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

Monthly:

• Revenue metrics

• Cohort analysis

• Churn breakdown

• LTV/CAC trends

Quarterly:

• Strategic metric review

• Long-term trend analysis

• Metric changes needed

Troubleshooting

Yaygın Hatalar & Çözümler

Hata Olası Sebep Çözüm

Vanity metrics focus Wrong KPI selection North Star alignment

Inconclusive A/B test Low sample size Extend duration

Data inconsistency Multiple sources Single source of truth

Dashboard unused Too complex Simplify to 5-7 KPIs

Debug Checklist

[ ] North Star metric defined mi? [ ] Metrics business goals'a aligned mi? [ ] Data collection accurate mi? [ ] Dashboard refreshed mi? [ ] A/B test sample sufficient mi? [ ] Statistical significance achieved mi?

Recovery Procedures

• Data Quality Issues → Flag affected metrics, exclude

• Inconclusive A/B → Extend test duration

• Misleading Metrics → Add context/segmentation

Master data-driven decision making and grow faster!