Picking the wrong location is one of the most expensive mistakes a retail business can make. A lease runs for years, fit-out costs are sunk on day one, and a store in the wrong spot can't be fixed with better marketing. So the decision deserves more than a drive around the neighbourhood and a gut feeling about the area.
The businesses already operating near a candidate site tell you a great deal about whether it's worth entering. How many competitors are there, how good are they, what other businesses sit alongside them, and where are the obvious gaps. Google Maps holds all of that, which makes it a practical first stop for site selection before anyone signs anything. This guide covers what the existing businesses in an area reveal, which signals matter, and how to pull the data with Livescraper.
Why Site Selection Is a Data Problem
Every candidate location comes with questions that have real answers sitting in the surrounding businesses. Is this area already saturated with what I sell? Are the competitors here strong or weak? Is there enough complementary activity nearby to bring the right customers past my door? Answering those from observation alone means visiting each site and eyeballing it, which is slow and easy to get wrong. The same questions answered from data across every candidate area at once give you a comparison instead of a hunch.
What the Existing Businesses Tell You
A few readings come straight out of Maps data once you have the businesses around a site:
- Competitor density. Count how many businesses in your category already sit within a radius. A high count can mean the area is proven, or it can mean it's saturated. Either way it's a number, not a guess.
- Quality of the competition. Ratings and review counts show whether the nearby competitors are strong and established or weak and beatable. A cluster of poorly rated competitors is a different opportunity than a cluster of great ones.
- Complementary businesses. Certain categories draw the same customers. A business that benefits from foot traffic wants to sit near the anchors that generate it, and the mix of nearby categories tells you whether that traffic exists.
- Gaps. An area with strong complementary activity but no business in your category is the kind of white space worth a closer look.
Watching for Cannibalization
For a business opening its second or fifth location, there's a second question: how close is too close to your own existing stores. Open a new branch inside an existing one's catchment and the two split the same customers rather than reaching new ones. Mapping your current locations alongside candidate sites, and measuring the distance, keeps a new store from eating into one you already run.
Who Uses This
Site selection from business data comes up for:
- Retail chains and franchisors planning where the next unit goes
- Food and beverage operators reading competitor and anchor density
- Service businesses with physical premises choosing a catchment
- Property and expansion teams comparing several candidate areas at once
Building the Analysis
The practical build is to pull your own category and a handful of complementary categories across every candidate area, keeping coordinates on each record. From there you count competitors per area, read their ratings, note the complementary mix, and measure distances to your existing locations. The area that comes out with reachable demand, beatable competition, and no cannibalization is the one worth visiting in person.
Key Livescraper Features for Site Selection
- Google Maps Data Scraper returns competitor and complementary listings with coordinates, category, rating, and review count for any candidate area.
- Multi-area pulls let you run the same categories across several candidate locations in one task, so the comparison is like for like.
- Filters isolate the exact categories you want to measure.
- Export with coordinates feeds a spreadsheet or mapping tool where you can measure density and distance.
What Maps Data Won't Tell You
Business data answers the competitive side of site selection well. It doesn't cover everything. Foot traffic counts, local demographics, rent levels, and visibility from the road all matter and sit outside Maps. Treat the business data as the layer that narrows a long list of candidate areas down to a short one, then bring in the other factors and a site visit for the final call.
A Worked Comparison
Picture two candidate areas for a new fitness studio. You pull "gym" and "fitness studio" across both, plus a few complementary categories like healthy food outlets and physiotherapy clinics, keeping coordinates and ratings.
Area one comes back with eight gyms inside a one-mile radius, most rated above 4.3, and a dense mix of complementary businesses. That reads as proven demand, but also as a crowded, well-served market where you'd be the ninth option against strong incumbents. Area two has three gyms, two of them rated below 3.8, sitting near a cluster of offices and cafes with no gym within walking distance of them. That reads as real demand that the current options aren't serving well, which is a more inviting opening for a new, better studio.
Without the data, area one looks more attractive on a drive-through, since it feels busy and established. The counts and ratings flip that read. The busy area is busy because it's saturated with good operators; the quieter one has a foot-traffic base and weak incumbents. Neither answer is final on its own, but the comparison turns a subjective "this feels like a better spot" into a defensible shortlist, which is exactly what you want before committing to a lease and a fit-out.
Conclusion
A store location is a long, expensive commitment, so the decision benefits from evidence rather than instinct. The businesses already around a candidate site tell you how crowded it is, how strong the competition is, what complementary activity exists, and whether you'd be cannibalising your own stores. Livescraper's Google Maps Data Scraper pulls that surrounding business data across every candidate area at once, turning site selection from a gut call into a comparison you can defend.
Related reading: How Google Maps Data Powers Location Intelligence, How to Size a Local Market Using Google Maps Business Data, How to Use Google Maps Reviews for Competitor Analysis.
Frequently asked questions
Can Google Maps data tell me if a location is good?
It answers the competitive part well: how many competitors are nearby, how strong they are, and what complementary businesses exist. It doesn't cover foot traffic, rent, or demographics, so it narrows the shortlist rather than making the final call.
How do I measure competitor density?
Pull your category across the candidate area with coordinates, then count how many listings fall within a set radius of the site. The coordinates are what make that measurable.
What are complementary businesses?
Categories that draw the same customers you want. Being near the businesses that generate the right foot traffic can matter as much as avoiding competitors.
How do I avoid cannibalizing my own stores?
Map your existing locations alongside the candidate site and measure the distance. If a new store sits inside an existing catchment, the two split customers rather than adding new ones.
Can I compare several areas at once?
Yes. Running the same categories across a list of candidate areas in one task gives a like-for-like comparison rather than separate, hard-to-line-up pulls.
