NPS9.ai
Net Promoter Score and customer sentiment analysis — survey orchestration, cohort segmentation, AI theme extraction from free-text feedback, and trend tracking.
Why I Built This
Most NPS programs die the same way: a number goes up or down each quarter, and nobody can explain why. NPS9 makes NPS usable again by treating the free-text answers as the actual signal — extracting themes, routing them to owners, and tracking whether the things customers complained about three months ago actually got fixed.
The Problem
NPS as a single number is directional at best. The real insight lives in thousands of free-text responses nobody has time to read, and most teams never connect what customers said to what the product team actually shipped.
How It Works
- Survey orchestration with cohort segmentation by plan, tenure, and product area
- AI theme extraction from free-text feedback — clusters, sentiment, and intensity
- Closed-loop routing: detractor responses flow to the right owner with context and suggested next steps
- Trend tracking that ties sentiment shifts to product releases, pricing changes, and outages
The Impact
NPS stops being a vanity metric — leadership sees what customers actually said, not just the score
Detractor response time drops sharply once closed-loop routing is wired in
Theme trends make it obvious which product bets moved the needle and which didn't
Executive reports generated in minutes instead of the quarterly synthesis scramble