Scraping E-commerce Data into Google Sheets: A Streamlined Approach

Building a robust data foundation for AI agents requires efficient data collection methods. One particularly effective approach involves scraping e-commerce data directly into Google Sheets using Make Automation (formerly Integromat).

Finding Your Data Source

The first step in this process is locating the sitemap XML of your target online store. This serves as your primary data source, containing structured information about all products available on the site.

Four Essential Make Modules

The automation process relies on four key modules working together. Let’s examine each one in reverse order:

1. Google Sheets Module

The final step in your workflow uses the “Add a row” function to push all collected data into your spreadsheet. This creates a clean, organized repository of product information.

2. Set Multiple Variables Module

This crucial module maps product fields to your Google Sheet headers, ensuring that each piece of data ends up in the correct column.

3. Iterator Module

The iterator loops through each item in the response, processing products one by one to prevent data overload.

4. HTTP Request Module

This initial module fetches the product data from the sitemap XML, forming the foundation of your entire workflow.

Testing Your Setup

Before running the complete automation, it’s essential to test the HTTP module individually. A successful test will display all store products in the output, confirming you’re on the right track.

Executing the Automation

With all modules properly configured, running the full scenario will populate your Google Sheet with comprehensive product data. This structured information is now ready to power sophisticated AI agents and analytics tools.

This streamlined approach to data collection creates an efficient foundation for AI development, allowing you to focus on building intelligence rather than gathering information.