2nd/10_Wiki/Topic_Business/From_Other/ARPU-ARPPU.md

---
id: wiki-2026-0508-arpu-arppu
title: ARPU / ARPPU
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [ARPU, ARPPU, Average Revenue Per User, Average Revenue Per Paying User]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [metrics, monetization, game-economy, saas, kpi]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: sql
  framework: bigquery-snowflake
---

# ARPU / ARPPU

## 매 한 줄
> **"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.

## 매 핵심

### 매 정의
- **ARPU** = Total Revenue / Total Active Users (DAU 또는 MAU 기준).
- **ARPPU** = Total Revenue / Paying Users.
- **Conversion Rate** = Paying Users / Active Users = ARPU / ARPPU.

### 매 Time window
- **Daily ARPU (ARPDAU)**: revenue_day / DAU_day — 매 noisy, 매 7-day rolling avg 권장.
- **Monthly ARPU (ARPMAU)**: revenue_month / MAU_month — 매 industry standard.
- **LTV-adjusted ARPU**: 매 cohort 기반 — 매 churn 반영.

### 매 응용
1. **F2P 게임**: 매 ARPPU $10–50, 매 conversion 1–5% → ARPU $0.10–2.50.
2. **SaaS B2C**: 매 conversion 5–15%, 매 ARPPU $5–30 → ARPU $0.50–4.
3. **AI 구독**: 매 ARPPU $20, 매 conversion 5–10%.

## 💻 패턴

### Pattern 1: SQL ARPU/ARPPU 계산
```sql
-- BigQuery: monthly ARPU & ARPPU
WITH monthly AS (
  SELECT
    DATE_TRUNC(event_date, MONTH) AS month,
    user_id,
    SUM(revenue_usd) AS user_revenue
  FROM events
  WHERE event_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 12 MONTH)
  GROUP BY month, user_id
)
SELECT
  month,
  COUNT(DISTINCT user_id) AS mau,
  COUNTIF(user_revenue > 0) AS paying_users,
  SUM(user_revenue) / COUNT(DISTINCT user_id) AS arpu,
  SAFE_DIVIDE(SUM(user_revenue), COUNTIF(user_revenue > 0)) AS arppu,
  SAFE_DIVIDE(COUNTIF(user_revenue > 0), COUNT(DISTINCT user_id)) AS conv_rate
FROM monthly
GROUP BY month
ORDER BY month;
```

### Pattern 2: Cohort ARPU (LTV-style)
```sql
SELECT
  install_cohort,
  DATE_DIFF(event_date, install_date, DAY) AS day_n,
  SUM(revenue_usd) / COUNT(DISTINCT user_id) AS cumulative_arpu
FROM user_events
GROUP BY install_cohort, day_n
ORDER BY install_cohort, day_n;
```

### Pattern 3: Whale segmentation
```sql
SELECT
  CASE
    WHEN user_revenue >= 1000 THEN 'whale'
    WHEN user_revenue >= 100  THEN 'dolphin'
    WHEN user_revenue >= 10   THEN 'minnow'
    ELSE 'free'
  END AS segment,
  COUNT(*) AS users,
  SUM(user_revenue) AS revenue,
  AVG(user_revenue) AS arppu_segment
FROM monthly
GROUP BY segment;
```

### Pattern 4: Python Pareto 검증
```python
import numpy as np
revenue = df['user_revenue'].sort_values(ascending=False).values
top_1pct = revenue[:int(len(revenue) * 0.01)].sum()
total = revenue.sum()
print(f"Top 1% contribute: {top_1pct / total:.1%}")  # 매 F2P 보통 50%+
```

### Pattern 5: ARPDAU rolling window
```sql
SELECT
  event_date,
  AVG(revenue / dau) OVER (
    ORDER BY event_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
  ) AS arpdau_7d
FROM daily_metrics;
```

## 매 결정 기준
| 상황 | Use which metric |
|---|---|
| 매 전체 monetization health | ARPU |
| 매 pricing / paywall 효과 | ARPPU |
| 매 funnel optimization | Conversion = ARPU/ARPPU |
| 매 long-term value | Cohort LTV (ARPU × retention 적분) |
| 매 whale dependence | Top 1% revenue share |

**기본값**: 매 ARPU + ARPPU + Conversion 매 trio 같이 보고. 매 single metric 의 misleading.

## 🔗 Graph
- 부모: [[유저 평균 매출(ARPU)]] · [[결제 사용자당 평균 매출(ARPPU)]]
- 응용: [[부분 유료화(Freemium) 게임 경제 모델링]] · [[가상 경제 시스템]]
- Adjacent: [[이탈률(Churn Rate)]] · [[수요와 공급(Supply and Demand)]]

## 🤖 LLM 활용
**언제**: 매 product analytics agent 가 매 monetization dashboard 생성 / 매 anomaly detection. 매 LLM 이 매 SQL 작성 + 매 ratio interpretation.
**언제 X**: 매 raw event-level data exploration — 매 BI tool (Looker, Metabase) 직접.

## ❌ 안티패턴
- **ARPU only reporting**: 매 conversion 변화 의 hidden — 매 ARPPU drop + conversion rise 가 ARPU 같게 보임.
- **Single-day snapshot**: 매 day-of-week / weekend effect → 매 7-day rolling 필수.
- **Mixing currencies**: 매 KRW/USD/EUR mixed → 매 normalize first.
- **Including refunds 의 X**: 매 refund 차감 안 하면 매 inflated ARPPU.

## 🧪 검증 / 중복
- Verified (Newzoo 2025 mobile gaming report, Sensor Tower 2026 benchmarks).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — definitions + 5 SQL patterns + 매 whale segmentation |