Every competitor location, placed on your map.
Physical footprint still decides where customers shop, eat and refuel. We compile points of interest from brand store locators, map and local listing platforms and delivery apps, with coordinates, categories, opening hours and change history.
Location datasets from different sources rarely agree. Branches open and close, addresses are written inconsistently, and store locators often omit coordinates, especially in fast-growing GCC and Latin American cities.
You get a clean, de-duplicated POI dataset with standardised addresses and coordinates. Openings and closures are tracked, so network planning and territory analysis reflect the current market.
Everything needed to run it in production.
Multi-source compilation
Locations collected from brand store locators, map and local listing platforms, delivery apps and directories.
Geocoding and validation
Addresses are geocoded where coordinates are missing, and points are checked against expected city boundaries.
Entity de-duplication
The same branch appearing on several sources is merged into one record with a confidence score.
Attributes
Category, brand, opening hours, phone, rating, review count and services such as drive-through or 24-hour opening.
Openings and closures
Each refresh is compared with the last to flag new, relocated and closed locations.
Address standardisation
Arabic and local-language addresses are kept alongside a standardised English form.
What lands in your systems.
| poi_id | brand | category | city | latitude | longitude | opening_hours | status |
|---|---|---|---|---|---|---|---|
| POI-AE-10231 | Coffee Chain A | Café | Dubai | 25.0772 | 55.1406 | 06:00-01:00 | open |
| POI-SA-20874 | Coffee Chain A | Café | Riyadh | 24.7136 | 46.6753 | 05:30-02:00 | new |
| POI-SG-30112 | Coffee Chain B | Café | Singapore | 1.3006 | 103.8390 | 07:00-22:00 | open |
| POI-CL-40561 | Coffee Chain C | Café | Santiago | -33.4172 | -70.6064 | 07:30-21:00 | relocated |
| POI-US-50993 | Coffee Chain B | Café | Austin | 30.2672 | -97.7431 | 06:00-20:00 | closed |
Illustrative rows. Your schema, field names and formats are agreed during scoping.
Who uses it, and for what.
Analysts track store openings and closures by chain as indicators of expansion or retrenchment. The history supports forecasts ahead of reported numbers.
Local marketing teams map competitor density around their own branches. Catchment-level campaigns are targeted where competition is heaviest.
Field sales teams build territory lists of outlets that stock their category. Routes are planned from accurate coordinates rather than outdated lists.
Location analysts combine POI data with footfall or demographic data to score potential sites. Clean coordinates and categories make the models reliable.
- Scope. Tell us the sources, fields and frequency. We confirm feasibility within a day.
- Free sample. A real sample from your own target source, in your format.
- Build. Engineers build extractors tuned to each source. No generic templates.
- Validate. Automated and manual QA on every run before anything ships.
- Deliver and monitor. Scheduled delivery, monitored pipelines, fast fixes when sites change.
Industries that use it
Where does the location data come from?
We combine brand store locators, map and local listing platforms, delivery apps and public directories. Using several sources improves coverage and lets us validate each location.
How do you handle duplicates across sources?
Records are matched on brand, name, address and distance between coordinates, then merged into a single entity. Each merged record lists the sources that confirmed it.
Can you detect store closures?
Yes. Locations that disappear from sources or are marked closed are flagged on each refresh. We confirm closures across more than one source where possible.
Do you include opening hours and ratings?
Yes, where published. Opening hours are standardised to a common format, and ratings and review counts are captured with their source.