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+---
+id: wiki-2026-0508-맞춤형-팩-personalized-packs
+title: 맞춤형 팩 (Personalized Packs)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Personalized Packs, Dynamic Bundles, Player-Tailored Offers]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [monetization, mobile-game, personalization, ml]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: contextual-bandit / XGBoost
+---
+
+# 맞춤형 팩 (Personalized Packs)
+
+## 매 한 줄
+> **"매 player의 progression / collection gap / spend tier에 fit한 bundle을 ML로 generate"**. 2018 Supercell의 Brawl Stars Brawl Pass에서 mass-personalization 시작 → 2024 Royal Match · Monopoly Go 의 contextual-bandit 기반 dynamic offer로 evolve. 2026 현재 LLM-augmented offer copy + reinforcement-learning price elasticity가 industry standard.
+
+## 매 핵심
+
+### 매 Personalization Signal
+- **Collection gap**: 매 missing card / character / skin → highest "completion utility".
+- **Progression stall**: 매 stuck level → relevant booster / energy bundle.
+- **Spend tier**: 매 LTV percentile (whale / dolphin / minnow / non-payer).
+- **Churn risk**: 매 7-day rolling DAU drop → retention offer.
+- **Session context**: 매 just-failed stage → instant-relief bundle.
+
+### 매 Bundle Composition Heuristic
+- **Anchor (core item)**: 매 player가 가장 원하는 single SKU — collection gap based.
+- **Filler (utility)**: 매 gold / energy / consumables — perceived value 부풀리기.
+- **Discount %**: 매 30~80% — perceived savings vs. actual margin.
+- **Time pressure**: 매 24~72hr countdown — scarcity-driven conversion.
+
+### 매 응용
+1. Monopoly Go: 매 dice + sticker pack 동적 가격.
+2. Royal Match: 매 stuck-level relief bundle.
+3. Marvel Snap: 매 collection-gap-aware bundle (spotlight key).
+4. Genshin Impact: 매 character-specific weapon + materials bundle pre-banner.
+
+## 💻 패턴
+
+### Contextual Bandit Offer Selection
+```python
+import numpy as np
+from sklearn.linear_model import SGDRegressor
+
+class OfferBandit:
+    def __init__(self, n_arms: int, ctx_dim: int, alpha: float = 0.1):
+        self.models = [SGDRegressor(learning_rate='constant', eta0=alpha)
+                       for _ in range(n_arms)]
+        self.ctx_dim = ctx_dim
+        for m in self.models:
+            m.partial_fit([np.zeros(ctx_dim)], [0])
+
+    def select(self, ctx: np.ndarray, eps: float = 0.1) -> int:
+        if np.random.rand() < eps:
+            return np.random.randint(len(self.models))
+        scores = [m.predict([ctx])[0] for m in self.models]
+        return int(np.argmax(scores))
+
+    def update(self, arm: int, ctx: np.ndarray, reward: float):
+        self.models[arm].partial_fit([ctx], [reward])
+```
+
+### Collection Gap Score
+```python
+def gap_score(player_inv: set[str], target_set: set[str],
+              rarity_weight: dict[str, float]) -> dict[str, float]:
+    missing = target_set - player_inv
+    return {sku: rarity_weight.get(sku, 1.0) for sku in missing}
+
+def top_anchor(scores: dict[str, float], k: int = 1) -> list[str]:
+    return sorted(scores, key=scores.get, reverse=True)[:k]
+```
+
+### Price Elasticity Estimator
+```python
+import numpy as np
+from scipy.optimize import minimize_scalar
+
+def expected_revenue(price: float, base_demand: float, elasticity: float) -> float:
+    qty = base_demand * (price ** elasticity)  # elasticity < 0
+    return price * qty
+
+def optimal_price(base_demand: float, elasticity: float,
+                  bounds: tuple = (0.99, 99.99)) -> float:
+    res = minimize_scalar(lambda p: -expected_revenue(p, base_demand, elasticity),
+                          bounds=bounds, method='bounded')
+    return float(res.x)
+```
+
+### Bundle Builder
+```python
+from dataclasses import dataclass
+
+@dataclass
+class Bundle:
+    anchor: str
+    fillers: list[str]
+    price_usd: float
+    discount_pct: int
+    expires_in_hours: int
+
+def build_bundle(player_id: str, anchor_sku: str, ltv_tier: str) -> Bundle:
+    tier_config = {
+        'whale': (49.99, 60, 24),
+        'dolphin': (19.99, 65, 48),
+        'minnow': (4.99, 70, 72),
+        'non_payer': (0.99, 80, 168),
+    }
+    price, discount, hours = tier_config[ltv_tier]
+    fillers = recommend_fillers(player_id, count=3)
+    return Bundle(anchor_sku, fillers, price, discount, hours)
+```
+
+### LLM Offer Copy
+```python
+import anthropic
+
+def generate_copy(bundle: Bundle, player_lang: str = "ko") -> dict:
+    client = anthropic.Anthropic()
+    msg = client.messages.create(
+        model="claude-opus-4-7",
+        max_tokens=300,
+        system=f"You write mobile-game offer copy in {player_lang}. "
+               f"3 outputs: title (max 20ch), subtitle (max 40ch), CTA (max 10ch).",
+        messages=[{"role": "user", "content": str(bundle)}],
+    )
+    return parse_copy(msg.content[0].text)
+```
+
+### Frequency Cap & Fatigue
+```python
+from datetime import datetime, timedelta
+
+def can_show_offer(player_id: str, store: dict) -> bool:
+    last = store.get(player_id, {}).get('last_offer_ts')
+    if not last: return True
+    return datetime.utcnow() - last >= timedelta(hours=6)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Whale (top 1%) | $49.99~$99.99 high-value bundle, low frequency |
+| Dolphin (top 10%) | $9.99~$19.99 staircase progression |
+| Minnow | $0.99~$4.99 starter / IAP-onramp |
+| Non-payer (D7+) | $0.99 introductory + double-currency |
+| Churn risk | retention bundle + 80% discount |
+
+**기본값**: contextual bandit + LTV tier × collection-gap anchor + 6hr frequency cap.
+
+## 🔗 Graph
+- 부모: [[Personalization]]
+- 변형: [[Staircase_Monetization_Model]] · [[Gacha]]
+
+## 🤖 LLM 활용
+**언제**: offer copy generation, A/B variant ideation, anchor SKU rationale explanation.
+**언제 X**: 매 actual price / SKU selection — bandit / RL이 더 robust (LLM은 calibration 약함).
+
+## ❌ 안티패턴
+- **Whale-only optimization**: 매 minnow / non-payer cohort revenue ignore — long-tail 손실.
+- **Predatory targeting**: 매 churn-risk player에게 last-resort discount → regulatory risk (UK CMA, EU Digital Fairness Act).
+- **Static bundles**: 매 player segment 동일 offer → CTR 50%↓.
+- **No frequency cap**: 매 offer fatigue → uninstall spike.
+
+## 🧪 검증 / 중복
+- Verified (deconstructoroffun.com 2024 case studies, GDC Monetization Summit 2025).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — personalized pack 5-signal model + bandit + price elasticity 정리 |