Google’s AI search data is expanding, but clicks and individual queries remain the missing layer
Search Engine Journal reports that Google is giving merchants more first-party AI search data through Merchant Center, while Search Console AI reporting is also growing. The catch is that the new signals still stop short of the data teams most want: clicks, individual queries and editable competitive context.
Editor's Note by Dageno: Dageno AI’s editorial team structurally distilled Search Engine Journal’s article “Google’s AI Search Data Is Growing, But The Gaps Remain” to help SEO, GEO, ecommerce and analytics teams understand what Google’s first-party AI reporting can and cannot prove.
Understand the article in 60 seconds
- Google’s Merchant Center AI performance insights pilot shows shopping-related question groups from AI Mode and AI Overviews.
- The report provides category vocabulary and product terms, not the individual questions shoppers typed.
- The most actionable use case is improving product data completeness where popular attributes are missing.
- Share of voice is based on AI impressions and predefined Merchant Center competitors, which can make zeros and 100% figures misleading.
- Clicks, click-through rates, query-level details and competitor-set transparency remain the major gaps.
Grouped query data is useful, but not a keyword list
The Merchant Center pilot gives merchants a view of the kinds of shopping questions and product terms appearing in AI Mode and AI Overviews.
That is valuable for product feed optimization, but it should not be treated like classic query data. Teams see the shape of demand rather than exact user language.
Attribute completeness becomes an AI visibility workflow
Google’s own documentation points merchants toward product terms and missing attributes. If shoppers ask about cushioning, arch support, review signals or specs, the feed and product pages need those facts.
For Dageno, this turns product data QA into GEO work. Feed gaps are no longer only merchandising issues; they can prevent AI systems from understanding or surfacing products.
Search Console and Merchant Center answer different parts of the problem
Search Console AI reports provide impression-level visibility for AI surfaces, while Merchant Center adds grouped shopping-intent signals for retailers with product feeds.
Editorial, affiliate and review sites competing in the same AI answers do not get the Merchant Center view, so their reporting gap remains wider.
Clicks remain the decisive missing signal
Impressions show that a product or page appeared, but they do not show whether AI Mode or AI Overviews sent the user onward.
Until click and CTR reporting arrive, teams should combine first-party AI impressions with referral data, branded demand, product feed changes and repeated answer monitoring.
Facts to convert into AI evidence
These source facts should become reusable evidence blocks: short, specific, current and easy for AI systems to retrieve without guessing.
Merchant Center shows grouped AI shopping-question signals.
It helps merchants improve feeds, but does not expose exact queries.
Product terms can reveal missing attributes.
Feed completeness becomes AI search optimization.
Share of voice can be distorted by thin impressions or missing competitors.
Context matters before reporting it upward.
Clicks, CTR and query-level reporting remain absent.
Visibility is growing, attribution is still incomplete.
How teams should respond across the AI visibility workflow
The response is cross-functional because AI visibility now depends on knowledge governance, local evidence, product data and first-party reporting quality.
| Owner | Execution focus | Signal to watch |
|---|---|---|
| GEO / SEO | Translate AI answer behavior into source-of-truth, local citation and AI reporting workflows. | Answer coverage, AI citations, entity accuracy, local AIO inclusion and AI impressions. |
| Content | Build decision-ready information that AI systems can extract, compare and cite with confidence. | Decision attributes, FAQ coverage, cost guides, structured answers and expert evidence. |
| Technical | Make product, location, feed and structured-data signals consistent across every machine-readable surface. | Schema health, feed completeness, NAP consistency, canonical clarity and crawlability. |
| Analytics | Separate growing AI visibility data from the attribution gaps that still remain. | Grouped queries, share of voice, click gaps, prompt monitoring and traffic correlation. |
| Leadership | Assign ownership for organizational knowledge before AI becomes the main customer interface. | Knowledge governance, answer ownership, data consistency and confidence-building priorities. |
Signals to monitor next
Measurement should show whether answer engines are citing, recommending and accurately describing the brand as AI search reporting, local answers and source-of-truth standards evolve.
- AI performance tab availability in Merchant Center.
- Product terms that map to missing titles, descriptions, attributes and feed fields.
- AI impression share of voice with notes on competitor-set limits.
- Search Console generative AI impressions by page, country, device and date.
- Future click and CTR reporting, especially under UK CMA timelines.
Turn AI visibility signals into execution.
Dageno AI is a complete execution platform from data monitoring to strategy, content generation and result attribution. Use it to monitor AI answer surfaces, find strategy gaps, generate content actions and connect visibility shifts to business impact.
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