| 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.
|
| context | fork |
| allowed-tools | ["Read","Write","Edit","Glob","Grep","WebSearch","WebFetch"] |
| execution_modes | {"default":"standard","supports":["quick","standard","deep"]} |
Feedback Loop Setup
Position in workflow: v0.9 Launch Metrics → v0.9 Feedback Loop Setup → v1.0 Market Adoption
Execution Mode
Default is standard. See .claude/rules/08-skill-execution-modes.md for selection logic.
| Mode | What this skill produces |
|---|
| quick | 1–2 channels (in-app + support); basic triage workflow |
| standard | 3–4 channels; full processing workflow + sentiment tracking + SLAs |
| deep | All channels + closed-loop tracking + voice-of-customer synthesis + escalation rules |
Consumes
This skill requires prior work from v0.9 Launch Metrics and v0.1-v0.8:
- GTM-* launch channels (from v0.9 GTM Strategy) — Active launch channels (Product Hunt, email, paid ads, etc.) become feedback sources; GTM- messaging and channels inform where feedback will arrive
- MON-* monitoring dashboards and alerts (from v0.8 Monitoring Setup) — MON- thresholds (latency, error rate, performance) define what qualifies as critical feedback; monitoring alerts can trigger deep-dive user research
- KPI-* launch targets and baselines (from v0.9 Launch Metrics) — KPI- thresholds (Day 1/7/30/90 targets) inform feedback urgency and trigger investigation when below target; baseline performance metrics (p95 latency, error rate, conversion rate) provide context for performance feedback
- CFD-* baseline entries (from v0.1-v0.4) — Baseline customer feedback hypotheses (user pain points, value propositions, competitive alternatives) become validation targets post-launch; feedback loop confirms or contradicts CFD- assumptions
- PER-* personas (from v0.4 Persona Definition) — Persona segments (PER-001 Startup Founder, PER-002 Team Lead) enable feedback categorization by user type and prioritization by persona importance
This skill assumes v0.9 Launch Metrics is live with KPI- thresholds established, GTM- channels are active, and MON- dashboards are displaying baseline metrics.
Produces
This skill creates/updates:
- CFD-* post-launch feedback entries (feedback capture specifications, channel/type-based) — Every piece of user feedback becomes a CFD- entry with source, sentiment, impact, and action taken; traced to GTM- channels and user personas
- Feedback processing workflow/matrix — Triage → Categorization → Prioritization → Action mapping showing how feedback flows from capture to ID updates (CFD- → FEA-/BR-/RISK- → EPIC-)
- CFD-* update entries — CFD- entries updated with resolution status, outcome, and follow-up evidence, enabling confidence progression (initial feedback → validated pattern → implemented action → confirmed outcome)
All CFD-* post-launch entries are evidential feedback records, not confidence-based themselves but supporting confidence scoring on OTHER IDs:
- Timestamped (when feedback was received, to track trends and velocity)
- Sourced (channel, user segment, user ID if available for follow-up)
- Categorized (UX | Performance | Feature Gap | Bug | Praise | Confusion for trend analysis)
- Prioritized (Critical/High/Medium/Low with impact justification)
- Actionable (every CFD- either triggers ID creation/update or documents "won't fix" decision)
- Closed-loop (user receives response and can verify resolution)
Example CFD- post-launch entries:
CFD-101: "Can't figure out how to export my data"
Type: Support Ticket
Source: Intercom (GTM-002 email → user support request)
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."
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."
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
Linked IDs: GTM-002 (email channel source), PER-001 (persona), KPI-104 (affected metric), FEA-025 (action taken), EPIC-05 (implementation)
---
CFD-102: NPS Detractor Response
Type: NPS Response
Source: In-App Survey (MON-005 trigger)
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."
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) — both trending down
Revenue Risk: High — performance is activation blocker
Action:
Response: N/A (anonymous survey)
Internal Action: Performance spike investigation started (MON-001 latency breach detected)
Linked IDs: RISK-012 (Performance Degradation) escalated, EPIC-06 prioritized for optimization
Status: In Progress
Resolution:
Outcome: Database query optimization deployed, latency restored to baseline
Date: 2025-01-22
Follow-up: Next NPS cycle (Day 30) will measure improvement
Linked IDs: MON-005 (dashboard source), PER-002, KPI-103, KPI-104, MON-001 (latency baseline), RISK-012, EPIC-06
---
CFD-103: Community Feature Request (Dark Mode)
Type: Community Post
Source: Discord #feature-requests (GTM-005 community channel)
Date: 2025-01-20
User Segment: Power Users (multiple PER-)
Verbatim: "Thread: 47 messages discussing dark mode. Summary: 15 unique users requesting."
Processed:
Category: Feature Gap
Sentiment: Neutral (constructive)
Priority: Medium
Frequency: Repeated (ongoing, 15 users vocal)
Impact Assessment:
Users Affected: 15+ vocal, likely more silent
KPI Impact: Minor — nice-to-have, not activation blocker; may reduce churn for night users
Revenue Risk: Low
Action:
Response: Community manager acknowledged, added to public roadmap
Internal Action: Added to backlog as P2 feature
Linked IDs: FEA-030 (Dark Mode) created, posted on public roadmap
Status: Acknowledged
Resolution:
Outcome: Pending — scheduled for Q2 release
Date: N/A
Follow-up: Posted on public roadmap
Linked IDs: GTM-005 (community channel), PER-* (multiple personas), FEA-030, public roadmap
Feedback → ID Flow
Each CFD- post-launch entry triggers cascading updates:
| Feedback Type | Creates/Updates | Confidence Impact | Example |
|---|
| Feature Request | FEA-, BR-FEA- | Increases FEA- confidence (user interview → beta validation) | CFD-101 (export request, 3rd this week) → FEA-025 (confidence: 2→3, source: support-requests-2025-01) |
| Performance Complaint | MON- threshold, RISK- escalation | Triggers MON- investigation; may update RISK- severity | CFD-102 (slow, 20% mention) → MON-001 threshold validation → RISK-012 escalation |
| UX Confusion | SCR-, UJ- refinement | Informs screen redesign without changing foundational journey | "Can't find export" → SCR-005 (export button placement) update |
| Bug Report | RISK- or direct fix | RISK- frequency increases → triggers prioritization | Critical bugs → P0 RISK- entry |
| Praise/Testimonial | CFD- (evidence), GTM- (social proof) | Confirms CFD- hypothesis; can become GTM- case study | "Love this feature!" → CFD- entry → GTM-015 (testimonial) |
This feedback loop enables evidence-driven iteration: feedback patterns → ID updates → implementation → launch validation → next iteration.
Downstream Connections
| Consumer | What It Uses | Example |
|---|
| v1.0 Market Adoption Planning | CFD- feedback patterns inform roadmap | 10× CFD- export requests → FEA-025 move to P1 |
| Product Development | CFD- → FEA-, BR- updates feed next EPIC | CFD-102 performance complaints → EPIC-06 optimization prioritized |
| Sales/Marketing | CFD- testimonials become GTM assets | CFD-103 community enthusiasm → GTM-015 case study |
| Support Team | CFD- patterns become FAQ and onboarding | Repeated "can't export" → FAQ article |
| Risk Management | CFD- negative trends escalate RISK- | NPS dropping → RISK-012 escalation |
| KPI Accountability | CFD- confirms KPI- achievement | KPI-104 (D7 Retention) gaps trigger CFD- investigation |
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?]
Note: See Produces section above for detailed CFD- examples with full traceability links.
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
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:
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
