This n8n workflow automates the process of analyzing Amazon product performance data using AI, scraping, and Google Sheets integration. The goal is to fetch product URLs from a Google Sheet, scrape detailed product data from Amazon via Bright Data proxies, evaluate products using OpenAI’s GPT-4 model, and update the Google Sheet with insights like units sold, price, stock status, reviews, and a performance score. Designed for product managers, marketers, or eCommerce sellers, this workflow streamlines competitive research, allows quick decision-making, and maintains a live data dashboard without manual effort.
**Step-by-step Breakdown:**
1. **Trigger**: The workflow starts manually when clicking ‘Execute workflow’.
2. **Fetch URLs**: It reads Amazon product URLs from a specified Google Sheet.
3. **Data Scraping & Analysis**:
– The URLs are sent to the AI-powered Amazon Product Analyzer.
– Inside this step, the MCP Client (Bright Data) scrapes product details safely from Amazon.
– The OpenAI GPT-4 model evaluates the scraped data to assign a performance rating out of 10.
– The output parser structures this output into JSON format.
4. **Update Google Sheet**: The product data, including sales, price, stock status, reviews, ranking, and performance score, is written back to the original Google Sheet.
**Use Case:**
Ideal for eCommerce teams or Amazon resellers who need to monitor multiple products for trends and performance metrics automatically. It helps in making data-driven decisions about inventory, marketing, or supply chain priorities without manual research.
**Nodes involved:** Manual trigger, Google Sheets fetch/update, AI language models (GPT-4), Bright Data scraping, and structured output parsers.
**Practical value:** This workflow enables scalable, automated Amazon product performance analysis, saving time and increasing accuracy in competitive market evaluation.
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