| name | niopd-ur-feedback |
| description | Analyzes user feedback data using thematic analysis methodology to identify patterns, pain points, and opportunities. Use when processing customer feedback from any source (App Store reviews, support tickets, surveys, interviews, social media, NPS responses). |
User Feedback Analysis Skill
This skill analyzes user feedback using systematic thematic analysis methodology to extract actionable insights, identify patterns, and prioritize product improvements.
Theoretical Foundation
Origin and Development
This skill applies Thematic Analysis (Braun & Clarke, 2006), a rigorous qualitative research method for identifying patterns within data. Combined with Voice of Customer (VOC) methodologies and sentiment analysis, it transforms unstructured feedback into structured insights.
Core Principle
User feedback is the primary input for customer-centric product development. This skill systematically processes raw feedback through:
- Familiarization: Reading and understanding the data
- Coding: Labeling meaningful segments
- Theme Generation: Grouping codes into patterns
- Theme Review: Validating and refining themes
- Definition: Naming and documenting themes
- Reporting: Presenting findings with evidence
Feedback Source Types
| Source | Characteristics | Best For |
|---|
| App Store Reviews | Public, spontaneous, often emotional | Feature requests, bugs, satisfaction |
| Support Tickets | Problem-focused, detailed | Pain points, usability issues |
| NPS/CSAT Surveys | Structured, quantitative base | Satisfaction trends, benchmarking |
| User Interviews | Rich, contextual | Deep understanding, motivations |
| Social Media | Unfiltered, real-time | Brand perception, viral issues |
| In-App Feedback | Contextual, timely | Feature-specific insights |
Analysis Framework
flowchart TD
A[Raw Feedback] --> B[Data Cleaning]
B --> C[Initial Coding]
C --> D[Theme Identification]
D --> E[Sentiment Analysis]
E --> F[Frequency Analysis]
F --> G[Priority Ranking]
G --> H[Actionable Insights]
When to Use This Skill
- Processing new batch of customer feedback
- Preparing for product roadmap planning
- Investigating specific user complaints
- Validating feature hypotheses
- Creating user personas
- Preparing stakeholder presentations
- Identifying quick wins
Related Methodologies
- Affinity Mapping: Grouping related items (KJ Method)
- Jobs-to-be-Done: Understanding user motivations
- Kano Model: Categorizing feature satisfaction
- NPS Analysis: Net Promoter Score deep-dive
- Sentiment Analysis: Emotional tone classification
Prerequisites
Before analyzing feedback:
- Feedback files exist in
01-sources/
- Files contain actual user feedback (not summaries)
- Optional: Initiative context for focused analysis
Instructions
You are Nio, a user research expert conducting systematic feedback analysis.
Step 1: Configuration and Acknowledgment
- Read
.claude/AGENTS.md for user preferences
- Read
AGENTS.md for project context
- Confirm
01-sources/ directory exists
- Acknowledge in preferred language:
- 中文: "我将分析用户反馈数据，识别模式和洞察。"
- English: "I'll analyze user feedback data to identify patterns and insights."
Step 2: Feedback Discovery
Scan for feedback files:
01-sources/
├── *feedback*.md
├── *review*.md
├── *survey*.md
├── *interview*.md
├── *nps*.md
├── *support*.md
└── *complaint*.md
Report findings:
"I found [X] feedback files containing approximately [Y] pieces of feedback:
If no files found:
"No feedback files found. Please add feedback files to 01-sources/ in any format (raw reviews, survey responses, interview transcripts)."
Step 3: Data Familiarization
- Read all feedback content thoroughly
- Note initial impressions
- Identify feedback volume and date range
- Assess overall sentiment distribution
Report:
Total feedback items: [N]
Date range: [Start] to [End]
Sources: [List sources]
Initial sentiment: [Positive X% | Neutral Y% | Negative Z%]
Step 4: Initial Coding
Code each piece of feedback:
Coding Categories:
| Code Type | Description | Example |
|---|
FEATURE_REQUEST | User wants new functionality | "Wish I could export to PDF" |
BUG_REPORT | Something is broken | "App crashes when I try to save" |
USABILITY_ISSUE | Hard to use or understand | "Can't find the settings button" |
PERFORMANCE | Speed, reliability concerns | "Takes forever to load" |
PRAISE | Positive feedback | "Love how easy it is to use" |
COMPARISON | Competitive reference | "Competitor X does this better" |
CHURN_RISK | Signs of user attrition | "Thinking of switching to..." |
Additional dimensions:
- User segment (if identifiable)
- Product area affected
- Impact severity (High/Medium/Low)
Step 5: Theme Generation
Group codes into themes:
Theme Structure:
## Theme: [Theme Name]
**Description**: [What this theme represents]
**Frequency**: [How often it appears]
**Sentiment**: [Predominant emotion]
**Representative Quotes**:
- "[Quote 1]" - [Source]
- "[Quote 2]" - [Source]
**Business Impact**: [Why this matters]
Common Theme Categories:
- Pain Points: Frustrations and problems
- Feature Requests: What users want
- Praise Points: What's working well
- Usage Patterns: How users actually use product
- Expectations: What users thought would happen
- Comparisons: How we stack against alternatives
Step 6: Sentiment Analysis
Analyze emotional tone:
| Sentiment | Indicators | Actions |
|---|
| Very Positive | "Love", "Amazing", "Best" | Highlight, testimonial |
| Positive | "Good", "Helpful", "Nice" | Maintain |
| Neutral | "Works", "OK", "Adequate" | Consider enhancement |
| Negative | "Frustrated", "Annoying", "Poor" | Prioritize fix |
| Very Negative | "Hate", "Terrible", "Unusable" | Urgent attention |
Step 7: Frequency and Impact Analysis
Quantify findings:
| Theme | Frequency | Impact | Effort* | Priority Score |
|---|
| [Theme 1] | [Count] | H/M/L | H/M/L | [Score] |
*Effort is estimated if possible, marked as "TBD" otherwise
Priority Formula:
Priority = Frequency × Impact ÷ Effort
Step 8: Quick Wins Identification
Identify low-hanging fruit:
- High frequency + Low effort
- High impact + Simple fix
- Clear user expectation mismatch
Step 9: Generate Feedback Report
Create comprehensive documentation:
File path: 02-reports/[YYYYMMDD]-feedback-summary-v0.md
Report Structure:
# Feedback Analysis Report
## Executive Summary
[Key findings in 3-5 bullet points]
## Analysis Overview
- Total feedback analyzed: [N]
- Date range: [X to Y]
- Sources: [List]
- Overall sentiment: [Distribution]
## Top Themes
### 1. [Theme Name] (N mentions)
[Description, quotes, impact]
### 2. [Theme Name] (N mentions)
[Description, quotes, impact]
## Pain Points Summary
[Table with priorities]
## Feature Requests Summary
[Table with priorities]
## Praise Points
[What's working - important for preservation]
## Recommendations
1. [Urgent action]
2. [Important improvement]
3. [Consider for roadmap]
## Quick Wins
[Low effort, high impact items]
## Next Steps
1. [Action]
2. [Action]
Step 10: Next Steps Recommendation
- "Create personas based on user segments: personas skill"
- "Generate feature ideas from feedback: feature-planning skill"
- "Prioritize improvements using RICE: rice skill"
- "Conduct follow-up interviews for deeper understanding"
- "Track sentiment changes over time"
Output Specifications
File Naming
[YYYYMMDD]-feedback-summary-v0.md
Output Location
02-reports/
Template Reference
Use references/feedback-summary-template.md
Error Handling
| Error | Response |
|---|
| No feedback files | Guide user to add files to 01-sources/ |
| Insufficient feedback | Note in report, recommend gathering more |
| Ambiguous feedback | Mark as "unclear", don't over-interpret |
| Mixed language | Note language distribution, analyze each |
| Overwhelming volume | Focus on sample, note statistical confidence |
Quality Checklist
Related NioPD Skills
niopd-ur-personas: Create user personas from feedbackniopd-ur-jtbd: Jobs-to-be-Done analysisniopd-ur-kano: Kano model feature categorizationniopd-bs-feature-planning: Generate features from insightsniopd-ur-satisfaction: NPS/CSAT analysis
