// ❌ FORBIDDEN: PII in event properties
track('user_signed_up', {
  email: 'user@example.com',     // PII!
  name: 'John Doe',              // PII!
  phone: '+1234567890',          // PII!
  ip_address: '192.168.1.1',     // PII!
  credit_card: '4111...',        // NEVER!
});

// ✅ REQUIRED: Anonymized/hashed identifiers only
track('user_signed_up', {
  user_id: hash('user@example.com'),  // Hashed
  plan: 'pro',
  source: 'organic',
  country: 'US',                       // Broad location OK
});

// Masking utilities
const maskEmail = (email) => {
  const [name, domain] = email.split('@');
  return `${name[0]}***@${domain}`;
};

// ❌ FORBIDDEN: Inconsistent naming
track('signup');                    // No object
track('newProject');                // camelCase
track('Upload File');               // Spaces and PascalCase
track('user-created');              // kebab-case
track('BUTTON_CLICKED');            // SCREAMING_CASE

// ✅ REQUIRED: object_action snake_case
track('user_signed_up');
track('project_created');
track('file_uploaded');
track('payment_completed');
track('checkout_started');

// ❌ FORBIDDEN: Vanity metrics without context
track('page_viewed');               // No insight
track('button_clicked');            // Too generic
track('app_opened');                // Doesn't indicate value

// ✅ REQUIRED: Actionable metrics tied to outcomes
track('feature_activated', {
  feature: 'dark_mode',
  time_to_activation_hours: 2.5,
  user_segment: 'power_user',
});

track('checkout_completed', {
  order_value: 99.99,
  items_count: 3,
  payment_method: 'credit_card',
  coupon_applied: true,
});

// ❌ FORBIDDEN: Drawing conclusions too early
// "After 100 users, variant B has 5% higher conversion!"
// This is not statistically significant.

// ✅ REQUIRED: Proper experiment setup
const experimentConfig = {
  name: 'new_checkout_flow',
  hypothesis: 'New flow increases conversion by 10%',

  // Statistical requirements
  significance_level: 0.05,      // 95% confidence
  power: 0.80,                   // 80% power
  minimum_detectable_effect: 0.10, // 10% lift

  // Calculated sample size
  sample_size_per_variant: 3842,

  // Guardrails
  max_duration_days: 14,
  stop_if_degradation: -0.05,    // Stop if 5% worse
};

✓ Captures product value delivery
✓ Correlates with revenue/growth
✓ Measurable and trackable
✓ Movable by product/engineering
✓ Understandable by entire org
✓ Leading (not lagging) indicator

North Star Metric
       ↓
┌──────┴──────┬──────────┬──────────┐
│             │          │          │
Input 1    Input 2   Input 3   Input 4
(Supporting metrics that drive NSM)

Example: Spotify
NSM: Time Spent Listening
├── Daily Active Users
├── Playlists Created
├── Songs Added to Library
└── Share/Social Actions

ACQUISITION → ACTIVATION → RETENTION → REFERRAL → REVENUE

• Website visitors
• App installs
• Sign-ups per channel
• Cost per acquisition (CPA)
• Channel conversion rates

// Landing page view
track('page_viewed', {
  page: 'landing',
  utm_source: 'google',
  utm_medium: 'cpc',
  utm_campaign: 'brand_search'
});

// Sign-up started
track('signup_started', {
  source: 'homepage_cta'
});

• Time to first action
• Activation rate (% completing key action)
• Setup completion rate
• Feature adoption rate

Slack:     Send 2,000 messages in team
Twitter:   Follow 30 users
Dropbox:   Upload first file
LinkedIn:  Connect with 5 people

// Activation milestone
track('activated', {
  user_id: 'usr_123',
  activation_action: 'first_project_created',
  time_to_activation_hours: 2.5
});

• Day 1/7/30 retention rate
• Weekly/Monthly active users (WAU/MAU)
• Churn rate
• Usage frequency
• Feature stickiness (DAU/MAU)

Day X Retention = Users returning on Day X / Total users in cohort

Example:
Cohort: 1000 users signed up Jan 1
Day 7: 300 returned
Day 7 Retention = 300/1000 = 30%

// Daily engagement
track('session_started', {
  user_id: 'usr_123',
  session_count: 42,
  days_since_signup: 15
});

• Viral coefficient (K-factor)
• Referral rate (% users referring)
• Invites sent per user
• Invite conversion rate
• Net Promoter Score (NPS)

K = (% users who refer) × (avg invites per user) × (invite conversion rate)

Example:
K = 0.20 × 5 × 0.30 = 0.30

K > 1: Viral growth (each user brings >1 new user)
K < 1: Need paid acquisition

// Referral actions
track('invite_sent', {
  user_id: 'usr_123',
  channel: 'email',
  recipients: 3
});

track('referral_converted', {
  referrer_id: 'usr_123',
  new_user_id: 'usr_456',
  channel: 'email'
});

• Monthly Recurring Revenue (MRR)
• Average Revenue Per User (ARPU)
• Customer Lifetime Value (LTV)
• LTV:CAC ratio
• Conversion to paid
• Revenue churn

LTV = ARPU × Gross Margin / Churn Rate

Example:
ARPU: $50/month
Gross Margin: 80%
Churn: 5%/month

LTV = $50 × 0.80 / 0.05 = $800

Healthy LTV:CAC ratio: 3:1 or higher

// Revenue events
track('subscription_started', {
  user_id: 'usr_123',
  plan: 'pro',
  mrr: 29.99,
  billing_cycle: 'monthly'
});

track('upgrade_completed', {
  user_id: 'usr_123',
  from_plan: 'basic',
  to_plan: 'pro',
  mrr_change: 20.00
});

undefined

---

Daily Active Users (DAU)
= Unique users performing key action per day

Monthly Active Users (MAU)
= Unique users performing key action per month

Stickiness = DAU / MAU × 100%
• 20%+ = Good (users engage 6+ days/month)
• 10-20% = Average
• <10% = Low engagement

Session Duration
= Average time between session start and end

Session Frequency
= Average sessions per user per time period

Retention Rate (Classic)
= Users active in Week N / Users in original cohort

Retention Rate (Bracket)
= Users active in Week N / Users active in Week 0

Churn Rate
= (Users at start - Users at end) / Users at start

Quick Ratio (Growth Health)
= (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR)
• >4 = Excellent growth
• 2-4 = Good
• <1 = Shrinking

Conversion Rate
= (Conversions / Total visitors) × 100%

Funnel Conversion
= (Users completing final step / Users entering funnel) × 100%

Time to Convert
= Median time from first touch to conversion

Monthly Recurring Revenue (MRR)
= Sum of all monthly subscription values

Annual Recurring Revenue (ARR)
= MRR × 12

Average Revenue Per User (ARPU)
= Total revenue / Number of users

Customer Lifetime Value (LTV)
= ARPU × Average customer lifetime (months)
OR
= ARPU × Gross Margin % / Monthly Churn Rate

Customer Acquisition Cost (CAC)
= Total sales & marketing spend / New customers acquired

LTV:CAC Ratio
= LTV / CAC
• >3:1 = Healthy
• 1:1 = Unsustainable

Payback Period
= CAC / (ARPU × Gross Margin %)
• <12 months = Good
• 12-18 months = Acceptable
• >18 months = Concerning

Object + Action pattern (recommended)

✓ user_signed_up
✓ project_created
✓ file_uploaded
✓ payment_completed

✗ signup (unclear)
✗ new_project (inconsistent)
✗ Upload File (inconsistent case)

// Standard event structure
{
  event: "checkout_completed",        // Event name
  timestamp: "2025-12-16T10:30:00Z",  // When
  user_id: "usr_123",                 // Who
  session_id: "ses_abc",              // Session context
  properties: {                       // Event-specific data
    order_id: "ord_789",
    total_amount: 99.99,
    currency: "USD",
    item_count: 3,
    payment_method: "credit_card",
    coupon_used: true,
    discount_amount: 10.00
  },
  context: {                          // Global context
    app_version: "2.4.1",
    platform: "web",
    user_agent: "...",
    ip: "192.168.1.1",
    locale: "en-US"
  }
}

undefined

undefined

// ✓ GOOD: No PII in events
track('user_signed_up', {
  user_id: hashUserId('user@example.com'),  // Hashed
  plan: 'pro',
  source: 'organic'
});

// ✗ BAD: Contains PII
track('user_signed_up', {
  email: 'user@example.com',  // PII!
  password: '...',            // Never log!
  credit_card: '...'          // Never log!
});

// Masking strategies
const maskEmail = (email) => {
  const [name, domain] = email.split('@');
  return `${name[0]}***@${domain}`;
};

const maskCard = (card) => `****${card.slice(-4)}`;