retention-optimization
appeeky/aso-skills
Diagnose and fix user churn with retention benchmarks, engagement tactics, and prioritized action plans.
What is retention-optimization?
Expert guidance for reducing churn and improving user lifetime value. Use when users mention retention, churn, engagement metrics (DAU/MAU), or why users are leaving. Provides industry benchmarks, activation/habit-formation strategies, and push notification tactics tailored to app category.
- Compare your retention metrics (Day 1, 7, 30) against industry benchmarks by app category
- Diagnose activation bottlenecks and time-to-value issues in the first session
- Design personalized push notification sequences and win-back campaigns
- Identify engagement cliffs and feature-discovery gaps between casual and power users
- Create prioritized action plans with estimated impact on monthly active users
- Optimize cancellation flows for subscription apps with targeted retention offers
How to install retention-optimization
npx skills add https://github.com/appeeky/aso-skills --skill retention-optimizationHow to use retention-optimization
- 1.Gather current retention metrics (Day 1, 7, 30 retention rates if available)
- 2.Identify your app category to compare against industry benchmarks
- 3.Share your monetization model (free, subscription, freemium) and current engagement features
- 4.Review the diagnostic output showing your biggest drop-off point and estimated impact
- 5.Implement the prioritized action plan starting with Week 1 quick wins (onboarding, push notifications)
- 6.Track changes with app-analytics skill to measure improvement
Use cases
- A game studio sees 20% Day 1 retention (below 25-30% benchmark) and needs to reduce onboarding friction
- A fitness app wants to build habit loops with streaks and daily content to improve Day 7 retention
- A productivity tool discovers power users engage with advanced features; needs feature-discovery prompts for casual users
- A subscription service experiences high cancellation; needs to diagnose churn reasons and offer alternatives
- A social app wants to re-engage dormant users with personalized win-back campaigns
- Mobile app product managers and growth leads
- Founders optimizing user lifetime value
- App marketers designing engagement campaigns
- Analytics teams diagnosing retention drop-offs
- Subscription app teams reducing cancellation rates
retention-optimization FAQ
app-launch focuses on first-time user experience and onboarding flow. retention-optimization covers the full user lifecycle from Day 1 through Day 30+ and addresses habit formation, engagement deepening, and churn prevention.
The framework recommends 3-5 notifications per week maximum, personalized by user behavior and always providing value. Day 1 welcome, Day 3 value reminder, Day 7 streak/progress, and Day 14+ feature discovery are key timing points.
Start with activation (reduce time-to-value to under 60 seconds, remove onboarding friction). Then add habit-formation triggers (streaks, daily content, personalized notifications). The action plan prioritizes quick wins first.
Ask why the user is canceling (too expensive, don't use enough, missing feature, found alternative), then offer targeted alternatives: discounts, feature highlights, roadmap sharing, or pause instead of cancel. Always make cancellation easy.
Social apps (30-35% Day 1, 8-12% Day 30) and Finance apps (20-25% Day 1, 5-8% Day 30) typically retain best. Games and E-commerce are lower (3-5% and 2-3% Day 30 respectively), so benchmarks vary dramatically by category.
Full instructions (SKILL.md)
Source of truth, from appeeky/aso-skills.
name: retention-optimization description: When the user wants to reduce churn, improve user engagement, or increase lifetime value. Also use when the user mentions "retention", "churn", "users leaving", "engagement", "DAU/MAU", "user activation", or "why are users uninstalling". For onboarding-specific issues, see app-launch. For monetization, see monetization-strategy. metadata: version: 1.0.0
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
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