What problem does it solve? Raw feedback from reviews, surveys, support tickets, and social listening is too voluminous and unstructured to act on directly. This Skill synthesizes that corpus into segments, sentiment scores, jobs to be done, and prioritized recommendations so product decisions rest on evidence rather than anecdote. ## Core Features & Use Cases - Segment identification: Groups feedback into at least three behavior- and need-based segments rather than demographics. - Sentiment scoring: Assigns an overall score from -1 to +1 per segment, with drivers and detractors, plus satisfaction read as product-segment fit. - Prioritized recommendations: Ranks pains by frequency, severity, and consequence, and outputs two to three highest-impact recommendations per segment with supporting quotes. - Use Case: A product manager with 500 app-store reviews and a quarter of support tickets runs the analysis to learn which user segment is at churn risk and which two fixes would have the highest impact. ## Quick Start Analyze the attached export of customer reviews and support tickets to identify user segments, score sentiment per segment, and recommend the highest-impact improvements.
