Retention = growth для мобильных приложений. Если D30 retention < 15% - не масштабируйте UA. Fix retention first.
#advanced #case #block-15 #mobile
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← 15.K2 - Кейс: E-commerce - от 0 до масштаба
Retention-Driven Growth Model
Key insight: 5% improvement in D30 retention → 25-40% increase in LTV → can outbid competitors on UA.
Кейс: Финтех-приложение (mobile banking KZ)
Baseline Metrics
| Metric | Before | Industry Benchmark |
|---|---|---|
| D1 Retention | 42% | 40-55% |
| D7 Retention | 22% | 25-35% |
| D30 Retention | 11% | 15-25% |
| Activation Rate | 35% (registration → first transaction) | 40-60% |
| ARPU (monthly) | ₸2,500 | - |
| CPI (blended) | ₸1,200 | ₸1,000-3,000 |
Diagnosis: D1 OK, but D1→D7 drop = 48%. Problem is between first open and habit formation.
Root Cause Analysis
| Drop-off point | Hypothesis | Data Source |
|---|---|---|
| D1→D3 | Onboarding too long (8 screens) | Amplitude funnel |
| D3→D7 | No trigger to return after first use | Push analytics |
| D7→D30 | Core value not discovered | Feature adoption metrics |
Experiments Roadmap
| # | Experiment | Metric | ICE | Result |
|---|---|---|---|---|
| 1 | Shorten onboarding: 8 → 4 screens | Activation rate | 8.3 | +12% activation ✅ |
| 2 | Push notification D1: «Complete setup → get ₸500 bonus» | D3 retention | 7.7 | +8% D3 retention ✅ |
| 3 | Smart push D5: transaction reminder | D7 retention | 7.0 | +5% D7 retention ✅ |
| 4 | Weekly financial summary (push + in-app) | D30 retention | 6.7 | +4% D30 retention ✅ |
| 5 | Referral: invite friend → both get ₸1,000 | K-factor | 6.3 | K-factor 0.15 → 0.28 ✅ |
| 6 | Gamification: savings challenge | D30 retention | 5.7 | +2% (modest) ⚠️ |
After Optimization
| Metric | Before | After | Δ |
|---|---|---|---|
| D1 Retention | 42% | 48% | +6pp |
| D7 Retention | 22% | 30% | +8pp |
| D30 Retention | 11% | 18% | +7pp |
| Activation | 35% | 47% | +12pp |
| ARPU | ₸2,500 | ₸3,200 | +28% |
| LTV (6mo) | ₸6,750 | ₸12,096 | +79% |
| Max CPI (profitable) | ₸1,687 | ₸3,024 | +79% |
Result: Can now spend 79% more per install and remain profitable → scale UA aggressively.
Mobile App Retention Toolkit
Push Notifications Strategy
| Timing | Message | Goal |
|---|---|---|
| D0 (install) | Welcome, complete setup | Activation |
| D1 | «Your account is ready - make first transfer» | First action |
| D3 | «₸500 bonus waiting - complete 1 transaction» | Habit start |
| D7 | «Weekly summary: you saved ₸12,500 this week» | Engagement |
| D14 | «New feature: bill payments in 1 tap» | Feature discovery |
| D30 | «Your monthly financial report is ready» | Long-term habit |
Rules:
- Frequency cap: max 1 push/day
- Opt-in optimization: ask AFTER first value (not on install)
- Personalize: name, specific amounts, relevant features
- Timing: based on user's typical active hours (Amplitude cohorts)
In-App Messages
| Trigger | Message | Format |
|---|---|---|
| First open | Onboarding flow (4 screens max) | Full-screen |
| After first transaction | «Great! Here's what else you can do» | Bottom sheet |
| 3 days inactive | «Complete a transfer → earn ₸500» | Banner |
| Feature unused after D7 | «Did you know you can pay bills here?» | Tooltip |
Feature Flags & Remote Config
| Tool | What it does |
|---|---|
| Firebase Remote Config | Change app behavior without release (feature toggles, copy, thresholds) |
| Firebase A/B Testing | Server-side A/B tests on config values |
| Statsig | Feature flags + experiments + analytics |
| LaunchDarkly | Enterprise feature management |
Attribution for Mobile
MMP Setup (AppsFlyer)
In-App Events tracked:
- af_complete_registration (activation)
- first_transaction (aha-moment)
- af_purchase (loan disbursed)
- monthly_active (custom, fires on D30)
OneLink: deferred deep linking for all campaigns
- Web ads → App Store → open specific screen
- Referral links → install → credited referrer
SKAN Conversion Value Schema:
Bit 0-1: Revenue bucket (0, ₸1-5K, ₸5-20K, ₸20K+)
Bit 2-3: Engagement (registered, first tx, 3+ tx, weekly active)
Bit 4-5: Time to convert (D0, D1, D2-3, D4-7)
🔧 Практика
- Для мобильного приложения (выберите тип: финтех / e-commerce / utility):
- Определите retention benchmarks (D1/D7/D30)
- Проанализируйте текущий D1→D7 и D7→D30 drop-off
- Предложите 5 экспериментов (push, onboarding, feature, referral)
- Рассчитайте: как +5pp D30 retention влияет на LTV и max CPI
- Создайте push notification calendar (D0-D30)
- Спроектируйте SKAN conversion value schema