Analyze Amazon review friction and revenue impact with Bright Data, OpenRouter and Google Sheets
UnknownWhat this workflow does
This AI workflow has 23 nodes and runs manually, when you click “Execute workflow”. Along the way it connects Google Sheets, OpenRouter Chat Model and Brightdata (community).
How it runs
Trigger: Manually, when you click “Execute workflow”.
- Set Product URL (Edit Fields (Set)): sets 1 field: url
- Start Review Scraping (Bright Data) (Bright Data): trigger collection by url (web scrapper)
- Wait: waits 30 seconds
- Format Bright Data Error (Edit Fields (Set)): sets 5 fields: errorSource, errorMessage, errorCode…
- Check Scraping Status (Bright Data): monitor progress snapshot (web scrapper)
- Log Scraping Error (Google Sheets): append
- Is Snapshot Ready? (If): checks a condition and branches into true / false
- Download Review Data (Bright Data): download snapshot (web scrapper)
- Detect Friction Signals (Code): runs custom JavaScript code
- Any Friction Detected? (If): checks a condition and branches into true / false
…plus 10 more steps. See the full node list below.
Services it uses
Before you upgrade to n8n 3.0
We can't fully verify this one: it relies on 1 community package, and n8n 3.0 turns unverified community packages off by default (N8N_UNVERIFIED_PACKAGES_ENABLED=false).
- Check that @brightdata/n8n-nodes-brightdata is verified or supports 3.0 (used in Start Review Scraping (Bright Data), Check Scraping Status, Download Review Data).
Nodes used (10)
- Manual Trigger
n8n-nodes-base.manualTrigger - Edit Fields (Set)
n8n-nodes-base.set - Bright Data
@brightdata/n8n-nodes-brightdata.brightData - Wait
n8n-nodes-base.wait - If
n8n-nodes-base.if - Code
n8n-nodes-base.code - AI Agent
@n8n/n8n-nodes-langchain.agent - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - Google Sheets
n8n-nodes-base.googleSheets - OpenRouter Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenRouter
From the author
Description This workflow automatically extracts Amazon product reviews and identifies hidden friction signals that are costing you conversions. It helps ecommerce and product teams turn customer complaints into measurable revenue opportunities. Overview This…