Google Maps Scraper for Business Leads (N8N)

Lead Generation; Just pulled 2,854 verified business emails from Los Angeles in 3 hours.

The old way:
• Manually searching Google Maps
• Writing down business names
• Visiting each website individually
• Hoping to find contact information
• Spending days for 15–20 leads

I built an automation that transforms this entire process:

It uses AI to break down cities into neighborhoods, so instead of just searching “HVAC companies in Los Angeles,” it searches every single district — Downtown, Hollywood, Echo Park, Westlake, and 30+ more areas.

The magic?

The system visits each business website automatically, finds real contact information, and validates it against the business domain. No more fake emails or wrong contacts.

What used to take an entire week now happens while I sleep.

Even better: The leads are higher quality because they’re automatically verified against business domains. No more wondering if you’re reaching the right person.

Three unexpected benefits I discovered:

1. Small businesses in local neighborhoods are often overlooked by competitors but make great clients
2. Validated emails have a 3x higher response rate than unverified ones
3. The neighborhood-by-neighborhood approach finds hidden gems that citywide searches miss,

Detailed Documentation for Make.com Workflow: Google Maps Scraper

Overview

The "Google Maps Scraper" workflow automates the process of generating location-based search queries, extracting data from Google Maps, and storing relevant information such as emails and locations into Airtable. This workflow ensures comprehensive and systematic data collection for targeted businesses and regions.

Workflow Components

1. Trigger Node

Airtable Trigger

• Node Name: Airtable Trigger
• Purpose: Monitors Airtable for new entries that require processing.
• Key Parameters:
  • Base ID: app7uvqIaU8s03Lls.
  • Table ID: tblmx8ut6NAO34oIP.
  • Trigger Field: Created.
  • View: Grid view.

2. Query Generation

LLM for Query Creation

• Node Name: Basic LLM Chain
• Purpose: Generates specific search queries based on business type and location.
• Key Parameters:
  • Prompt: Instructs the AI to create exhaustive search queries using the format business type + in + subregion.
  • Input Variables: Business, City.
  • Output Format: JSON with the field locations.

3. Query Structuring

Output Parser

• Node Name: Structure for Google Maps
• Purpose: Formats the AI-generated queries into a structured JSON format for further processing.

4. Data Extraction

Google Maps Query Execution

• Node Name: Execute Workflow
• Purpose: Executes the Google Maps Scraper workflow with each query generated.
• Key Parameters:
  • Workflow ID: Google maps scraper.
  • Mode: Processes each query individually.

Extract Locations

• Node Name: Get Location
• Purpose: Retrieves Google Maps results for the provided search query.

5. Data Processing

Domain Extraction

• Node Name: Extract Domains
• Purpose: Extracts domain names from the URLs returned by Google Maps.
• Method: Uses regex to isolate domains from URLs.

Email Extraction

• Node Name: Find Domain Related Emails
• Purpose: Searches for emails on the extracted domains using regex.
  • Fallback Mechanism: Logs "nothing found" if no emails are identified.

6. Filtering and Validation

Filter Emails

• Node Name: Emails with Valid Domains
• Purpose: Filters out invalid email addresses or entries without domain matches.

Duplicate Removal

• Node Name: Remove Duplicates
• Purpose: Eliminates duplicate emails from the extracted results.

7. Storage and Logging

Airtable Integration

• Node Name: Create Records
• Purpose: Logs the processed data into Airtable for record-keeping and analysis.
• Key Parameters:
  • Table ID: tblXbe3gEdSiqI0Gv.
  • Data Fields: Email, Search Term, Location.

Workflow Logic

1. Trigger:
   • Monitors Airtable for new entries and begins processing.
2. Query Generation:
   • AI generates search queries based on the business type and city provided.
3. Data Collection:
   • Executes the Google Maps scraper workflow for each generated query.
   • Extracts domains and searches for email addresses linked to each domain.
4. Filtering and Validation:
   • Filters out invalid or duplicate email addresses.
5. Storage:
   • Logs the extracted data into Airtable for further use.

Error Handling

• Invalid Inputs:
  • Logs errors if mandatory fields (e.g., Business or City) are missing.
• Query Failures:
  • Retries failed queries and skips after three unsuccessful attempts.
• Email Extraction Errors:
  • Logs "nothing found" for domains with no identifiable emails.
• Duplicate Data:
  • Ensures only unique records are logged in Airtable.

Customization Options

1. Search Query Format:
   • Modify the prompt to adjust query formats or include additional parameters (e.g., ratings, reviews).
2. Target Regions:
   • Expand or restrict the list of cities or neighborhoods processed.
3. Output Fields:
   • Add or remove fields in Airtable to include additional metadata (e.g., phone numbers).
4. Error Notifications:
   • Implement Slack or email alerts for error notifications.
5. Execution Frequency:
   • Adjust the Airtable trigger to process entries at a different frequency.

Conclusion

The "Google Maps Scraper" workflow is an efficient and scalable solution for collecting and managing location-specific business data. Its flexibility and error-handling capabilities make it suitable for various use cases, including lead generation and market research.