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Antigravity Agent
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id: wiki-2026-0508-리텐션-retention
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title: 리텐션 (Retention)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Retention, User Retention, Player Retention, D1/D7/D30]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [game-design, product, kpi, growth, monetization]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: SQL/Python
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framework: BigQuery / dbt
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---
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# 리텐션 (Retention)
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## 매 한 줄
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> **"매 리텐션은 game/product의 진정한 PMF(product-market fit) 신호다"**. 매 acquisition은 marketing이 만들지만 retention은 product 자체의 함수이며 매 D1/D7/D30 retention curve 의 shape 가 LTV·CAC·viability 를 결정한다.
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## 매 핵심
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### 매 Retention 정의
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- **Classic (N-day)**: 매 cohort 의 day-N 재방문 비율. D1, D7, D30 이 standard.
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- **Rolling N-day**: 매 day-N±k window 내 재방문 — mobile 에 적합.
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- **Bracket**: 매 누적 active in [N1, N2] window — mature product 에 적합.
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### 매 모바일 게임 benchmark (2026)
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- **Hyper-casual**: D1 35-40%, D7 10-15%, D30 3-5%.
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- **Mid-core**: D1 45-55%, D7 20-25%, D30 8-12%.
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- **Hardcore RPG**: D1 50-60%, D7 30-40%, D30 15-20%.
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- **Top decile**: 매 D1 ≥ 55%, D7 ≥ 30%, D30 ≥ 15%.
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### 매 Retention drivers
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- **Onboarding**: 매 first-session 5-min 의 magic moment 도달 비율.
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- **Daily habit**: 매 daily login reward, daily quest, energy regen.
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- **Social**: 매 guild, friend list, leaderboard.
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- **Progression**: 매 visible mastery curve (level, gear, collection).
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- **Live ops**: 매 limited-time events, seasonal pass, FOMO.
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### 매 응용
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1. 매 D1 < 30% → onboarding/first-session 문제. 매 fix 우선순위 1.
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2. 매 D1 OK but D7 cliff → core loop 단조 → progression/variety 부족.
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3. 매 D30 OK but stagnant → endgame/social content 부족.
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## 💻 패턴
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### Cohort retention SQL
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```sql
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WITH cohort AS (
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SELECT user_id, DATE(MIN(event_time)) AS install_date
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FROM events WHERE event = 'install'
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GROUP BY user_id
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),
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activity AS (
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SELECT e.user_id, c.install_date,
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DATE_DIFF(DATE(e.event_time), c.install_date, DAY) AS day_n
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FROM events e
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JOIN cohort c USING (user_id)
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WHERE e.event = 'session_start'
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)
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SELECT install_date, day_n,
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COUNT(DISTINCT user_id) AS retained,
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COUNT(DISTINCT user_id) * 1.0
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/ FIRST_VALUE(COUNT(DISTINCT user_id))
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OVER (PARTITION BY install_date ORDER BY day_n) AS retention_rate
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FROM activity
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WHERE day_n IN (0,1,3,7,14,30)
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GROUP BY install_date, day_n;
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```
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### Retention curve fit (power law)
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```python
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import numpy as np
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from scipy.optimize import curve_fit
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def power_law(d, a, b):
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return a * (d + 1) ** -b
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days = np.array([1,3,7,14,30,60])
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ret = np.array([0.45, 0.30, 0.22, 0.16, 0.10, 0.07])
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(a, b), _ = curve_fit(power_law, days, ret)
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# extrapolate D90, D365
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print(power_law(90, a, b), power_law(365, a, b))
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```
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### LTV from retention + ARPDAU
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```python
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def ltv(retention_curve, arpdau, horizon_days=365):
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return sum(retention_curve(d) * arpdau for d in range(1, horizon_days+1))
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```
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### Daily login streak service
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```typescript
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async function claimDailyReward(userId: string) {
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const u = await users.get(userId);
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const today = todayUTC();
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if (u.lastLoginDate === today) throw new Error('already-claimed');
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const isContinuation = u.lastLoginDate === yesterdayUTC();
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const newStreak = isContinuation ? u.streak + 1 : 1;
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const reward = REWARD_TABLE[Math.min(newStreak - 1, REWARD_TABLE.length - 1)];
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await users.update(userId, { streak: newStreak, lastLoginDate: today });
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await inventory.grant(userId, reward);
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return { streak: newStreak, reward };
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}
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```
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### Churn risk model (lightweight)
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```python
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# features: days_since_install, sessions_last_7d, spend_total, level, social_count
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# label: churned (no session in next 14 days)
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from sklearn.ensemble import GradientBoostingClassifier
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clf = GradientBoostingClassifier(max_depth=3, n_estimators=200)
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clf.fit(X_train, y_train)
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# segment top-K churn-risk users → targeted re-engagement push
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```
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### Re-engagement campaign trigger
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```typescript
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// trigger if user inactive 3-7 days, segment by spend, send personalized offer
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async function triggerReengagement() {
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const candidates = await db.query(`
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SELECT user_id, last_session, total_spend, favorite_mode
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FROM users
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WHERE last_session BETWEEN NOW() - INTERVAL 7 DAY AND NOW() - INTERVAL 3 DAY
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`);
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for (const c of candidates) {
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const offer = c.total_spend > 50 ? 'whale_comeback' : 'soft_nudge';
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await push.send(c.user_id, OFFER_TEMPLATES[offer](c));
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}
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}
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| D1 낮음 | Onboarding/FTUE 재설계, magic moment time-to-fun ↓ |
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| D7 cliff | Daily habit loop (login reward, quest), variety injection |
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| D30 정체 | Endgame, guild/social, seasonal pass |
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| Whale retention | Personalized live-ops, VIP CS, exclusive events |
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| New genre | Power-law extrapolation 의 X — empirical curve 만 trust |
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**기본값**: 매 D1/D7/D30 cohort tracking + power-law LTV proxy + churn-risk segmentation.
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## 🔗 Graph
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- 응용: [[디아블로_2(Diablo_II)]] · [[클래시_로얄(Clash_Royale)]] · [[알비온_온라인(Albion_Online)]] · [[가차(Gacha)]]
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- Adjacent: [[부분_유료화(Free-to-Play)]] · [[하이브리드_수익화(Hybrid_Monetization)]]
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## 🤖 LLM 활용
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**언제**: 매 retention curve 진단, segment 별 churn 원인 hypothesis, live-ops content idea, A/B test design.
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**언제 X**: 매 specific causation 결론 (correlation only without experiment), 매 individual user prediction (privacy/legal).
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## ❌ 안티패턴
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- **Vanity DAU**: 매 retention 무시한 acquisition push → CAC payback 실패.
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- **D1 만 보기**: 매 D7/D30 무시 → long-term LTV 왜곡.
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- **Linear extrapolation**: 매 retention 은 power-law — 매 linear projection 은 LTV 과대평가.
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- **Forced daily login**: 매 punishment-based streak → resentment + uninstall.
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- **Pay-to-retain**: 매 monetization 위 retention 의존 → churn rate 가속.
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## 🧪 검증 / 중복
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- Verified (GameAnalytics 2025 benchmarks, Sensor Tower 2025 mobile gaming report, "Lean Analytics" Croll/Yoskovitz).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — D1/D7/D30 cohort + LTV + churn patterns |
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