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
How this looks in practice
A campaign might start with a single trade in a single city, say plumbers across one metro area. The collection returns several hundred listings; de-duplication removes the repeated ones; website checks drop the dead domains; and enrichment adds a named owner and a verified address where one can be found. The final file is small enough to work by hand and specific enough that the first email can mention the area the business actually serves.
Common mistakes
Poorly collected local data usually fails in the same ways.
- Deduplicating by business name, which misses the same shop listed three ways
- Trusting the first category shown, which is often the provider guess rather than the real trade
- Leaving phone numbers in mixed formats so dialling and matching both break
- Skipping the website check, so dead domains reach the sales team
Each of these is cheap to fix during cleaning and expensive to fix after import.
What to measure
Report rows collected, rows surviving de-duplication, websites that resolve, and emails that pass verification. Those four numbers tell a client exactly how much usable data they received. Pair the local list with a verified contact layer described in where to find B2B leads, and run the addresses through the checks in how to verify email addresses before delivery.
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.