Make.com (http://Make.com) Automation for Google

Make.com Automation for Google Sheets Data Comparison & Real Estate Change Detection. My Role is as Make.com Automation Engineer | Data Workflow Specialist | Google Sheets Automation Expert | API Integration Consultant I Built a Make.com automation system that compares daily Google Sheets datasets to detect changes in real estate data such as price, status, and DOM. Implemented structured data comparison logic, webhook triggers, and event-based workflows to generate automated snapshots. Designed scalable workflow automation, API integrations, and data processing pipelines to eliminate manual tracking and enable real-time business insights. Case Study, Using Make.com, Google Sheets automation, workflow automation, API integration, webhooks, data processing, dashboard reporting, lead generation automation, AI automation, and business automation, I developed a daily change-detection system that automatically compares datasets, identifies key differences, and triggers real-time workflow actions for real estate tracking.

The client needed an automation that could compare today’s vs yesterday’s property data inside Google Sheets and detect changes in status, price, and days on market (DOM), then generate a structured snapshot output based on a predefined template.

I began by designing a data comparison architecture inside Google Sheets, where both datasets (current and previous) were normalized into structured tabs. I created a system that aligns records using unique identifiers (such as property ID or address), ensuring accurate row-by-row comparison.

Next, I built a Make.com automation scenario that runs daily using scheduled triggers. The workflow pulls both datasets, processes them using iterator and aggregator modules, and applies comparison logic to detect:

Price changes

Status updates (active, sold, pending)

DOM variations

New or removed listings

To handle this efficiently, I implemented conditional routing and filtering logic so only records with meaningful changes proceed through the automation. This significantly reduced unnecessary processing and improved performance.

Once changes are detected, the system automatically formats the data into a structured snapshot output, based on the client’s PDF template. I mapped each data field dynamically and ensured consistent formatting for reporting and decision-making.

I also designed the system to write processed “old vs new” data into a separate Google Sheets tab, making it easy to audit changes, debug workflows, and maintain historical tracking.

To enhance reliability, I added:

Error handling and fallback routes

Logging for change tracking

Modular workflow design for scalability

Clean data mapping for future API integrations

Key Results Delivered

Automated daily real estate data comparison

Eliminated manual spreadsheet tracking

Improved accuracy in detecting property changes

Reduced processing noise using filtered workflows

Enabled real-time snapshot generation

Created scalable system for future automation expansion