Product Analytics

SkillDatabases & data

Measure what matters with proper event tracking, funnels, cohorts, and metrics. Use when setting up analytics, tracking features, or understanding behavior.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Product Analytics skill

What this skill tells your AI

The instructions your AI receives, as published by nicepkg/ai-workflow in workflows/marketing-pro-workflow/.claude/skills/product-analytics/SKILL.md and read by ahel’s review.

Measure what matters and make data-driven decisions.

North Star Metric

The ONE metric that represents customer value

Examples:
  Slack: Weekly Active Users
  Airbnb: Nights Booked
  Spotify: Time Listening
  Shopify: GMV

Your North Star should: ✅ Represent customer value
  ✅ Correlate with revenue
  ✅ Be measurable frequently
  ✅ Rally the team

Key Metrics Hierarchy

North Star Metric
  ├── Input Metrics (drive North Star)
  │   ├── Acquisition
  │   ├── Activation
  │   └── Retention
  └── KPIs (business health)
      ├── Revenue
      ├── Churn
      └── LTV

Event Tracking

// Track user actions
analytics.track('Button Clicked', {
  button_name: 'signup',
  page: 'homepage',
  user_id: '123'
})

// Track page views
analytics.page('Homepage', {
  referrer: document.referrer,
  path: window.location.pathname
})

// Identify users
analytics.identify('user-123', {
  email: 'user@example.com',
  plan: 'pro',
  created_at: '2024-01-15'
})

Funnel Analysis

Sign-up Funnel:
  1. Land on homepage: 10,000 (100%)
  2. Click signup: 2,000 (20%)
  3. Fill form: 1,000 (10%)
  4. Verify email: 800 (8%)
  5. Complete onboarding: 400 (4%)

Insights:
  - Biggest drop: Homepage to signup (80% lost)
  - Fix: Clarify value prop, add social proof

Cohort Analysis

Week 1 Cohort (Jan 1-7):
  - D1: 80% active
  - D7: 40% active
  - D30: 20% active

Week 2 Cohort (Jan 8-14):
  - D1: 85% active (+5%)
  - D7: 50% active (+10%)
  - D30: 30% active (+10%)

Insight: Onboarding changes improved retention!

Retention Curves

Good Retention:
  - D1: 60-80%
  - D7: 40-60%
  - D30: 30-50%
  - Flattening curve (good!)

Bad Retention:
  - D1: 40%
  - D7: 10%
  - D30: 2%
  - Steep drop-off (bad!)

Key Metrics to Track

Acquisition

  • Traffic sources (organic, paid, referral)
  • Cost per click (CPC)
  • Conversion rate (visitor → signup)

Activation

  • Signup → first core action
  • Time to value
  • Onboarding completion rate

Retention

  • DAU / MAU (stickiness)
  • Retention rate D1, D7, D30
  • Churn rate

Revenue

  • MRR / ARR
  • ARPU (Average Revenue Per User)
  • LTV (Lifetime Value)
  • LTV:CAC ratio

Referral

  • Viral coefficient
  • Referral signups
  • NPS (Net Promoter Score)

## Tools

```yaml
Event Tracking:
  - Mixpanel (best for products)
  - Amplitude (good alternative)
  - PostHog (open-source)

Session Recording:
  - FullStory
  - LogRocket
  - Hotjar

A/B Testing:
  - Optimizely
  - VWO
  - Google Optimize (free)

Dashboard Design

Executive Dashboard:
  - North Star Metric (big number)
  - Revenue (MRR/ARR)
  - Key metric trends (graphs)

Product Dashboard:
  - Active users (DAU/WAU/MAU)
  - Feature usage
  - Retention cohorts
  - Funnels

Marketing Dashboard:
  - Traffic sources
  - Conversion rates
  - Cost per acquisition
  - ROI by channel

Summary

Great analytics:

  • ✅ One North Star Metric
  • ✅ Track everything
  • ✅ Regular review (weekly)
  • ✅ Share insights widely
  • ✅ Act on data quickly

Signals

GitHub stars
283
Forks
48
Last commit
Jan 2026
Advanced
Catalog kind
skill
Gateway key
product-analytics-nicepkg
Source
github.com/nicepkg/ai-workflow