2nd/10_Wiki/Topics/Economics & Algorithms/Dynamic Pricing.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-dynamic-pricing	Dynamic Pricing	10_Wiki/Topics	verified	self	동적 가격 책정 변동 가격제 Surge Pricing	none	A	0.9	applied	economics pricing monetization ml		2026-05-10	pending	language framework Python scikit-learn / XGBoost

Dynamic Pricing

매 한 줄

"매 가격은 매 순간 다르다". 매 수요·재고·시간·세그먼트 signal 의 기반에서 매 price 가 매 real-time 의 조정. 매 Uber surge, 매 airline yield management, 매 2026 게임 IAP A/B price 의 mainstream.

매 핵심

매 입력 signal

• 수요 (Demand): 매 search volume, 매 conversion rate, 매 cart-add rate.
• 공급 (Supply / Inventory): 매 남은 stock, 매 server capacity.
• 시간 (Time): 매 hour-of-day, 매 day-of-week, 매 holiday.
• 사용자 segment: 매 LTV tier, 매 churn risk, 매 region 의 PPP.
• 경쟁사 price: 매 web scraping 의 competitor catalog.

매 알고리즘 family

• Rule-based: 매 if (inventory < 20%) then price *= 1.3.
• Elasticity model: 매 demand curve 의 fit → 매 revenue-maximizing point 의 추출.
• Bandit / RL: 매 contextual bandit 의 사용 — 매 explore vs exploit.
• Deep learning: 매 transformer 의 시퀀스 → 매 next-period price prediction.

매 응용

1. Airline / hotel yield management (매 origin domain).
2. Ride-sharing surge (Uber, Lyft).
3. E-commerce flash sale + personalized coupon.
4. Game IAP regional pricing + LTV tier offer.

💻 패턴

Elasticity 추정 (log-log regression)

import numpy as np
import statsmodels.api as sm

# price, qty observed across past promotions
log_p = np.log(prices)
log_q = np.log(quantities)
X = sm.add_constant(log_p)
model = sm.OLS(log_q, X).fit()
elasticity = model.params[1]  # 매 typical -1.2 ~ -2.5
print(f"Price elasticity: {elasticity:.2f}")

Revenue-maximizing price (constant elasticity)

def optimal_price(cost, elasticity):
    """매 monopoly markup formula: P* = c * e/(e+1) for e<-1"""
    if elasticity >= -1:
        raise ValueError("Inelastic demand — revenue unbounded")
    return cost * elasticity / (elasticity + 1)

print(optimal_price(cost=2.0, elasticity=-1.5))  # → 6.0

Contextual bandit (LinUCB)

import numpy as np

class LinUCB:
    def __init__(self, n_arms, n_features, alpha=1.0):
        self.alpha = alpha
        self.A = [np.eye(n_features) for _ in range(n_arms)]
        self.b = [np.zeros(n_features) for _ in range(n_arms)]

    def select(self, context):
        ucb = []
        for a in range(len(self.A)):
            A_inv = np.linalg.inv(self.A[a])
            theta = A_inv @ self.b[a]
            mu = context @ theta
            sigma = self.alpha * np.sqrt(context @ A_inv @ context)
            ucb.append(mu + sigma)
        return int(np.argmax(ucb))

    def update(self, arm, context, reward):
        self.A[arm] += np.outer(context, context)
        self.b[arm] += reward * context

XGBoost demand forecaster

import xgboost as xgb
import pandas as pd

features = ["price", "hour", "dow", "is_holiday", "competitor_price",
            "inventory", "user_ltv_tier", "region_ppp"]
dtrain = xgb.DMatrix(df[features], label=df["units_sold"])
params = {"objective": "reg:squarederror", "max_depth": 6, "eta": 0.1}
model = xgb.train(params, dtrain, num_boost_round=300)

def expected_revenue(price, ctx):
    ctx2 = {**ctx, "price": price}
    qty = model.predict(xgb.DMatrix(pd.DataFrame([ctx2])))[0]
    return price * qty

Personalized price (LTV tier)

def personalized_price(base_price, user):
    tier = user["ltv_tier"]  # "whale", "dolphin", "minnow"
    region_factor = REGION_PPP[user["country"]]  # 매 0.4 ~ 1.2
    tier_factor = {"whale": 1.0, "dolphin": 0.85, "minnow": 0.6}[tier]
    return round(base_price * region_factor * tier_factor, 2)

Surge guardrails

def safe_surge(base, raw_multiplier):
    # 매 PR backlash 의 prevent
    capped = min(raw_multiplier, 3.0)
    floored = max(capped, 0.7)
    return base * floored

A/B price test (Bayesian)

import numpy as np
from scipy.stats import beta

def bayesian_ab(buyers_a, visitors_a, buyers_b, visitors_b, n_sim=100_000):
    a = beta(1 + buyers_a, 1 + visitors_a - buyers_a).rvs(n_sim)
    b = beta(1 + buyers_b, 1 + visitors_b - buyers_b).rvs(n_sim)
    return float(np.mean(b > a))  # 매 P(B > A)

매 결정 기준

상황	Approach
매 stable demand, 매 cost-plus	Rule-based + manual ladder
매 elastic, 매 abundant data	Elasticity model + grid search
매 cold start, 매 many SKUs	Contextual bandit
매 high-stakes (regulated)	Constrained optimization + audit log

기본값: 매 elasticity model 의 시작, 매 enough data 의 수집 후 contextual bandit 의 graduate.

🔗 Graph

• 부모: 게임 수익화 모델 · Microeconomics
• 변형: Surge Pricing · Yield Management · Personalized Pricing
• 응용: IAP_In_App_Purchase · Live-ops
• Adjacent: A/B Testing · Contextual Bandit · LTV

🤖 LLM 활용

언제: 매 elasticity 추정 의 EDA, 매 price ladder design, 매 A/B test 의 statistical analysis. 언제 X: 매 production pricing decision 의 single LLM call — 매 hallucination risk 의 too high.

❌ 안티패턴

• Race-to-bottom: 매 competitor 의 blind matching → margin collapse.
• Surge backlash: 매 cap 없는 multiplier → user trust 의 손상 (매 Uber NYE 8x 의 사례).
• Personalization 의 leak: 매 same item 의 different price 의 user-visible → fairness backlash.
• Cold-start naïveté: 매 new SKU 에 매 zero data 의 RL 의 직접 deploy → wild swing.

🧪 검증 / 중복

• Verified (Phillips 2005 Pricing and Revenue Optimization; Uber Engineering blog 2023).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — full content with elasticity, bandit, A/B patterns