Clothing and accessories retailers use catalog.xmap.ai to map mall and street co-tenancy, track competitor openings and closures, and find the whitespace worth expanding into.
catalog.xmap.ai is a self-serve database of 140M+ real business locations across 40+ countries. Every record is an actual, physically-located business (not an estimate) with up to 36 structured fields: name, full address, latitude/longitude, category (three levels deep), brand and chain affiliation, hours of operation, phone and website, star rating and review count, open/closed status, and more.
You filter the catalog by category, brand, or geography in a live web interface, watch the matching record count update as you narrow the search, and export the results as a clean CSV file, ready to open in a spreadsheet, load into GIS software, or join against your own data.
The dataset refreshes every 30 days, so an export reflects recent openings, closures, and changes rather than a static snapshot from years ago.
A center's tenant mix can change within a season, last year's anchor may be this year's vacancy.
A seasonal or pop-up location gets weeks of lead time, not the months a full lease review usually takes.
Knowing which price tiers already crowd a submarket shapes whether a new store fits or fights for share.
See exactly which other retailers share a center or corridor before committing to a lease.
Monitor a rival chain's footprint monthly to react to expansion or pull-back before it shows up in earnings.
Rank candidate sites by nearby traffic, dwell time, and category density in a single export.
Spot submarkets underserved at a given price point, from fast fashion to premium boutique.
Move fast on a short-term location by scoring candidate sites in the same afternoon you find them.
Check a proposed new store against your own network before approving it, to avoid cannibalization.
Store count by price tier in a candidate submarket.
Every export carries 36 columns; these are the ones fashion teams use most.
| Field | Type | Why it matters here |
|---|---|---|
| venue_hierarchy | string | Ties a store to its mall or shopping center, and its co-tenants |
| brand_name / chain_flag | string / boolean | Separates national chains from independent boutiques |
| price_tier | string | Positions competitors and gaps by price point |
| category_level_3 | string | Narrows to apparel, footwear, accessories, or specialty format |
| traffic_score / dwell_time | numeric | Ranks candidate sites and centers by relative footfall |
| open_status | string | Flags recent openings and closures for monthly monitoring |
| rating / review_count | numeric | Signals which nearby retailers are actually performing |
| latitude / longitude | float | Drives every trade-area and drive-time calculation |
A fashion retailer is deciding between two shopping centers for its next store. The team exports each center's full tenant list with price tier and traffic score, sees that one center is already saturated with fast-fashion competitors while the other has a gap at their exact price point, and signs the lease with the data to back it up.
We'll pull a live sample for your category and market.