Google Maps Negative Review Intelligence Automation
Developed a robust n8n workflow to scrape Google Maps for business listings across multiple cities, capture key details, and extract negative reviews into structured tables for CRM and outreach automation. Manual extraction of business listings and reviews from Google Maps was slow, inconsistent, and lacked structured output for further enrichment and outreach
Business Listings
Negative Review Filter
Search Coverage
Data Structure
Manual extraction of business listings and reviews from Google Maps was slow, inconsistent, and lacked structured output for further enrichment and outreach.
Built a scalable n8n automation engine that allows keyword + city/zip input, scrapes structured business and review data via Serp API, consolidates results in Airtable with supporting tables for bad reviews, and links competitors automatically.
After implementation, Automated n8n workflow with bulk input, structured tables for business info, reviews, and bad reviews, competitor names included, ready for enrichment and outreach.
Behind the Scenes
How the system moved from problem to controlled execution.
Problem
Manual extraction of business listings and reviews from Google Maps was slow, inconsistent, and lacked structured output for further enrichment and outreach. Need to search multiple cities or zip codes for a given keyword. Manual Google Maps scraping is rate-limited and fragile. Business and review data were not consolidated in one place. Negative reviews were not separated or easily accessible.
System Built
Built a scalable n8n automation engine that allows keyword + city/zip input, scrapes structured business and review data via Serp API, consolidates results in Airtable with supporting tables for bad reviews, and links competitors automatically. Workflow covered: Input form allows keyword + zip codes/cities or bulk CSV upload; Construct query strings in the format '{keyword} near {zip/city}'; Fetch business results per city from Serp API; Extract business details: name, website, number of reviews, location; Identify local competitor names and attach to each business.
What Changed
100% business listings automated. 3 or below filtered negative reviews. 100% search results per query coverage. Fully Structured company + competitor + review consolidation. Bulk CSV or manual input handling for keywords and cities. Per-business and per-review processing with loop nodes.
Before / After
What changed after the system was rebuilt.
Data collection
Before
Manual Google Maps scraping
After
Fully automated n8n workflow
Review filtering
Before
No negative review filtering
After
3 or below filtered automatically
Data consolidation
Before
Scattered across sources
After
Three-table Airtable architecture
Error handling
Before
Workflow breaks on errors
After
Fault-isolated processing
Delivery Scope
What was included in the system delivery.
n8n workflow for multi-city Google Maps business extraction
Serp API business and review extraction logic
Airtable Places, Reviews, and Bad Reviews table architecture
Negative review filtering for 3-star-and-below feedback
Bulk keyword and city input handling
Controls
Checks built in to keep the workflow reliable.
Fault-isolated processing so one record does not stop the workflow
Separate tables prevent review data from mixing with business records
Negative reviews are filtered before entering outreach-ready views
Competitor names are attached consistently to related businesses
Bulk input processing supports repeatable city and keyword runs
Tools & Stack
Tools used to build, connect, and deliver this system
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