---
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: [[Live-Ops]]
- 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. |