
customer-feedback-analyzer
Synthesize user feedback from multiple channels and identify patterns to inform product decisions. Use when analyzing feedback, prioritizing feature requests, conducting NPS surveys, or understanding user sentiment. Covers feedback collection, categorization, prioritization frameworks, and closing the feedback loop.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/daffy0208/ai-dev-standards --skill customer-feedback-analyzer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads customer-feedback-analyzer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Customer Feedback Analyzer
Collect, analyze, and prioritize user feedback to inform product decisions.
Core Principle
Never collect feedback you won't act on. Collecting feedback creates expectation of action. If you can't commit to reviewing and acting on it, don't ask for it. Destroys trust.
Feedback Channels
1. In-App Feedback Widget
Best for: Contextual feedback, low friction
// Contextual feedback
<FeedbackWidget
context={{
page: 'dashboard',
feature: 'export',
user_action: 'clicked_export'
}}
placeholder="How can we improve exports?"
/>
Pros: High quality (contextual), immediate Cons: Can interrupt user flow
2. NPS Surveys
Best for: Measuring overall satisfaction and loyalty
Question: "How likely are you to recommend [Product] to a friend or colleague?"
Scale: 0-10
Scoring:
Promoters (9-10): Love your product, will advocate
Passives (7-8): Satisfied but not enthusiastic
Detractors (0-6): Unhappy, will churn
NPS = % Promoters - % Detractors
Benchmarks:
Excellent: ≥50
Good: 30-49
Needs Work: <30
Follow-up question: "What's the main reason for your score?"
3. Support Tickets
Best for: Identifying recurring issues
Pattern Recognition:
- Same issue reported 5+ times → UX problem, not edge case
- Support time > 10 min per ticket → Needs better docs
- Ticket volume spike → Recent deploy likely caused issue
4. User Interviews
Best for: Deep qualitative insights
Interview Structure:
1. Background (5 min): Their role, use case
2. Problem Discovery (10 min): Challenges they face
3. Solution Validation (10 min): Show prototype, get reaction
4. Wrap-up (5 min): Any other feedback?
Sample Size: 5-10 users per persona
5. Feature Request Voting
Best for: Prioritizing roadmap
Tools: Canny, ProductBoard, Upvoty
Benefits:
- See most requested features
- Reduce duplicate requests
- Public roadmap transparency
- Close the loop automatically
Avoid:
- Building everything requested
- Letting voters drive strategy
6. Exit Interviews
Best for: Understanding why users churn
Key Questions:
- What made you decide to cancel?
- What feature/change would have kept you?
- What are you switching to?
- What did we do well?
Feedback Categorization
By Type
Bug: Something broken
- "Export fails with >100 rows"
- Priority: Fix immediately
Feature Request: New capability
- "Add Slack integration"
- Priority: Vote/validate
Enhancement: Improve existing feature
- "Export should include timestamps"
- Priority: Nice to have
Usability: Confusing UX
- "Can't find where to invite team members"
- Priority: High (friction)
Performance: Speed issue
- "Dashboard loads slowly"
- Priority: Depends on impact
By Severity
Critical: Blocks core workflow
- "Can't save projects"
- Action: Hotfix immediately
High: Significant friction
- "Onboarding confusing"
- Action: Fix this sprint
Medium: Minor annoyance
- "Button text unclear"
- Action: Fix next quarter
Low: Edge case or cosmetic
- "Spacing looks off on mobile"
- Action: Backlog
By Frequency
Widespread: 50+ reports
→ High priority
Common: 10-50 reports
→ Medium priority
Occasional: 5-10 reports
→ Low priority, monitor
Rare: <5 reports
→ Likely edge case, document workaround
By User Segment
Power Users: High engagement, experienced
→ Actionable, technical feedback
New Users: Recently signed up
→ Onboarding issues, first impressions
Churned Users: Cancelled/inactive
→ Why did they leave?
Enterprise: Paying customers
→ Security, compliance, integrations
Prioritization Framework
Priority Score Formula
Score = Impact (1-5) × Frequency (1-5) × Strategic Alignment (1-5)
Score ≥ 40: High Priority (next sprint)
Score 20-39: Medium Priority (next quarter)
Score < 20: Low Priority (backlog or never)
Example:
Feedback: "Add Slack integration"
Impact: 4 (significantly improves collaboration)
Frequency: 5 (50+ requests)
Strategic Alignment: 4 (fits roadmap)
Score: 4 × 5 × 4 = 80
Decision: HIGH PRIORITY - Build next sprint
Feedback: "Change button color"
Impact: 1 (minor cosmetic)
Frequency: 1 (1 person mentioned)
Strategic Alignment: 1 (not strategic)
Score: 1 × 1 × 1 = 1
Decision: LOW PRIORITY - Backlog
Close the Feedback Loop
1. Acknowledge
Email Template:
Subject: Thanks for your feedback!
Hi [Name],
Thanks for taking the time to share your thoughts on [topic].
We review all feedback and use it to prioritize our roadmap. I've shared
your input with the product team.
You can track feature requests on our public roadmap: [link]
Thanks for helping us improve!
[Your Name]
2. Act
Decision Tree: Is it reported 10+ times?
Yes → Add to roadmap
No → Monitor
Does it align with strategy?
Yes → Prioritize
No → Document why not
Can we ship in 2 weeks?
Yes → Quick win, do it
No → Add to backlog
3. Notify Users Who Requested It
Email Template:
Subject: You asked for [Feature] - it's live!
Hi [Name],
Remember when you asked us to add [feature]? Good news - it's live!
[Screenshot/GIF of feature]
Here's how it works:
1. [Step 1]
2. [Step 2]
Try it now: [Link]
Thanks for the feedback that made this happen.
[Your Name]
P.S. Have more ideas? Reply to this email.
4. Public Changelog
## [Feature] is now live!
Requested by 47 users, [Feature] lets you [benefit].
How it works:
- [Key point 1]
- [Key point 2]
Try it: [Link]
Thanks to everyone who suggested this!
Common Feedback Patterns
Squeaky Wheel Syndrome
Problem: Vocal minority ≠ Real need
Example:
- 1 user emails daily about dark mode
- Analytics show 2% use dark mode
Action: Validate with data before building
Silent Churn
Problem: Users leave without complaining
Example:
- Retention drops from 40% to 30%
- No feedback, no complaints
Action:
- Proactive exit interviews
- Check analytics for drop-off points
- Run usability tests
Feature Bloat Risk
Problem: Building everything requested leads to bloat
Example:
- 'Add Excel export'
- 'Add CSV export'
- 'Add JSON export'
- 'Add PDF export'
Action: Build generic solution, not every variant
Synthesis & Reporting
Weekly Feedback Summary
feedback_summary:
period: "2024-01-15 to 2024-01-22"
total_items: 87
top_themes:
- theme: "Slack Integration"
frequency: 23
severity: high
example_quotes:
- "We need Slack notifications"
- "Can't notify team without Slack"
recommended_action: "Build Slack integration next sprint"
- theme: "Slow Dashboard Load"
frequency: 15
severity: medium
example_quotes:
- "Dashboard takes 10+ seconds"
- "Performance is terrible"
recommended_action: "Optimize queries, add caching"
- theme: "Mobile App Request"
frequency: 8
severity: low
example_quotes:
- "I want a mobile app"
recommended_action: "Monitor, not enough demand yet"
nps:
score: 42
detractor_reasons:
- "Too expensive" (12 mentions)
- "Missing features" (8 mentions)
- "Slow performance" (5 mentions)
prioritized_backlog:
- feedback: "Add Slack integration"
score: 80
priority: high
- feedback: "Optimize dashboard performance"
score: 45
priority: medium
- feedback: "Mobile app"
score: 16
priority: low
Tools & Software
Feedback Collection:
- In-app: Canny, UserVoice, Intercom
- Surveys: Typeform, SurveyMonkey, Delighted (NPS)
- User Research: Calendly, Zoom, UserTesting.com
Analysis:
- Qualitative: Dovetail, Notion, Airtable
- Quantitative: Excel, Google Sheets, Tableau
- Sentiment: MonkeyLearn, Lexalytics
Roadmap Transparency:
- Public Roadmap: Canny, ProductBoard, Trello
- Changelog: Headway, ReleaseNotes.io, Beamer
Feedback Cadence
Daily:
- Review support tickets
- Monitor in-app feedback
Weekly:
- Synthesize themes
- Share with product team
- Prioritize top requests
Monthly:
- Send NPS survey
- Review feature requests
- Update public roadmap
Quarterly:
- User interviews (5-10)
- Exit surveys for churned users
- Competitive feedback analysis
Quick Start Checklist
- Set up in-app feedback widget
- Schedule NPS survey (monthly)
- Create feedback tracking spreadsheet
- Review support tickets weekly
- Conduct 2-3 user interviews
- Set up public roadmap (optional)
- Create email templates for acknowledging feedback
- Document feedback categorization process
Common Pitfalls
❌ Collecting feedback without acting: Damages trust ❌ Building everything requested: Feature bloat ❌ Not validating with data: Vocal minority ≠ majority ❌ Ignoring silent majority: Not everyone gives feedback ❌ No follow-up: Users want to know you listened
Summary
Great feedback analysis:
- ✅ Multiple channels (surveys, tickets, interviews)
- ✅ Categorize and prioritize systematically
- ✅ Act on high-priority feedback quickly
- ✅ Close the loop (notify users)
- ✅ Balance requests with strategy
- ✅ Share insights with team
FAQ
What does the customer-feedback-analyzer skill do?
Synthesize user feedback from multiple channels and identify patterns to inform product decisions. Use when analyzing feedback, prioritizing feature requests, conducting NPS surveys, or understanding user sentiment. Covers feedback collection, categorization, prioritization frameworks, and closing the feedback loop.
How do I install customer-feedback-analyzer?
Run npx skills add https://github.com/daffy0208/ai-dev-standards --skill customer-feedback-analyzer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/customer-feedback-analyzer/ (personal) or .claude/skills/customer-feedback-analyzer/ (project).
Which AI agents can use customer-feedback-analyzer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is customer-feedback-analyzer safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains customer-feedback-analyzer?
It is published by daffy0208 in daffy0208/ai-dev-standards. It has 187 recorded installs.
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