product-analytics · git:20251218.65d8cd4 · 2025-12-18 · sha256 da48d36d05f60014
product-analytics git:20251218.65d8cd4A
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---
name: product-analytics
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!
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}`;
};
```
### 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 = {
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
};
```
---
## 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
- [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