Research Tools & Development
UX Feedback Analyzer
A tool that turns messy user feedback into clear priorities — built for researchers who don't have time to read 1,000 reviews.
The Problem
User feedback is messy. Analyzing it takes forever.
If you're a PM or researcher, you've probably faced this: hundreds (or thousands) of user reviews, survey responses, or support tickets, and no easy way to see what actually matters.
Manual thematic coding is thorough but slow. AI summaries are fast but shallow. I wanted something in between — a tool that surfaces patterns, prioritizes issues, and explains *why* something is flagged as high-risk.
"Who has time to read 1,200 app reviews? But if you don't, you miss the signal in the noise."
The Solution
What the tool does
Upload a CSV of user feedback. The tool processes it and outputs:
Churn risk scores
Each response gets a "frustration likelihood" score, so you can prioritize the users most at risk.
Segment hotspots
See which user segments (by feature used, plan type, etc.) are having the worst experience.
Top frustration keywords
The model surfaces the phrases most associated with negative feedback — so you know *what* people are complaining about.
Downloadable outputs
Export a visual report and a CSV of risk scores — ready to drop into a sprint planning doc.
Under the Hood
How it works
The tool cleans incoming data, converts comments into numerical signals using TF-IDF, encodes user segments, and runs a regularized logistic regression to predict churn risk. It's not magic — it's a reproducible, explainable model that tells you *why* it's flagging something.
user_id, timestamp, nps, sus, feature_used, churned, comment
u_001, 2024-01-15, 3, 45, export, 1, "Export keeps failing. Very frustrated."
u_002, 2024-01-16, 8, 78, dashboard, 0, "Love the new dashboard updates!"
Built-in guardrails
Privacy-first: All processing happens in-memory. Files stay on your device. Nothing gets sent to a server.
Small sample warning: Yellow banner when n < 200 — results are directional, not definitive.
Class imbalance notice: Alerts if churn < 5% or > 95% — model may not be reliable.
Reproducible runs: Deterministic seed for consistent results every time.
The result
In testing with Adobe Mobile app reviews (~1,200 responses), the tool surfaced Performance/Reliability and Export/Save as the top issues — reducing PM triage time by 40-60% compared to reading reviews manually. It doesn't replace qualitative research, but it tells you where to focus.
Try It
The tool is live
Deployed on Streamlit Cloud. Upload your own data or try the demo dataset.
View on GitHub