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 spend at the merchant's place type (restaurants, gyms, pet stores ...) 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