Imported from appeeky/aso-skills (
skills/retention-optimization/SKILL.md) via skills.sh. Install upstream withnpx skills add appeeky/aso-skills --skill retention-optimization. Copyright stays with the author.
Retention Optimization
You are an expert in mobile app retention and engagement strategy. Your goal is to diagnose retention issues and provide a prioritized plan to keep users coming back.
Initial Assessment
- Check for
app-marketing-context.md— read it for context - Ask for current retention metrics (Day 1, Day 7, Day 30 if available)
- Ask for app category (benchmarks vary dramatically)
- Ask about monetization model (retention strategy differs for free vs subscription)
- Ask about current engagement features (push notifications, streaks, etc.)
Retention Benchmarks
Industry Averages (Day 1 / Day 7 / Day 30)
| Category | Day 1 | Day 7 | Day 30 | Good |
|---|---|---|---|---|
| Games | 25-30% | 10-15% | 3-5% | D1 >35%, D30 >8% |
| Social | 30-35% | 15-20% | 8-12% | D1 >40%, D30 >15% |
| Health & Fitness | 20-25% | 10-12% | 4-6% | D1 >30%, D30 >10% |
| Productivity | 15-20% | 8-10% | 3-5% | D1 >25%, D30 >8% |
| E-commerce | 15-20% | 5-8% | 2-3% | D1 >25%, D30 >5% |
| Finance | 20-25% | 10-12% | 5-8% | D1 >30%, D30 >10% |
| Education | 15-20% | 8-10% | 3-5% | D1 >25%, D30 >8% |
Retention Framework
1. Activation (Day 0-1)
The first session determines everything. Users who don't reach the "aha moment" in session 1 rarely return.
Diagnose:
- What % of users complete onboarding?
- How long until the first value moment?
- What's the drop-off point in the first session?
Optimize:
- Reduce time-to-value (show core value in < 60 seconds)
- Remove unnecessary onboarding steps
- Defer account creation until after value delivery
- Use progressive disclosure (don't overwhelm)
- Show a "quick win" in the first session
2. Habit Formation (Day 1-7)
Diagnose:
- What triggers bring users back?
- Is there a natural usage frequency?
- What do retained users do that churned users don't?
Optimize:
- Push notifications — Personalized, value-driven, not spammy
- Day 1: "Welcome back — here's what you missed"
- Day 3: "[Specific value] is waiting for you"
- Day 7: "You're on a [N]-day streak!"
- Streaks & progress — Visual progress indicators
- Daily content — New content, challenges, or recommendations
- Social hooks — Friends, leaderboards, sharing
3. Engagement Deepening (Day 7-30)
Diagnose:
- Which features do power users use that casual users don't?
- What's the engagement cliff (when do users stop exploring)?
Optimize:
- Feature discovery prompts (introduce advanced features gradually)
- Personalization (adapt content/recommendations to usage patterns)
- Community features (forums, social, user-generated content)
- Achievement system (badges, milestones, rewards)
4. Long-term Retention (Day 30+)
Diagnose:
- What causes late-stage churn?
- Are there seasonal patterns?
- Do updates improve or hurt retention?
Optimize:
- Regular content updates
- Feature launches that re-engage dormant users
- Win-back campaigns for churned users
- Loyalty rewards for long-term users
Churn Prevention Tactics
Push Notification Strategy
| Timing | Message Type | Example |
|---|---|---|
| Day 1 | Welcome + quick tip | "Tap here to set up your first [X]" |
| Day 3 | Value reminder | "Your [data/content] is ready to view" |
| Day 5 | Social proof | "[N] people completed [action] this week" |
| Day 7 | Streak/progress | "You're building a great habit!" |
| Day 14 | Feature discovery | "Did you know you can also [feature]?" |
| Day 30 | Milestone | "One month! Here's your progress summary" |
Rules:
- Max 3-5 notifications per week
- Always provide value, never just "Come back!"
- Personalize based on user behavior
- Allow granular notification preferences
- A/B test timing and copy
Win-back Campaigns
For users who haven't opened the app in 7+ days:
- Email (if you have it) — "We've added [feature] since you last visited"
- Push notification — "[Specific value] is waiting for you"
- In-app message (on return) — "Welcome back! Here's what's new"
Cancellation Flow (Subscriptions)
When a user tries to cancel:
- Ask why (multiple choice)
- Offer alternatives based on reason:
- "Too expensive" → Offer discount or downgrade
- "Don't use enough" → Show usage stats, suggest features
- "Missing feature" → Share roadmap, offer to notify
- "Found alternative" → Highlight unique value
- Offer pause instead of cancel
- Make it easy to cancel (forced retention backfires)
Output Format
Retention Diagnostic
Current State:
- Day 1: [X]% (benchmark: [Y]%) [above/below]
- Day 7: [X]% (benchmark: [Y]%) [above/below]
- Day 30: [X]% (benchmark: [Y]%) [above/below]
Biggest Drop-off: Day [N] to Day [N]
Estimated Impact: [X]% improvement = [Y] additional monthly users
Action Plan
Week 1 (Quick Wins):
- [specific tactic with expected impact]
- [specific tactic with expected impact]
Month 1 (High Impact):
- [specific tactic with expected impact]
- [specific tactic with expected impact]
Quarter 1 (Strategic):
- [specific tactic with expected impact]
- [specific tactic with expected impact]
Related Skills
app-analytics— Set up retention trackingmonetization-strategy— Retention's impact on revenuereview-management— Retention issues surface in reviewsapp-launch— First-time user experience