git:20251218.65d8cd4 to git:20260523.d39dd11

2 added, 202 removed. Audit A to A.

---

description: Product analytics and growth expert. Use when designing event tracking, defining metrics, running A/B tests, or analyzing retention. Covers AARRR framework, funnel analysis, cohort analysis, and experimentation.

---

# Product Analytics

## Core Principles

- **Metrics over vanity** — Focus on actionable metrics tied to business outcomes

- **Data-driven decisions** — Hypothesize, measure, learn, iterate

- **User-centric measurement** — Track behavior, not just pageviews

- **Statistical rigor** — Understand significance, avoid false positives

- **Privacy-first** — Respect user data, comply with GDPR/CCPA

- **North Star focus** — Align all teams around one key metric

---

## Hard Rules (Must Follow)

> These rules are mandatory. Violating them means the skill is not working correctly.

### No PII in Events

**Events must NEVER contain personally identifiable information.**

```javascript

// ❌ FORBIDDEN: PII in event properties

track('user_signed_up', {

email: 'user@example.com', // 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}`;

};

```

### Object_Action Event Naming

**All event names must follow the object_action snake_case format.**

```javascript

// ❌ 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');

```

### Actionable Metrics Only

**Track metrics that drive decisions, not vanity metrics.**

```javascript

// ❌ 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,

});

```

### Statistical Rigor for Experiments

**A/B tests must have proper sample size and significance thresholds.**

```javascript

// ❌ 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 = {

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

};

```

---

## Quick Reference

### When to Use What

| Scenario | Framework/Tool | Key Metric |

|----------|---------------|------------|

| Overall product health | North Star Metric | Time spent listening (Spotify), Nights booked (Airbnb) |

| Growth optimization | AARRR (Pirate Metrics) | Conversion rates per stage |

| Feature validation | A/B Testing | Statistical significance (p < 0.05) |

| User engagement | Cohort Analysis | Day 1/7/30 retention rates |

| Conversion optimization | Funnel Analysis | Drop-off rates per step |

| Feature impact | Attribution Modeling | Multi-touch attribution |

| Experiment success | Statistical Testing | Power, significance, effect size |

---

## North Star Metric

### Definition

A North Star Metric is the **one metric** that best captures the core value your product delivers to customers. When this metric grows sustainably, your business succeeds.

### Characteristics of Good NSMs

```

✓ Captures product value delivery

✓ Correlates with revenue/growth

✓ Measurable and trackable

✓ Movable by product/engineering

✓ Understandable by entire org

✓ Leading (not lagging) indicator

```

### Examples by Company

| Company | North Star Metric | Why It Works |

|---------|------------------|--------------|

| **Spotify** | Time Spent Listening | Core value = music enjoyment |

| **Airbnb** | Nights Booked | Revenue driver + value delivered |

| **Slack** | Daily Active Teams | Engagement = product stickiness |

| **Facebook** | Monthly Active Users | Network effect foundation |

| **Amplitude** | Weekly Learning Users | Value = analytics insights |

| **Dropbox** | Active Users Sharing Files | Core product behavior |

### NSM Framework

```

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

```

### How to Define Your NSM

1. **Identify core value proposition**

- What job does your product do for users?

- When do users get "aha!" moment?

2. **Find the metric that represents this value**

- Transaction completed? (e.g., Nights Booked)

- Time engaged? (e.g., Time Listening)

- Content created? (e.g., Messages Sent)

3. **Validate it correlates with business success**

- Does NSM increase → revenue increases?

- Can product changes move this metric?

4. **Define supporting input metrics**

- What user behaviors drive NSM?

- Break into 3-5 key inputs

---

## AARRR Framework (Pirate Metrics)

### Overview

The AARRR framework tracks the customer lifecycle across five stages:

```

ACQUISITION → ACTIVATION → RETENTION → REFERRAL → REVENUE

```

### Stage Definitions

#### 1. Acquisition

**When users discover your product**

**Key Questions:**

- Where do users come from?

- Which channels have best quality users?

- What's the cost per acquisition (CPA)?

**Metrics:**

```

• Website visitors

• App installs

• Sign-ups per channel

• Cost per acquisition (CPA)

• Channel conversion rates

```

**Example Events:**

```javascript

// 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'

});

```

#### 2. Activation

**When users experience core product value**

**Key Questions:**

- What's the "aha!" moment?

- How long to first value?

- What % reach activation?

**Metrics:**

```

• Time to first action

• Activation rate (% completing key action)

• Setup completion rate

• Feature adoption rate

```

**Example "Aha!" Moments:**

```

Slack: Send 2,000 messages in team

Twitter: Follow 30 users

Dropbox: Upload first file

LinkedIn: Connect with 5 people

```

**Example Events:**

```javascript

// Activation milestone

track('activated', {

user_id: 'usr_123',

activation_action: 'first_project_created',

time_to_activation_hours: 2.5

});

```

#### 3. Retention

**When users keep coming back**

**Key Questions:**

- What's Day 1/7/30 retention?

- Which cohorts retain best?

- What drives churn?

**Metrics:**

```

• Day 1/7/30 retention rate

• Weekly/Monthly active users (WAU/MAU)

• Churn rate

• Usage frequency

• Feature stickiness (DAU/MAU)

```

**Retention Calculation:**

```

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%

```

**Example Events:**

```javascript

// Daily engagement

track('session_started', {

user_id: 'usr_123',

session_count: 42,

days_since_signup: 15

});

```

#### 4. Referral

**When users recommend your product**

**Key Questions:**

- What's the viral coefficient (K-factor)?

- Which users refer most?

- What referral incentives work?

**Metrics:**

```

• Viral coefficient (K-factor)

• Referral rate (% users referring)

• Invites sent per user

• Invite conversion rate

• Net Promoter Score (NPS)

```

**Viral Coefficient:**

```

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

```

**Example Events:**

```javascript

// 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'

});

```

#### 5. Revenue

**When users generate business value**

**Key Questions:**

- What's customer lifetime value (LTV)?

- What's LTV:CAC ratio?

- Which segments monetize best?

**Metrics:**

```

• Monthly Recurring Revenue (MRR)

• Average Revenue Per User (ARPU)

• Customer Lifetime Value (LTV)

• LTV:CAC ratio

• Conversion to paid

• Revenue churn

```

**LTV Calculation:**

```

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

```

**Example Events:**

```javascript

// 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

});

```

### AARRR Metrics Dashboard

```markdown

## Acquisition

- Total visitors: 50,000

- Sign-ups: 2,500 (5% conversion)

- Top channels: Organic (40%), Paid (30%), Referral (20%)

## Activation

- Activated users: 1,750 (70% of sign-ups)

- Time to activation: 3.2 hours (median)

- Activation funnel drop-off: 30% at setup step 2

## Retention

- Day 1: 60%

- Day 7: 35%

- Day 30: 20%

- Churn: 5%/month

## Referral

- K-factor: 0.4

- Users referring: 15%

- Invites per user: 4.2

- Invite conversion: 25%

## Revenue

- MRR: $125,000

- ARPU: $50

- LTV: $800

- LTV:CAC: 4:1

- Conversion to paid: 25%

```

---

- ## Key Metrics & Formulas

- ### Engagement Metrics

- ```

- 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 Metrics

- ```

- 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 Metrics

- ```

- 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

- ```

- ### Revenue Metrics

- ```

- 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

- ```

- ---

- ## Event Tracking Best Practices

- ### Event Naming Convention

- ```

- Object + Action pattern (recommended)

- ✓ user_signed_up

- ✓ project_created

- ✓ file_uploaded

- ✓ payment_completed

- ✗ signup (unclear)

- ✗ new_project (inconsistent)

- ✗ Upload File (inconsistent case)

- ```

- ### Event Properties Structure

- ```javascript

- // 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"

- }

- ```

- ### Critical Events to Track

- ```markdown

- ## User Lifecycle

- - user_signed_up

- - user_activated (first key action)

- - user_onboarded (completed setup)

- - user_upgraded (plan change)

- - user_churned (canceled/inactive)

- ## Feature Usage

- - feature_viewed

- - feature_used

- - feature_completed

- ## Commerce

- - product_viewed

- - product_added_to_cart

- - checkout_started

- - payment_completed

- - order_fulfilled

- ## Engagement

- - session_started

- - session_ended

- - page_viewed

- - search_performed

- - content_shared

- ## Errors

- - error_occurred

- - payment_failed

- - api_error

- ```

- ### Privacy & Compliance

- ```javascript

- // ✓ 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)}`;

- ```

- ---

- ## See Also

+ ## Extended Reference

- - [reference/event-tracking.md](reference/event-tracking.md) — Event tracking and data modeling guide

- - [reference/metrics-framework.md](reference/metrics-framework.md) — North Star, AARRR, key metrics deep dive

- - [reference/experimentation.md](reference/experimentation.md) — A/B testing and statistical best practices

- - [reference/retention.md](reference/retention.md) — Cohort analysis and retention strategies

- - [templates/tracking-plan.md](templates/tracking-plan.md) — Event tracking plan template

+ Detailed material starting at `## Key Metrics & Formulas` has been moved to [`reference/extended.md`](reference/extended.md) to keep this skill concise. Load that reference when the task requires the moved examples, command catalogs, checklists, platform details, or implementation templates.