Business school faculty and doctoral researchers use catalog.xmap.ai to study retail structure, franchise networks, and market competition for publications and teaching cases.
Study competitive concentration and chain versus independent share by category.
Ground a teaching case in a franchise brand's real geographic footprint.
Build a contact list of businesses in a target area for interviews or field studies.
Cite a transparently sourced, standardized dataset in a peer-reviewed paper.
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.
| Field | Type | Why it matters here |
|---|---|---|
| brand_name / chain_flag | string / boolean | Separates chain from independent business for market-structure research |
| category_level_1–3 | string | A standardized taxonomy for cross-industry comparison |
| phone / website | string | Builds a contact list for interview or survey recruitment |
| open_status | string | Tracks franchise expansion and contraction over time |
A faculty researcher exports a franchise brand's full location history across three years, tracks its expansion pattern by market, and builds a teaching case around the sequencing decisions that pattern reveals.