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Product Analytics

  • 231 installs
  • 33 repo stars
  • Updated December 25, 2025
  • daffy0208/ai-dev-standards

Define product metrics, funnels, and experiment readouts so engineering and PM decisions trace to measurable outcomes instead of intuition.

About

AI dev standards skill codifying product analytics practice: name core metrics, design funnels and cohorts, standardize event taxonomies, review experiments, and translate data into concrete product decisions engineering can implement.

  • North-star and guardrail metrics
  • Funnel and cohort framing
  • Event taxonomy standards
  • Experiment design checks
  • Actionable insight writeups

Product Analytics by the numbers

  • 231 all-time installs (skills.sh)
  • Ranked #623 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
  • Data as of Jul 30, 2026 (Skillselion catalog sync)
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Installs231
repo stars33
Last updatedDecember 25, 2025
Repositorydaffy0208/ai-dev-standards

What it does

Define product metrics, funnels, and experiment readouts so engineering and PM decisions trace to measurable outcomes instead of intuition.

Files

SKILL.mdMarkdownGitHub ↗

Product Analytics

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

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

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