The Scenario
You manage retail partnerships for a CPG brand. Someone from the sales team just dropped a sheet on you: 300 retail partner locations — store name in column A, address in column B — and a request to get Placekeys for all of them before the end-of-week data sync with the brand's retail analytics platform.
The catch: this isn't a generic address list. A CVS and a Target can share the same strip mall address. If you submit address-only, Placekey returns the building's ID, not the specific store's ID. Your analytics platform is POI-level. You need a Placekey that distinguishes the CVS from the Target at 400 Commerce Drive.
The bad version:
- Submit addresses only and hope the generic building-level ID is good enough — it won't be, and you'll find out during the join when everything collapses to the wrong POI.
- Read through the Placekey API docs, figure out that the location_name parameter is what improves match specificity, write a script or configure a tool that passes both name and address together, debug the ones that still return no match.
- Spend the rest of the day reviewing which rows came back with a valid ID and which ones need manual correction.
The analytics platform sync is Friday. Getting this wrong means the CPG team spends next week looking at foot traffic data attributed to the wrong store format.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Google Sheet. It reads your store name and address columns, passes both to the Placekey API using the location_name parameter, and writes the higher-accuracy ID back — no configuration, no script.
Here is the prompt:
For each row, get a Placekey using both the location name in column A and the address in columns B–E to maximize matching accuracy, and write the result to column F
What You Get
- Column F: a Placekey that distinguishes the specific branded location from co-located stores at the same address.
- Rows where Placekey can't match even with the name included get a blank in column F — visible so you can review those locations before the sync.
- The API call uses location_name + street_address + city + region + postal_code, which is the full parameter set Placekey uses for highest-confidence POI matching.
What If the Data Is Not Quite Ready
The store name column has inconsistent brand name formats — normalize first
Before submitting to Placekey, normalize common brand name variants in column A (e.g., "CVS Pharmacy" and "CVS" → "CVS", "Walmart Supercenter" and "Walmart" → "Walmart"), then submit all rows with location_name (A) and address (B–E) and write the Placekey to column F
Some rows have a combined address in column B instead of split fields
Parse the full address in column B into street, city, state, and zip and write them to helper columns G–J, then submit location_name (A) and parsed address (G–J) to Placekey for each row and write the Placekey to column F
Flag any rows that return no match so you can send them back to the sales team
Submit all rows to Placekey with location_name (A), street_address (B), city (C), region (D), postal_code (E), iso_country_code 'US' — write Placekey to column F and flag any rows that return no match with 'NO MATCH' in column G
The full pipeline: normalize names, submit with name + address, flag no-matches, summarize
Normalize brand name variants in column A, parse any combined addresses in column B into components, submit all 300 rows to Placekey with location_name and full address parameters, write Placekey to column F, flag no-matches with 'NO MATCH' in column G, and add a summary in cell I1 with total submitted, matched, and unmatched counts
The combined prompt gets the list to the analytics platform ready in one shot — names normalized, Placekeys assigned, gaps flagged, summary included.
Try It
Open the retail partner sheet in Google Sheets and get the 7-day free trial of SheetXAI. Ask it to match all 300 locations using name and address together. Also useful: bulk Placekey assignment and the full Placekey hub.