Shopping demand

Shopping demand shows the searches an AI assistant runs while building a product carousel on your prompts — and ranks them so you can see which shopping topics are rising, fading, or brand new. It is a focused view of your fan-outs, narrowed to the answers that actually produced a shelf.

What it does

When ChatGPT answers a shopping-style question, it usually runs its own web searches first, then renders a product carousel. MentionFlow already captures those searches as fan-outs. Shopping demand keeps only the fan-outs that came from answers where a carousel appeared, then compares the current window against the window just before it — so a single list can tell you what is growing, what is slipping, and what just showed up.

Every query here is a real search the assistant ran, lifted verbatim from the stored answer — never simulated.

Who it's for

• Merchandisers and category managers watching which product topics AI shoppers are asking about right now.
• SEO and content teams who want early signal on rising shopping demand before it shows up in traditional keyword tools.
• Anyone auditing a shelf who wants to know whether interest in a topic is climbing (worth a product or a prompt) or fading.

Try it

1. Open Shopping and find the Shopping demand card, or go straight to Fan-outs and switch the View selector from All fan-outs to Shopping demand.
2. The header reads Query fan-outs, with a line describing the window — for example "the searches assistants ran while building shopping carousels on your prompts — last 7 days vs the prior 7 days."
3. Set the time window with the date-range control at the top of the app. The whole view compares that window against the equal-length window before it.
4. Use the Mode selector to switch between Top, Trending, Losing, and New (explained below). The table re-ranks the same queries under each mode.
5. Read a row: the Searched query, its Change vs prior window, how many Answers ran it now and in the Prior window, an Est. AI volume, and the Triggered by prompts that caused the search.
6. Turn a promising query into something you monitor: click Track on its row to create a tracked prompt from it. A query already in your portfolio shows a tracked marker instead.
7. Click the CSV button in the header to export the current mode exactly as shown.

The four modes

Each mode is the same set of shopping queries, filtered and sorted a different way:

• Top — the queries with the highest answer count in the current window. This is the only mode that works on day one, because it needs no earlier window to compare against.
• Trending — the queries growing the most versus the prior window. A query that is brand new appears under New, not here.
• Losing — the queries declining the most versus the prior window. A query that ran before but has now vanished entirely counts as the strongest decline and is included here.
• New — queries that ran in the current window but were absent from the prior one.

Trending, Losing, and New all need a prior window to compare against. Until one exists, those modes say so plainly rather than guessing.

How it's computed

• Scope. Demand is limited to fan-outs from answers that produced a shopping carousel — the search is joined to a captured placement. It is not split by product, category, or merchant: it ranks queries, not products. The only things you choose are the date window and the mode.
• The window. The current window is your selected range (7, 14, or 28 days, or a custom span); it is compared against the prior equal-length window. The default is 7 days.
• Change (delta). A query's change is the movement in its answer count versus the prior window. When there is no prior window, or the query had no runs before, the change is shown as new — never a fabricated "+100%".
• Honesty rules. A missing estimated volume is an em-dash, meaning "no data", not zero. If no prior window has been observed yet, the Change column is an em-dash and the note reads "no prior 7-day window observed yet — change and the Trending / Losing / New modes unlock once one exists" (the day count follows your window). When there is only one day of shopping answers so far, the view warns that "one day is a snapshot, not a trend." These follow the data-honesty rule.
• Empty states are specific. With no shopping fan-outs at all, the view explains that these searches "appear once a shopping-intent prompt triggers a carousel." Each mode also has its own "nothing to rank yet" copy when it filters to zero rows.
• Export. The CSV button downloads the current mode from /api/export/shopping-demand. Empty prior/delta cells stay empty (unknown), never zero.

Limits

• Effectively ChatGPT-sourced today. A query needs two things to appear here: an engine payload that exposes the searches it ran (ChatGPT and Perplexity — see Fan-outs) and a captured carousel on the same answer. Shopping placements now come from four engines (see Engine coverage), but ChatGPT is currently the only engine delivering both halves — Perplexity's carousels are still awaiting vendor data, and the Google and Copilot channels don't expose their searches.
• Live workspaces only. The demo workspace shows labelled sample data.
• Estimated volumes require DataForSEO credentials; without them, each volume is an em-dash rather than a guess.

Related

• Fan-outs — the full list of searches assistants ran, across all prompts.
• Engine coverage — which engines deliver shopping data today.
• Shopping analytics — who wins the shelf and how your presence moves.
• Shopping overview — how carousels are captured and matched to your catalog.
• Prompts — where a tracked query becomes a monitored question.
• Data honesty — the em-dash and single-day-snapshot rules.