Introduction
For years, Google Merchant Center told retailers one main story: whether their product data was approved, disapproved, or somewhere in between. As of late May 2026, it is starting to tell a very different one — how your products actually show up when shoppers research and buy through AI.
According to Search Engine Land's reporting, Google is rolling out AI performance insights inside Merchant Center to help retailers track product visibility across AI-powered shopping experiences. The feature set was first previewed at Google Marketing Live and is now being detailed for merchants. For a small retailer who has spent the last two years wondering whether AI search is quietly eating into their store traffic, this is the closest thing to a dashboard we have seen.
But “new dashboard” and “useful dashboard” are not the same thing. The honest read is that these insights are a starting point — a way to see directional patterns, not a precise revenue ledger. Below, we break down exactly what the four new reports show, what they deliberately leave out, and the concrete feed and structured-data work a small business should prioritize before chasing every shiny new AI surface.
Key Takeaways
- Merchant Center is adding four AI shopping reports: share of voice, shopping funnel performance, product term insights, and product attribute insights.
- The reports benchmark your visibility against similar retailers across AI surfaces like AI Mode, AI Overviews, and the Gemini app — they do not hand you exact revenue or conversion figures.
- Google is rolling the insights out to the U.S., Canada, Australia, India, and New Zealand “in the coming months,” with no firm date.
- The most actionable report for small retailers is product attribute insights, which flags incomplete specs like color, material, and style.
- The winning move is boring but effective: clean, complete product data beats trying to be everywhere at once.
- Pair the new insights with conversational attributes, Google's companion feature for optimizing listings for natural-language queries.
What Exactly Is Google Showing in These New Merchant Center Insights?
The headline feature is a reporting module Google calls AI performance insights, built around four distinct report types. Based on Search Engine Land's coverage and Google's own Merchant Center documentation, here is what each one does.
Share of voice insights benchmark your brand's visibility against similar retailers. Rather than an absolute score, this is a relative measure: how often your products surface on AI-driven shopping experiences across Search and Gemini compared to comparable stores. In Google's framing, it covers shopping journeys that begin in AI Mode, AI Overviews, or the Gemini app.
Shopping funnel performance tracks how products move across the discovery, evaluation, and purchase stages. The idea is to show where in the journey AI surfaces are introducing your products and where shoppers drop off.
Product term insights reveal the popular conversational shopping queries that are surfacing products in your category. This is arguably the most interesting report for content and merchandising decisions, because it exposes the natural-language phrasing real shoppers use.
Product attribute insights highlight incomplete product specifications — missing color, material, style, and similar structured fields — across your feed. This is the report that turns directly into a task list.
Taken together, these reports are Google's attempt to give merchants the kind of “AI share-of-voice” view that third-party tools have been selling for the past year. The difference is that Google is measuring its own surfaces, which is both the strength and the limitation.
| Report | What it answers | How a small retailer uses it |
|---|---|---|
| Share of voice | How visible am I vs. similar stores? | Spot category-level gaps; set realistic expectations |
| Shopping funnel performance | Where do AI shoppers find and lose me? | Diagnose drop-off between discovery and purchase |
| Product term insights | What language do AI shoppers use? | Inform titles, descriptions, and FAQ content |
| Product attribute insights | What data is missing from my feed? | Build a prioritized feed-fix list |
What Don't These Insights Tell You?
This is where honesty matters more than hype. The reports are benchmarking and diagnostic tools, not a profit-and-loss statement for AI search.
Based on the available reporting, the insights do not hand you exact conversion rates, revenue attributed to AI surfaces, or granular competitive metrics beyond general share-of-voice benchmarking. Share of voice is relative and anonymized — you learn that you trail or lead “similar retailers,” not which specific competitor outranked you or by how much in dollars. That is a meaningful gap for a business trying to justify spend, and it is worth setting that expectation internally before anyone treats the dashboard as a revenue report.
There is also a rollout caveat. Google has said the AI performance insights are expected in the U.S., Canada, Australia, India, and New Zealand “in the coming months,” without a specific launch date as of the May 28, 2026 reporting. If you log into Merchant Center today and do not see these reports, that is expected — they are arriving in stages.
Finally, share of voice across AI surfaces is inherently noisier than classic Shopping data. AI Mode and Gemini responses vary by phrasing, session, and personalization, so two shoppers asking similar questions may see different products. Treat the trend line, not any single data point, as the signal. This is the same lesson we covered when we wrote about how AI search reshapes e-commerce discovery: the surfaces are real, but the measurement is still maturing.
How Do AI Performance Insights Connect to Conversational Attributes?
The visibility reports did not arrive alone. In its May 20 Merchant Center update, Google paired the insights with a companion feature called conversational attributes — and the two are meant to work as a loop.
Conversational attributes let retailers add richer, natural-language product data directly in Merchant Center so Google's AI systems can match listings to conversational queries. Where a traditional feed might list “material: cotton,” conversational attributes give you room to express the kind of context a shopper actually asks about: how something fits, what it pairs with, what it is good for. Google has indicated conversational attributes are rolling out globally, broader than the staged regional rollout of the insights themselves.
The intended workflow is straightforward. Product term insights show you the conversational queries surfacing in your category. Conversational attributes give you a place to answer those queries inside your feed. Then share of voice and funnel reports tell you whether the changes moved your visibility. It is a measure-adjust-remeasure cycle, and it rewards retailers who treat their product feed less like a spreadsheet and more like SEO content. We made that same argument in our guide to optimizing product feeds for AI search, and Google's own tooling now nudges merchants in that direction.
Which Feed Fixes Should a Small Retailer Prioritize First?
Here is the practical core. New reports are only useful if they change what you do on Monday morning. For a small store with limited hours and no dedicated feed manager, we recommend working in this order.
- Close attribute gaps first. Product attribute insights exist precisely to flag missing specs. Color, size, material, gender, age group, and product category are the fields AI systems lean on to match products to specific questions. This is the highest-leverage work because it is concrete, it is finite, and Google is literally pointing at the gaps for you.
- Rewrite titles and descriptions for how people ask, not just what you sell. Product term insights reveal conversational phrasing. If shoppers in your category ask for “waterproof work boots for standing all day,” your titles and descriptions should be able to answer that, not just say “Men's Boot, Style 4471.” This overlaps heavily with answer-engine optimization, and it is the same discipline that earns citations in AI search.
- Add conversational attributes where they matter most. You do not need to enrich every SKU on day one. Start with your best sellers and highest-margin items, since those are where improved visibility translates fastest into revenue.
- Fix the boring data-quality issues. Accurate pricing, availability, GTINs, and high-quality images still underpin everything. AI surfaces are not a workaround for a messy feed — they amplify whatever quality is already there.
- Measure with patience. Give changes a few weeks before reading the share-of-voice trend, and resist the urge to overhaul everything at once. If you change ten things and visibility moves, you will not know which change did the work.
| Priority | Task | Effort | Why it matters |
|---|---|---|---|
| 1 | Fill missing attributes | Low–medium | Direct match signal; Google flags them for you |
| 2 | Rewrite titles/descriptions | Medium | Aligns with conversational queries |
| 3 | Add conversational attributes | Medium | Answers shopper questions in-feed |
| 4 | Fix pricing, GTINs, images | Low | Foundational data quality |
| 5 | Monitor share-of-voice trend | Low (ongoing) | Confirms whether changes worked |
If you sell through a custom storefront and your feed is generated automatically, some of this work happens at the data-source level rather than in Merchant Center directly. That is where having a clean product data layer — the kind we build into e-commerce development projects — saves hours of manual editing later.
Should Small Retailers Chase Every AI Surface, or Pick Their Spots?
It is tempting to read “AI shopping is everywhere” as “I need to be everywhere.” We would push back on that. The realistic constraint for most Northeast Indiana retailers is time, not ambition.
The good news is that the foundational work — complete, well-described, accurately priced product data — pays off across nearly every AI surface at once, because they all draw on the same underlying feed. That is very different from the old playbook of maintaining separate optimizations for a dozen channels. A retailer who gets their Merchant Center feed genuinely clean is simultaneously improving how they appear in AI Mode, AI Overviews, Gemini shopping, and increasingly in third-party assistants. We have written before about the broader zero-click retail reality for Fort Wayne stores, and the through-line is the same: the data layer is the leverage point.
Where we would be cautious is in over-investing in surface-specific tactics that may not last. The AI shopping landscape is shifting monthly, and features that look essential today can be deprecated or merged tomorrow. Anchor your effort in durable fundamentals — accurate data, clear descriptions, real product imagery — and treat surface-specific tuning as the lighter, second layer. The same logic applies to paid placement; we cover the trade-offs in our look at product feed ads for small e-commerce and at Google's AI Max shopping controls.
What This Means for a Fort Wayne or Northeast Indiana Retailer
Picture a boutique on Broadway, a hardware store in Auburn, or a specialty outfitter in DeKalb County. None of them has a full-time feed analyst, and all of them are competing in AI search against national chains with deep merchandising teams. Do these new insights help or just add to the overwhelm?
Used well, they help — precisely because they narrow the work. A local hardware store does not need to win share of voice for every category nationally; it needs its actual inventory described accurately so that a nearby shopper asking Gemini for “where to buy a specific fitting in Fort Wayne” gets matched. Product attribute insights give that store a finite, prioritized list of what to fix. Product term insights tell it the real language shoppers use, which often differs from the manufacturer's catalog wording.
Our recommendation for local retailers is to block ninety minutes a week against the attribute-gap report and nothing else for the first month. Resist the pull to chase every surface. Northeast Indiana businesses already have a built-in advantage in specificity and local relevance — when you combine clean product data with genuine local context, you compete on the dimensions where national chains are weakest. That is the same hyper-local edge we see drive results across Allen and DeKalb counties.
How Button Block Helps
If your product data is scattered across a point-of-sale system, a website, and a spreadsheet, getting it clean enough to benefit from these insights can feel like a project you will never start. That is where we come in. Button Block helps Northeast Indiana retailers build and maintain product feeds that AI surfaces can actually read, connect Merchant Center to a reliable data source, and turn the new insight reports into a short, prioritized action list instead of another dashboard to ignore.
If you want a second set of eyes on your feed before these reports fully roll out, take a look at our e-commerce development services or reach out for a straightforward conversation about where your product data stands today. No jargon, no pressure — just a clear picture of what to fix first.
Ready to Make Your Product Feed AI-Ready?
Button Block helps Fort Wayne and Northeast Indiana retailers build clean, AI-readable product feeds and turn Merchant Center insights into a short, prioritized action list. Let's find your biggest gaps first.
Frequently Asked Questions
- What are Google's new AI shopping visibility insights in Merchant Center?
- They are a set of four reports — share of voice, shopping funnel performance, product term insights, and product attribute insights — that show how your products surface across AI-powered shopping experiences like AI Mode, AI Overviews, and the Gemini app. The reports benchmark your visibility against similar retailers rather than reporting exact revenue.
- When will AI performance insights be available in my Merchant Center account?
- Google has said the insights are rolling out to the U.S., Canada, Australia, India, and New Zealand "in the coming months," without a specific date as of late May 2026. If you do not see the reports yet, that is expected, since the rollout is staged by region.
- Do these insights show exactly how much revenue AI search drives?
- No. The reports focus on relative visibility and funnel diagnostics, not precise conversion rates or revenue attribution. Treat them as a directional signal for where to improve your product data, not as a profit-and-loss statement for AI shopping.
- What is the difference between AI performance insights and conversational attributes?
- AI performance insights are reporting tools that show how you are doing. Conversational attributes are an input feature that lets you add natural-language product data so Google’s AI can match your listings to conversational queries. You use the insights to find gaps and conversational attributes to fill them.
- What should a small retailer fix first?
- Start with the product attribute insights report, which flags missing specifications like color, material, and style. Closing those gaps is concrete, finite, and directly improves how AI surfaces match your products. Then refine titles and descriptions to match the conversational queries shoppers actually use.
- Do I need to optimize separately for AI Mode, Gemini, and AI Overviews?
- Largely no. These surfaces draw on the same underlying product feed, so clean, complete, well-described data improves your visibility across all of them at once. Surface-specific tuning is a lighter second layer, not the foundation.
- Do these insights help a small Fort Wayne or Northeast Indiana retailer compete with national chains?
- Yes, mainly because they narrow the work. A local store does not need to win share of voice nationally — it needs its real inventory described accurately so that a nearby shopper asking Gemini for a specific item in Fort Wayne gets matched. Product attribute insights hand you a finite, prioritized fix list, and your local specificity is an advantage national chains struggle to replicate.
Sources & Further Reading
- Search Engine Land: searchengineland.com/google-adds-ai-shopping-visibility-insights-to-merchant-center — Google adds AI shopping visibility insights to Merchant Center
- Search Engine Land: searchengineland.com/google-launches-ai-performance-insights-and-conversational-attributes — AI performance insights and conversational attributes in Merchant Center
- Google (The Keyword): blog.google/products-and-platforms/products/shopping/shopping-updates-google-marketing-live — How we're helping retailers thrive with new features and AI tools on Google
- Google Merchant Center Help: support.google.com/merchants/answer/17117204 — Insights for AI-powered shopping experiences coming soon