Feedback Optimization AI
Feedback Optimization AI applies clustering, sentiment detection, and behavior analytics to help you identify themes, pain points, and opportunities for roadmap evolution.
Key Use Cases
NPS Comment Clustering
Group qualitative responses into themes and trends
Feature Prioritization
Rank requests by volume, sentiment, and effort-to-impact
In-app Behavior Signals
Monitor drop-offs, rage clicks, and success moments
Product QA Loops
Detect bugs, UX issues, and friction from support chats
How It Works
Step 1
Data Ingestion: Pull from Intercom, Hotjar, Google Play, App Store, etc.
Step 2
Semantic Analysis: NLP classifies and summarizes feedback into topics
Step 3
Clustering: Group similar themes using embedding models
Step 4
Prioritization Models: Score items based on impact, volume, and urgency
Key Benefits
Perfect For
Founders & PMs
Make product bets based on patterns, not anecdotes
Growth Teams
Connect feedback to conversion or churn outcomes
UX Researchers
Spot qualitative trends that quantify user friction
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