# Retention optimization Human Guide

## What This Is For
Provides expert guidance for guidance for mobile app retention and engagement strategy. It gives the agent a clearer input/output frame for retention optimization: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Retention optimization agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for retention optimization.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Retention optimization guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- 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.)
- What % of users complete onboarding?
- How long until the first value moment?
- What's the drop-off point in the first session?
- 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)

## Decision Points And Nuance
The original skill emphasizes: Initial Assessment, Retention Benchmarks, Industry Averages (Day 1 / Day 7 / Day 30), Retention Framework, Activation (Day 0-1), Habit Formation (Day 1-7), Engagement Deepening (Day 7-30), Long-term Retention (Day 30+), Churn Prevention Tactics, Push Notification Strategy.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- The first session determines everything. Users who don't reach the "aha moment" in session 1 rarely return.
- Use progressive disclosure (don't overwhelm)
- What do retained users do that churned users don't?
- Which features do power users use that casual users don't?
- Always provide value, never just "Come back!"
- "Don't use enough" → Show usage stats, suggest features

## Copy-And-Paste Prompt
```text
Use the Retention optimization human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the eronred/aso-skills skill entry for `retention-optimization`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# 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:**