Mine user complaints and generate insight reports with Olostep, Gemini and Google Docs

AI Complaint Mining & Insight Extraction

This n8n template automates complaint mining from unstructured text sources and turns raw user feedback into clear, actionable insights. It uses AI to identify recurring complaints, pain points, and themes, helping teams understand what users are unhappy about and why.

Who’s it for - Product managers identifying recurring user pain points - Customer support and success teams - Founders validating product-market fit issues - UX researchers analyzing qualitative feedback - Anyone dealing with large volumes of complaints or negative feedback

How it works / What it does 1. Trigger - The workflow starts with a manual trigger, form submission, or imported text source containing user complaints.

2. Data Preparation - Raw complaint text is cleaned, normalized, and split into individual complaint entries. - Ensures consistent input for AI processing.

3. AI Complaint Analysis - An AI model analyzes each complaint to identify: - Core issue - Complaint category - Emotional tone - Severity or urgency

4. Pattern Detection - Complaints are grouped by similarity to uncover recurring issues and themes. - Highlights the most frequent and impactful problems.

5. Insight Extraction - AI summarizes key insights such as: - Top recurring complaints - Root causes - Suggested improvement areas