1. Fetch the profile
We pull the public Google Business Profile through the Google Places API: category, address, hours, photos, star rating, review count and the most recent reviews. No scraping, no logged-in data.
Every score in our scan comes from public Google data and one fixed calculation. This page opens up the whole method: which sources we use, how we calculate, and where the data stops.
We pull the public Google Business Profile through the Google Places API: category, address, hours, photos, star rating, review count and the most recent reviews. No scraping, no logged-in data.
We run a search for the primary category inside the business's own city and record where the profile lands among local competitors. Position is therefore category- and city-specific, never a national average.
The score uses a Bayesian average: rating × log10(review count + 1), smoothed toward the category mean. That stops 5.0 from 3 reviews outranking 4.6 from 400 reviews.
From the fetched reviews we compute the reply rate and how recent the newest review is. Replies and recency weigh more heavily than raw volume, because both are activity signals.
We check the fields Google itself uses to consider a profile complete: categories, website, phone, hours, photos, services and description. Every missing field lowers the score.
An honest method names its limits too. These are ours:
The scan is free and takes about a minute. Prefer to see the numbers across every business we scanned first?