product-analytics-integrator

Product Analytics Integrator

Overview

This skill provides comprehensive guidance for implementing and optimizing product analytics infrastructure. It covers event tracking design, SDK integration, data governance, dashboard creation, and behavioral analysis using industry-leading tools like PostHog, Mixpanel, Google Analytics 4, and Amplitude.

Core Responsibilities

1. Event Tracking Design

Design semantic event models using Object-Action framework
Establish naming conventions and taxonomy standards
Define event properties and user attributes
Create tracking plans with clear documentation
Validate event instrumentation completeness

2. Analytics SDK Integration

Integrate PostHog, Mixpanel, GA4, or Amplitude SDKs
Configure autocapture vs custom event strategies
Implement privacy-compliant tracking
Set up cross-platform tracking (web, mobile, backend)
Validate data flow and event delivery

3. Dashboard & Reporting

Design KPI dashboards for product metrics
Create funnel analyses for conversion optimization
Build cohort retention analyses
Set up automated Slack/email reports
Implement real-time monitoring for critical events

4. Data Interpretation

Analyze user behavior patterns and trends
Identify drop-off points in user journeys
Measure feature adoption and engagement
Calculate retention curves and churn rates
Generate actionable insights from metrics

Event Naming Convention Standards

Object-Action Framework

Format: Object Action or Category: Object Action

Best Practice: Use Title Case for event names, snake_case for properties

Examples:

✅ Button Clicked
✅ Form Submitted
✅ Product Added
✅ app: Onboarding Completed
✅ checkout: Payment Failed
❌ button_clicked (wrong casing)
❌ ClickedButton (wrong order)
❌ User clicked the submit button (too verbose)

Category Prefixes

Add context for where events originate:

app: - Main application events
site: - Marketing website events
checkout: - Checkout flow events
onboarding: - User onboarding events
settings: - Account settings events

Examples:

app: Dashboard Viewed
site: Newsletter Subscribed
checkout: Payment Completed
onboarding: Step Completed
settings: Password Changed

Verb Tense Guidelines

Recommended: Past tense (event already happened)

Examples:

✅ Product Added (clear: action completed)
✅ User Signed Up
✅ Page Viewed
❌ Product Add (confusing: incomplete action?)
❌ User Sign Up

Property Naming

Format: snake_case for all event properties

Best Practices:

Descriptive and clear names
Include units where relevant
Use consistent data types
Document expected values

Examples:

analytics.track('Product Added', {
  product_id: 'prod_123',
  product_name: 'Premium Subscription',
  product_category: 'subscriptions',
  price_usd: 29.99,
  currency: 'USD',
  quantity: 1,
  discount_applied: true,
  discount_code: 'SAVE20'
});

Standard Property Types

Common Property Patterns:

// Identifiers (suffix with _id)
user_id, session_id, product_id, order_id

// Names (suffix with _name)
product_name, feature_name, campaign_name

// Status/State (suffix with _status or _state)
payment_status, order_status, subscription_state

// Timestamps (suffix with _at)
created_at, completed_at, canceled_at

// Durations (suffix with _seconds or _ms)
session_duration_seconds, load_time_ms

// Counts (suffix with _count)
item_count, retry_count, view_count

// Booleans (prefix with is_ or has_)
is_premium, has_discount, is_mobile

// Categories/Types (suffix with _type or _category)
product_type, event_type, user_category

Platform-Specific Implementation

PostHog Integration

Best For: Product-focused teams needing session replay, feature flags, and A/B testing

Key Features:

Autocapture with minimal setup
Built-in session replay
Feature flags and experiments
Self-hosted option for data privacy
SQL access for custom queries

Setup Example (JavaScript):

// Install: npm install posthog-js

import posthog from 'posthog-js'

posthog.init('<project_api_key>', {
  api_host: 'https://us.i.posthog.com',
  defaults: '2025-05-24', // Use latest defaults
  capture_pageview: 'history_change', // For SPAs
  autocapture: true, // Enable autocapture
  session_recording: {
    maskAllInputs: true, // Privacy-first
    maskTextSelector: '.sensitive'
  }
})

// Custom event tracking posthog.capture('Product Added', { product_id: 'prod_123', product_name: 'Premium Plan', price_usd: 29.99 })

// Identify users posthog.identify('user_123', { email: 'user@example.com', plan: 'premium', signup_date: '2025-01-15' })


**Best Practices**:
- Use autocapture initially, then add custom events for critical actions
- Enable session replay for debugging user issues
- Set up feature flags for gradual rollouts
- Use reverse proxy to avoid ad blockers

### Mixpanel Integration

**Best For**: Deep behavioral analysis with advanced segmentation

**Key Features**:
- Event-driven architecture
- Powerful cohort analysis
- Real-time data updates
- Advanced funnel analysis
- A/B test result analysis
**Setup Example (JavaScript)**:
```javascript
// Install: npm install mixpanel-browser

import mixpanel from 'mixpanel-browser'

mixpanel.init('<project_token>', {
  track_pageview: true,
  persistence: 'localStorage'
})

// Track events
mixpanel.track('Product Added', {
  product_id: 'prod_123',
  product_name: 'Premium Plan',
  product_category: 'subscriptions',
  price_usd: 29.99
})

// Identify users
mixpanel.identify('user_123')
mixpanel.people.set({
  '$email': 'user@example.com',
  '$name': 'John Doe',
  'plan': 'premium',
  'signup_date': '2025-01-15'
})

// Track revenue
mixpanel.people.track_charge(29.99, {
  '$time': new Date().toISOString(),
  'product_id': 'prod_123'
})

Best Practices:

Track user properties for detailed segmentation
Use Mixpanel's people profiles for user-centric analysis
Set up custom dashboards for key metrics
Track revenue events for monetization analysis

Google Analytics 4 Integration

Best For: Marketing-focused teams tracking acquisition and content

Key Features:

Free tier with generous limits
Deep Google Ads integration
Predictive metrics (churn, purchase probability)
Cross-platform tracking (web + app)
BigQuery export

Setup Example (JavaScript):

// Add to <head>
<!-- Google tag (gtag.js) -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-XXXXXXXXXX"></script>
<script>
  window.dataLayer = window.dataLayer || [];
  function gtag(){dataLayer.push(arguments);}
  gtag('js', new Date());
  gtag('config', 'G-XXXXXXXXXX');
</script>

// Custom event tracking
gtag('event', 'product_added', {
  product_id: 'prod_123',
  product_name: 'Premium Plan',
  product_category: 'subscriptions',
  price: 29.99,
  currency: 'USD'
});

Best Practices:

Use Google Tag Manager for flexible tracking
Set up enhanced ecommerce for online stores
Configure custom dimensions/metrics for product-specific data
Enable BigQuery export for advanced analysis

Amplitude Integration

Best For: Enterprise teams needing advanced behavioral analytics

Key Features:

Industry-leading cohort analysis
Behavioral insights and predictions
Multi-step funnel analysis
User journey mapping
Robust data governance tools

Setup Example (JavaScript):

// Install: npm install @amplitude/analytics-browser
import * as amplitude from '@amplitude/analytics-browser'

amplitude.init('<api_key>', {
  defaultTracking: {
    sessions: true,
    pageViews: true,
    formInteractions: true
  }
})

// Track events
amplitude.track('Product Added', {
  product_id: 'prod_123',
  product_name: 'Premium Plan',
  product_category: 'subscriptions',
  price_usd: 29.99
})

// Identify users
amplitude.setUserId('user_123')
amplitude.identify(
  new amplitude.Identify()
    .set('email', 'user@example.com')
    .set('plan', 'premium')
    .set('signup_date', '2025-01-15')
)

Best Practices:

Use Amplitude's taxonomy feature for data governance
Set up retention cohorts to measure feature stickiness
Create user journey maps for complex flows
Use Amplitude Data for tracking plan management

Event Tracking Strategy

Autocapture vs Custom Events

Autocapture (PostHog, Heap):

✅ Fast setup with zero code
✅ Retroactive event definition
✅ No missing interactions
❌ High data volume
❌ Noisy/unclear event names
❌ Difficult to filter signal from noise

Custom Events (Mixpanel, Amplitude):

✅ Clean, intentional data
✅ Clear event semantics
✅ Lower data volume
✅ Business-focused tracking
❌ Requires developer implementation
❌ Can miss unexpected behaviors
❌ Not retroactive

Recommended Hybrid Approach:

Start with autocapture for quick insights
Identify critical user actions
Implement custom events for these actions
Keep autocapture for exploratory analysis
Gradually refine based on insights

Critical Events to Track

User Lifecycle:

User Signed Up
Onboarding Completed
First Action Completed
Feature Discovered
Subscription Started
Subscription Canceled
Account Deleted

Product Engagement:

Page Viewed
Feature Used
Search Performed
Filter Applied
Content Created
Content Shared
Help Accessed

Conversion Funnel:

Product Viewed
Product Added to Cart
Checkout Started
Payment Information Entered
Order Completed
Order Failed

Feature Adoption:

Feature Viewed
Feature Interaction Started
Feature Action Completed
Feature Shared
Feedback Provided

Dashboard Design Patterns

1. Product Health Dashboard

Metrics:

Daily/Weekly/Monthly Active Users (DAU/WAU/MAU)
User retention curves (D1, D7, D30)
Feature adoption rates
Session duration averages
Crash/error rates

Recommended Visualizations:

Line charts for DAU/WAU/MAU trends
Retention curves (cohort retention)
Feature usage heatmap
Top features by engagement

2. Conversion Funnel Dashboard

Metrics:

Funnel conversion rates by stage
Drop-off points identification
Time between funnel steps
Conversion by user segment
Revenue per conversion

Recommended Visualizations:

Funnel chart with drop-off rates
Cohort conversion over time
Segment comparison (A/B testing)

3. Engagement Dashboard

Metrics:

Feature engagement frequency
Power user identification
Engagement distribution
Content interaction rates
Session depth metrics

Recommended Visualizations:

Feature usage matrix
User engagement distribution
Session flow diagrams

4. Retention Dashboard

Metrics:

N-day retention rates
Churn prediction
Feature stickiness
Activation correlation
Resurrection rates

Recommended Visualizations:

Retention curves by cohort
Churn risk segments
Feature impact on retention

Cohort Analysis Patterns

Cohort Types

1. Time-Based Cohorts:

Sign-up week/month cohorts
Feature launch cohorts
Campaign cohorts
Seasonal cohorts

2. Behavioral Cohorts:

Power users (>X actions/week)
Trial users
Paying customers
Churned users
At-risk users

3. Demographic Cohorts:

Geographic segments
Plan type segments
Company size segments
Industry segments

Cohort Analysis Examples

Example 1: Feature Adoption by Cohort

Question: Do users who sign up in Q1 2025 adopt Feature X faster than Q4 2024 users?

Setup:
1. Create cohorts by signup quarter
2. Track "Feature X Used" event
3. Measure time-to-feature-adoption
4. Compare adoption curves

Example 2: Retention by Acquisition Channel

Question: Which marketing channel brings the most retained users?

Setup:
1. Create cohorts by utm_source
2. Measure D7, D30, D90 retention
3. Calculate retention curves
4. Identify highest-quality channels

Example 3: Feature Impact on Retention

Question: Does using Feature Y improve 30-day retention?

Setup:
1. Create cohort: Users who used Feature Y in first week
2. Create cohort: Users who did NOT use Feature Y
3. Compare 30-day retention rates
4. Calculate retention lift

Data Validation & Quality

Validation Checklist

Before Launch:

Events fire on correct user actions
Event properties include all required fields
Property data types match specification
Events fire exactly once (no duplicates)
User identification works correctly
Cross-platform tracking links properly
Privacy compliance (PII masking) verified
QA environment tracking separated from production

Post-Launch Monitoring:

Event volume matches expectations
No unexpected spikes/drops in event counts
Property value distributions are reasonable
No null/undefined values where unexpected
Event timestamps are accurate
User counts align with known metrics

Common Data Quality Issues

Issue 1: Duplicate Events

Symptom: Same event fires multiple times for single action
Root Cause: Multiple SDK initializations, button double-clicks
Fix: Debounce event tracking, ensure single SDK init

Issue 2: Missing Properties

Symptom: Events missing critical properties
Root Cause: Async data loading, undefined variables
Fix: Validate properties exist before tracking, use defaults

Issue 3: Inconsistent Naming

Symptom: Same event with multiple names
Root Cause: No naming convention enforcement
Fix: Implement validation layer, use TypeScript types

Issue 4: PII Leakage

Symptom: Email, phone, or sensitive data in events
Root Cause: Autocapture capturing form inputs
Fix: Mask sensitive fields, use data governance rules

Testing Strategy

Manual Testing:

Open browser developer tools
Trigger user actions
Verify events in network tab or analytics debugger
Check event properties match specification
Test across devices and browsers

Automated Testing (Example with Jest):

import { render, fireEvent } from '@testing-library/react'
import { trackEvent } from './analytics'

jest.mock('./analytics')

test('tracks Product Added event on button click', () => {
  const { getByText } = render(<AddToCartButton product={mockProduct} />)
  
  fireEvent.click(getByText('Add to Cart'))
  
  expect(trackEvent).toHaveBeenCalledWith('Product Added', {
    product_id: 'prod_123',
    product_name: 'Premium Plan',
    price_usd: 29.99
  })
})

Quick Reference: Event Examples

Authentication Events

// Sign Up
track('User Signed Up', {
  signup_method: 'email', // 'google', 'github'
  referral_source: 'organic',
  utm_campaign: 'spring_promo'
})

// Login
track('User Logged In', {
  login_method: 'email',
  is_new_device: true,
  session_id: 'sess_abc123'
})

// Logout
track('User Logged Out', {
  session_duration_seconds: 1847,
  pages_viewed: 12
})

Ecommerce Events

// Product View
track('Product Viewed', {
  product_id: 'prod_123',
  product_name: 'Premium Subscription',
  product_category: 'subscriptions',
  price_usd: 29.99,
  referrer: 'search_results'
})

// Add to Cart
track('Product Added', {
  product_id: 'prod_123',
  product_name: 'Premium Subscription',
  price_usd: 29.99,
  quantity: 1,
  cart_value_usd: 29.99,
  cart_item_count: 1
})

// Purchase
track('Order Completed', {
  order_id: 'order_789',
  revenue_usd: 29.99,
  tax_usd: 2.40,
  shipping_usd: 0,
  total_usd: 32.39,
  items: [
    { product_id: 'prod_123', quantity: 1, price_usd: 29.99 }
  ],
  payment_method: 'stripe',
  coupon_code: 'SAVE20'
})

Feature Usage Events

// Feature Discovery
track('Feature Discovered', {
  feature_name: 'advanced_filters',
  discovery_method: 'tooltip',
  days_since_signup: 3
})

// Feature Used
track('Feature Used', {
  feature_name: 'advanced_filters',
  usage_count: 1,
  session_id: 'sess_abc123'
})

// Feature Shared
track('Feature Shared', {
  feature_name: 'report_builder',
  share_method: 'email',
  recipient_count: 3
})

Resources

Analytics Platform Documentation

PostHog: https://posthog.com/docs
Mixpanel: https://docs.mixpanel.com
Google Analytics 4: https://support.google.com/analytics
Amplitude: https://amplitude.com/docs

Best Practices Guides

Segment Data Collection: https://segment.com/academy/collecting-data/
Amplitude Data Taxonomy: https://amplitude.com/docs/data/data-planning-playbook
PostHog Event Tracking: https://posthog.com/tutorials/event-tracking-guide

Conclusion

Effective product analytics requires:

Clear event taxonomy with consistent naming
Strategic instrumentation focused on critical user actions
Robust data validation to ensure quality
Meaningful dashboards that drive decisions
Continuous iteration based on insights

Follow this skill's guidelines to build analytics infrastructure that scales with your product and provides reliable insights for data-driven decisions.

About this skill

Overview

Core Responsibilities

1. Event Tracking Design

2. Analytics SDK Integration

3. Dashboard & Reporting

4. Data Interpretation

Event Naming Convention Standards

Object-Action Framework

Category Prefixes

Verb Tense Guidelines

Property Naming

Standard Property Types

Platform-Specific Implementation

PostHog Integration

Google Analytics 4 Integration

Amplitude Integration

Event Tracking Strategy

Autocapture vs Custom Events

Critical Events to Track

Dashboard Design Patterns

1. Product Health Dashboard

2. Conversion Funnel Dashboard

3. Engagement Dashboard

4. Retention Dashboard

Cohort Analysis Patterns

Cohort Types

Cohort Analysis Examples

Data Validation & Quality

Validation Checklist

Common Data Quality Issues

Testing Strategy

Quick Reference: Event Examples

Authentication Events

Ecommerce Events

Feature Usage Events

Resources

Analytics Platform Documentation

Best Practices Guides

Conclusion