Lead Finder by WappBlaster

Google Maps scraper for B2B SaaS teams

SaaS companies selling to location-based SMBs.

Last reviewed

Where the model fits B2B SaaS teams

  • Category filtering approximates ICP better than firmographic databases do for local businesses.
  • Multi-city sweeps build TAM lists a data vendor would price per thousand rows.
  • Review count works as a maturity proxy: businesses investing in reputation buy software.

The general tradeoffs between desktop, cloud and extension extraction are covered in the comparison; this page is about how the desktop model is actually worked by B2B SaaS teams.

A working playbook

  1. Define ICP as Maps categories plus review-count band, then sweep your launch metros against it.
  2. Size TAM from the de-duplicated counts per metro before committing sales headcount to a region.
  3. Route by digital footprint: rows with websites and 50+ reviews to email sequences, the rest to SDR calls.
  4. Track category density per metro over quarters; expansion markets show up in the data before they show up in inbound.

Volume, honestly

For products sold outside location-based SMB niches, a firmographic database remains the right tool; Maps data wins specifically where the buyer is a storefront or service area business.

What the export gives you to work with

Every row carries business name, phone, website, full address, coordinates, star rating, review count, category, opening hours where published, and social links where listed. The two fields that do the most work for B2B SaaS teams are the review count, which is the best free qualification signal local data has, and the website URL, which splits every list into rows that can take email outreach and rows that need the phone. Email itself is discovered from the linked website, not the listing, with realistic coverage of 20-40% of rows; the email extraction guide explains why.

Related

Or browse all 50 industry and audience guides.

Frequently asked

Why do B2B SaaS teams use a desktop scraper instead of a cloud API?
Mostly pricing shape and control. A desktop licence is a flat $20 per year with unlimited rows, so cost does not scale with thoroughness, and extraction runs from your own connection rather than a shared proxy pool. The tradeoff is real: a desktop app cannot run unattended on a server or be triggered by another system, so workflows needing scheduled or programmatic extraction still belong on a cloud API.
What volume should B2B SaaS teams plan around?
For products sold outside location-based SMB niches, a firmographic database remains the right tool; Maps data wins specifically where the buyer is a storefront or service area business.
Does it get past the 120-result limit?
Yes, by grid extraction: the target area is divided into smaller cells, each cell is searched separately, and results are merged and de-duplicated on place ID. The 120-result ceiling applies to a single query, so coverage scales with the number of cells rather than being capped.