Retention Optimization

appeeky/aso-skills/skills/retention-optimization

作者 appeeky3919d7c27402無授權條款2.1K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫2 天前更新

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.

AI 產生的概覽

診斷行動應用程式的留存問題,並提出降低流失、提升參與度的優先順序計畫。

功能
引導評估留存指標、應用程式類別、變現模式和參與功能,並將第 1 天、第 7 天與第 30 天留存率與產業基準比較。內容涵蓋啟用、習慣養成、參與度深化與長期留存,並提出防止流失的策略,例如推播通知節奏、召回活動與訂閱取消流程。最終產出留存診斷報告,以及依週、月、季劃分的行動計畫。
適用情境
適用於使用者想降低流失、提升參與度或提高終身價值,或提到留存、流失、使用者離開、DAU/MAU、啟用、解除安裝等情況。新手引導與變現相關問題會轉介至其他技能。
執行需求
不需要指令碼或工具,僅為說明性內容。若存在 app-marketing-context.md 檔案會選擇性讀取,並向使用者詢問留存指標與應用程式資訊。

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)

CategoryDay 1Day 7Day 30Good
Games25-30%10-15%3-5%D1 >35%, D30 >8%
Social30-35%15-20%8-12%D1 >40%, D30 >15%
Health & Fitness20-25%10-12%4-6%D1 >30%, D30 >10%
Productivity15-20%8-10%3-5%D1 >25%, D30 >8%
E-commerce15-20%5-8%2-3%D1 >25%, D30 >5%
Finance20-25%10-12%5-8%D1 >30%, D30 >10%
Education15-20%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

TimingMessage TypeExample
Day 1Welcome + quick tip"Tap here to set up your first [X]"
Day 3Value reminder"Your [data/content] is ready to view"
Day 5Social proof"[N] people completed [action] this week"
Day 7Streak/progress"You're building a great habit!"
Day 14Feature discovery"Did you know you can also [feature]?"
Day 30Milestone"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

來源與署名

來源:appeeky/aso-skills位於skills/retention-optimization提交3919d7c

授權條款: 無授權條款

內容歸原作者所有。SourceWeft 從公開儲存庫中收錄這些內容。

檢舉或申請下架