
prd-v09-feedback-loop-setup
by mattgierhart
PRD-driven Context Engineering: A systematic approach to building AI-powered products using progressive documentation and context-aware development workflows
SKILL.md
name: prd-v09-feedback-loop-setup description: > Establish channels and processes for capturing and processing post-launch feedback during PRD v0.9 Go-to-Market. Triggers on requests to set up feedback systems, capture user input, or when user asks "how do we collect feedback?", "feedback loop", "user research", "post-launch feedback", "customer feedback", "NPS", "voice of customer". Outputs CFD- entries specialized for post-launch feedback capture.
Feedback Loop Setup
Position in workflow: v0.9 Launch Metrics → v0.9 Feedback Loop Setup → v1.0 Market Adoption
Purpose
Establish systematic channels for capturing, processing, and acting on post-launch user feedback—closing the loop between user experience and product iteration.
Core Concept: Feedback as Fuel
Feedback is not a task to complete—it is fuel for iteration. Every piece of feedback should flow into the ID graph, informing future CFD-, BR-, FEA-, or RISK- entries. If feedback sits in a spreadsheet, it's not feedback—it's noise.
Feedback Channels
| Channel | Type | Best For | Response Time |
|---|---|---|---|
| In-App | Prompted | Contextual reactions | Real-time |
| Support | Reactive | Issues, requests | <24h |
| Community | Proactive | Discussion, ideas | Ongoing |
| Surveys | Scheduled | Structured data | Periodic |
| Analytics | Passive | Behavior signals | Continuous |
Execution
-
Map feedback touchpoints
- Where do users already reach out?
- Where should we actively prompt?
- What channels from GTM- are active?
-
Design feedback capture
- In-app widgets (NPS, CSAT, feature requests)
- Support ticket taxonomy
- Community moderation workflow
- Survey schedule and instruments
-
Define processing workflow
- Who triages incoming feedback?
- How does it become CFD- entries?
- What triggers action?
-
Establish feedback → ID flow
- Feedback → CFD-
- CFD- → BR-, FEA-, RISK- updates
- Updates → EPIC- for implementation
-
Set up monitoring
- Volume metrics
- Sentiment tracking
- Response time SLAs
-
Create CFD- entries for post-launch feedback
CFD- Output Template (Post-Launch Feedback)
CFD-XXX: [Feedback Title]
Type: [Support Ticket | Feature Request | Bug Report | NPS Response | Community Post | Survey Response]
Source: [Intercom | Zendesk | Discord | In-App | Email | Twitter]
Date: [When received]
User Segment: [PER-XXX if identifiable]
Verbatim: "[Exact user quote or description]"
Processed:
Category: [UX | Performance | Feature Gap | Bug | Praise | Confusion]
Sentiment: [Positive | Neutral | Negative | Frustrated]
Priority: [Critical | High | Medium | Low]
Frequency: [One-off | Repeated | Trending]
Impact Assessment:
Users Affected: [Count or estimate]
KPI Impact: [KPI-XXX affected if applicable]
Revenue Risk: [High | Medium | Low | None]
Action:
Response: [How we responded to user]
Internal Action: [What we're doing about it]
Linked IDs: [BR-XXX, FEA-XXX, RISK-XXX created/updated]
Status: [New | Acknowledged | In Progress | Resolved | Won't Fix]
Resolution:
Outcome: [What happened]
Date: [When resolved]
Follow-up: [Did we close the loop with user?]
Example CFD- entries:
CFD-101: "Can't figure out how to export my data"
Type: Support Ticket
Source: Intercom
Date: 2025-01-15
User Segment: PER-001 (Startup Founder)
Verbatim: "I've been using the tool for a week and I can't find
any way to export my work. I need to share results with
my team. Is this possible? If not, this is a dealbreaker."
Processed:
Category: Feature Gap
Sentiment: Frustrated
Priority: High
Frequency: Repeated (3rd request this week)
Impact Assessment:
Users Affected: ~50 (based on support volume)
KPI Impact: KPI-104 (D7 Retention) — export needed for team use case
Revenue Risk: High — multiple users mentioned "dealbreaker"
Action:
Response: "Thanks for reaching out! Export is on our roadmap.
We're prioritizing this for our next release."
Internal Action: Escalated to product team, added to backlog
Linked IDs: FEA-025 (Export Feature) created, EPIC-05 updated
Status: In Progress
Resolution:
Outcome: FEA-025 shipped in v1.2
Date: 2025-02-01
Follow-up: Emailed user with release notes
CFD-102: NPS Detractor Response
Type: NPS Response
Source: In-App Survey
Date: 2025-01-18
User Segment: PER-002 (Team Lead)
Verbatim: "Score: 4. Too slow. Takes forever to load projects
and I give up waiting half the time."
Processed:
Category: Performance
Sentiment: Negative
Priority: Critical
Frequency: Trending (NPS dropped 10 points this week)
Impact Assessment:
Users Affected: ~200 (20% of NPS responses mention speed)
KPI Impact: KPI-103 (Activation), KPI-104 (Retention)
Revenue Risk: High — performance is activation blocker
Action:
Response: N/A (anonymous survey)
Internal Action: Performance spike investigation started
Linked IDs: RISK-012 (Performance Degradation) escalated
Status: In Progress
Resolution:
Outcome: Database query optimization deployed
Date: 2025-01-22
Follow-up: Next NPS cycle will measure improvement
CFD-103: Community Feature Discussion
Type: Community Post
Source: Discord #feature-requests
Date: 2025-01-20
User Segment: Power Users (multiple PER-)
Verbatim: "Thread: 47 messages discussing dark mode.
Summary: 15 unique users requesting dark mode.
Top comment: 'I work at night and this is eye-strain city.'"
Processed:
Category: Feature Gap
Sentiment: Neutral (constructive)
Priority: Medium
Frequency: Repeated (ongoing thread)
Impact Assessment:
Users Affected: 15+ vocal, likely more silent
KPI Impact: Minor — nice-to-have, not activation blocker
Revenue Risk: Low
Action:
Response: Community manager acknowledged, added to public roadmap
Internal Action: Added to backlog as P2
Linked IDs: FEA-030 (Dark Mode) created
Status: Acknowledged
Resolution:
Outcome: Pending — scheduled for Q2
Date: N/A
Follow-up: Posted on public roadmap
Feedback Collection Methods
In-App Feedback
| Method | When to Use | Question |
|---|---|---|
| NPS | After activation, monthly | "How likely to recommend?" (0-10) |
| CSAT | After support interaction | "How satisfied?" (1-5) |
| CES | After key action | "How easy was this?" (1-7) |
| Feature Request | Persistent widget | "What's missing?" |
| Bug Report | Error states | "What went wrong?" |
Survey Cadence
| Survey | Frequency | Purpose |
|---|---|---|
| NPS | Monthly | Overall sentiment tracking |
| Onboarding Exit | After churn signal | Why didn't they activate? |
| Feature Satisfaction | Post-release | Did this solve the problem? |
| Annual Deep Dive | Yearly | Strategic feedback |
Passive Signals
| Signal | What It Indicates | Action Trigger |
|---|---|---|
| Rage clicks | Frustration | UX investigation |
| Drop-off | Confusion or friction | Funnel analysis |
| Feature abandonment | Poor value delivery | User interview |
| Error rates | Technical issues | Bug investigation |
Feedback Processing Workflow
CAPTURE → TRIAGE → CATEGORIZE → PRIORITIZE → ACTION → CLOSE LOOP
1. CAPTURE
- All channels → central inbox
2. TRIAGE (Daily)
- Critical: <4h response
- High: <24h response
- Medium/Low: Weekly review
3. CATEGORIZE
- Apply CFD- template
- Link to existing IDs
4. PRIORITIZE
- Frequency × Impact × Revenue Risk
- Weekly prioritization meeting
5. ACTION
- Create/update IDs (BR-, FEA-, RISK-)
- Add to EPIC- backlog
- Communicate internally
6. CLOSE LOOP
- Respond to user
- Update CFD- status
- Verify resolution
Feedback → ID Flow
| Feedback Type | Creates/Updates | Example |
|---|---|---|
| Feature Request | FEA-, BR-FEA- | CFD-101 → FEA-025 |
| Bug Report | RISK- (or direct fix) | CFD-102 → RISK-012 |
| UX Confusion | SCR-, UJ- refinement | "Can't find X" → SCR-005 update |
| Performance | MON-, RISK- | "Too slow" → MON-010 threshold |
| Praise | CFD- (testimonial), GTM- | "Love this!" → GTM-015 (social proof) |
Sentiment Monitoring
Track aggregate sentiment over time:
| Metric | Calculation | Target |
|---|---|---|
| NPS | % Promoters - % Detractors | >30 |
| CSAT | % Satisfied (4-5) | >80% |
| Support Volume | Tickets per 100 users | <5 |
| Response Time | Median first response | <4h |
| Resolution Rate | % resolved within SLA | >90% |
Anti-Patterns
| Pattern | Signal | Fix |
|---|---|---|
| Feedback graveyard | Collect but never act | Mandate weekly triage meeting |
| Only negative | No positive feedback captured | Celebrate wins, capture praise |
| No closing loop | Users never hear back | Require follow-up on High+ priority |
| Volume without insight | "We got 500 tickets" | Categorize and trend analysis |
| Building in silence | Ship features, don't validate | Post-release surveys |
| Anecdote-driven | "One user said..." | Require frequency data |
Quality Gates
Before proceeding to v1.0 Market Adoption:
- All feedback channels identified and configured
- In-app feedback widgets deployed
- Support ticket taxonomy defined
- Community monitoring active
- Processing workflow documented and assigned
- Feedback → ID flow established
- Sentiment metrics baselined
Downstream Connections
| Consumer | What It Uses | Example |
|---|---|---|
| v1.0 Planning | CFD- feedback informs roadmap | CFD-101 frequency → FEA-025 priority |
| Product Development | CFD- → FEA-, BR- updates | "Users need X" → FEA-030 |
| Support Team | CFD- patterns for FAQ | Repeated CFD-102 → knowledge base |
| Marketing | CFD- testimonials for GTM- | Positive CFD- → case study |
| Risk Management | CFD- negative trends → RISK- | Sentiment drop → RISK-015 |
Detailed References
- Feedback channel setup: See
references/channel-setup.md - CFD- post-launch template: See
assets/cfd-feedback-template.md - Survey question bank: See
references/survey-questions.md - Sentiment analysis guide: See
references/sentiment-guide.md
Score
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