🎯

product-analytics

🎯Skill

from majiayu000/claude-arsenal

What it does

Helps product teams design event tracking, define metrics, run A/B tests, and analyze user behavior using the AARRR framework and data-driven insights.

📦

Part of

majiayu000/claude-arsenal(24 items)

product-analytics

Installation

Install ScriptRun install script

curl -fsSL https://raw.githubusercontent.com/majiayu000/claude-arsenal/main/install.sh | bash

git cloneClone repository

git clone https://github.com/majiayu000/claude-arsenal.git

Shell ScriptRun shell script

./install.sh --skills typescript-project,python-project,devops-excellence

Shell ScriptRun shell script

./install.sh --all

📖 Extracted from docs: majiayu000/claude-arsenal

Need more details? View full documentation on GitHub →

6Installs

AddedFeb 4, 2026

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Skill Details

SKILL.md

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.

Overview

# 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

Identify core value proposition

- What job does your product do for users?

- When do users get "aha!" moment?

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)

Validate it correlates with business success

- Does NSM increase → revenue increases?

- Can product changes move this metric?

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)

= 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)};

```

---

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product-analytics

Installation

Skill Details

Overview

Core Principles

Hard Rules (Must Follow)

No PII in Events

Object_Action Event Naming

Actionable Metrics Only

Statistical Rigor for Experiments

Quick Reference

When to Use What

North Star Metric

Definition

Characteristics of Good NSMs

Examples by Company

NSM Framework

How to Define Your NSM

AARRR Framework (Pirate Metrics)

Overview

Stage Definitions

AARRR Metrics Dashboard

Acquisition

Activation

Retention

Referral

Revenue

Key Metrics & Formulas

Engagement Metrics

Retention Metrics

Conversion Metrics

Revenue Metrics

Event Tracking Best Practices

Event Naming Convention

Event Properties Structure

Critical Events to Track

User Lifecycle

Feature Usage

Commerce

Engagement

Errors

Privacy & Compliance

See Also

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