home / skills / mattgierhart / prd-driven-context-engineering / prd-v09-feedback-loop-setup
This skill helps you establish and optimize post-launch feedback loops, transforming user input into actionable CFD-, BR-, and RISK- updates.
npx playbooks add skill mattgierhart/prd-driven-context-engineering --skill prd-v09-feedback-loop-setupReview the files below or copy the command above to add this skill to your agents.
---
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
1. **Map feedback touchpoints**
- Where do users already reach out?
- Where should we actively prompt?
- What channels from GTM- are active?
2. **Design feedback capture**
- In-app widgets (NPS, CSAT, feature requests)
- Support ticket taxonomy
- Community moderation workflow
- Survey schedule and instruments
3. **Define processing workflow**
- Who triages incoming feedback?
- How does it become CFD- entries?
- What triggers action?
4. **Establish feedback → ID flow**
- Feedback → CFD-
- CFD- → BR-, FEA-, RISK- updates
- Updates → EPIC- for implementation
5. **Set up monitoring**
- Volume metrics
- Sentiment tracking
- Response time SLAs
6. **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`
This skill sets up repeatable channels and processes to capture, process, and act on post-launch feedback during PRD v0.9 Go-to-Market. It converts raw user input into structured CFD- entries and links feedback into the ID graph so product teams can prioritize and close the loop. The goal is measurable feedback flow, not just data collection.
On trigger, the skill maps touchpoints and configures capture points (in-app widgets, support taxonomy, surveys, community hooks, analytics). Incoming items are routed to a central inbox, triaged by priority and SLA, and turned into CFD- entries using a standard template. CFD- entries are linked to BR-, FEA-, RISK-, or EPIC- IDs and tracked through response and resolution workflows.
How do I prioritize feedback?
Score by Frequency × Impact × Revenue Risk and review weekly in a prioritization meeting.
What channels should be first to deploy?
Start with in-app (contextual) and support (reactive) plus analytics for passive signals; add surveys and community workflows next.