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Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
name analytics-metrics-kpi version 2.0.0 description Master metrics definition, KPI tracking, dashboarding, A/B testing, and data-driven decision making. Use data to guide product decisions. sasmp_version 1.3.0 bonded_agent 06-analytics-metrics bond_type PRIMARY_BOND parameters [{"name":"product_stage","type":"string","enum":["pre-launch","growth","mature"],"required":true},{"name":"metric_category","type":"string","enum":["acquisition","activation","retention","revenue","referral"]}] retry_logic {"max_attempts":3,"backoff":"exponential"} logging {"level":"info","hooks":["start","complete","error"]}
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/appSignup Rate - % who sign up (target: 10-15%)Free-to-Paid Conversion - % free users who pay (target: 2-5%)CAC - Cost per acquired customerCAC 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 useFeature Adoption - % who try key featuresDay 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 UsersDAU/MAU Ratio - Stickiness (target: 20-30%)Feature Usage - % using key features
Session Length
Session Frequency - Times per weekJan 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
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
Average Revenue Per User (ARPU)
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)
MRR / ARR (vs target, vs month ago)
New customers (weekly, monthly)
Churn rate (%)
NPS score
Engagement (DAU, MAU)
Key initiatives status
Product Dashboard (Product Team)
Funnel metrics (signup → paid)
Feature adoption
Engagement metrics
User feedback score
A/B test results
Support ticket volume
Financial Dashboard (Finance/Operations)
MRR / ARR
Customer acquisition cost
Customer lifetime value
Gross margin
CAC payback period
Revenue by segment
Churn by cohort
Health Dashboard (Operations)
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
Control: Keep current versionTreatment: New versionSample size: Enough users to be statisticalDuration: 2-4 weeks minimumMetric: 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%
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%
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
UI copy optimization
CTA button colors
Email subject lines
Notification triggers
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
System uptime
Error rates
Support response time
Funnel metrics
Feature adoption
Key engagement metrics
Test results
Revenue metrics
Cohort analysis
Churn breakdown
LTV/CAC trends
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, excludeInconclusive A/B → Extend test durationMisleading Metrics → Add context/segmentation
Master data-driven decision making and grow faster!
