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>
191 lines
6.2 KiB
Markdown
191 lines
6.2 KiB
Markdown
---
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id: wiki-2026-0508-user-acquisition-ua
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title: User Acquisition (UA)
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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: [UA, Paid Acquisition, Mobile UA]
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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: [marketing, mobile, growth, performance-marketing]
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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: Python
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framework: AppsFlyer/Adjust SDK
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---
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# User Acquisition (UA)
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 funnel
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1. **Impression** — ad shown (CPM).
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2. **Click** — user tap (CTR 1-3%).
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3. **Install** — store install (IPM 0.5-2%).
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4. **Activation** — first session, tutorial complete.
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5. **Monetization** — IAP/ad revenue.
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6. **Retention** — D1/D7/D30.
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### 매 KPI
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- **CPI**: Cost Per Install ($0.30-$5).
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- **CPA**: Cost Per Action (purchase, level X).
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- **ROAS**: Return on Ad Spend — D7/D30/D90.
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- **LTV**: Lifetime Value (predicted D180/D360).
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- **Payback period**: 매 spend recovery 시점.
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### 매 channels (2026)
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- **Self-attributing networks (SAN)**: Meta, TikTok, Google, Unity Ads, ironSource, AppLovin.
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- **DSPs**: Liftoff, Moloco, Vungle, Mintegral.
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- **Owned/cross-promo**: 매 portfolio publisher 만 의 leverage.
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- **Influencer**: TikTok creators, YouTube playthrough.
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## 💻 패턴
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### LTV prediction (gradient boost on D7 features)
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```python
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import lightgbm as lgb
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import pandas as pd
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def train_ltv_model(cohorts: pd.DataFrame):
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features = [
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"sessions_d7", "iap_count_d7", "iap_value_d7",
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"ad_views_d7", "level_reached_d7", "session_len_avg_d7",
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"country", "platform", "channel"
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]
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target = "ltv_d180"
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X, y = cohorts[features], cohorts[target]
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model = lgb.LGBMRegressor(n_estimators=500, learning_rate=0.03,
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num_leaves=63, min_data_in_leaf=200)
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model.fit(X, y, categorical_feature=["country","platform","channel"])
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return model
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def predict_ltv(model, user_d7_data):
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return model.predict(user_d7_data)[0]
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```
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### Bid optimization (channel-level pacing)
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```python
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def optimize_daily_bids(channels: list[str], budget: float) -> dict:
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perf = {c: get_recent_perf(c, days=3) for c in channels}
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target_roas = 1.20 # D30 break-even + margin
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bids = {}
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remaining = budget
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sorted_ch = sorted(channels, key=lambda c: perf[c]["pred_roas"], reverse=True)
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for c in sorted_ch:
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if perf[c]["pred_roas"] >= target_roas:
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spend = min(perf[c]["capacity"], remaining * 0.4)
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bids[c] = {"bid_cpi": perf[c]["target_cpi"], "budget": spend}
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remaining -= spend
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else:
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bids[c] = {"bid_cpi": perf[c]["target_cpi"] * 0.7, "budget": 0}
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return bids
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```
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### SKAN 4.0 conversion value encoding
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```swift
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// iOS 14.5+ SKAdNetwork 4.0
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import StoreKit
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func updateSKANConversion(user: User) {
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let coarseValue: SKAdNetwork.CoarseConversionValue
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let fineValue: Int
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switch user.revenueD3 {
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case 0..<5: coarseValue = .low; fineValue = encodeFine(user)
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case 5..<25: coarseValue = .medium; fineValue = encodeFine(user)
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default: coarseValue = .high; fineValue = encodeFine(user)
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}
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SKAdNetwork.updatePostbackConversionValue(
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fineValue,
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coarseValue: coarseValue,
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lockWindow: false
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) { error in if let e = error { Log.warn("SKAN: \(e)") } }
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}
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func encodeFine(_ u: User) -> Int {
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var v = 0
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if u.tutorialDone { v |= 0x01 }
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if u.purchasedD3 { v |= 0x02 }
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if u.adImpressions > 5 { v |= 0x04 }
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return v & 0x3F // 6 bits
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}
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```
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### Creative testing (Thompson sampling)
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```python
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import numpy as np
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class CreativeBandit:
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def __init__(self, creatives: list[str]):
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self.alpha = {c: 1 for c in creatives} # installs
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self.beta = {c: 1 for c in creatives} # non-installs
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def select(self) -> str:
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samples = {c: np.random.beta(self.alpha[c], self.beta[c])
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for c in self.alpha}
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return max(samples, key=samples.get)
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def update(self, creative: str, installed: bool):
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if installed: self.alpha[creative] += 1
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else: self.beta[creative] += 1
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```
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### Media Mix Modeling (privacy-safe)
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```python
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import statsmodels.api as sm
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def fit_mmm(weekly_data: pd.DataFrame):
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# Adstock + saturation transformations
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for ch in ["meta", "tiktok", "google", "applovin"]:
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weekly_data[f"{ch}_adstock"] = adstock(weekly_data[f"{ch}_spend"], decay=0.5)
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weekly_data[f"{ch}_sat"] = hill_saturation(weekly_data[f"{ch}_adstock"])
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X = weekly_data[[f"{ch}_sat" for ch in CHANNELS] + ["seasonality"]]
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y = weekly_data["installs"]
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model = sm.OLS(y, sm.add_constant(X)).fit()
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return model
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| New game soft launch | $50-100K, 5-7 geos, 14-day window |
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| Scale phase | Channel diversify, 3+ networks |
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| iOS 14.5+ | SKAN 4.0 + probabilistic + MMM |
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| Android Privacy Sandbox | Topics API + on-device |
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| Unprofitable channel | Pause, retest creative quarterly |
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**기본값**: 매 D7 ROAS 25% gate + 매 portfolio diversification across 3+ networks.
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## 🔗 Graph
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- 부모: [[Performance Marketing]]
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- 변형: [[ASO]]
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- 응용: [[CPI (Cost Per Install)]] · [[Live Operations (LiveOps)]]
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## 🤖 LLM 활용
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**언제**: Creative copy variants, ad concept brainstorming, channel performance summary.
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**언제 X**: 매 actual bid 의 결정 — 매 model + human 의 영역.
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## ❌ 안티패턴
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- **Last-click attribution only**: 매 cross-channel synergy 의 무시 — MMM 미사용.
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- **Vanity CPI focus**: 매 cheap install 추구 → 매 low-LTV cohort 의 floods.
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- **No creative refresh**: 매 ad fatigue 무시 — 매 2-week cycle 필요.
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- **Geo over-concentration**: 매 US/UK only 의 risk — 매 emerging market 의 ignore.
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## 🧪 검증 / 중복
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- Verified (AppsFlyer 2026 mobile marketing index, Adjust mobile growth report 2025).
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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 — UA full lifecycle w/ SKAN 4.0 + MMM |
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