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Google Merchant Center’s AI Performance Insights reveals AI share of voice, purchase stages, product terms, and attribute gaps. Here is what brands should do next.
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Google Merchant Center is beginning to make AI Performance Insights available to merchants. For the first time, brands can use an official Google dashboard to see which consumer needs their products cover across AI Mode, AI Overviews, and Gemini, where those needs sit in the buying journey, and how much AI exposure they receive compared with similar brands.
After reviewing the report, however, I became even more convinced of one thing:
The most valuable data in the next phase of Generative Engine Optimization, or GEO, will not be how many times AI mentions a brand. It will be where customer demand is concentrated and whether the brand can actually satisfy that demand.
Not long ago, I wrote about Google Search Console and Bing Webmaster Tools beginning to provide data related to AI search.
My conclusion at the time was that GEO was moving from a new concept built largely on third-party monitoring and industry judgment toward a stage where official platform data could validate it.
Microsoft began reporting citations, while Google Search Console began reporting AI impressions. At a minimum, this proved one thing: brand visibility and citations inside AI-generated answers had become metrics that search platforms were willing to track formally.
That round of updates mainly addressed the website and content layer:
Now Google has taken another step forward.
This time, the data has entered Merchant Center, which means it has moved into the product and ecommerce layer.
Google's AI Performance Insights is beginning to show merchants how consumers discover their products through AI Mode, AI Overviews, and Gemini, how much Share of Voice their brands hold against comparable products, and which product terms and attributes users care about most.
This is not merely another report.
It represents Google's attempt to answer a question that matters far more than whether a product received exposure:
What do consumers actually care about when they shop through AI?
Based on the interfaces and official explanations currently available, AI Performance Insights contains four major categories of data.
The first metric is a brand's Share of Voice within Google's AI shopping experiences.
In simple terms, it estimates how much AI exposure a brand and its products receive for relevant shopping needs, then compares that exposure with similar brands automatically identified by Google.
Figure 1: Example of Google Merchant Center AI Performance Insights, showing a brand's AI Share of Voice and the average for similar brands.
This metric may sound similar to the Visibility Score used by many GEO tools, but there is an important difference.
Third-party GEO tools typically:
Define a set of prompts, query multiple models on a recurring basis, and calculate how frequently a brand appears.
Google's data comes from its own AI shopping environments, including AI Mode, AI Overviews, and Gemini. It is therefore closer to actual exposure inside Google's ecosystem, although it cannot measure performance outside that ecosystem.
Its value is not that it replaces every third-party monitoring tool. Its value is that it gives brands an official baseline.
Ecommerce brands can now determine more confidently:
My products are not merely appearing in a handful of test prompts. They have genuinely entered Google's AI Shopping demand landscape.
However, Share of Voice should not be interpreted as market share.
Google automatically chooses the comparison brands, and merchants cannot fully control the competitive set. Sample size, product categorization, and the scope of Google's entity recognition may also affect the percentage.
A Share of Voice of 100% does not necessarily mean that a brand has monopolized its category. A score of 0% does not necessarily mean that it has no exposure at all.
The metric is better used to identify trends than as a standalone measure of business performance.
Google also classifies AI shopping queries according to the purchase funnel:
For example:
Figure 2: AI Performance Insights divides shopping demand into Discovery, Evaluation, and Purchase stages.
I believe this is more valuable than measuring AI exposure alone.
In the past, many GEO monitoring programs simply counted whether a brand appeared.
But appearing in an answer to "What is an air purifier?" has very different commercial value from appearing in "What are the best air purifiers for homes with pets?"
The first query may be general education. The second is already close to a purchase decision.
Without distinguishing intent and decision stage, 100 brand mentions may be less valuable than 10 recommendations attached to high-purchase-intent queries.
By placing this data directly inside Merchant Center, Google is effectively telling merchants:
AI Shopping should not be judged by total exposure alone. You need to know where that exposure occurs in the purchase journey.
Google also provides Product Terms, which are product concepts and expressions that consumers frequently use during AI-assisted shopping.
This requires careful interpretation.
Google is not providing a complete list of raw prompts. It is providing aggregated product terms and query types.
That structure fits the nature of AI search.
In traditional search, a keyword may be very short:
running shoesAn AI user may instead ask:
I run four times a week, I am heavier than average, and my knees often feel uncomfortable. Are there any running shoes with better cushioning for long-distance, easy-paced runs?
That single question contains multiple dimensions:
If this query were broken down using a traditional keyword report, the data would become fragmented.
From the perspective of consumer intent, however, the core demand is clear:
Well-cushioned long-distance running shoes for heavier runners with knee discomfort.
Instead of handing merchants every long prompt individually, Google aggregates these queries into more manageable demand themes, product terms, and purchase stages.
Industry analysis of the report has reached a similar conclusion: more data is becoming available, but merchants still cannot see the full raw query, click, and conversion journey.
This is an important signal for the GEO industry.
The most valuable unit of analysis in the future may not be an isolated prompt. It may be:
A cluster of differently worded queries driven by the same underlying customer intent.
The fourth category covers the product attributes users care about and whether those attributes are missing from a brand's product data.
Users may frequently ask about:
Suppose consumers repeatedly ask whether a product is suitable for outdoor use, but the product feed, structured data, and product page never describe its usage environment. Google will struggle to determine whether the product should be recommended.
Google Merchant Center has always relied on product data submitted by merchants to match products with relevant queries. Google has also consistently emphasized that accurate and complete product information directly affects whether products can be displayed correctly.
In the past, merchants optimized feeds primarily for Shopping Ads and free product listings.
Now those same fields are also beginning to influence product discovery in AI Mode, AI Overviews, and Gemini.
The role of the product feed is changing:
It is no longer just a data source for advertising. It is becoming the foundational language AI uses to understand a product.
The most underestimated part of this update is not that Google has introduced Share of Voice. It is how Google has chosen to organize the data.
Google has not simply handed merchants a list of AI keywords.
Instead, the AI Shopping report is organized around:
Why?
Because AI users do not consistently use a small, fixed set of keywords.
The same need may be expressed in dozens of ways.
For example, a user shopping for an air purifier for a small home might ask:
These questions look completely different at the sentence level.
From a commercial-demand perspective, however, they may belong to a few stable intent clusters:
What truly influences AI recommendations is not whether a brand happens to cover one exact prompt. It is whether:
The brand's product information and content assets comprehensively cover the recurring demand structures customers use when making decisions.
This is why I have long believed that measuring GEO with only a few dozen predefined prompts creates a false sense of certainty.
A company can carefully select 100 questions and report that brand visibility increased by 20%.
But if those questions do not represent what customers genuinely care about, or if they omit high-value purchase scenarios, the 20% increase means very little.
Google is also moving away from isolated queries and toward aggregated demand and intent.
Looking at the evolution of official platform data, Google is beginning to fill in three layers.
The first layer is Search Console, which reports impressions for website content across AI search features.
The second layer is Merchant Center, which reports product Share of Voice, shopping stages, product terms, and attribute demand in AI Shopping scenarios.
The third layer is Google Ads and Merchant Center, which continue to connect product feeds, advertising, and transaction data.
Even so, the most important gaps remain.
Merchants currently see aggregated terms and query types, not a complete prompt-by-prompt record.
A brand may know that a product received AI exposure, but it may not know how many clicks a specific class of AI queries generated.
There is still no complete query-level attribution chain connecting:
The report can show the outcome, but it may not fully explain whether:
The report mainly answers:
Has the brand entered a particular category of demand within Google AI Shopping?
It does not yet fully answer:
Why did the brand appear, why was it absent, and how can the probability of appearing be improved systematically?
Industry coverage has summarized the update accurately: Google's AI search data is expanding, but major gaps remain around clicks, exact queries, competitive sets, and conversion attribution.
More official reports do not automatically eliminate the value of third-party tools.
However, products that rely only on a process of "define prompts, ask repeatedly, calculate mention rate" will become increasingly limited.
Google can already tell merchants:
If a third-party product still reports only:
Your brand appeared in 13 out of 50 prompts.
That is clearly not enough.
Third-party GEO products will need to deliver at least three things that official platform data still cannot provide.
Google can explain only the Google ecosystem.
Brands still need to understand their performance across ChatGPT, Perplexity, Copilot, Claude, and other AI entry points.
Each platform has different data sources, citation mechanisms, product catalogs, and recommendation logic.
Official platforms show only the data they collect and aggregate.
Brands still need a complete structure of industry demand, including:
Real business value does not come from telling a brand, "You did not appear."
It comes from explaining:
As official data matures, third-party tools need to evolve from monitoring systems into analysis and decision systems.
I do not recommend responding to this update by immediately chasing another AI Share of Voice number.
A more practical approach is to review four areas first.
Many independent ecommerce brands provide only the most basic information in their Merchant Center feeds:
But the questions consumers ask through AI are far more complex.
They ask:
If this information exists only in the customer service team's knowledge, or is scattered across images without appearing in the feed, structured data, or crawlable product pages, AI will struggle to understand the product accurately.
Many brand product pages look attractive, but their content consists mainly of:
This type of page may work for someone who has already decided to purchase, but it is poorly suited to helping AI answer complex questions.
A product page that is easier for AI to understand and recommend should clearly explain:
Brands are accustomed to creating content around product names:
AI Shopping users more often express needs through scenarios:
The content structure of the future needs to expand from a product catalog into a customer decision catalog.
AI Performance Insights cannot prove revenue growth on its own.
A more reasonable approach is to combine:
Only then can a brand evaluate:
Most early GEO products began with prompt monitoring.
A customer entered a set of questions, the tool queried AI models on a schedule, and the system recorded whether the brand appeared, where it ranked, and which sources were cited.
That was valuable in the industry's early stage because brands first needed to know whether they appeared in AI-generated answers at all.
The limitations, however, have become increasingly obvious:
Dageno is moving from limited prompt monitoring toward comprehensive industry-intent coverage.
We are less interested in:
How many times did the brand appear in these 100 predefined questions?
We are more interested in:
Across the entire industry, in which scenarios do users ask questions? Which purchase stages do those needs belong to? Which demands have already been captured by competitors? In which intent clusters is the brand completely absent? Is that absence caused by product information, content structure, trust sources, or brand awareness?
Google's report is also organizing AI data around purchase stages, product terms, query types, and product attributes.
That aligns with our view of where the industry is heading:
The fundamental unit of AI search will not remain an individual prompt. It will gradually become an aggregated user intent and decision scenario.
Google can provide data from Google's ecosystem.
Dageno needs to add cross-platform coverage, industry-wide demand intelligence, and causal explanations.
The goal is not to give clients another screenshot showing that "AI visibility increased by 10%."
The goal is to help brands build a new market-demand map that answers:
Google Merchant Center's launch of AI Performance Insights is an important update.
For the first time, ecommerce brands can use an official dashboard to see their products' exposure share in Google AI Shopping, the purchase stages associated with that exposure, the product terms users care about, and gaps in product attributes.
But the most important meaning of this update is not that Google has added another AI visibility metric.
It is that Google is publicly acknowledging:
Competition in AI Shopping is not merely about whether a product has been indexed. It is about whether the product can match the real demand hidden behind the user's natural language.
In the past, merchants optimized titles and feeds around keywords.
Next, brands will need to reorganize their entire product-information system around audiences, scenarios, attributes, problems, and purchase stages.
This also means GEO will increasingly resemble a formal growth discipline rather than a new concept that proves its value through manual prompting.
The data loop is still incomplete.
Major gaps remain around exact prompts, clicks, recommendation logic, and order attribution.
But the number of dashboards is growing.
The next stage will not be won by the first brand to buy an AI visibility tool. It will be won by the brand that first connects industry demand, product data, content assets, and official performance metrics.
Users are already making purchase decisions through AI.
What brands need to compete for is not merely one more impression.
It is the right to be included in the user's final answer.
Updated by
Updated on Jul 22, 2026
Updated by
Tim
Tim is the co-founder of Dageno and a serial AI SaaS entrepreneur, focused on data-driven growth systems. He has led multiple AI SaaS products from early concept to production, with hands-on experience across product strategy, data pipelines, and AI-powered search optimization. At Dageno, Tim works on building practical GEO and AI visibility solutions that help brands understand how generative models retrieve, rank, and cite information across modern search and discovery platforms.
