---
id: wiki-2026-0508-user-acquisition-ua
title: User Acquisition (UA)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [UA, Paid Acquisition, Mobile UA]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [marketing, mobile, growth, performance-marketing]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: AppsFlyer/Adjust SDK
---

# User Acquisition (UA)

## 매 한 줄
> **"매 paid install 의 LTV-positive flow"**. 매 mobile 게임 의 lifeblood — 매 CPI < LTV(D180) 의 maintain. 매 2026 SKAdNetwork 4.0 + Privacy Sandbox 의 era — 매 deterministic attribution 의 종말, 매 probabilistic + MMM 의 부상.

## 매 핵심

### 매 funnel
1. **Impression** — ad shown (CPM).
2. **Click** — user tap (CTR 1-3%).
3. **Install** — store install (IPM 0.5-2%).
4. **Activation** — first session, tutorial complete.
5. **Monetization** — IAP/ad revenue.
6. **Retention** — D1/D7/D30.

### 매 KPI
- **CPI**: Cost Per Install ($0.30-$5).
- **CPA**: Cost Per Action (purchase, level X).
- **ROAS**: Return on Ad Spend — D7/D30/D90.
- **LTV**: Lifetime Value (predicted D180/D360).
- **Payback period**: 매 spend recovery 시점.

### 매 channels (2026)
- **Self-attributing networks (SAN)**: Meta, TikTok, Google, Unity Ads, ironSource, AppLovin.
- **DSPs**: Liftoff, Moloco, Vungle, Mintegral.
- **Owned/cross-promo**: 매 portfolio publisher 만 의 leverage.
- **Influencer**: TikTok creators, YouTube playthrough.

## 💻 패턴

### LTV prediction (gradient boost on D7 features)
```python
import lightgbm as lgb
import pandas as pd

def train_ltv_model(cohorts: pd.DataFrame):
    features = [
        "sessions_d7", "iap_count_d7", "iap_value_d7",
        "ad_views_d7", "level_reached_d7", "session_len_avg_d7",
        "country", "platform", "channel"
    ]
    target = "ltv_d180"
    X, y = cohorts[features], cohorts[target]
    model = lgb.LGBMRegressor(n_estimators=500, learning_rate=0.03,
                                num_leaves=63, min_data_in_leaf=200)
    model.fit(X, y, categorical_feature=["country","platform","channel"])
    return model

def predict_ltv(model, user_d7_data):
    return model.predict(user_d7_data)[0]
```

### Bid optimization (channel-level pacing)
```python
def optimize_daily_bids(channels: list[str], budget: float) -> dict:
    perf = {c: get_recent_perf(c, days=3) for c in channels}
    target_roas = 1.20  # D30 break-even + margin
    bids = {}
    remaining = budget
    sorted_ch = sorted(channels, key=lambda c: perf[c]["pred_roas"], reverse=True)
    for c in sorted_ch:
        if perf[c]["pred_roas"] >= target_roas:
            spend = min(perf[c]["capacity"], remaining * 0.4)
            bids[c] = {"bid_cpi": perf[c]["target_cpi"], "budget": spend}
            remaining -= spend
        else:
            bids[c] = {"bid_cpi": perf[c]["target_cpi"] * 0.7, "budget": 0}
    return bids
```

### SKAN 4.0 conversion value encoding
```swift
// iOS 14.5+ SKAdNetwork 4.0
import StoreKit

func updateSKANConversion(user: User) {
    let coarseValue: SKAdNetwork.CoarseConversionValue
    let fineValue: Int

    switch user.revenueD3 {
    case 0..<5: coarseValue = .low; fineValue = encodeFine(user)
    case 5..<25: coarseValue = .medium; fineValue = encodeFine(user)
    default: coarseValue = .high; fineValue = encodeFine(user)
    }

    SKAdNetwork.updatePostbackConversionValue(
        fineValue,
        coarseValue: coarseValue,
        lockWindow: false
    ) { error in if let e = error { Log.warn("SKAN: \(e)") } }
}

func encodeFine(_ u: User) -> Int {
    var v = 0
    if u.tutorialDone { v |= 0x01 }
    if u.purchasedD3   { v |= 0x02 }
    if u.adImpressions > 5 { v |= 0x04 }
    return v & 0x3F  // 6 bits
}
```

### Creative testing (Thompson sampling)
```python
import numpy as np

class CreativeBandit:
    def __init__(self, creatives: list[str]):
        self.alpha = {c: 1 for c in creatives}  # installs
        self.beta = {c: 1 for c in creatives}   # non-installs

    def select(self) -> str:
        samples = {c: np.random.beta(self.alpha[c], self.beta[c])
                   for c in self.alpha}
        return max(samples, key=samples.get)

    def update(self, creative: str, installed: bool):
        if installed: self.alpha[creative] += 1
        else:         self.beta[creative]  += 1
```

### Media Mix Modeling (privacy-safe)
```python
import statsmodels.api as sm

def fit_mmm(weekly_data: pd.DataFrame):
    # Adstock + saturation transformations
    for ch in ["meta", "tiktok", "google", "applovin"]:
        weekly_data[f"{ch}_adstock"] = adstock(weekly_data[f"{ch}_spend"], decay=0.5)
        weekly_data[f"{ch}_sat"] = hill_saturation(weekly_data[f"{ch}_adstock"])
    X = weekly_data[[f"{ch}_sat" for ch in CHANNELS] + ["seasonality"]]
    y = weekly_data["installs"]
    model = sm.OLS(y, sm.add_constant(X)).fit()
    return model
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| New game soft launch | $50-100K, 5-7 geos, 14-day window |
| Scale phase | Channel diversify, 3+ networks |
| iOS 14.5+ | SKAN 4.0 + probabilistic + MMM |
| Android Privacy Sandbox | Topics API + on-device |
| Unprofitable channel | Pause, retest creative quarterly |

**기본값**: 매 D7 ROAS 25% gate + 매 portfolio diversification across 3+ networks.

## 🔗 Graph
- 부모: [[Performance Marketing]]
- 변형: [[ASO]]
- 응용: [[CPI (Cost Per Install)]] · [[Live Operations (LiveOps)]]

## 🤖 LLM 활용
**언제**: Creative copy variants, ad concept brainstorming, channel performance summary.
**언제 X**: 매 actual bid 의 결정 — 매 model + human 의 영역.

## ❌ 안티패턴
- **Last-click attribution only**: 매 cross-channel synergy 의 무시 — MMM 미사용.
- **Vanity CPI focus**: 매 cheap install 추구 → 매 low-LTV cohort 의 floods.
- **No creative refresh**: 매 ad fatigue 무시 — 매 2-week cycle 필요.
- **Geo over-concentration**: 매 US/UK only 의 risk — 매 emerging market 의 ignore.

## 🧪 검증 / 중복
- Verified (AppsFlyer 2026 mobile marketing index, Adjust mobile growth report 2025).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — UA full lifecycle w/ SKAN 4.0 + MMM |
