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+---
+id: wiki-2026-0508-dynamic-pricing-offers
+title: Dynamic Pricing & Offers
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [dynamic pricing, surge pricing, demand pricing, personalized offer, price optimization]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.92
+verification_status: applied
+tags: [pricing, dynamic-pricing, optimization, ml, revenue, demand, ecommerce]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: scikit-learn / XGBoost / OR-Tools
+---
+
+# Dynamic Pricing & Offers
+
+## 매 한 줄
+> **"매 supply / demand / inventory / context 의 real-time 의 price 의 adjust"**. 매 Uber surge, 매 airline yield, 매 hotel revenue management. 매 modern: 매 ML + RL + causal inference. 매 ethical: 매 fairness + 매 backlash.
+
+## 매 핵심
+
+### 매 method
+- **Rule-based**: 매 demand-tier.
+- **Elasticity model**: 매 price-demand curve.
+- **MAB / bandit**: 매 explore.
+- **RL**: 매 long-horizon reward.
+- **Causal**: 매 price 의 effect 의 estimate.
+
+### 매 famous
+- **Uber surge**: 매 multiplier.
+- **Airline yield**: 매 fare class + restriction.
+- **Hotel RM**: 매 occupancy + LOS.
+- **Amazon**: 매 millions / day price change.
+- **Ticketmaster**: 매 dynamic event.
+
+### 매 ethical / risk
+- **Backlash**: 매 ride 의 surge during emergency.
+- **Discrimination**: 매 protected attribute 의 proxy.
+- **Trust erosion**: 매 frequent change 의 detect.
+- **Regulation**: 매 EU price personalization disclosure.
+
+### 매 응용
+1. **Ride-hailing**: 매 surge.
+2. **Travel**: 매 yield management.
+3. **Retail**: 매 markdown.
+4. **Energy**: 매 time-of-use.
+5. **Subscription**: 매 win-back offer.
+6. **Gaming**: 매 IAP discount.
+
+## 💻 패턴
+
+### Price elasticity estimation
+```python
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+# 매 log-log: log(Q) = a + b * log(P) + ε
+def estimate_elasticity(prices, quantities):
+    log_p = np.log(prices).reshape(-1, 1)
+    log_q = np.log(quantities)
+    model = LinearRegression().fit(log_p, log_q)
+    elasticity = model.coef_[0]  # 매 typically negative
+    return elasticity
+```
+
+### Optimal price (revenue maximize)
+```python
+def optimal_price(cost, base_demand, elasticity, p_min, p_max):
+    """매 max revenue: 매 (p - c) * Q(p)."""
+    def revenue(p):
+        q = base_demand * (p / p_min) ** elasticity
+        return (p - cost) * q
+    
+    from scipy.optimize import minimize_scalar
+    result = minimize_scalar(lambda p: -revenue(p), bounds=(p_min, p_max), method='bounded')
+    return result.x
+```
+
+### Surge multiplier (Uber-style)
+```python
+def surge_multiplier(active_drivers, pending_requests):
+    ratio = pending_requests / max(active_drivers, 1)
+    if ratio < 0.5: return 1.0
+    elif ratio < 1.0: return 1.2
+    elif ratio < 2.0: return 1.5
+    elif ratio < 3.0: return 2.0
+    else: return min(3.0, 1.0 + ratio * 0.5)
+```
+
+### Yield management (hotel)
+```python
+class YieldManager:
+    def __init__(self, total_rooms, days_to_event):
+        self.total = total_rooms
+        self.dte = days_to_event
+        self.booked = 0
+    
+    def price(self, base_price, demand_signal):
+        occupancy = self.booked / self.total
+        proximity = max(0, 1 - self.dte / 30)  # 매 closer → urgency
+        
+        multi = 1 + 0.5 * occupancy + 0.3 * proximity + 0.2 * demand_signal
+        return base_price * multi
+```
+
+### Contextual bandit price
+```python
+class PriceBandit:
+    def __init__(self, price_options):
+        self.prices = price_options
+        self.alpha = np.ones(len(price_options))
+        self.beta = np.ones(len(price_options))
+    
+    def select(self, context):
+        # 매 Thompson sample
+        samples = np.random.beta(self.alpha, self.beta)
+        return self.prices[np.argmax(samples)]
+    
+    def update(self, price_idx, purchased):
+        if purchased: self.alpha[price_idx] += 1
+        else: self.beta[price_idx] += 1
+```
+
+### Personalized offer
+```python
+def personalized_discount(user, base_price, cost):
+    """매 user lifetime value 의 discount 의 fund."""
+    ltv = predict_ltv(user)
+    sensitivity = predict_price_sensitivity(user)
+    
+    if sensitivity > 0.8 and ltv > base_price * 5:
+        # 매 churn risk + valuable → 매 deep discount
+        return base_price * 0.7
+    elif sensitivity > 0.5:
+        return base_price * 0.9
+    return base_price
+```
+
+### Win-back offer
+```python
+def winback_offer(user):
+    if user.last_active_days_ago < 30: return None
+    if user.churn_score > 0.7:
+        return {
+            'type': 'discount',
+            'value': 0.5,
+            'expires': now() + timedelta(days=7),
+            'urgency': 'high',
+        }
+    return None
+```
+
+### Causal effect of price (DoWhy)
+```python
+import dowhy
+def estimate_price_causal_effect(df):
+    model = dowhy.CausalModel(
+        data=df,
+        treatment='price',
+        outcome='purchased',
+        common_causes=['user_segment', 'season', 'inventory'],
+    )
+    estimand = model.identify_effect()
+    estimate = model.estimate_effect(estimand, method_name='backdoor.linear_regression')
+    return estimate.value  # 매 price 의 1 unit 의 purchase 의 marginal effect
+```
+
+### Anti-discrimination check
+```python
+def fairness_audit(prices_by_segment):
+    """매 protected attribute 의 disparate pricing."""
+    grouped = prices_by_segment.groupby('protected_attr')['price']
+    means = grouped.mean()
+    if (means.max() - means.min()) / means.mean() > 0.05:
+        return 'WARN: >5% pricing disparity by protected attribute'
+    return 'OK'
+```
+
+### Inventory-aware (markdown)
+```python
+def markdown(item, days_remaining, inventory):
+    """매 perishable / seasonal 의 markdown."""
+    if days_remaining < 7:
+        if inventory > 50: return 0.5
+        elif inventory > 20: return 0.7
+        else: return 0.9
+    return 1.0
+```
+
+### A/B testing dynamic pricing
+```python
+def split_test(user_id):
+    bucket = hash(user_id) % 100
+    if bucket < 50: return 'control', static_price
+    elif bucket < 75: return 'A', dynamic_price_v1
+    else: return 'B', dynamic_price_v2
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Real-time supply/demand | Surge multiplier |
+| Long-horizon | Yield management |
+| Few price tiers | Bandit |
+| Continuous price | Elasticity model + optimize |
+| Personalization | Bandit + user features |
+| High-stakes | Causal estimation |
+
+**기본값**: 매 elasticity model + 매 inventory-aware + 매 personalization (LTV) + 매 fairness audit + 매 A/B.
+
+## 🔗 Graph
+- 변형: [[Surge-Pricing]]
+- 응용: [[Causal-Inference]] · [[Multi-Armed-Bandit]] · [[Reinforcement-Learning]]
+- Adjacent: [[E-commerce-Optimization]] · [[Dynamic-Creative-Optimization]]
+
+## 🤖 LLM 활용
+**언제**: 매 inventory perishable. 매 demand variable. 매 personalization.
+**언제 X**: 매 regulated commodity. 매 trust-critical (medication).
+
+## ❌ 안티패턴
+- **No fairness audit**: 매 discrimination.
+- **No inventory awareness**: 매 stockout / waste.
+- **Surge without cap**: 매 emergency price gouge.
+- **No experimentation**: 매 elasticity 의 stale.
+- **Personalize without disclosure**: 매 EU compliance.
+
+## 🧪 검증 / 중복
+- Verified (Pricing literature, Uber/Amazon engineering posts).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — pricing + 매 elasticity / surge / yield / bandit / fairness code |