Retention Optimization logo

Retention Optimization

CommunityPopular
Eronred
retention-optimization

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.

Overview

PublisherEronred
Repositoryaso-skills
Skill nameretention-optimization
Stars
1.9K
Forks
116
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Eronred on GitHub. Read the source before you install it.

Installation

Install the Retention Optimization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Eronred/aso-skills.git /tmp/aso-skills
mkdir -p .claude/skills
cp -r /tmp/aso-skills/skills/retention-optimization .claude/skills/retention-optimization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Retention Optimization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Retention Optimization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Retention Optimization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Frequently asked questions

What does the Retention Optimization AI skill do?

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.

Why use Retention Optimization on TypingMind?

Because you install it once and use it with any model. Retention Optimization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Retention Optimization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Eronred/aso-skills/tree/main/skills/retention-optimization. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Retention Optimization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Retention Optimization?

As many as you like. As long as a model supports skills, you can use Retention Optimization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Retention Optimization AI skill free?

Yes. It is published on GitHub by Eronred under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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