---
id: wiki-2026-0508-하이브리드-수익화-hybrid-monetization
title: 하이브리드 수익화 (Hybrid Monetization)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Hybrid Monetization, 하이브리드 수익화]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [hybrid, monetization, iap, iaa]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: machinations
---

# 하이브리드 수익화 (Hybrid Monetization)

## 매 한 줄
> **"매 IAP + IAA segment 별 결합으로 LTV 극대화"**. 매 2026 모바일 dominant model — 매 hyper-casual 가 hybrid-casual 로 진화하면서 mainstream. 매 non-payer 는 ad-load 로 monetize, payer 는 IAP 로 friction-free experience 제공.

## 매 핵심

### 매 segment 전략
- **Non-payer (95%)**: rewarded video + interstitial.
- **Minnow (3%)**: starter packs, small IAP.
- **Whale (top 2%)**: high-value bundles, VIP, no-ads.
- **Mixed**: ad-removal IAP 로 transition path 제공.

### 매 KPI
- **ARPDAU**: IAP ARPDAU + ad ARPDAU 합산.
- **Ad LTV** vs **IAP LTV**: cohort 별 비교.
- **Cannibalization**: IAP 가 광고 매출을 잠식하는지 측정.
- **No-ads conversion**: ad-removal IAP rate.

### 매 응용
1. Royal Match: puzzle + ad + IAP combo.
2. Subway Surfers: 광고 중심 + cosmetic IAP.
3. Archero: IAA + IAP gem currency.

## 💻 패턴

### Segment-based ad load
```python
def ad_frequency(user):
    if user.is_whale:
        return 0  # no ads
    if user.has_paid:
        return 1  # rewarded only
    return 3  # full ad load
```

### Dual revenue tracking
```python
def compute_arpdau(users, day):
    iap_rev = sum(u.iap_today for u in users if u.active(day))
    ad_rev = sum(u.ad_revenue_today for u in users if u.active(day))
    dau = sum(1 for u in users if u.active(day))
    return {
        "iap_arpdau": iap_rev / dau,
        "ad_arpdau": ad_rev / dau,
        "total_arpdau": (iap_rev + ad_rev) / dau,
    }
```

### Rewarded video offer
```python
class RewardedAd:
    def show(self, user, reward):
        if not ad_network.has_fill():
            return None
        ad_network.play(user)
        user.grant(reward)
        analytics.track("rewarded_complete", user, reward)
```

### A/B ad placement
```python
def assign_variant(user_id):
    bucket = hash(user_id) % 100
    return "high_load" if bucket < 50 else "low_load"
```

### Whale exclusion
```python
def should_show_ad(user, ad_type):
    if user.lifetime_spend > 50:
        return False
    if ad_type == "interstitial" and user.session_seconds < 60:
        return False
    return True
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Hyper-casual | IAA-heavy, light IAP (ad removal) |
| Mid-core | IAP-primary + rewarded video |
| Casual puzzle | Hybrid 50/50 |
| Hardcore RPG | IAP-only, no ads |

**기본값**: hybrid + ad-removal IAP path.

## 🔗 Graph
- 부모: [[게임 수익화 모델]]
- 변형: [[하이브리드 캐주얼(Hybrid-Casual)]] · [[부분 유료화(Free-to-Play)]]
- 응용: [[인앱 구매(IAP)]] · [[인앱 광고(IAA)]]
- Adjacent: [[지불 용의 (Willingness to Pay)]] · [[고객 유지율(Retention)]]

## 🤖 LLM 활용
**언제**: hybrid monetization design, ad-IAP balance, segment 전략 질문.
**언제 X**: pure premium / 단일 model 게임.

## ❌ 안티패턴
- **Whale ad bombing**: 매 whale 에게 광고 노출 → churn risk.
- **Pre-monetization 0 ads**: 매 non-payer LTV = 0.
- **Cannibalization 무시**: 매 ad placement 가 IAP intent 잠식.

## 🧪 검증 / 중복
- Verified (Liftoff, AppLovin 2025 hybrid reports).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — hybrid monetization 정리 (segment 전략, dual ARPDAU) |
