In this project, we scraped Google Maps data using Python and Selenium to gather contact details of cleaning companies. The extracted data, including company names, emails, and phone numbers, was cleaned and formatted before being uploaded into a searchable business directory. This effort expanded the client’s database and improved the utility of their cleaner services platform.
Google Maps Data Scraping of Cleaner Companies
Challenge & Solution
Inconsistent and Unstructured Business Data
Google Maps listings often contain unstructured or inconsistent business information, especially for small local service providers. Extracting accurate and complete data such as emails and contact numbers posed a significant challenge due to variable formats and anti-scraping restrictions.
Automated Scraping with Data Cleaning Pipeline
We developed a Python automation script using the Selenium driver to navigate Google Maps results programmatically. The script captured business names, emails, phone numbers, and service categories. Post-extraction, we implemented a custom data-cleaning module to remove duplicates, standardize formats, and validate email structures. Once cleaned, the dataset was integrated into the client’s directory for public search.
A Robust, Searchable Cleaner Directory
The final dataset contained hundreds of accurately profiled cleaning companies, searchable by location or service type. This not only enhanced the client’s platform but also positioned it as a more credible and data-rich directory for users seeking local cleaners.
Our Process
We began by defining scraping parameters and target search terms on Google Maps, focusing on cleaning-related services. Using Selenium, we simulated human-like browsing to bypass rate limits and rendered dynamic content. We collected all visible business details into a CSV structure, then cleaned and normalized the dataset with custom Python scripts. Finally, we imported the cleaned data into the directory’s backend database, verified the searchability of each listing, and tested user access flow.
Result Driven
Over 500+ Valid Cleaning Company Profiles Added
Our scraping process successfully captured over 500 usable company profiles with contact information—ready for directory upload.
Automated Scraping with Data Cleaning Pipeline
Automating the scraping and cleaning process significantly reduced time and labor costs, replacing what would have taken days of manual entry.
Enhanced Search Experience for Directory Users
The updated directory now enables users to find cleaning companies quickly, filtered by service and region, improving the overall UX and platform value.
