review-management

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• SKILL.md

SKILL.md (first lines)

# Review Management

Expert in app review strategy and reputation management. Goal: turn reviews into growth lever.

# Assessment
1. Read `app-marketing-context.md`
2. App ID (to fetch reviews)
3. Target country (default: US)
4. Current rating + trend (improving/declining?)
5. Currently responding to reviews?

# Sentiment Categories
Bugs/Crashes -> fix + respond with timeline
Feature Requests -> track frequency, consider roadmap
UX Complaints -> prioritize UX improvements
Pricing Complaints -> review monetization
Love/Praise -> thank, ask for sharing
Competitor Mentions -> understand gaps

# Metrics Targets
Avg rating: 4.5+ (below 4.0 hurts conversion significantly)
Trend: stable or improving
Review velocity: consistent (sudden drops = prompt issues)
Response rate: 100% of negative
Response time: <24hrs

# Rating Prompt (SKStoreReviewController)
WHEN: after positive experience (task completed, goal achieved), 3+ sessions, 7+ days usage
NEVER: after crash/error/frustration, during onboarding/first session
Apple rules: max 3 calls per 365 days per device, Apple controls display, no customization
Triggers: achievement, streak (N consecutive days), value delivery (saved money/time), delight moment

# Negative Review Response (HEAR)
H=Hear (acknowledge specific issue), E=Empathize, A=Act (explain fix/timeline), R=Resolve (invite support contact)

Templates:
Bug: "Thank you for reporting. Fixed in [X.X] releasing [date]. Please update and let us know."
Feature request: "Great suggestion, added to roadmap. Stay tuned."
Vague negative: "Sorry about your experience. Please reach out to [support email] with details so we can help."

DON'T: be defensive, copy-paste same response, ignore negatives, ask to change rating (guideline violation), offer incentives

# Detractor->Advocate
Fix issue -> respond acknowledging fix -> follow up via support -> many users update review after resolution

# Competitor Review Mining
Find: unmet needs, common complaints (your opportunity), switching triggers, feature expectations (table stakes)

# Your Review Patterns
Analyze: most mentioned features (pos+neg), user segments, emotional language, competitor mentions

# Output
Review Health Report:
```
Rating: [X.X] stars ([trend up/down/stable]), Total: [N], Last 30d: [N] reviews [X.X] avg, Response rate: [X]%
Top Issues: 1.[issue]x[N] 2.[issue]x[N] 3.[issue]x[N]
Top Praise: 1.[praise]x[N] 2.[praise]x[N]
```
Action Plan: Immediate (respond to negatives), This week (fix top bug, optimize prompt), This month (top feature request, competitor review analysis)
Response drafts for most impactful negatives.

# Related
aso-audit, retention-optimization, competitor-analysis, app-analytics

From asker26/claude-task-framework, MIT.

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