Iceflower

Peer centres for a given shopping centre -- optionally within one state or one revenue tier -- each with its revenue, customers and biggest tenants by revenue. Peers share the subject's venue type (a shopping centre compares against shopping centres, an airport against airports, an open-air retail area against retail areas) and tiers are computed within that type; `summary.subject` reports the subject's own tier and size. Use it to see what comparable centres host that this one does not.

mall_compare

Full profile for one merchant: headline metrics (revenue, transactions, customers, stores, growth, basket, visit cadence, geography) plus its RFM customer segments, as a readable document. The segment table is the `rfm_table` grain (the merchant's store selection over a fixed 24-month window), not `rfm_segments`' whole-merchant grain -- their totals differ by design. Takes an `id` from `search`. For filtered or comparative analysis use `search_merchants` and `rfm_table` directly.

fetch

Individual stores for the named merchants -- address, latitude/longitude, transactions and revenue -- ranked by revenue, optionally narrowed to one city, state or metro area (a US Census MSA or a UK Travel To Work Area). `store_id` is an opaque per-store identifier; treat it as a whole, never parse it. `mall_lookup` tenant rows carry the same values, so the two answers join on it.

store_locations

One row per named merchant from the merchant directory: category, mapped physical store count (0 for online merchants), panel transactions, revenue, customers and average transaction. The cheapest way to size a brand before asking anything else about it. Names that do not resolve are listed in `summary.not_found` and the rest still answer.

merchant_summary

Merchants with physical stores in a city (locality plus region), across a whole state, or across a metro area (a US Census MSA or a UK Travel To Work Area), ranked by store revenue: store count, transactions and revenue. An optional category filter narrows it. The way to find a brand's local competitors. Give either region (optionally with locality) or metro.

merchants_in_area

Monthly revenue, transactions and customers per merchant over a window. Optionally scoped to the stores in one city (locality plus region) or a whole state (region alone); with neither it is national and covers all channels.

revenue_timeseries

Customer segmentation (RFM: recency, frequency, monetary) for a merchant, a region, or any combination of the two. Returns the ten standard segments -- Champions, Loyal, Potential Loyalists, Recent, Promising, Need Attention, About to Sleep, At Risk, Can't Lose, Hibernating -- with each segment's customer count and share, revenue and share, lifetime value, average order value, visit-count and days-since-last-visit ranges, and its share-of-wallet mix. Share-of-wallet splits each segment's customers four ways by how much they spend and how much of their category wallet the merchant captures: Low Value (spends little, small share), Barnacles (spends little, large share), High Potential (spends a lot, small share -- the growth opportunity), True Loyalists (spends a lot, large share). At least one filter is required. Filters combine: merchants AND categories narrow the brand side, countries, regions AND metros narrow the geography, and a bbox narrows to a map viewport. Without a merchant filter, only physical stores are covered (a category or region on its own never includes online merchants). Use `rfm_segments` instead for ONE whole merchant: it reports year-over-year movement per segment and lets you move the window, neither of which this tool can do. This one is for any selection smaller or broader than a brand -- a region, a category, a viewport. Their totals for the same merchant differ because they are built from different grains; that is expected and not a contradiction to report.

rfm_table

The standard RFM segment table for one whole merchant: per segment, customers and revenue with their shares, lifetime value, average order value, change against the year-earlier window and the share-of-wallet mix. For a region, a category or a map viewport rather than a whole brand, use `rfm_table`.

rfm_segments

Monthly-active retention for a merchant's sign-up cohort: the share of households that made a first purchase together and are still active, by months since. The cohort window adapts to the panel's coverage.

retention_curve

Find merchants on the Iceflower consumer panel by name. Returns matching merchant identifiers to pass to `fetch` for the full profile, or to `search_merchants` and `rfm_table` for specific metrics. Use `search_merchants` instead when you want metrics, filtering or ranking -- this tool only resolves names.

search