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

Example A/B Test

Hypothesis: Moving signup button above fold increases conversion 15%

Setup:

Control: Current design
Treatment: Button moved above fold
Success metric: Conversion rate (signup / visit)
Sample size: 10,000 users per variant
Duration: 2 weeks
Confidence: 95%

Results:

Control: 2.0% conversion (200 signups from 10K visitors)
Treatment: 2.8% conversion (280 signups from 10K visitors)
Improvement: 40% increase (0.8% / 2% = 40%)
P-value: 0.02 (statistically significant!)
Decision: SHIP IT - Roll out to 100%

Test Ideas by Priority

High Priority (Start Here):

Signup flow optimization (biggest funnel)
Onboarding experience
Pricing page clarity
Feature discoverability

Medium Priority:

UI copy optimization
CTA button colors
Email subject lines
Notification triggers

Low Priority:

Micro-copy tweaks
Animation effects
Color scheme changes

Metric Pitfalls to Avoid

Vanity Metrics

❌ "We have 1M page views!" ✓ "We have 50K daily active users, growing 10% monthly"

Actionable vs Non-Actionable

❌ "User satisfaction increased" (what changed?) ✓ "Onboarding completion rate 65% → 78% (↑20%)" (clear action)

Correlation vs Causation

❌ "Ice cream sales correlate with drownings" ✓ Understand actual causation, not just correlation

Look-Alike Metrics

❌ Track MRR but not Customer LTV (can grow MRR by spending more on acquisition) ✓ Track both acquisition efficiency AND retention

Metrics Review Cadence

Daily:

System uptime
Error rates
Support response time

Weekly:

Funnel metrics
Feature adoption
Key engagement metrics
Test results