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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-20 23:52:15 +09:00

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title

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

Impression — ad shown (CPM).
Click — user tap (CTR 1-3%).
Install — store install (IPM 0.5-2%).
Activation — first session, tutorial complete.
Monetization — IAP/ad revenue.
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)

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)

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

// 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)

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)

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

6.2 KiB Raw Blame History