The Scenario
A retail site consultant has spent the week evaluating 20 candidate locations for a new store. Each candidate is a lat/lng pair in a Google Sheet. Before the recommendation goes to the client, the consultant needs to know how many competitor stores are within 1 kilometer of each site — competitive density is the deciding factor, and the client wants numbers, not gut feeling.
The bad version:
- Open Google Maps, drop a pin at candidate location 1, manually look at what's around it in a 1 km radius, count the competitors, type the count into the sheet. Repeat 19 more times.
- The category filter in Google Maps doesn't match Radar's place category system, so you're squinting at map pins trying to decide what counts as a competitor.
- By candidate 12 you've lost track of whether you were searching "grocery stores" or "supermarkets" for the last few entries. The counts aren't comparable.
The recommendation deck is due Monday. It's Friday afternoon.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Google Sheet. It reads the coordinate pairs, calls Radar's place search API for each location, and writes the result back into the sheet — with a consistent category filter applied to every row.
For each row in this sheet (lat in A, lng in B), search Radar for all places within 1000 meters matching the category food-beverage and write the place count, nearest place name, and nearest place distance to columns C, D, and E
What You Get
- Column C: count of matching places within the search radius for each candidate location
- Column D: name of the nearest matching place
- Column E: distance to the nearest match in meters
- Consistent category filter across all 20 rows — no judgment calls about what counts
What If the Data Is Not Quite Ready
You need to search for a different category — retail instead of food-beverage
For each row in this sheet (lat in A, lng in B), search Radar for all places within 500 meters in the retail category and write the count, nearest place name, and nearest distance (meters) to columns C, D, and E
You want to run searches at multiple radii to build a density gradient
For each of the 20 candidate rows (lat in A, lng in B): search Radar for retail places within 250 meters and write the count to column C, then within 500 meters to column D, then within 1000 meters to column E
Some candidate locations have been eliminated and should be skipped
For each row where column C is not "Eliminated": search Radar for retail places within 1000 meters of the coordinates in columns A and B and write count, nearest name, and nearest distance to columns D, E, and F
Search, score competitive density, and add a recommendation flag in one pass
For each of the 20 candidates (lat in A, lng in B): search Radar retail places within 1000 meters, write the count to column C and nearest place name to column D, then add a flag in column E: "High Competition" if count is 10 or more, "Moderate" if 4-9, "Low" if under 4
That flag column gives you the recommendation tier without a separate scoring step.
Try It
Get the 7-day free trial of SheetXAI and open any Google Sheet with location coordinates — ask it to search for nearby Radar places at each point and write the count and nearest result back into the sheet. To check geofence coverage instead, see finding geofences near coordinates, or return to the Radar integration overview.