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