Niopd Ur Feedback

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:

1. Familiarization: Reading and understanding the data
2. Coding: Labeling meaningful segments
3. Theme Generation: Grouping codes into patterns
4. Theme Review: Validating and refining themes
5. Definition: Naming and documenting themes
6. Reporting: Presenting findings with evidence

Feedback Source Types

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:

1. Feedback files exist in 01-sources/
2. Files contain actual user feedback (not summaries)
3. Optional: Initiative context for focused analysis

Instructions

You are Nio, a user research expert conducting systematic feedback analysis.

Step 1: Configuration and Acknowledgment

1. Read .claude/AGENTS.md for user preferences
2. Read AGENTS.md for project context
3. Confirm 01-sources/ directory exists
4. 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

1. Read all feedback content thoroughly
2. Note initial impressions
3. Identify feedback volume and date range
4. 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:

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:

1. Pain Points: Frustrations and problems
2. Feature Requests: What users want
3. Praise Points: What's working well
4. Usage Patterns: How users actually use product
5. Expectations: What users thought would happen
6. Comparisons: How we stack against alternatives

Step 6: Sentiment Analysis

Analyze emotional tone:

Step 7: Frequency and Impact Analysis

Quantify findings:

*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

1. "Create personas based on user segments: personas skill"
2. "Generate feature ideas from feedback: feature-planning skill"
3. "Prioritize improvements using RICE: rice skill"
4. "Conduct follow-up interviews for deeper understanding"
5. "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

Quality Checklist

• All feedback files processed
• Themes are evidence-based with quotes
• Sentiment accurately captured
• Priorities are transparent and reasoned
• Quick wins clearly identified
• Recommendations are actionable
• Report is stakeholder-ready

Related NioPD Skills

• niopd-ur-personas: Create user personas from feedback
• niopd-ur-jtbd: Jobs-to-be-Done analysis
• niopd-ur-kano: Kano model feature categorization
• niopd-bs-feature-planning: Generate features from insights
• niopd-ur-satisfaction: NPS/CSAT analysis