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liqiongyu
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analyzing-user-feedback

Synthesize user feedback into normalized tables, themes, and recommendations.

51|8|Updated Jan 22, 2026
Search Tags:#feedback loop#customer insights#product discovery#recommendations#user feedback#theme analysis
One-click install
npx skills add https://github.com/liqiongyu/lenny_skills_plus --skill analyzing-user-feedback-liqiongyu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analyzing-user-feedback
Source: https://github.com/liqiongyu/lenny_skills_plus/tree/main/skills/analyzing-user-feedback
Command: npx skills add https://github.com/liqiongyu/lenny_skills_plus --skill analyzing-user-feedback-liqiongyu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill transforms raw user feedback from various sources into structured, actionable insights, helping product teams understand user pain points and prioritize improvements effectively.

Core Features & Use Cases

  • Feedback Synthesis: Aggregates and normalizes feedback from support tickets, surveys, reviews, and more.
  • Theme Identification: Identifies key themes, quantifies their frequency and severity, and provides supporting evidence.
  • Actionable Recommendations: Generates concrete recommendations for product improvements, bug fixes, and strategic decisions.
  • Use Case: Analyze 90 days of support tickets and churn survey comments for your B2B SaaS product to identify the top 5 reasons users struggle with onboarding and propose specific UX fixes.

Quick Start

Use analyzing-user-feedback to synthesize support tickets from the last 90 days about onboarding for SMB users, producing themes, evidence, and top fixes.

Frequently Asked Questions about analyzing-user-feedback

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I synthesize user feedback from multiple sources into actionable insights?▼

You can synthesize raw user feedback from support tickets, surveys, and reviews by normalizing it into data tables, identifying key themes with supporting evidence, and generating actionable product improvement recommendations.

How does theme analysis work for customer feedback?▼

Theme analysis for customer feedback works by processing normalized data tables to identify key themes, quantifying their frequency and severity by segment and source, and linking each theme with supporting evidence from the raw input.

Can I analyze churn survey comments and support tickets together?▼

Yes, you can analyze churn survey comments and support tickets together by aggregating feedback from multiple sources to produce a comprehensive analysis pack that quantifies issues by segment and identifies underlying churn reasons.

What is the best way to identify top reasons users struggle with onboarding?▼

The best way to identify onboarding struggle reasons is to analyze support tickets and churn survey comments, synthesizing the raw input to quantify issue severity by user segment and propose specific UX fixes.

Does user feedback analysis work for B2B SaaS product discovery?▼

Yes, user feedback analysis works for B2B SaaS product discovery by supporting voice-of-customer initiatives and feature request analysis to generate concrete recommendations for strategic product decisions and feedback loop design.

When do I need to normalize raw customer feedback data?▼

You need to normalize raw customer feedback data when synthesizing input from disparate sources like reviews and support tickets, ensuring the aggregated data can be accurately quantified by segment and theme for analysis.

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