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