Count Your Competitors' Reviews Before You Open a New Location
More competitors isn't always worse. Total competitor Google reviews can be a proxy for local demand. Here's how to test it against your own locations.
Before you open a new location, count your competitors' Google reviews in that area. The intuition says more competitors means a harder market. But for businesses like food delivery, the opposite often holds: areas where competitors have many reviews are areas where people already order a lot. If you trust your product, a busy market is easier to win share in than an empty one.
Why would more competition be a good sign?
Reviews are a by-product of orders. An area where competitors have collected thousands of reviews is an area where the habit already exists: people order online, compare options and leave feedback. You don't have to create demand there. You only need to win part of it.
An area with few competitors and few reviews might be an untapped opportunity. More often, it's a market where people simply don't order much.
The plain number of competitors tells you little. The total number of reviews they've collected tells you far more.
How do you run this analysis for your business?
- List the areas you're considering, plus the areas where you already operate.
- Collect competitors per area from Google Maps: every business in your category within your delivery or catchment radius.
- Record each competitor's review count and rating. For a handful of towns you can do this by hand. For dozens, a scraper or a Places data API does it in minutes.
- Sum the reviews per area. That's your demand proxy.
- Check it against your own history. Do your best existing locations sit in high-review areas? If yes, you have a signal you can trust. If not, the proxy doesn't hold for your category.
The check against your own data is the step people skip, and it's the one that makes this more than a nice theory.
What else do competitor reviews tell you?
The same scrape gives you the text of the reviews. Read them for recurring complaints: slow delivery, cold food, the same mistakes over and over. That tells you what to promise in a new area, and what your own operations have to get right on day one.
An AI model is good at this part. Give it a few hundred reviews per competitor and ask for the top complaints with counts and example quotes.
Where does this fit in site selection?
It's one input, not the whole model. A solid process also includes:
- a tool that scans commercial property listings against fixed criteria (size, parking, gas, electrical capacity) and tracks price changes;
- a revenue estimate based on the real delivery or catchment area, not just the town's population;
- people physically visiting the shortlisted places.
What are the caveats?
- Categories differ. Review volume tracks ordering habits well for delivery food and consumer services. For a dentist or a B2B service it may mean much less.
- Old reviews inflate counts. A competitor with 2,000 reviews collected over ten years isn't the same as one with 2,000 from the last two. If you can, weight recent reviews.
- Respect the terms of service of whatever source you collect from, and prefer official APIs when they exist.
Where should you start?
Take your three best and three weakest locations and add up competitor reviews around each. It's an afternoon of work, and you'll know whether the signal holds for you.
If you want this turned into a repeatable site-scoring tool, that's something I build.
