Retention Optimization
Diagnose mobile churn and get a prioritized retention plan tied to Day 1/7/30 benchmarks for your app category.
Overview
Retention Optimization is an agent skill most often used in Grow (also Validate scope checks and Launch onboarding handoffs) that diagnoses mobile churn against category benchmarks and delivers a prioritized retention an
Install
npx skills add https://github.com/eronred/aso-skills --skill retention-optimizationWhat is this skill?
- Structured initial assessment: context file, retention metrics, category, monetization, and engagement features
- Category-specific Day 1 / Day 7 / Day 30 benchmark table with “good” thresholds (games, social, health, productivity, e-
- Expert framing for diagnosing why users leave and prioritizing fixes vs onboarding (app-launch) and monetization (moneti
- Actionable engagement levers: push notifications, streaks, and model-aware strategy for free vs subscription apps
- Benchmark table covers 6 app categories with Day 1, Day 7, and Day 30 industry averages plus “good” targets
- Initial assessment lists 5 context questions including metrics, category, monetization, and engagement features
Adoption & trust: 1.7k installs on skills.sh; 1.5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You have installs but Day 1/7/30 retention sags and you cannot tell whether the gap is onboarding, engagement design, or category-normal decay.
Who is it for?
Indie mobile apps with some retention numbers (or estimates) who want category-aware targets and a structured engagement plan before coding random features.
Skip if: Teams with no mobile app, pure web-only SaaS lifecycle email strategy, or cases where the spec is only pre-launch onboarding copy with zero usage data yet.
When should I use this skill?
User wants to reduce churn, improve engagement or LTV, or mentions retention, churn, users leaving, DAU/MAU, user activation, or uninstalls.
What do I get? / Deliverables
You leave with benchmark-aligned retention diagnosis, prioritized engagement actions, and clear pointers to app-launch or monetization-strategy when those are the real blockers.
- Prioritized retention diagnosis aligned to category benchmarks
- Engagement feature recommendations (notifications, streaks, activation paths)
- Cross-references when onboarding or monetization skills are the better next step
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Retention and engagement compounding sit in Grow once users are acquired; this skill is shelved under lifecycle because it targets ongoing usage, not first-time ASO listing work. Lifecycle covers activation, habit loops, push/streaks, and LTV—exactly what the retention playbook optimizes after install.
Where it fits
Sanity-check whether planned MVP features support Day 7 habits before committing to a six-month roadmap.
Connect store positioning and first-week experience so messaging matches what retained users actually value.
Prioritize push, streaks, and activation fixes when Day 30 trails category “good” thresholds.
Frame which retention metrics to collect and how to read them against the skill’s benchmark table.
How it compares
Use for ongoing lifecycle retention strategy—not as a substitute for dedicated onboarding launch playbooks or monetization pricing skills.
Common Questions / FAQ
Who is retention-optimization for?
Solo and indie builders shipping consumer or subscription mobile apps who need to interpret churn, DAU/MAU, and activation against realistic category benchmarks.
When should I use retention-optimization?
Use in Grow/lifecycle when users uninstall or engagement drops; in Launch when post-install experience ties to store conversion; in Validate when prototype retention assumptions need sanity checks before a full build.
Is retention-optimization safe to install?
It is advisory content with no built-in shell or network calls in the skill itself; review the Security Audits panel on this page before installing any skill from the repo.
SKILL.md
READMESKILL.md - Retention Optimization
# 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" |