How to Scrape Google Maps Business Data

Google Maps is the largest directory of local businesses. Scraping it with ScrapingLab lets you build lead lists, monitor competitors, and aggregate business intelligence.

What You Can Extract

• Business name and category
• Full address and coordinates
• Phone number and website URL
• Star rating and total review count
• Operating hours
• Popular times data
• Individual review text and ratings

Step-by-Step with ScrapingLab

1. Create Your Search Task

Start with a Google Maps search URL. Structure your URL to target a specific query and location:

https://www.google.com/maps/search/restaurants+in+new+york/

2. Build Your Workflow

1. Navigate — Go to the Maps search URL
2. Wait — Wait for the results panel to load (3-5 seconds)
3. Scroll — Scroll the results panel to load more listings (Google Maps loads results dynamically)
4. Extract — From each business listing:
   • Business name
   • Rating and review count
   • Address
   • Category/type
   • Phone number (if visible)
5. Screenshot — Capture the results for reference

3. Extract Individual Business Details

For deeper data on each business:

1. Click — Click on a business listing to open its detail panel
2. Wait — Wait for the detail panel to load
3. Extract — Get additional fields:
   • Full address
   • Website URL
   • Operating hours
   • Photos count
4. Navigate back — Return to the search results
5. Loop — Repeat for each listing

4. Schedule and Export

• Run weekly to discover new businesses in your target area
• Export to CSV for sales team prospecting
• Send to webhooks to feed your CRM

Common Challenges

Infinite Scroll

Google Maps uses infinite scroll — results load as you scroll. Use the Scroll step multiple times in your workflow to load more listings before extraction.

Dynamic Content

Maps is heavily JavaScript-driven. ScrapingLab’s browser engine handles this, but allow extra wait times (3-5 seconds) for content to fully render.

Geo-targeting

Results vary by location. ScrapingLab’s proxy rotation means requests come from different regions. For consistent results, include the city/state in your search query.

Best Practices

• Be specific — Use detailed search queries like “italian restaurants in brooklyn” rather than broad terms
• Limit batch size — Scrape 50-100 businesses per run rather than thousands
• Verify data — Use screenshots to spot-check that extraction is accurate
• Track changes — Schedule recurring runs to monitor new businesses, rating changes, and closures