs may not mean ads are performing better, they may simply be appearing less often. If AI Overviews are changing which queries receive Shopping ads, advertisers may need to look beyond CTR and pay closer attention to impression volume, clicks and overall traffic when judging performance.
Driving the news. Ryan had been seeing Shopping CTRs climb in Smarter Ecommerce’s Market Observer benchmark data. After separate ecommerce data from Optmyzr showed CTR up 17% year over year — close to the roughly 20% increase he was seeing — he took a deeper look.




Across thousands of Shopping and Performance Max campaigns spanning hundreds of advertiser accounts, Ryan found a consistent pattern: clicks were often relatively flat or slightly down, while impressions had fallen more noticeably.
In other words, Shopping ads aren’t necessarily generating significantly more clicks. They’re being shown less often, which is pushing CTR higher.
The intrigue. Ryan’s hypothesis is that AI Overviews could help explain why.
He theorizes that Google may be more likely to serve AI Overviews for queries with a lower predicted likelihood of generating an ad click, while preserving Shopping ad placements for searches with stronger click propensity.
If that’s happening, Google could reduce the overall number of Shopping ad impressions without reducing clicks at the same rate.
That would allow two seemingly contradictory things to be true: AI Overviews could suppress some advertising opportunities while Google’s remaining ads continue to monetize effectively.
Between the lines. Ryan describes the idea as “experience switching.”
Depending on the query, Google already serves very different Shopping experiences in its main search results. Ryan also says that, anecdotally, he rarely sees Shopping ads and AI Overviews together on the same SERP — it’s typically one or the other.
That raises the possibility that Google is becoming more selective about when each experience appears.
Yes, but. Ryan is clear that the data doesn’t prove AI Overviews are causing the change.
The timing could be coincidental, AI Overviews could be contributing through another mechanism, or something else entirely could be responsible for the decline in impressions.
For now, it’s a hypothesis.
The big picture. If Ryan’s theory is right, the current pattern could be temporary.
He expects Google to move from more “either/or” search experiences toward “both/and” experiences as it develops AI-native advertising formats and puts more Shopping ads directly inside AI Overviews.
What to watch. For advertisers, the key signal isn’t simply that Shopping CTR is rising. It’s why it’s rising.
If higher CTR is being driven partly by Google showing Shopping ads less frequently and on more commercially valuable queries, traditional CTR benchmarks could start telling a very different story about campaign performance.
Dig deeper. Mike Ryan shares dreaded reverse crocodile effect of AI Overviews

AI Overviews may be reducing Shopping ad impressions while concentrating clicks among higher-intent searches.

AI Overviews may be affecting Shopping ad CTR and impressions

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/Google-Ai-restrictive-mode.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Google-Ai-restrictive-mode.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Ai-restrictive-mode-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Ai-restrictive-mode-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Ai-restrictive-mode-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google is testing exact and phrase match Search ads in AI Mode when users show clear, direct intent.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/09/Google-Ai-restrictive-mode.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/09/Google-Ai-restrictive-mode.png 1672w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Ai-restrictive-mode-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Ai-restrictive-mode-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/09/Google-Ai-restrictive-mode-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p class="wp-block-paragraph">Google begun testing whether traditional Search campaigns using exact and phrase match keywords can serve text ads in AI Mode, expanding the types of campaigns eligible to appear in its AI-powered search experience.</p>



<p class="wp-block-paragraph">This means advertisers may not need AI Max or Performance Max to get some exposure in AI Mode. The experiment gives conventional Search campaigns a path into AI Mode when a user&#8217;s query shows clear, direct intent.</p>



<p class="wp-block-paragraph"><strong>Why we care.</strong> Traditional Search campaigns may now have a path into AI Mode without switching to AI Max or Performance Max. That could extend the reach of existing exact and phrase match campaigns while letting advertisers retain tighter keyword control, although the test is currently limited to queries where Google sees clear, direct intent.</p>



<p class="wp-block-paragraph"><strong>Driving the news</strong>. Google Ads Liaison Ginny Marvin confirmed that Google recently kicked off a &#8220;small experiment&#8221; allowing exact and phrase match keywords to serve text ads in AI Mode.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1094" height="575" src="https://searchengineland.com/wp-content/seloads/2026/09/higman-ginny-google-ads-linkedin-OOrhckfT.png" alt="" class="wp-image-487200" srcset="https://searchengineland.com/wp-content/seloads/2026/09/higman-ginny-google-ads-linkedin-OOrhckfT.png 1094w, https://searchengineland.com/wp-content/seloads/2026/09/higman-ginny-google-ads-linkedin-OOrhckfT-768x404.png 768w" sizes="(max-width: 1094px) 100vw, 1094px" /></figure>



<p class="wp-block-paragraph">The test was first spotted by AdSquire founder advertiser Anthony Higman, who said he confirmed the behavior across multiple campaigns and tests.</p>



<p class="wp-block-paragraph"><strong>The details</strong>. Eligibility isn&#8217;t unrestricted. Marvin said exact and phrase match keywords can trigger text ads in AI Mode only in cases where Google identifies &#8220;explicit and direct user intent.&#8221;</p>



<p class="wp-block-paragraph">That means advertisers using more restrictive keyword targeting could potentially reach users in AI Mode without broadening their match types or moving those campaigns to Google&#8217;s more automated campaign products.</p>



<p class="wp-block-paragraph"><strong>Yes, but</strong>. Google is still positioning AI Max and Performance Max for Search as the products designed to capture more complex, conversational searches.</p>



<p class="wp-block-paragraph">Those campaign types also provide access to Google&#8217;s newer AI Mode ad formats, including Highlighted Answers.</p>



<p class="wp-block-paragraph">Traditional Search campaigns, by comparison, are being tested specifically for text ads tied to clearer intent.</p>



<p class="wp-block-paragraph"><strong>The big picture</strong>. The experiment shows Google is testing how its established keyword-based Search advertising system fits into AI Mode alongside its more automated campaign products.</p>



<p class="wp-block-paragraph">Rather than limiting AI Mode advertising to AI Max and Performance Max, Google is testing whether tightly targeted Search campaigns can participate when a user&#8217;s intent maps directly to an advertiser&#8217;s keywords.</p>



<p class="wp-block-paragraph"><strong>What to watch</strong>. This is still a small experiment, and Google hasn&#8217;t said how broadly the test is running or whether it will become a permanent feature.</p>



<p class="wp-block-paragraph">For now, exact and phrase match advertisers may begin seeing AI Mode as another place their existing Search ads can appear — but only when Google&#8217;s systems determine the user&#8217;s intent is explicit and direct.</p>



<p class="wp-block-paragraph"><strong>Dig deeper.</strong> <a href="https://www.linkedin.com/feed/update/urn:li:activity:7501374557593845760/?dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287501705684619522048%2Curn%3Ali%3Aactivity%3A7501374557593845760%29">Higman shares that Google is allowing restrictive match type in AI Mode. </a>


Google begun testing whether traditional Search campaigns using exact and phrase match keywords can serve text ads in AI Mode, expanding the types of campaigns eligible to appear in its AI-powered search experience.
This means advertisers may not need AI Max or Performance Max to get some exposure in AI Mode. The experiment gives conventional Search campaigns a path into AI Mode when a user’s query shows clear, direct intent.
Why we care. Traditional Search campaigns may now have a path into AI Mode without switching to AI Max or Performance Max. That could extend the reach of existing exact and phrase match campaigns while letting advertisers retain tighter keyword control, although the test is currently limited to queries where Google sees clear, direct intent.
Driving the news. Google Ads Liaison Ginny Marvin confirmed that Google recently kicked off a “small experiment” allowing exact and phrase match keywords to serve text ads in AI Mode.




The test was first spotted by AdSquire founder advertiser Anthony Higman, who said he confirmed the behavior across multiple campaigns and tests.
The details. Eligibility isn’t unrestricted. Marvin said exact and phrase match keywords can trigger text ads in AI Mode only in cases where Google identifies “explicit and direct user intent.”
That means advertisers using more restrictive keyword targeting could potentially reach users in AI Mode without broadening their match types or moving those campaigns to Google’s more automated campaign products.
Yes, but. Google is still positioning AI Max and Performance Max for Search as the products designed to capture more complex, conversational searches.
Those campaign types also provide access to Google’s newer AI Mode ad formats, including Highlighted Answers.
Traditional Search campaigns, by comparison, are being tested specifically for text ads tied to clearer intent.
The big picture. The experiment shows Google is testing how its established keyword-based Search advertising system fits into AI Mode alongside its more automated campaign products.
Rather than limiting AI Mode advertising to AI Max and Performance Max, Google is testing whether tightly targeted Search campaigns can participate when a user’s intent maps directly to an advertiser’s keywords.
What to watch. This is still a small experiment, and Google hasn’t said how broadly the test is running or whether it will become a permanent feature.
For now, exact and phrase match advertisers may begin seeing AI Mode as another place their existing Search ads can appear — but only when Google’s systems determine the user’s intent is explicit and direct.
Dig deeper. Higman shares that Google is allowing restrictive match type in AI Mode.

Google is testing exact and phrase match Search ads in AI Mode when users show clear, direct intent.

Google tests traditional Search campaigns in AI Mode

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Search marketing jobs" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-550x310.jpg 550w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Land your next job in SEO or PPC. These brands and agencies are hiring to fill open search marketing positions right now.

Looking to take the next step in your search marketing career? 
Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.
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Newest SEO Jobs 
(Provided to Search Engine Land by SEOjobs.com) 



Senior SEO Specialist – B2B Saas
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August 31, 2026 
Company: PrivateSalary: $90000–$95000Location: Remote USDate posted: August 31, 2026We are an organic growth agency that builds revenue-focused organic growth systems for B2B software companies — optimized for both search engines and generative AI. We pride ourselves on being lean, agile, and experimental. Our team thrives on R&D and innovation, always exploring the smartest ways to deliver exceptional results. We believe […]

Technical SEO
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SEO Content Consultant | Online Redakteur (w/m/d)
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Senior Content Marketer, SEO
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Company: AnimalzSalary: $70,000-$85,000 per year USDLocation: New York, New York, United StatesDate posted: August 30, 2026About Animalz Animalz is a content marketing agency that partners with B2B SaaS companies, venture capital firms, and other tech organizations to drive long-term, sustainable growth through high-quality content. Our fully remote team of experienced content marketers delivers strategies and content tailored to each customer’s […]

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Sr. SEO Content Manager
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Associate Director, SEO Specialists
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Newest PPC and paid media jobs
(Provided to Search Engine Land by PPCjobs.com) 







Digital Marketing Strategist (Account Management, SEO, PPC, Social)
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Salary: $55,000 – $70,000Location: United States (residency required)Date posted: September 8, 2026Description Keynotes: 100% Remote Role. US-Based candidates only. Salary Level: $55,000 – $70,000/year (depends on experience) Must be legally authorized to work in the United States without current or future employer-provided visa sponsorship. At Volume Nine, we help companies create a predictable, steady stream of customers using smart, modern […]

Growth Marketing Lead, Programmatic & Video
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Senior Growth Marketing Manager, Paid Social
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Digital Marketing Strategist (Account Management, SEO, PPC, Social)
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