2nd/10_Wiki/Topics/AI_and_ML/G-Stack Principles.md

---
id: wiki-2026-0508-g-stack-principles
title: G-Stack Principles
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [G-Stack, growth stack, growth engineering, growth marketing]
duplicate_of: none
source_trust_level: B
confidence_score: 0.82
verification_status: applied
tags: [growth, growth-stack, marketing, analytics, experimentation]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: TypeScript / Python
  framework: GA4 / Mixpanel / GrowthBook / Amplitude
---

# G-Stack Principles (Growth Stack)

## 매 한 줄
> **"매 acquisition + activation + retention + revenue + referral 의 의 의 의 modern data + tooling stack"**. 매 product-led growth (PLG), 매 experimentation, 매 personalization. 매 stack: GA4 + Mixpanel/Amplitude + GrowthBook + Segment + Customer.io.

## 매 핵심

### 매 AARRR (Pirate metrics)
- **Acquisition**.
- **Activation**: 매 'aha' moment.
- **Retention**: 매 D1, D7, D30.
- **Revenue**.
- **Referral**.

### 매 stack tier
- **Analytics**: GA4, Mixpanel, Amplitude, PostHog.
- **CDP**: Segment, RudderStack.
- **Experimentation**: GrowthBook, Optimizely, LaunchDarkly.
- **Marketing automation**: Customer.io, Braze, Iterable.
- **CRM**: Salesforce, HubSpot.
- **Reverse ETL**: Hightouch, Census.
- **Warehouse**: Snowflake, BigQuery.

### 매 응용
1. **Onboarding optimize**.
2. **Retention email / push**.
3. **Pricing test**.
4. **Feature flag rollout**.
5. **Personalization**.

## 💻 패턴

### Event tracking (Segment)
```typescript
import { Analytics } from '@segment/analytics-next';
const analytics = new Analytics({ writeKey });
analytics.track('Sign Up Completed', {
  plan: 'pro',
  source: 'landing',
});
```

### A/B test (GrowthBook)
```typescript
import { GrowthBook } from '@growthbook/growthbook';
const gb = new GrowthBook({ apiHost, clientKey });
await gb.loadFeatures();
gb.setAttributes({ id: userId, country });

if (gb.isOn('new_pricing_page')) showNewPricing();
const variation = gb.getValue('hero_headline', 'default');
```

### Funnel analysis (Mixpanel)
```python
import mixpanel
mp = mixpanel.Mixpanel(token)
mp.track(distinct_id, 'Funnel Step', {'step': 1, 'name': 'view_landing'})
```

### Cohort retention
```python
import pandas as pd
def retention_curve(events_df):
    events_df['cohort'] = events_df.groupby('user_id').date.transform('min').dt.to_period('M')
    events_df['period'] = (events_df.date.dt.to_period('M') - events_df.cohort).apply(lambda x: x.n)
    return events_df.groupby(['cohort', 'period']).user_id.nunique().unstack()
```

### Activation prediction
```python
import xgboost as xgb
def predict_activation(user_features):
    """매 user 의 의 의 activate?"""
    return xgb.XGBClassifier().fit(X_train, y_train).predict_proba(user_features)[:, 1]
```

### Segment + reverse ETL
```typescript
// 매 warehouse → tool sync
// 매 Hightouch config:
{
  source: 'snowflake',
  query: 'SELECT email, ltv FROM users WHERE ltv > 1000',
  destination: 'customer_io',
  mode: 'upsert',
  on: 'email',
}
```

### Email automation (Customer.io)
```python
import requests
requests.post(f'https://api.customer.io/v1/campaigns/{cid}/triggers',
    json={'recipients': {'segment': {'id': 1}}, 'data': {'name': '{{first_name}}'}})
```

### Cohort-based feature flag
```typescript
gb.setAttributes({
  id: user.id,
  cohort: user.cohort,
  isPro: user.plan === 'pro',
});
if (gb.isOn('new_dashboard')) showNewDashboard();
```

### LTV prediction
```python
def predict_ltv(user_features):
    """매 first 30-day signal → 매 12-month LTV."""
    return xgb.XGBRegressor().fit(X_train, y_train).predict(user_features)
```

### Churn prediction
```python
def churn_probability(user_features):
    return classifier.predict_proba(user_features)[:, 1]

def trigger_winback(users):
    high_churn = users[churn_probability(user_features) > 0.7]
    customer_io.send_campaign('winback', high_churn)
```

### Multi-touch attribution
```python
def first_touch_attribution(touchpoints):
    return touchpoints[0]

def linear_attribution(touchpoints):
    return [(tp, 1/len(touchpoints)) for tp in touchpoints]

def time_decay_attribution(touchpoints, half_life_days=7):
    weights = [0.5 ** ((now - tp.date).days / half_life_days) for tp in touchpoints]
    total = sum(weights)
    return [(tp, w/total) for tp, w in zip(touchpoints, weights)]
```

### Eval metric
```python
def growth_metrics(users, period_days=30):
    return {
        'D1_retention': retention(users, day=1),
        'D7_retention': retention(users, day=7),
        'D30_retention': retention(users, day=30),
        'avg_LTV': mean([u.ltv for u in users]),
        'CAC': sum(u.acquisition_cost for u in users) / len(users),
        'NRR': net_revenue_retention(users, period_days),
    }
```

### Onboarding flow optimize
```python
def onboarding_funnel(events):
    steps = ['signup', 'verify_email', 'create_first_project', 'invite_teammate', 'first_action']
    drop_offs = []
    for i in range(1, len(steps)):
        rate = events[steps[i]].nunique() / events[steps[i-1]].nunique()
        drop_offs.append((steps[i-1], steps[i], 1 - rate))
    return drop_offs
```

## 매 결정 기준
| 상황 | Tool |
|---|---|
| Product analytics | Mixpanel / Amplitude / PostHog |
| Pipeline | Segment + warehouse + Hightouch |
| Experimentation | GrowthBook / Optimizely |
| Email | Customer.io / Braze |
| Open-source | PostHog + GrowthBook |
| Enterprise | Amplitude + LaunchDarkly + Braze |

**기본값**: 매 Segment + 매 warehouse + 매 PostHog/Amplitude + 매 GrowthBook + 매 Customer.io + 매 cohort retention focus.

## 🔗 Graph
- 응용: [[E-commerce-Optimization]] · [[Dynamic-Creative-Optimization]]
- Adjacent: [[Feature-Flag]]

## 🤖 LLM 활용
**언제**: 매 SaaS / consumer product. 매 PLG.
**언제 X**: 매 enterprise sales-led only.

## ❌ 안티패턴
- **Vanity metrics**: 매 page views, signups.
- **No experimentation discipline**: 매 noise as truth.
- **Tool sprawl**: 매 5+ overlapping.
- **No cohort analysis**: 매 average misleading.

## 🧪 검증 / 중복
- Verified (Reforge, GrowthBook docs, Segment docs, Pirate metrics McClure).
- 신뢰도 B.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — AARRR + 매 Segment / GrowthBook / cohort / LTV / churn code |