/retention-optimization

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

1. Check for `app-marketing-context.md` — read it for context 2. Ask for **current retention metrics** (Day 1, Day 7, Day 30 if available) 3. Ask for **app category** (benchmarks vary dramatically) 4. Ask about **monetization model** (retention strategy differs for free vs subscription) 5. 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: 1. **Email** (if you have it) — "We've added [feature] since you last visited" 2. **Push notification** — "[Specific value] is waiting for you" 3. **In-app message** (on return) — "Welcome back! Here's what's new"

Cancellation Flow (Subscriptions)

When a user tries to cancel: 1. Ask why (multiple choice) 2. 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

3. Offer pause instead of cancel 4. 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):** 1. [specific tactic with expected impact] 2. [specific tactic with expected impact]

**Month 1 (High Impact):** 1. [specific tactic with expected impact] 2. [specific tactic with expected impact]

**Quarter 1 (Strategic):** 1. [specific tactic with expected impact] 2. [specific tactic with expected impact]

Related Skills

• `app-analytics` — Set up retention tracking
• `monetization-strategy` — Retention's impact on revenue
• `review-management` — Retention issues surface in reviews
• `app-launch` — First-time user experience