Feature · AI Improvement Analysis

Your roadmap,
argued with evidence.

Claude reads every improvement request from your “somewhat disappointed” users, clusters them into themes, and ranks each by a transparent impact score. Every claim links back to verbatim quotes — so when you reprioritize the roadmap, you bring receipts.

AI Improvement Analysis
Last run 1 min ago · 5 themes from 14 responses
↻ Re-run analysis
#01Add missing features to close competitor gap

Multiple users — including target personas — feel the product lacks features that competing tools already offer. Closing this gap is critical to retaining and converting high-value users.

IMPACT
15.0
Evidence ›
“Missing some features that competitors offer.”
4 responses28.6% of responses● High severity (7.0)50% target persona
competitor featuresfeature parityfeature gap
#02Improve search power and relevance
4 responses · Medium severity (6.0) · 50% target persona
IMPACT
12.9
#03Lower pricing for individual users
2 responses · High severity (7.0) · 100% target persona
IMPACT
10.0
EVIDENCE · THEME #01
Verbatim requests that landed in this theme
“Missing some features that competitors offer.”
Rebecca C. · 4/13/2026
“I keep a second tool open just for the gaps.”
Elizabeth W. · 4/13/2026
“Feature parity would make this a no-brainer.”
Sarah H. · 4/18/2026
No black box

The impact score is a formula, not a vibe.

Every theme’s rank is computed from things you can check: how many users raised it, how severe their language is, whether they’re target personas, and which PMF segment they sit in. Sort the table by any factor. Disagree with the AI? The verbatims are one click away.

IMPACT SCORE
frequency × severity
× persona weight × PMF segment × 100
frequency — share of responses mentioning the theme
severity — 1–10, judged from the language used
persona weight — boosted when target personas are affected
PMF segment — “somewhat disappointed” voices weigh most
Competitive Mentions
8 mentions of 1 distinct competitor, across 91 analyzed responses
Competitor
Mentions
Sentiment
In q3
In q6
Excel
8
8
0
Positive Neutral Negative
Also in every run

Know who you’re really losing to.

Sentiment is scored on every free-text answer, and competitor names are detected and aggregated — with the sentiment around each mention. When users name your alternative in “what would you use instead?”, that’s intelligence worth having on one screen. PII is redacted before any text reaches the model, and analysis runs under a per-organization daily cost ceiling.