AI Customer Feedback Miner: Turn Reviews, Support Tickets & Comments Into Product Priorities
Built by KineticMatrix ·
About this build
A Claude and n8n powered workflow that collects customer feedback from reviews, support tickets, surveys, and community comments, groups similar complaints automatically, identifies recurring problems, and turns the raw feedback into clear product improvement priorities.
What was built
I built this after noticing how easy it is for useful customer feedback to get buried. A customer might say "checkout keeps freezing," another might say "I can't complete my order," and someone else might write "the payment page gets stuck." A human reading all three might immediately recognise that they are probably describing the same problem. The workflow is designed to make that connection automatically. Step 1: Collect the feedback n8n pulls feedback from different sources such as support tickets, customer surveys, product reviews, and community discussions. Each entry is cleaned and stored in Airtable with the source, date, customer message, and product area. Step 2: Analyse the feedback Claude analyses each piece of feedback and identifies the main topic, sentiment, urgency, customer intent, and whether the comment contains a specific product problem. Step 3: Group similar problems Instead of treating every comment as a separate issue, the workflow groups feedback that describes the same underlying problem. For example: "Checkout keeps freezing." "Payment page won't load." "Can't finish my order." These can all be grouped under something like Checkout Failure rather than appearing as three unrelated complaints. Step 4: Calculate priority Each issue receives a priority score based on factors such as frequency, severity, customer impact, and whether the problem is affecting an important part of the customer journey. This helps separate "three people requested a dark mode" from "47 customers can't complete checkout." Step 5: Create the product summary Claude generates a short summary for each major issue explaining: What customers are experiencing How frequently it appears Who appears to be affected The likely business impact Example customer feedback Suggested next action The goal is to give the product team something they can actually use instead of another giant spreadsheet of comments. Step 6: Alert the team High-priority issues trigger a Slack notification containing the issue summary, frequency, severity, and a few representative customer comments. The team doesn't need to wait until the end of the month to discover that a major problem is suddenly appearing. What I would build next I'd add a trend detection layer that compares feedback week over week. That would make it possible to catch things like: "Checkout complaints increased 180% this week." or "Customers mentioning delivery delays have doubled since last month." The bigger goal is to move from simply analysing feedback to detecting emerging customer problems before they become major issues.