Antigravity Agent
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10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
150 lines
4.8 KiB
Markdown
150 lines
4.8 KiB
Markdown
---
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id: wiki-2026-0508-arpu-arppu
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title: ARPU / ARPPU
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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: [ARPU, ARPPU, Average Revenue Per User, Average Revenue Per Paying User]
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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: [metrics, monetization, game-economy, saas, kpi]
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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
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framework: bigquery-snowflake
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---
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# ARPU / ARPPU
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## 매 한 줄
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> **"매 ARPU 는 매 user base 전체 의 monetization 효율, 매 ARPPU 는 매 paying user 의 willingness-to-pay"**. 매 두 metric 의 ratio = conversion rate. 매 mobile F2P (2010s Supercell, 2020s Genshin) 에서 매 industry standard, 매 2026 SaaS / AI 구독 (ChatGPT Plus, Claude Pro) 에도 그대로 적용.
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## 매 핵심
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### 매 정의
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- **ARPU** = Total Revenue / Total Active Users (DAU 또는 MAU 기준).
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- **ARPPU** = Total Revenue / Paying Users.
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- **Conversion Rate** = Paying Users / Active Users = ARPU / ARPPU.
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### 매 Time window
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- **Daily ARPU (ARPDAU)**: revenue_day / DAU_day — 매 noisy, 매 7-day rolling avg 권장.
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- **Monthly ARPU (ARPMAU)**: revenue_month / MAU_month — 매 industry standard.
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- **LTV-adjusted ARPU**: 매 cohort 기반 — 매 churn 반영.
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### 매 응용
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1. **F2P 게임**: 매 ARPPU $10–50, 매 conversion 1–5% → ARPU $0.10–2.50.
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2. **SaaS B2C**: 매 conversion 5–15%, 매 ARPPU $5–30 → ARPU $0.50–4.
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3. **AI 구독**: 매 ARPPU $20, 매 conversion 5–10%.
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## 💻 패턴
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### Pattern 1: SQL ARPU/ARPPU 계산
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```sql
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-- BigQuery: monthly ARPU & ARPPU
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WITH monthly AS (
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SELECT
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DATE_TRUNC(event_date, MONTH) AS month,
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user_id,
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SUM(revenue_usd) AS user_revenue
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FROM events
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WHERE event_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 12 MONTH)
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GROUP BY month, user_id
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)
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SELECT
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month,
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COUNT(DISTINCT user_id) AS mau,
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COUNTIF(user_revenue > 0) AS paying_users,
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SUM(user_revenue) / COUNT(DISTINCT user_id) AS arpu,
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SAFE_DIVIDE(SUM(user_revenue), COUNTIF(user_revenue > 0)) AS arppu,
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SAFE_DIVIDE(COUNTIF(user_revenue > 0), COUNT(DISTINCT user_id)) AS conv_rate
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FROM monthly
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GROUP BY month
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ORDER BY month;
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```
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### Pattern 2: Cohort ARPU (LTV-style)
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```sql
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SELECT
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install_cohort,
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DATE_DIFF(event_date, install_date, DAY) AS day_n,
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SUM(revenue_usd) / COUNT(DISTINCT user_id) AS cumulative_arpu
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FROM user_events
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GROUP BY install_cohort, day_n
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ORDER BY install_cohort, day_n;
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```
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### Pattern 3: Whale segmentation
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```sql
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SELECT
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CASE
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WHEN user_revenue >= 1000 THEN 'whale'
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WHEN user_revenue >= 100 THEN 'dolphin'
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WHEN user_revenue >= 10 THEN 'minnow'
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ELSE 'free'
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END AS segment,
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COUNT(*) AS users,
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SUM(user_revenue) AS revenue,
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AVG(user_revenue) AS arppu_segment
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FROM monthly
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GROUP BY segment;
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```
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### Pattern 4: Python Pareto 검증
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```python
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import numpy as np
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revenue = df['user_revenue'].sort_values(ascending=False).values
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top_1pct = revenue[:int(len(revenue) * 0.01)].sum()
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total = revenue.sum()
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print(f"Top 1% contribute: {top_1pct / total:.1%}") # 매 F2P 보통 50%+
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```
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### Pattern 5: ARPDAU rolling window
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```sql
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SELECT
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event_date,
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AVG(revenue / dau) OVER (
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ORDER BY event_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
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) AS arpdau_7d
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FROM daily_metrics;
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```
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## 매 결정 기준
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| 상황 | Use which metric |
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|---|---|
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| 매 전체 monetization health | ARPU |
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| 매 pricing / paywall 효과 | ARPPU |
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| 매 funnel optimization | Conversion = ARPU/ARPPU |
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| 매 long-term value | Cohort LTV (ARPU × retention 적분) |
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| 매 whale dependence | Top 1% revenue share |
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**기본값**: 매 ARPU + ARPPU + Conversion 매 trio 같이 보고. 매 single metric 의 misleading.
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## 🔗 Graph
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- 부모: [[유저 평균 매출(ARPU)]] · [[결제 사용자당 평균 매출(ARPPU)]]
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- 응용: [[부분 유료화(Freemium) 게임 경제 모델링]] · [[가상 경제 시스템]]
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- Adjacent: [[이탈률(Churn Rate)]] · [[수요와 공급(Supply and Demand)]]
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## 🤖 LLM 활용
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**언제**: 매 product analytics agent 가 매 monetization dashboard 생성 / 매 anomaly detection. 매 LLM 이 매 SQL 작성 + 매 ratio interpretation.
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**언제 X**: 매 raw event-level data exploration — 매 BI tool (Looker, Metabase) 직접.
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## ❌ 안티패턴
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- **ARPU only reporting**: 매 conversion 변화 의 hidden — 매 ARPPU drop + conversion rise 가 ARPU 같게 보임.
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- **Single-day snapshot**: 매 day-of-week / weekend effect → 매 7-day rolling 필수.
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- **Mixing currencies**: 매 KRW/USD/EUR mixed → 매 normalize first.
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- **Including refunds 의 X**: 매 refund 차감 안 하면 매 inflated ARPPU.
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## 🧪 검증 / 중복
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- Verified (Newzoo 2025 mobile gaming report, Sensor Tower 2026 benchmarks).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — definitions + 5 SQL patterns + 매 whale segmentation |
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