Antigravity Agent
183 lines
6.5 KiB
Markdown
183 lines
6.5 KiB
Markdown
---
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id: wiki-2026-0508-전자상거래-소비자-참여-및-보상-시스템-최적화
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title: 전자상거래 소비자 참여 및 보상 시스템 최적화
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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: [E-commerce Engagement, Reward System Optimization, 보상 시스템]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.88
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verification_status: applied
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tags: [e-commerce, growth, rewards, personalization]
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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: typescript
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framework: Next.js/Node
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---
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# 전자상거래 소비자 참여 및 보상 시스템 최적화
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## 매 한 줄
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> **"매 reward 는 dopamine schedule 의 design"**. e-commerce engagement 는 LTV (lifetime value) 곡선의 변형 문제로, 매 variable-ratio reinforcement + loss-aversion + social proof 의 조합. 2026 현재 매 ML personalization (reinforcement learning bandits) 으로 매 user 마다 다른 reward 를 발사.
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## 매 핵심
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### 매 4대 lever
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- **Frequency**: visit cadence — 매 daily login bonus, streak
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- **Depth**: AOV (average order value) — 매 threshold reward, free shipping bar
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- **Breadth**: category exploration — 매 cross-sell coupon
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- **Retention**: churn prevention — 매 win-back, lapsed user offer
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### 매 reward 분류
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- **Hedonic**: tier badge, exclusive access — 매 status driver
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- **Utilitarian**: point/coupon, cashback — 매 economic driver
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- **Social**: referral, leaderboard — 매 viral driver
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- **Surprise**: random box, lucky draw — 매 dopamine spike
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### 매 응용
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1. Subscription churn 감소 — predictive offer 발사.
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2. Cart abandonment 회복 — personalized incentive.
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3. New-user onboarding — 매 first-week reward sequence.
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## 💻 패턴
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### Multi-armed bandit reward selector
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```typescript
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class ContextualBandit {
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private weights: Map<string, number[]> = new Map()
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selectReward(userCtx: UserContext, candidates: Reward[]): Reward {
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const arms = candidates.map(r => ({
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reward: r,
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score: this.predict(userCtx, r),
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}))
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// Thompson sampling
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const sampled = arms.map(a => ({
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...a,
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sample: a.score + this.gaussianNoise(0.1),
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}))
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return sampled.sort((a, b) => b.sample - a.sample)[0].reward
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}
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update(userCtx: UserContext, reward: Reward, outcome: number) {
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const key = this.featureKey(userCtx, reward)
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const w = this.weights.get(key) ?? [0, 0]
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w[0] += outcome
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w[1] += 1
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this.weights.set(key, w)
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}
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}
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```
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### LTV-aware coupon sizing
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```typescript
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function couponAmount(user: UserProfile, basket: Cart): number {
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const ltvDecile = user.ltvPercentile
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const marginalLTV = user.predictedLTV - user.realizedLTV
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// 매 high-LTV / low-realized = 매 invest more
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const cap = Math.min(marginalLTV * 0.15, basket.total * 0.25)
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// 매 churn risk multiplier
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const risk = 1 + user.churnProb * 0.5
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return Math.round(cap * risk)
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}
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```
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### Streak / habit loop
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```typescript
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class StreakEngine {
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async onLogin(userId: string, now: Date) {
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const last = await this.getLastLogin(userId)
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const days = daysBetween(last, now)
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let streak = days === 1 ? (await this.getStreak(userId)) + 1 : 1
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if (streak > 0 && streak % 7 === 0) {
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await this.grantReward(userId, { type: "weekly_streak", multiplier: streak / 7 })
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}
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await this.setStreak(userId, streak)
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}
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}
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```
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### Cart abandonment recovery
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```typescript
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async function abandonRecovery(cart: Cart, user: User) {
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const elapsed = Date.now() - cart.lastUpdate
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if (elapsed < 1000 * 60 * 30) return // 매 too early
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const offer = await bandit.selectReward(user.context, [
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{ type: "free_shipping" },
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{ type: "5_percent_off" },
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{ type: "loyalty_2x_points" },
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])
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await sendEmail(user.email, renderRecovery(cart, offer))
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await sendPush(user.deviceTokens, renderPush(offer))
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}
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```
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### Gamified tier progression
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```typescript
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type Tier = "bronze" | "silver" | "gold" | "platinum"
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const TIER_THRESHOLDS: Record<Tier, number> = {
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bronze: 0, silver: 500, gold: 2000, platinum: 10000,
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}
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function tierProgress(spend: number): { tier: Tier; progress: number; nextAt: number } {
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const tiers: Tier[] = ["bronze", "silver", "gold", "platinum"]
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let cur: Tier = "bronze"
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for (const t of tiers) if (spend >= TIER_THRESHOLDS[t]) cur = t
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const next = tiers[tiers.indexOf(cur) + 1]
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if (!next) return { tier: cur, progress: 1, nextAt: spend }
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const nextAt = TIER_THRESHOLDS[next]
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return { tier: cur, progress: (spend - TIER_THRESHOLDS[cur]) / (nextAt - TIER_THRESHOLDS[cur]), nextAt }
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}
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```
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### Referral viral loop
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```typescript
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async function referralReward(referrer: string, referee: string, firstOrder: Order) {
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if (firstOrder.total < 30) return // 매 anti-fraud floor
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await grantPoints(referrer, 500)
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await grantCoupon(referee, { value: 10, expiresInDays: 30 })
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await track("referral_completed", { referrer, referee, orderValue: firstOrder.total })
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}
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```
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## 매 결정 기준
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| 상황 | Reward type |
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|---|---|
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| New user (D0-D7) | Welcome bundle + first-purchase bonus |
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| Active mid-LTV | Streak + tier progress |
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| High-LTV at churn risk | Personalized win-back, hedonic perk |
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| Low-LTV, high acquisition cost | Bundle / referral, no deep coupon |
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| Cart abandonment | Free shipping (cheap) → coupon (expensive) escalate |
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**기본값**: contextual bandit + LTV-capped budget + 매 hedonic + utilitarian mix.
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## 🔗 Graph
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- 부모: [[E-commerce_Platforms]] · [[Growth_Engineering]]
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- 변형: [[하이브리드_수익화(Hybrid_Monetization)]] · [[부분_유료화(Free-to-Play)]]
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- 응용: [[리텐션(Retention)]] · [[가차(Gacha)]]
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- Adjacent: [[Nudge_Theory]] · [[FOMO (Fear of Missing Out)]] · [[Telemetry_Balancing]]
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## 🤖 LLM 활용
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**언제**: copy generation (개인화 message), reward strategy ideation, A/B test hypothesis.
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**언제 X**: 매 real-time bandit decision (latency budget < 50ms) — pre-trained model 로.
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## ❌ 안티패턴
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- **All-or-nothing coupon**: 매 같은 coupon 모두에게 — margin 폭탄.
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- **Streak punishment**: 매 1일 miss 로 reset — 매 user frustration.
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- **Dark pattern reward**: 매 hidden cost / forced opt-in — 매 churn 가속.
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- **Tier inflation**: 매 너무 빠른 tier-up — 매 status 의미 상실.
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- **Reward 의 marginal cost 무시**: 매 LTV 보다 큰 reward — unit economics 붕괴.
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## 🧪 검증 / 중복
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- Verified (Reichheld Loyalty Effect, Hooked by Nir Eyal, Shopify rewards docs).
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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 — e-commerce engagement & reward optimization |
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