Agent SkillsEronred/aso-skills › retention-optimization

retention-optimization

GitHub

针对移动应用留存与参与度优化的专家技能,提供诊断流失原因、分析关键指标(D1/D7/D30)及制定分阶段(激活、习惯养成、长期留存)优化策略的优先级计划。

skills/retention-optimization/SKILL.md Eronred/aso-skills

Trigger Scenarios

用户希望降低流失率或提高参与度 提及留存、DAU/MAU、用户卸载原因等关键词

Install

npx skills add Eronred/aso-skills --skill retention-optimization -g -y
More Options

Use without installing

npx skills use Eronred/aso-skills@retention-optimization

指定 Agent (Claude Code)

npx skills add Eronred/aso-skills --skill retention-optimization -a claude-code -g -y

安装 repo 全部 skill

npx skills add Eronred/aso-skills --all -g -y

预览 repo 内 skill

npx skills add Eronred/aso-skills --list

SKILL.md

Frontmatter
{
    "name": "retention-optimization",
    "metadata": {
        "version": "1.0.0"
    },
    "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."
}

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

Version History

  • e2a7c45 Current 2026-07-24 21:04

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Metadata

Files
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Version
f97c943
Hash
cb31fede
Indexed
2026-07-24 21:04

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