Mapping Hidden Opportunities: Automated Google Maps Intelligence for Business Contacts

Date: November 10, 2018

In 2018, organisations with large contact databases faced a recurring problem: they knew who their entities were on paper, but not where they were in the real world or how to reach them effectively. Our MapsScrap initiative brought together Google Maps automation, contact intelligence, and database integration to turn fragmented records into a geocoded, searchable asset for the business intelligence unit.

The Challenge
Our client maintained extensive internal databases of entities (such as schools, public institutions, and organisations) with basic identifiers but incomplete or inconsistent contact details. Key challenges included:

• Missing Real-World Context: Many entities lacked validated addresses, phone numbers, websites, or geo-coordinates.
• Inconsistent Data Quality: Legacy records contained partial, duplicated, or outdated information.
• Manual Research Overload: Analysts spent hours manually searching Google Maps for each entity to verify locations and collect contact details.
• Limited Segmentation: Without coordinates, it was difficult to segment entities by region, municipality, or proximity for campaigns and field operations.
• No Centralised Store: Scraped or manually collected data was scattered across spreadsheets instead of a unified, queryable database.

The traditional approach—manual Google Maps lookups and spreadsheet maintenance—did not scale to tens of thousands of entities.

The Solution: Automated Google Maps Intelligence Platform
We designed a Google Maps–based scraping and enrichment platform that systematically transformed internal entity records into structured, geo-referenced business intelligence.

Data Acquisition and Scraping Engine

• Google Maps Automation: Built a Selenium-based scraper to search entities directly in Google Maps, using their names, locations, and national identifiers (NIF).
• Smart Querying: Automatically combined entity names with municipalities (or defaulted to a country when necessary) to maximise match quality.
• Main vs. Related Results: Distinguished between the primary entity and nearby or alternative results for richer context.

Structured Contact and Location Enrichment

• Contact Details Extraction: Captured names, addresses, categories, websites, phone numbers, and descriptive texts from Google Maps business profiles.
• Geocoding and Plus Codes: Stored coordinates and location codes to enable precise mapping and spatial analysis.
• Source Attribution: Tagged each record with the origin of the result (main business, nearby, alternative, etc.) to support quality control.

Database Architecture and Integration

• SQLite Intelligence Layer: Stored all scraped places in a dedicated places table inside a portable SQLite database (maps_data.db), with timestamps and source fields for auditing.
• MySQL Integration: Connected to existing MySQL databases to read entity lists and update processing status.
• Processed-Status Flags: Marked NIFs as processed once their Google Maps profiles had been evaluated, preventing duplicate work and enabling incremental runs.
• Batch Processing and Restart Logic: Implemented controlled batch sizes, browser restarts, and robust error handling for long scraping sessions.

Bulk Import Pipelines from External Sources

• CSV and TXT Importers: Developed loaders for third-party scrap exports (Import_*.txt, Export*.txt) feeding into central SQLite databases (GMaps.db, GMaps_XXX.db).
• Dynamic Schema Handling: Normalised column names and created tables on the fly based on expected structures.
• High-Volume Inserts with Progress Tracking: Used chunked inserts and progress bars (tqdm) to handle large files efficiently while monitoring load progress.
• Encoding Detection: Automatically detected file encodings (via chardet) to avoid data corruption in multi-source environments.

Key Features Delivered

1. Automated Entity Enrichment: Systematic retrieval of addresses, contacts, and coordinates for thousands of records.
2. Geocoded Contact Intelligence: Structured location data enabling map-based reporting and regional segmentation.
3. Incremental Processing: NIF-based tracking ensured each entity was processed once and could be revisited when needed.
4. Portable Intelligence Databases: SQLite-based stores (maps_data.db, GMaps.db, GMaps_XXX.db) that could be queried and shared across teams.
5. Seamless BI Integration: Ready-to-use data for business intelligence workflows, CRM enrichment, and targeted outreach.

Technical Implementation
The platform was built on a pragmatic and portable stack:

• Python Automation: Core logic implemented in Python for scraping, parsing, and database operations.
• Selenium + BeautifulSoup: Browser automation combined with HTML parsing for robust data extraction from Google Maps.
• SQLite and MySQL: Dual-database model—SQLite for local intelligence storage and MySQL for integration with existing systems.
• Resilient Batch Processing: Controlled batch sizes, browser restarts, and detailed logging for long-running sessions.
• File Ingestion Pipelines: CSV/TXT import tools with automatic encoding detection, schema creation, and chunked inserts.

Results Achieved

• Massive Time Savings: Automated lookups replaced manual Google Maps searches, freeing analysts for higher-value work.
• Improved Data Quality: Contact and location fields were standardised and centralised, reducing duplication and errors.
• Actionable Geo-Intelligence: Enabled map-based planning for campaigns, visits, and territory management.
• Reusable Infrastructure: The same pipelines could be applied to new data sources and regions with minimal changes.

Client Impact
“For the first time, we had a complete, geo-referenced view of our target entities,” said the head of the business intelligence unit. “What used to be scattered spreadsheets and incomplete addresses became a consolidated, searchable asset that supported real decision-making.”

Why This Project Matters
This 2018 initiative marked an important step in bringing location-aware intelligence into traditional contact databases. By combining Google Maps automation, robust data ingestion, and portable databases, we turned raw entity lists into a strategic asset for the business intelligence unit—laying the groundwork for later AI-driven projects in data extraction and enrichment.

Lessons Learned

• Location data is a critical dimension of business intelligence, not just a secondary attribute.
• Incremental, status-aware processing is essential for scaling large enrichment pipelines.
• Portable SQLite databases are powerful tools for bridging operational scripts and BI analysis.
• Investing early in data quality and structure simplifies future AI and analytics initiatives.