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Master metrics definition, KPI tracking, dashboarding, A/B testing, and data-driven decision making. Use data to guide product decisions.
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SOC 職業分類に基づく
| 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"]} |
Become data-driven. Define meaningful metrics, build dashboards, run experiments, and make decisions based on data, not intuition.
Definition: One metric that best captures the value your product delivers.
Characteristics:
Examples:
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:
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:
Goal: Users regularly use product
Daily/Monthly Metrics:
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%)
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
NPS (Net Promoter Score)
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+)
Weekly Updates:
Frequency: Weekly
Daily/Weekly:
Frequency: Daily updates
Monthly:
Frequency: Monthly
Realtime:
Frequency: Realtime/hourly
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."
Experiment Design:
Confidence Level: 95% (industry standard)
P-Value: Probability result is random chance
Hypothesis: Moving signup button above fold increases conversion 15%
Setup:
Results:
High Priority (Start Here):
Medium Priority:
Low Priority:
❌ "We have 1M page views!" ✓ "We have 50K daily active users, growing 10% monthly"
❌ "User satisfaction increased" (what changed?) ✓ "Onboarding completion rate 65% → 78% (↑20%)" (clear action)
❌ "Ice cream sales correlate with drownings" ✓ Understand actual causation, not just correlation
❌ Track MRR but not Customer LTV (can grow MRR by spending more on acquisition) ✓ Track both acquisition efficiency AND retention
Daily:
Weekly:
Monthly:
Quarterly:
| 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 |
[ ] 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?
Master data-driven decision making and grow faster!