Case Study (NPS)
Improving Product Direction Using AI-Analyzed NPS Feedback
Many PMs struggle to extract actionable insights from qualitative feedback like NPS comments, user interviews, or support tickets — that’s where AI can be a game-changer.
The Problem:
Product teams receive tons of unstructured qualitative data:
NPS open-ended responses
App store reviews
Support tickets
User interviews
But:
It’s time-consuming to read manually
Hard to spot patterns or trends
Feedback often gets siloed or ignored
How AI Helps:
Text Clustering
Groups similar feedback automatically
See common pain points
Sentiment Analysis
Tags feedback as positive, neutral, or negative
Spot overall mood or spikes
Theme Detection
Finds recurring topics/themes (e.g., “performance issues”, “pricing”)
Prioritize what to fix
Auto-summarization
Condenses thousands of comments into key insights
Saves hours of reading
Urgency Tagging
Flags critical/high-impact comments
Enables faster response loops
🧭 Context:
You're a PM for a SaaS productivity tool. NPS is decent (score: 38), but user growth has slowed. Leadership asks:
“What are customers actually saying?”
Tools Used:
AI Text Analysis Platform (e.g., MonkeyLearn, Chattermill, or a custom OpenAI model)
NPS Collection Platform (like Delighted, Typeform, or In-app surveys)
Slack or Notion integration for live summaries
Step-by-Step Breakdown:
1. Data Collection
Collected 3,000+ open-text NPS comments over 3 months.
2. AI Analysis
Used sentiment analysis → 22% negative, 54% neutral, 24% positive
Clustered responses by theme:
"Too many notifications" — 420 comments
"Confusing settings panel" — 390 comments
"Great collaboration tools" — 300 comments
Summarized into top 5 insights, with AI-generated heatmap showing trends over time.
3. PM Action
Prioritized redesigning settings UI (Q2 OKR item)
Worked with growth team to add in-app notification customization
Created a “Voice of Customer” Notion page that auto-updates via AI summary every week
4. Outcome
NPS score improved from 38 → 47 in 2 quarters
Support tickets about settings confusion dropped 32%
Internal teams now check AI-generated summaries weekly
Key PM Wins:
Turned noise into signal without spending hours reading
Data-driven roadmap alignment — features tied directly to real user pain
Improved cross-team visibility of user feedback
Bonus Tip for You:
You don’t need to build it all from scratch. Tools like:
Chattermill
Thematic
MonkeyLearn
or even Zapier + ChatGPT + Google Sheets can be set up quickly to automate a lot of this.
Last updated