Analytics Metrics Kpi

Analytics & Metrics Skill

Become data-driven. Define meaningful metrics, build dashboards, run experiments, and make decisions based on data, not intuition.

Metrics Framework (Acquisition → Revenue)

North Star Metric

Definition: One metric that best captures the value your product delivers.

Characteristics:

• Directly tied to business success
• Driven by product improvements
• Leading indicator of revenue
• Understandable to whole company

Examples:

• Slack: Daily Active Users (DAU)
• Airbnb: Booked Nights
• YouTube: Watch Time
• Uber: Rides Completed
• Stripe: Payment Volume Processed

Funnel Metrics (Acquisition)

Total Visitors: 100,000/month
↓ 20% conversion
Free Signups: 20,000
↓ 10% free-to-paid
Paid Customers: 2,000

CAC: $50 (marketing + sales spend / customers acquired)
LTCAC: $100 (all customer acquisition costs)

Metrics to Track:

• Traffic - Total visitors to website/app
• Signup Rate - % who sign up (target: 10-15%)
• Free-to-Paid Conversion - % free users who pay (target: 2-5%)
• CAC - Cost per acquired customer
• CAC Payback - Months to recover CAC from revenue (target: < 12 months)

Activation Metrics

Goal: New users become active users

Free Signups: 2,000
↓ 30% onboard successfully
Activated: 600
↓ 60% remain active Day 7
Day 7 Active: 360

Metrics to Track:

• Onboarding Completion Rate - % who complete setup (target: 50-80%)
• Time to First Value - Hours to first successful use
• Feature Adoption - % who try key features
• Day 1/7/30 Retention - % active those days (target: 40/25/15)

Engagement Metrics

Goal: Users regularly use product

Daily/Monthly Metrics:

• DAU/MAU - Daily/Monthly Active Users
• DAU/MAU Ratio - Stickiness (target: 20-30%)
• Feature Usage - % using key features
• Session Length - Minutes per session
• Session Frequency - Times per week

Cohort Analysis Example:

Jan Cohort (1,000 signups):
- Day 1: 600 active (60%)
- Day 7: 360 active (36%)
- Day 30: 180 active (18%)
- Month 3: 90 active (9%)

Feb Cohort (1,500 signups):
- Day 1: 1050 active (70%) ← Improving!
- Day 7: 630 active (42%)
- Day 30: 300 active (20%)

Retention Metrics

Goal: Users stay and continue paying

Month 1: 1,000 customers
Month 2: 900 active (90% retained)
Month 3: 810 active (90% of month 2)
Month 12: 314 active (31% annual retention)

Churn Rate: % lost each period

• Monthly churn: (Customers Lost / Month Start) × 100
• Annual churn: 1 - (Ending / Starting)
• Target for SaaS: < 5% monthly churn

NPS (Net Promoter Score)

• Question: "How likely to recommend (0-10)?"
• Score = % Promoters (9-10) - % Detractors (0-6)
• Range: -100 to +100
• Target: 50+ (world-class)

Revenue Metrics

Monthly Recurring Revenue (MRR)

MRR = (Total paid customers) × (average subscription price)
Growth MRR = New MRR + Expansion MRR - Churn MRR

Annual Run Rate (ARR)

ARR = MRR × 12

Average Revenue Per User (ARPU)

ARPU = MRR / Total Users

Customer Lifetime Value (LTV)

LTV = (ARPU × Gross Margin %) / Monthly Churn %

Example:
ARPU: $100
Gross Margin: 80%
Monthly Churn: 5%
LTV = ($100 × 80%) / 5% = $1,600

If CAC = $400: LTV/CAC = 4x ✓ (target: 3x+)

Dashboard Architecture

Executive Dashboard (C-Level)

Weekly Updates:

• MRR / ARR (vs target, vs month ago)
• New customers (weekly, monthly)
• Churn rate (%)
• NPS score
• Engagement (DAU, MAU)
• Key initiatives status

Frequency: Weekly

Product Dashboard (Product Team)

Daily/Weekly:

• Funnel metrics (signup → paid)
• Feature adoption
• Engagement metrics
• User feedback score
• A/B test results
• Support ticket volume

Frequency: Daily updates

Financial Dashboard (Finance/Operations)

Monthly:

• MRR / ARR
• Customer acquisition cost
• Customer lifetime value
• Gross margin
• CAC payback period
• Revenue by segment
• Churn by cohort

Frequency: Monthly

Health Dashboard (Operations)

Realtime:

• System uptime (%)
• Error rate (%)
• Response time (p95)
• Database performance
• Support ticket response time
• Support backlog

Frequency: Realtime/hourly

A/B Testing (Experimentation)

Test Planning

Hypothesis: "If we change X, then Y will improve, because Z"

Example: "If we move signup button above the fold, then conversion will improve 15%, because users won't scroll."

Test Structure

Experiment Design:

• Control: Keep current version
• Treatment: New version
• Sample size: Enough users to be statistical
• Duration: 2-4 weeks minimum
• Metric: Clear success metric

Statistical Significance

Confidence Level: 95% (industry standard)

• Means 5% chance of false positive
• Need enough samples (typically 1000-10K per variant)
• Use calculator for exact sample size

P-Value: Probability result is random chance

• P < 0.05: Statistically significant
• P > 0.05: Not significant, inconclusive