Intelligent E-commerce Product Information Collection and Structured Processing Workflow

Workflow Name

Intelligent E-commerce Product Information Collection and Structured Processing Workflow

Key Features and Highlights

This workflow automatically scrapes HTML content from specified e-commerce web pages and leverages an AI language model (OpenRouter Chat Model based on OpenAI GPT-4.1) to intelligently extract key product information such as name, description, rating, number of reviews, and price. It performs cleaning and structuring of the raw webpage content and automatically writes the processed data into Google Sheets, achieving end-to-end automated collection and management. Highlights include multi-step data cleansing, structured output parsing, deep integration with the AI model, and seamless connection with Google Sheets.

Core Problems Addressed

• Traditional web data scraping often struggles with complex HTML structures, messy content, and data that is difficult to utilize directly.
• Manual extraction and organization of e-commerce product information is time-consuming, labor-intensive, and prone to errors.
• There is a need for an automated and intelligent solution to improve data collection efficiency and data quality.

Application Scenarios

• Market Research: Automatically gather product information and user reviews from competitors’ e-commerce platforms.
• E-commerce Operations: Monitor price, rating, and review changes for own or competitor products.
• Data Analysis: Provide accurate product data inputs for data science and business intelligence.
• Content Aggregation: Build foundational data for product comparison websites or recommendation systems.

Main Workflow Steps

1. Retrieve URL List for Collection: Read target e-commerce page URLs from Google Sheets.
2. Batch Process URLs: Use batch splitting modules to process URLs one by one.
3. Webpage Content Scraping: Call Brightdata’s Web Scraper API to obtain raw webpage HTML.
4. HTML Cleaning: Use custom function nodes to remove irrelevant tags, scripts, styles, and excessive blank lines, retaining only structured textual content.
5. AI-Powered Information Extraction: Utilize the OpenRouter Chat Model based on GPT-4.1 to extract product information from the cleaned HTML, generating product data in a predefined JSON structure.
6. Structured Output Parsing: Parse the AI model’s returned data to ensure completeness and correct formatting of fields.
7. Split Multiple Results: Separate multiple extracted product entries into individual records.
8. Write Results to Spreadsheet: Append the organized product name, description, rating, review count, and price into Google Sheets.
9. Loop Execution: Continue processing the next batch of URLs to achieve full workflow automation.

Involved Systems or Services

• Brightdata Web Scraper API: Efficiently scrape target webpage HTML content.
• OpenRouter Chat Model (GPT-4.1): Natural language processing and intelligent data extraction.
• Google Sheets: Store URLs for collection tasks and final collected results, enabling data management and sharing.
• n8n Automation Platform: Orchestrate the above services to build the automated workflow.

Target Users and Value

• E-commerce Data Analysts and Operations Personnel: Quickly acquire large volumes of product data to support decision-making.
• Market Research and Competitive Intelligence Teams: Monitor competitive landscape and market dynamics in real time.
• Data Engineers and Automation Enthusiasts: Build flexible and efficient web data collection and processing pipelines.
• Content Aggregation Platform Developers: Establish stable and accurate product information sourcing.

This workflow significantly lowers the barrier for e-commerce product information collection by enabling intelligent, batch, and structured processing, enhancing data collection efficiency and accuracy to support business intelligence and operational optimization.