Scraping Google Maps reviews for competitor research
Updated July 22, 2026 · 6 min read
Reviews are the only place where your competitors' customers describe, in their own words and unprompted, what went wrong and what they'd pay for. Survey data costs money and flatters you. Reviews are free and don't.
The trick is reading them at volume rather than one at a time, which means getting them out of the web page and into a spreadsheet.
What review data is good for
Three things, mostly. Finding recurring complaints in a category — the operational failure everyone in the market shares is a positioning opportunity. Finding the vocabulary customers actually use, which is better ad copy than anything you'll write from scratch. And tracking a specific competitor over time: a sudden run of one-star reviews mentioning staff usually means someone left.
It's also the fastest way to understand a niche you don't know. Read two hundred reviews of pest-control companies and you'll know more about how that market is won than any industry report will tell you.
Getting the reviews out
- 1
Pick your comparison set deliberately
Start from a scraped list of the category in your area, then choose competitors on a basis you can defend — the top ten by review count, everyone above 4.5, or the three that keep beating you in the map pack.
- 2
Run the review scraper on those listings
Hubertino has a separate Google Maps review scraper alongside the listing scraper, so you can go from “all dentists in this metro” to “every review of these eight practices” without leaving the tool.
- 3
Read the extremes first
One- and two-star reviews tell you where the category breaks. Five-star reviews tell you which specific staff member or detail people bother to name — that's the thing worth copying.
- 4
Then read the middle
Three-star reviews are where the honest, unemotional feedback lives. They're the smallest bucket and the most useful one.
How to read it without fooling yourself
Review volume tracks how long a business has existed and how hard it asks for reviews, not how good it is. A 4.9 with 30 reviews and a 4.5 with 900 are not comparable, and the second business is almost certainly bigger.
Ratings also cluster by category — a 4.2 is mediocre for a dentist and respectable for an airport parking lot. Compare within category, in the same metro, or the numbers mean nothing.
And reviews are self-selected: people who write them are disproportionately delighted or furious. Treat the themes as real and the proportions as unreliable.
A concrete use: the complaint-to-offer move
Tally the complaint themes across a category — “never called back”, “quoted one price, charged another”, “two weeks to get an appointment”. Whichever appears most is the promise your own marketing should lead with, and it's also the opening line of a decent cold email to businesses in that trade if what you sell fixes it.
Common questions
Can I get the reviewer's contact details?
No, and you shouldn't want to. Reviewers are private individuals; the useful output is aggregate themes about businesses, not a list of people.
How far back do reviews go?
Listings hold years of history, but recency is what matters — a complaint pattern from 2021 may have been fixed. Weight the last twelve months heavily.
Should I track this on a schedule?
For a handful of direct competitors, yes. Re-pull quarterly and diff; the delta is more informative than the snapshot.