
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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| Installs | 231 |
|---|---|
| repo stars | ★ 33 |
| Last updated | December 25, 2025 |
| Repository | daffy0208/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
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 teamKey Metrics Hierarchy
North Star Metric
├── Input Metrics (drive North Star)
│ ├── Acquisition
│ ├── Activation
│ └── Retention
└── KPIs (business health)
├── Revenue
├── Churn
└── LTVEvent 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 proofCohort 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 channelSummary
Great analytics:
- ✅ One North Star Metric
- ✅ Track everything
- ✅ Regular review (weekly)
- ✅ Share insights widely
- ✅ Act on data quickly
name: product-analytics
kind: skill
description: Measure what matters with proper event tracking, funnels, cohorts, and
metrics. Use when setting up analytics, tracking features, or understanding behavior.
preconditions:
- check: project_initialized
description: Project environment is set up
required: true
effects:
- provides_capability
domains: &id001
- ai
- rag
- frontend
- testing
- product
- design
- data
cost: medium
latency: medium
risk_level: low
side_effects:
- modifies_files
- creates_artifacts
idempotent: false
success_signal: product-analytics capability successfully applied
failure_signals:
- Prerequisites not met
- Configuration error
compatibility:
requires: []
conflicts_with: []
composes_with: []
enables: []
observability:
logs:
- Applying product-analytics...
- product-analytics completed
metrics:
- execution_time_ms
- success_rate
metadata:
version: 1.0.0
created_at: '2025-10-30'
tags: *id001
examples: []