Local business data is one of the most frequently requested datasets, and also one of the most frequently delivered badly. Collection is the easy part; cleaning is where the value is created.
What you can gather
- Business name and primary category
- Address, city, postcode and country
- Website and phone number
- Rating and review count
- Opening hours and, where present, service areas
The method
Search by category and city, scroll through the results, then open each listing and extract the fields. Browser automation is required because the page loads its content dynamically, and generous randomised delays keep the request pattern reasonable.
Where an official Places API is available, prefer it. It is faster, permitted and far less fragile than driving a browser for hours.
Cleaning matters more than collecting
- Deduplicate by phone number and by website domain, not by name
- Standardise categories into your own taxonomy so filters work
- Flag chains and franchises separately, since they buy differently
- Verify that each website resolves before delivery
- Split address into street, city and postcode columns
Local data is messier than people expect. Three listings for the same business with slightly different names is normal, and only domain and phone matching resolves it reliably.
Typical uses
Local SEO prospecting, route and territory planning, market mapping, franchise research and competitive density analysis. For an agency, a clean local list is often the fastest way to build a credible pipeline in a new city.
Enrichment makes it valuable
Raw listings produce a category, a name and a phone number. Add the decision maker, a verified email and a note about their current web presence, and the same data becomes an actionable sales list rather than a directory export.
Respect the source
Keep request rates sensible, avoid hammering the service, and never present the data as officially licensed when it is not. Responsible collection keeps the dataset useful and keeps you out of trouble.