Product Analytics Agent Skill

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

1k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

275

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/nicepkg/ai-workflow --skill product-analytics

The instruction itself

13 sections, as written by the author

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: '[email protected]',
  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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How to use it

Copy the folder

Take nicepkg/product-analytics from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.