Lead Finder by WappBlaster

Google Maps scraper for recruiters

Recruitment firms mapping employers by industry and geography.

Last reviewed

Where the model fits recruiters

  • Employer discovery by category and area, including the small firms job boards never see.
  • Phone numbers reach owners directly in SMB niches where LinkedIn presence is thin.
  • Density mapping shows which metros can actually absorb a candidate pool.

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 recruiters.

A working playbook

  1. Sweep the category across the commute radius, not the city name; candidates think in travel time.
  2. Use review count to size firms roughly; a 300-review clinic hires differently to a 5-review one.
  3. Call rather than email in trades and healthcare, where the decision maker answers the listed number.
  4. Keep quarterly snapshots; new listings are new employers before they ever post a vacancy.

Volume, honestly

Candidate-side sourcing stays on LinkedIn and job boards; Maps data covers the employer side, and works best in categories underrepresented there.

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 recruiters 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 recruiters 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 recruiters plan around?
Candidate-side sourcing stays on LinkedIn and job boards; Maps data covers the employer side, and works best in categories underrepresented there.
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.