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+---
+id: wiki-2026-0508-data-driven-personalization
+title: Data-Driven Personalization
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [personalization, dynamic-personalization, player-segmentation]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [data, personalization, segmentation, ml, mobile-game]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: feature-store-ml
+---
+
+# Data-Driven Personalization
+
+## 매 한 줄
+> **"매 player 의 behavior data 로 매 content / offer / difficulty 의 individual tuning"**. 매 segment 단위 → 매 ML-driven individual 단위 의 매 evolution. 매 2026 standard 는 매 real-time feature store + 매 contextual bandit + 매 explainable rule overlay.
+
+## 매 핵심
+
+### 매 segmentation tier
+- **Static segment**: 매 country, install_source.
+- **Behavior segment**: 매 spender / dolphin / minnow / F2P.
+- **Lifecycle**: 매 new / engaged / churning / churned.
+- **ML cluster**: 매 k-means / autoencoder embedding.
+
+### 매 personalization surface
+- **Offer pricing**: 매 player-specific bundle.
+- **Difficulty**: 매 adaptive level.
+- **Content order**: 매 onboarding sequence.
+- **Push timing**: 매 individual best-time.
+
+### 매 응용
+1. 매 LTV uplift (매 10-30%).
+2. 매 retention curve flattening.
+3. 매 cohort-specific event design.
+
+## 💻 패턴
+
+### Player feature vector
+```python
+from dataclasses import dataclass
+from typing import Literal
+
+@dataclass
+class PlayerFeatures:
+    days_since_install: int
+    sessions_7d: int
+    sessions_30d: int
+    spend_total: float
+    spend_30d: float
+    last_spend_days: int
+    avg_session_min: float
+    progression_level: int
+    cohort: Literal["new", "engaged", "churning", "churned"]
+    country: str
+
+def derive_cohort(p: PlayerFeatures) -> str:
+    if p.days_since_install < 7: return "new"
+    if p.sessions_7d == 0: return "churned"
+    if p.sessions_7d < p.sessions_30d / 8: return "churning"
+    return "engaged"
+```
+
+### Contextual bandit (offer selection)
+```python
+import numpy as np
+
+class LinUCB:
+    def __init__(self, n_arms: int, n_features: int, alpha: float = 1.0):
+        self.A = [np.eye(n_features) for _ in range(n_arms)]
+        self.b = [np.zeros(n_features) for _ in range(n_arms)]
+        self.alpha = alpha
+
+    def select(self, x: np.ndarray) -> int:
+        scores = []
+        for a in range(len(self.A)):
+            A_inv = np.linalg.inv(self.A[a])
+            theta = A_inv @ self.b[a]
+            ucb = theta @ x + self.alpha * np.sqrt(x @ A_inv @ x)
+            scores.append(ucb)
+        return int(np.argmax(scores))
+
+    def update(self, arm: int, x: np.ndarray, reward: float):
+        self.A[arm] += np.outer(x, x)
+        self.b[arm] += reward * x
+```
+
+### Adaptive difficulty (player skill estimate)
+```python
+def estimate_skill(recent_attempts: list[dict]) -> float:
+    """recent_attempts: [{success: bool, level_difficulty: float}]"""
+    if not recent_attempts: return 0.5
+    total_w = sum(0.9 ** i for i in range(len(recent_attempts)))
+    skill = sum(
+        (a["level_difficulty"] if a["success"] else a["level_difficulty"] - 0.2)
+        * (0.9 ** i)
+        for i, a in enumerate(recent_attempts)
+    ) / total_w
+    return max(0.0, min(1.0, skill))
+
+def next_difficulty(skill: float, target_winrate: float = 0.65) -> float:
+    # Want challenge slightly above skill
+    return skill + (1 - target_winrate) * 0.3
+```
+
+### Real-time feature store query
+```python
+from datetime import datetime
+
+class FeatureStore:
+    def __init__(self, redis_client, warehouse_client):
+        self.redis = redis_client
+        self.warehouse = warehouse_client
+
+    async def get_player_features(self, player_id: str) -> PlayerFeatures:
+        # Hot features from Redis
+        hot = await self.redis.hgetall(f"player:{player_id}:hot")
+        # Cold features from warehouse (cached)
+        cold = await self.warehouse.query(
+            f"SELECT * FROM player_cold WHERE id = '{player_id}'"
+        )
+        return PlayerFeatures(**{**cold, **hot})
+```
+
+### Explainable overlay (rule + ML)
+```python
+def select_offer_with_guardrails(p: PlayerFeatures, ml_pick: int, offers: list) -> int:
+    # Guardrails override ML
+    if p.spend_total == 0 and offers[ml_pick].price > 9.99:
+        return offers.index(STARTER_PACK_499)  # never expensive to non-spenders
+    if p.cohort == "churning":
+        return offers.index(WIN_BACK_OFFER)
+    if p.country in HIGH_RISK_COUNTRIES and offers[ml_pick].price > 4.99:
+        return offers.index(LOW_PRICE_DEFAULT)
+    return ml_pick
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 매 small data | Static segment + rules |
+| 매 medium data | Behavior segment + A/B |
+| 매 large data | ML cluster + contextual bandit |
+| 매 regulated market | Rule guardrails 의 mandatory |
+
+**기본값**: 매 segment + bandit + 매 rule guardrails 의 layered.
+
+## 🔗 Graph
+- 부모: [[LiveOps]]
+- 변형: [[Dynamic Offers]] · [[Adaptive-Difficulty]]
+- 응용: [[Game_Monetization_Strategy]] · [[Capybara GO!]]
+- Adjacent: [[CPI (Cost Per Install)]] · [[Gacha Mechanics Analysis]]
+
+## 🤖 LLM 활용
+**언제**: 매 segmentation design, bandit setup, feature engineering.
+**언제 X**: 매 cold-start product — 매 data 부족.
+
+## ❌ 안티패턴
+- **Predatory targeting**: 매 vulnerable player 의 매 high-spend offer.
+- **Black-box only**: 매 explainability 없음 → 매 regulator + designer 둘 다 lost.
+- **Stale features**: 매 hourly batch → 매 real-time signal miss.
+- **Over-segmentation**: 매 sample size 부족.
+
+## 🧪 검증 / 중복
+- Verified (Unity LiveOps 2025 report, GameAnalytics Personalization Whitepaper).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — data-driven personalization with bandit + guardrails. |