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Customer feedback analysis — sentiment detection, NPS/CSAT frameworks, feature request clustering, support ticket triage, churn signal detection, and feedback-to-roadmap translation
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SOC 職業分類に基づく
| name | feedback-analysis |
| description | Customer feedback analysis — sentiment detection, NPS/CSAT frameworks, feature request clustering, support ticket triage, churn signal detection, and feedback-to-roadmap translation |
| version | 1.0.0 |
| model | sonnet |
| invoked_by | both |
| user_invocable | true |
| tools | ["Read","Write","Bash","WebSearch","WebFetch"] |
| agents | ["feedback-synthesizer"] |
| category | domain-specific |
| tags | ["feedback","sentiment","nps","csat","churn","triage","roadmap","clustering","customer-experience"] |
| verified | false |
| best_practices | ["Unify all feedback channels before any analysis (prevents blind spots)","Detect mismatch between numerical score and text sentiment","Use hierarchical taxonomy for ticket triage (30-50 tags max)","Correlate feature requests with churn risk for roadmap prioritization","Always produce an actionable output — analysis without action has no value"] |
| error_handling | graceful |
| streaming | supported |
| source | builtin |
| trust_score | 100 |
| provenance_sha | db198bf01e37b2d9 |
Customer feedback analysis transforms raw feedback into actionable intelligence across six interconnected capability areas. All capabilities share a common data pipeline: unified multi-channel feedback collection feeds sentiment detection, which powers NPS/CSAT scoring, feature clustering, ticket triage, churn signals, and ultimately roadmap prioritization.
Six Capability Areas:
Invoke Skill({ skill: 'feedback-analysis' }) when:
Iron Law: All analysis degrades without unified data. Single-channel view creates blind spots.
Channels to unify:
- In-app surveys (NPS, CSAT, CES)
- Support tickets (Zendesk, Intercom, Freshdesk)
- App store reviews (iOS, Android)
- Social mentions (Twitter/X, Reddit, LinkedIn)
- Chat transcripts (live chat, chatbot logs)
- Product reviews (G2, Capterra, Trustpilot)
- Email responses
Output: A unified feedback dataset with source, timestamp, channel, user tier, and raw text per item.
Method: NLP-based multi-dimensional sentiment classification.
Classifications:
Key Pattern — Mismatch Detection (Critical Insight):
A customer scoring 8 (NPS Passive) with deeply negative text is high churn risk. A customer scoring 6 (NPS Detractor) with positive text is recoverable. Mismatch = highest priority segment for intervention.
Mismatch Types:
- High score + negative text → At-risk, intervention needed
- Low score + positive text → Recoverable, reduce friction
- Neutral score + high emotion → Emerging issue, monitor closely
Output: Sentiment-tagged dataset with polarity, emotion, intensity, and mismatch flags.
Dual-Track Analysis: Process numerical scores AND open-text responses in parallel.
NPS Segments:
CSAT Layers:
Multi-Dimensional Segmentation:
Dimensions to segment by:
- User tier (free, pro, enterprise)
- Acquisition channel
- Product area (onboarding, core feature, billing, support)
- Agent/team (for support CSAT)
- Cohort (joined date, plan upgrade date)
- Region/language
Output: NPS/CSAT dashboard data with trend lines, mismatch segments, and causation narratives.
Method: Group verbatims into themes without manual tagging using pattern detection.
Clustering Dimensions:
Category Taxonomy:
Request Categories:
- Bug Report (broken functionality)
- Feature Gap (missing capability)
- UX Friction (confusing/slow workflow)
- Performance Issue (speed, reliability)
- Integration Request (connect to other tools)
- Pricing Feedback (too expensive, wrong tier)
- Documentation Gap (can't figure out how to use it)
Prioritization Formula:
Priority Score = (Frequency × 0.3) + (Emotion Weight × 0.3) + (Churn Correlation × 0.4)
Output: Ranked feature request list with evidence count, sentiment weight, and churn correlation per cluster.
Taxonomy Design: Hierarchical, max 30-50 tags to prevent tag bloat.
Recommended Taxonomy Structure:
Level 1 (Category): Level 2 (Subcategory): Level 3 (Root Cause):
- Technical Issue - Login/Auth - Password reset broken
- Billing - Charge dispute - Double-charged
- Feature Usage - Onboarding - Setup wizard unclear
- Performance - Slow response - Database timeout
- Integration - API error - Rate limit exceeded
- Account Management - Team permissions - Role not propagating
Triage Modes:
Priority Scoring:
Ticket Priority = Urgency (language cues) + Impact (user tier/revenue) + Sentiment (frustration level)
- P0: Critical + Enterprise user + High frustration
- P1: High urgency + Any paid user + Negative sentiment
- P2: Medium urgency + Any user + Neutral/negative
- P3: Low urgency + Any user + Neutral
Output: Categorized and prioritized ticket queue with taxonomy assignments and routing rules.
Behavioral Profile Clustering:
User Engagement Profiles:
- Power User: High session frequency, feature breadth, collaborative
- Dabbler: Irregular sessions, single workflow, no integrations
- One-Feature User: Deep single-feature use, no expansion
- Trial Tourist: Onboarding complete, then disengaged
Early Warning Signals (detect BEFORE explicit churn):
Churn Risk Scoring:
Churn Risk = (Behavioral signals × 0.4) + (Feedback sentiment × 0.3) + (Support ticket pattern × 0.3)
Risk Tiers:
- High (>0.7): Trigger immediate retention playbook
- Medium (0.4-0.7): Proactive outreach + success check-in
- Low (<0.4): Monitor, standard touchpoints
Reason Code Generation: Each high-risk user gets a human-readable reason code:
Output: Churn risk cohort with risk scores, reason codes, and triggered playbook recommendations.
Input: Completed phases 1-6 (sentiment, NPS/CSAT, clusters, triage, churn signals)
Prioritization Matrix:
Roadmap Score = (Feature Request Frequency × 0.25)
+ (Churn Correlation × 0.35)
+ (NPS Impact × 0.25)
+ (Support Volume × 0.15)
Stakeholder Output Format:
## Roadmap Recommendation: [Feature/Fix Name]
**Evidence Summary**: [N] users requested this across [channels]
**Sentiment**: [Avg. emotional weight and polarity]
**Churn Correlation**: [% of churned users mentioned this]
**NPS Impact**: [Correlation to Detractor-to-Promoter potential]
**Support Impact**: [Ticket volume and priority distribution]
**Recommended Action**: [Implement / Investigate / Defer / Decline]
**Priority Tier**: P0 / P1 / P2 / P3
**Supporting Quotes**: [3-5 verbatim user quotes]
Continuous Loop: Feed roadmap decisions back into feedback collection ("Did we solve the problem?").
Output: Ranked roadmap items with quantitative evidence, stakeholder narrative, and action recommendations.
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Analyzing only NPS scores without text | Misses mismatch segments (fake Promoters, recoverable Detractors) | Always run dual-track score + text analysis |
| Flat taxonomy with 200+ ticket tags | Agents use first matching tag; root cause data is lost | Hierarchical taxonomy, max 50 leaf nodes |
| Clustering by frequency alone | Missing features that don't come up often but cause 80% of churn | Weight clusters by churn correlation (0.4 weight) |
| Waiting for explicit churn to detect it | Post-churn analysis doesn't save the customer | Behavioral early warning signals, 14-day detection horizon |
| Roadmap items without evidence count | Stakeholders can't evaluate priority or trade-offs | Every roadmap item needs: frequency, sentiment weight, churn %, quotes |
| Single-channel feedback collection | Blind spots by channel; social complaints ≠ support tickets | Unify all channels before analysis |
Input validated against schemas/input.schema.json before execution.
Output contract defined in schemas/output.schema.json.
Pre-execution hook: hooks/pre-execute.cjs
Post-execution hook (observability): hooks/post-execute.cjs
Before starting:
Read .claude/context/memory/learnings.md
Check for:
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: If it's not in memory, it didn't happen.