For e-commerce & shopping brands

When a shopper asks an AI assistant "what's the best [product] under [price]?", the answer often isn't a paragraph — it's a product carousel. Those shelves are the new endcap, and most brands have no idea whether their products are on them. MentionFlow captures the carousels AI engines render on your tracked prompts and matches every product against your own catalog, so you can see exactly where you land on the AI shelf, who's beating you to slot one, and which shopping demand is rising before a keyword tool shows it.

This guide is the merchandiser's playbook. It is also honest about which engines carry shelf data today — because a shelf metric you can't trust is worse than none.

The vocabulary, fixed up front

• A placement is one product in one slot of one carousel.
• A carousel is the set of placements sharing a single run — the shelf as the engine rendered it that time.
• Placements are matched against your uploaded catalog, so the numbers describe your products, not the shelf in general.

Shopping metrics run over a rolling 7-day window and count active-prompt runs only — a paused prompt's old carousels don't prop up today's numbers. Full model in Shopping overview.

Step 1 — Load your catalog

Your catalog is the bridge between "products I sell" and "products the AI shelf featured". There are three ways in, all preview-then-confirm, re-validated on the server, and idempotent (re-running corrects rather than duplicates):

See Product imports, plus the per-source guides for Shopify and Google Merchant Center.

Step 2 — Read the shelf

Shopping analytics is the home screen: four summary tiles, a presence trend, a top-30 product leaderboard, the 40 most recent carousels to audit, and a CSV export. Zoom into any product on its product-detail page:

• Win rate = slot-#1 placements ÷ total appearances.
• Visibility share = distinct carousels featuring the product ÷ distinct carousels in the window (holding two slots of one carousel is not double-counted).
• Average slot = mean position — and an honest em-dash when the product never appeared, because there is no average of nothing.
• Price vs shelf compares your price to the shelf's typical price (the median of per-carousel medians, so one 12-slot carousel can't outweigh three small ones). A delta shows only when both sides share a known currency; otherwise it's an em-dash with the reason stated.

A shopping-gap recommendation fires in Actions when there were at least three carousels and you had zero own placements — a clear signal that a shelf exists and you're missing from it.

Step 3 — Track rising demand

Shopping demand is your early-warning system. It takes the real web searches assistants ran while building a carousel on your prompts — lifted verbatim from the stored answer, never simulated — and ranks them four ways:

• Top — highest answer count now (the only mode that works on day one).
• Trending — growing most versus the prior window.
• Losing — declining most (a query that vanished entirely counts as the strongest decline).
• New — ran this window, absent from the prior one.

Turn any rising query into something you monitor with one Track click. This is category demand you can act on before it surfaces in traditional keyword tools.

Step 4 — See how AI describes you

Product attributes reads how AI answers describe products — the material, specs, and ratings the text literally states — and lines your product up against the brands winning the most shelf space. Nothing is inferred: an attribute appears only when an answer states it verbatim, ratings must be written with digits, and every value keeps its evidence quote.

Which engines carry the shelf — honestly

This is the part to read before you quote a shelf number. Shopping data no longer comes from one engine, but coverage varies, and MentionFlow tells "not sampled" apart from "awaiting vendor data" apart from a real measured zero:

Claude, DeepSeek, Grok, and Mistral answer through model APIs with no shopping surface, so they never appear. All shopping metrics blend every collecting engine into one view of the shelf; the Engine coverage strip on /shopping is the only per-engine breakdown. Two honesty gates keep multi-engine shelves clean: Google products count only when the result actually rendered an AI Overview (a plain Shopping module on an answerless page is excluded), and Copilot cards count only with real commerce evidence — a price or a rating — so a list of name-drops can't fabricate a shelf. Full detail in Engine coverage.

What this adds up to

You upload the catalog once; MentionFlow watches the AI shelves your shopping prompts trigger, tells you your win rate, visibility share, and average slot per product, and flags the shelves you're absent from. The demand view tells you what shoppers are asking AI right now, and the coverage strip keeps you honest about which engines those numbers represent. Shopping analytics is not plan-gated — it runs on every live workspace; the demo shows labelled sample data.

Related

• Shopping overview — how carousels are captured and matched.
• Product catalog — the matching gate and backfill.
• Product imports — CSV, Shopify, and Google Merchant Center.
• Shopping demand — the four momentum modes.
• Engine coverage — which engines carry the shelf today.
• Product attributes — how AI describes your product (rolling out).