retention-optimization
eronred/aso-skills
Diagnose and fix user churn with retention benchmarks, activation tactics, and engagement strategies.
What is retention-optimization?
Expert guidance for reducing churn and improving user lifetime value. Provides industry benchmarks, diagnostic frameworks across activation/habit/engagement phases, and prioritized tactics like push notification strategies, win-back campaigns, and cancellation flows. Use when users mention retention, churn, engagement metrics (DAU/MAU), or user activation problems.
- Compare your retention metrics (D1/D7/D30) against industry benchmarks by app category
- Diagnose drop-off points across activation, habit formation, engagement deepening, and long-term retention phases
- Generate prioritized action plans with expected impact estimates
- Design push notification strategies with timing, messaging, and frequency rules
- Create win-back campaigns and cancellation flow strategies to prevent churn
How to install retention-optimization
npx skills add https://github.com/eronred/aso-skills --skill retention-optimization- Current retention metrics (Day 1, Day 7, Day 30 if available)
- App category for benchmark comparison
- Monetization model (free, subscription, freemium, etc.)
- List of current engagement features (push notifications, streaks, etc.)
How to use retention-optimization
- 1.Gather your app's current retention metrics and category, then ask the skill to compare against benchmarks
- 2.Identify which phase (activation, habit, engagement, long-term) has the biggest drop-off
- 3.Review the recommended tactics for that phase and prioritize by expected impact
- 4.Implement quick wins in Week 1, high-impact changes in Month 1, and strategic initiatives in Quarter 1
- 5.Track changes to D1/D7/D30 metrics to measure improvement
Use cases
- Analyze why Day 7 retention is 8% when benchmark is 15% and identify the biggest drop-off point
- Design a 4-week push notification sequence to build habit formation after onboarding
- Create a cancellation flow that offers discounts or pauses instead of losing subscription users
- Evaluate which features power users engage with that casual users miss, then plan feature discovery prompts
- Plan a win-back campaign for users inactive 7+ days with email, push, and in-app messaging
- Product managers optimizing user retention and lifetime value
- Mobile app founders diagnosing churn problems
- Growth teams designing engagement and re-engagement campaigns
- Subscription app teams building cancellation prevention flows
retention-optimization FAQ
app-launch focuses on first-time user experience and onboarding flow. This skill covers the full retention lifecycle from Day 0 through Day 30+ and addresses why users stop coming back.
The skill recommends 3-5 notifications per week maximum. Use the timing table (Day 1 welcome, Day 3 value reminder, Day 7 streak, etc.) and always personalize based on user behavior.
Use the diagnostic framework to identify the biggest drop-off point (e.g., Day 1→3 suggests activation issues, Day 7→30 suggests habit formation). Then implement phase-specific tactics from the action plan.
No. The skill advises making cancellation easy while offering alternatives (discount, downgrade, pause). Forced retention backfires and damages trust.
Track D1, D7, and D30 retention before and after each tactic. The skill provides estimated impact percentages to help prioritize which changes to measure first.
Full instructions (SKILL.md)
Source of truth, from eronred/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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