Google Maps data extraction
Google Maps lead extractor
A list of businesses is not a list of leads. The difference is whether each row carries enough signal for a rep to know why they are calling. This page covers turning a Maps search into rows that a sales team can actually work, and which fields do the qualifying.
What you get
- Business name, phone, website, address and category per row
- Rating and review count for qualification, not decoration
- Public email addresses pulled from the business website
- Filters that run before extraction so you spend credits on fits
- Column layout that maps cleanly onto CRM import fields
- Google Maps URL per row so a rep can verify the listing in one click
The fields that decide whether a row is workable
Name and address identify a business. Phone and email make it contactable. What makes it a lead is the qualifying data: whether it has a website, how many reviews it has collected, and what its rating looks like. Those three fields are what let a rep open with something specific rather than a generic pitch, and they are the difference between a list and a directory dump.
Qualify before extraction, not after
Filtering in the spreadsheet afterwards means you already spent credits on rows you deleted. Applying filters first is cheaper and faster. If you sell websites, filter for listings with no website. If you sell reputation management, filter for a rating below four or a review count under ten. The export then arrives pre-qualified instead of needing a cleaning pass.
Review count as a proxy for how established a business is
A business with four reviews and one with four hundred are different prospects even in the same category and city. Low counts often mean a newer business, a weak online presence, or nobody managing the listing, which makes them receptive to marketing services. High counts with a strong rating mean an established operator who is harder to sell to but worth more if you land them. Segment the list rather than mailing it uniformly.
Getting the export into a CRM without a cleanup pass
Most CRM imports fail on column naming and on multi-value fields. The export keeps one business per row with a fixed column order, so the mapping you set up on the first import keeps working on later ones. Import a batch of fifty first and look at how the records landed before pushing several thousand into a live pipeline.
Keeping the list current
Local business data goes stale quickly. Businesses close, move, rebrand and change numbers. A list built six months ago will have a measurable bounce and dead-number rate today. Re-running the same searches quarterly and comparing against what you already have is cheaper than working a decayed list and blaming the channel.
Frequently asked questions
What makes a row a lead rather than just a listing?
A contact method plus a qualifying signal. Phone or email makes it reachable, and website status, rating and review count give a rep a reason to open the conversation.
Should I filter before or after exporting?
Before. Filtering after extraction means you have already spent credits on rows you are about to delete.
Which filter works best for selling web design?
Listings with no website attached. They are the clearest signal in the dataset and the pitch writes itself.
Can I import the export directly into a CRM?
Yes. Columns are one business per row in a fixed order, so a field mapping set up once keeps working. Test with a small batch before a full import.
How often should a lead list be refreshed?
Quarterly for most local categories. Businesses close and change numbers often enough that older lists carry a noticeable bounce rate.