Using Google Maps data for franchise and site-selection research
Updated July 22, 2026 · 6 min read
Before signing a lease or granting a franchise territory, someone has to answer: how many competitors are already here, how are they doing, and is this area under- or over-served? Listing data answers the first question well, the second roughly, and the third only in combination with things Maps doesn't know.
Here's how to use it without over-claiming.
What the data genuinely supports
Competitor density per zip code, which is the foundational number. Scrape your category across every zip in a region and you have a saturation map: how many coffee shops, gyms, urgent-care clinics or car washes per area, and where the gaps are.
Co-tenancy patterns, too. Scrape the categories that tend to sit near your concept — a gym near a smoothie bar, a nail salon near a supermarket — and the overlap shows where your kind of retail cluster already exists. Coordinates in the export make this a mapping exercise rather than a guessing one.
And competitive quality: ratings and review counts across a zip tell you whether the incumbents are strong or whether there's a market of tired 3.6-star operators.
A workable process
- 1
Define the competitive set narrowly and broadly
Direct competitors (same category) and substitutes (the categories a customer would choose instead). Run both. The substitute count is usually the one people forget and later regret.
- 2
Scrape the whole region by zip code
Saturation analysis is meaningless if searches truncate. One search per zip keeps each result set well under Google's feed cap, so per-area counts are comparable rather than all pinned at the same ceiling.
- 3
Normalise by population or households
Raw counts favour dense zips. Bring in census or ONS population figures and compute businesses per 10,000 residents — that's the number that identifies genuine under-service.
- 4
Look at review velocity as a demand proxy
Total reviews divided by roughly how long a business has been operating hints at throughput. Treat this as directional only: review counts reflect how hard a business asks for reviews as much as how busy it is.
- 5
Re-run before committing
Listings change. A site that looked under-served six months ago may have two new entrants. Re-scrape and diff before a lease is signed.
What Maps data cannot tell you
Revenue. Foot traffic. Rent. Lease terms. Traffic counts on the road outside. Demographics of who walks past. Whether the anchor tenant in that plaza is about to leave. None of that is in a listing, and any analysis that implies otherwise is dressing up a competitor count as a feasibility study.
Use listing data for the competitive layer and pair it with mobility data, demographic data and an actual site visit for the rest. It's the cheapest layer to acquire, and the one most often skipped.
Franchise territory design
For franchisors, the same per-zip counts feed territory definition: how many zips constitute a viable exclusive territory given the density of the target customer base, and where territories would cannibalise each other. The territory-list guide covers the mechanics of grouping zips into balanced units, which is the same problem in a different context.
Common questions
Can I measure how busy a location is?
Not from the export. Rating and review count are the available proxies and they're weak ones. Anything stronger requires foot-traffic or transaction data from a different kind of provider.
How large an area can I scrape for this?
As large as you're willing to pay for at one credit per business delivered. Whole states are routine; the practical limit is budget, and unused reservations refund automatically if a search finds less than estimated.