Agent skill

prd-v09-feedback-loop-setup

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.

Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/prd-v09-feedback-loop-setup

SKILL.md

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

Didn't find tool you were looking for?

Be as detailed as possible for better results