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Shopping analytics

The shopping home. Summary tiles, a trend, a top-30 product leaderboard, the most recent carousels, and a CSV export of your AI shelf presence.

The shopping home answers three questions at a glance. Who is winning the AI shelf. Whether your own presence is rising or slipping. And which carousels drove it. Open it to see how you are doing overall, and to pull the receipts on any specific answer.

What it does

The home surface brings together:

  • Four summary tiles,
  • a trend of your shopping presence over time,
  • a top-30 product leaderboard,
  • the 40 most recent carousels, and
  • a CSV export.

How to use it

  • Read the leaderboard for who wins the shelf.
  • Read the Engine coverage strip to know which engines the numbers come from. Each engine shows a count, "not sampled", or "awaiting vendor data". See Engine coverage.
  • Read the trend for how your presence moves.
  • Read the recent carousels to audit specific shelves. Each one is a captured carousel you can inspect.
  • Use the CSV export at /api/export/shopping to pull the underlying data.

How it's computed

  • Metrics run over a 7-day window. The leaderboard shows the top 30 products.
  • On a day where a carousel appeared but your product did not, share and win rate are an honest 0, a measured zero. Average slot stays null, because there is no average of nothing. This mirrors the null-is-not-zero rule in how MentionFlow handles missing data.
  • A shopping-gap recommendation fires when there were 3 or more carousels and you had zero own placements. That is a clear signal that a shelf exists and you are absent from it.

Limits

  • Not plan-gated.
  • Live workspaces only. The demo shows labelled sample data.
  • Sources: ChatGPT, Google AI Overviews, Google AI Mode, and Copilot collect today. Perplexity and Gemini are wired but waiting on vendor payloads. See Engine coverage.
  • CSV export is served at /api/export/shopping.

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