---
id: wiki-2026-0508-cpi-cost-per-install
title: CPI (Cost Per Install)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [CPI, cost-per-install, install-cost]
duplicate_of: none
source_trust_level: A
confidence_score: 0.92
verification_status: applied
tags: [mobile, marketing, ua, monetization, ltv]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: analytics
  framework: ua-channel
---

# CPI (Cost Per Install)

## 매 한 줄
> **"매 신규 install 1건당 marketing 비용"**. 매 mobile UA (User Acquisition) 의 가장 fundamental metric. 매 LTV (lifetime value) 와 매 짝 — 매 LTV > CPI 면 매 ROI positive. 매 2026 iOS ATT post-era 에서 매 CPI 는 매 $3-15 (US tier-1) 의 일반.

## 매 핵심

### 매 정의
- **CPI = 총 ad spend / 매 attributed install 수**.
- **paid CPI**: 매 광고로 attributed 된 매 install 만.
- **blended CPI**: 매 paid + organic 합산.
- **organic uplift**: 매 paid campaign 에 의한 매 organic install 증가.

### 매 영향 요인
- **Geo**: 매 US/JP/KR > EU > LatAm > India.
- **Platform**: 매 iOS 가 매 Android 보다 매 2-4x 비쌈.
- **Genre**: 매 mid-core RPG > casual > hyper-casual.
- **Creative**: 매 video > playable > static.
- **Targeting**: 매 lookalike < broad < whale-target.

### 매 응용
1. 매 ROAS (Return on Ad Spend) D7/D30 추적.
2. 매 channel mix optimization.
3. 매 creative A/B 의 매 cost-efficiency 비교.

## 💻 패턴

### CPI 계산
```typescript
type Campaign = {
  id: string;
  spend: number;
  attributed_installs: number;
  organic_installs: number;
};

function paidCPI(c: Campaign): number {
  return c.attributed_installs > 0
    ? c.spend / c.attributed_installs
    : Infinity;
}

function blendedCPI(c: Campaign): number {
  const total = c.attributed_installs + c.organic_installs;
  return total > 0 ? c.spend / total : Infinity;
}

function organicUplift(c: Campaign, baseline_organic: number): number {
  return Math.max(0, c.organic_installs - baseline_organic);
}
```

### Channel ROAS rollup
```typescript
type ChannelDay = {
  channel: "facebook" | "google" | "tiktok" | "applovin";
  date: string;
  spend: number;
  installs: number;
  d7_revenue: number;
  d30_revenue: number;
};

function rollupROAS(days: ChannelDay[]) {
  const byCh = new Map<string, ChannelDay>();
  for (const d of days) {
    const cur = byCh.get(d.channel) ?? { ...d, spend: 0, installs: 0, d7_revenue: 0, d30_revenue: 0 };
    cur.spend += d.spend;
    cur.installs += d.installs;
    cur.d7_revenue += d.d7_revenue;
    cur.d30_revenue += d.d30_revenue;
    byCh.set(d.channel, cur);
  }
  return [...byCh.values()].map(c => ({
    channel: c.channel,
    cpi: c.spend / c.installs,
    roas_d7: c.d7_revenue / c.spend,
    roas_d30: c.d30_revenue / c.spend,
  }));
}
```

### Bid-cap calculator (target ROAS)
```typescript
function maxBidForROAS(target_roas: number, expected_ltv_d30: number): number {
  // CPI <= LTV / target_roas
  return expected_ltv_d30 / target_roas;
}

// Example: $5 LTV, target 1.2 ROAS → max CPI $4.17
```

### Cohort LTV vs CPI tracker
```typescript
function cohortPayback(cohort_installs: number, cpi: number, daily_arpu: number[]): number {
  let cum = 0;
  for (let day = 0; day < daily_arpu.length; day++) {
    cum += daily_arpu[day];
    if (cum >= cpi) return day; // payback day
  }
  return -1; // not paid back
}
```

### SKAdNetwork-aware attribution (iOS post-ATT)
```typescript
interface SKANPostback {
  campaign_id: number;     // 0-99
  conversion_value: number; // 0-63 (6-bit)
  redownload: boolean;
}

function decodeCV(cv: number): { revenue_bucket: number; engagement: number } {
  // Custom schema — common: bits 0-3 revenue, 4-5 engagement
  return {
    revenue_bucket: cv & 0b1111,
    engagement: (cv >> 4) & 0b11,
  };
}
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 매 hyper-casual | Low CPI ($0.5-2) + IAA monetization |
| 매 casual | Medium CPI ($2-5) + IAP + IAA |
| 매 mid-core RPG | High CPI ($5-15) + deep IAP |
| 매 4X / strategy | Very high CPI ($15-50) + whale-LTV |

**기본값**: 매 D7 ROAS > 0.3, D30 ROAS > 0.7 의 매 channel 만 scale.

## 🔗 Graph
- 응용: [[Game_Monetization_Strategy]] · [[Capybara GO!]]
- Adjacent: [[Dynamic Offers]] · [[Data-Driven Personalization]]

## 🤖 LLM 활용
**언제**: 매 UA budget planning, channel comparison, ROAS analysis.
**언제 X**: 매 organic-only product — 매 paid UA 의 X.

## ❌ 안티패턴
- **Blended-only 추적**: 매 paid 의 incrementality 의 hidden.
- **CPI 만 tracking**: 매 LTV 의 무시 — 매 negative ROI scaling.
- **Day-1 만 보기**: 매 long-tail 의 무시.
- **iOS = Android 가정**: 매 2-4x 차이.

## 🧪 검증 / 중복
- Verified (AppsFlyer 2025 benchmark, Liftoff Casual Gaming Apps Report).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — CPI definition + UA channel measurement patterns. |
