Electronics and home-goods retailers use catalog.xmap.ai to map power-center co-tenancy, track competitor openings, and find trade areas with the household density to support a big-box format.
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.
Getting the co-tenancy and traffic read right matters more when the footprint is this large.
Stores increasingly need to justify their footprint against online-only competitors.
A big-box store's traffic depends heavily on which anchors and neighbors share the center.
See exactly which anchors and neighbors share a center before committing to a lease.
Monitor a rival chain's new locations as a signal of where the category is expanding.
Weigh a candidate site by nearby residential density that supports a big-box format.
Spot trade areas with electronics or home-goods demand but thin competitive coverage.
Decide which stores to downsize or close by seeing overlap with nearby locations.
Rank candidate power-center sites by traffic score and dwell time.
Anchor and co-tenant category counts within a candidate center.
Every export carries 36 columns; these are the ones this category uses most.
| Field | Type | Why it matters here |
|---|---|---|
| venue_hierarchy | string | Ties a store to its power center and its anchor tenants |
| brand_name / chain_flag | string / boolean | Separates national chains from independent specialty retailers |
| category_level_3 | string | Narrows to electronics, furniture, home improvement, or specialty format |
| traffic_score / dwell_time | numeric | Ranks candidate centers by relative footfall and visit length |
| 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 household-density calculation |
| hours_of_operation | string | Reveals extended-hours competitive gaps in a trade area |
An electronics retailer is offered anchor space in a new power center. The team exports the center's full tenant roster with traffic score, confirms the anchor mix supports the expected footfall, and checks that no competing electronics retailer already sits in the same center before signing.
We'll pull a live sample for your category and market.