SaaS Metrics Coach Skill

Overview

Production-ready SaaS metrics toolkit for calculating MRR/ARR, analyzing cohort retention, and evaluating unit economics. Designed for SaaS founders, finance teams, and growth operators who need precise subscription revenue analysis without spreadsheet gymnastics.

Clarify First

Before calculating, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Which metric set — MRR/ARR growth, cohort retention, or unit economics (selects the script and the input format: subscription CSV, activity CSV, or metrics JSON)
• Reporting period + currency handling — the window and how multi-currency MRR is normalized (changes every revenue and churn figure)
• Gross margin — the margin to apply (drives LTV and CAC payback; LTV = ARPU x margin / churn)
• Churn definition — gross vs. net, logo vs. revenue (changes churn rate, NRR, and the health-flag verdict)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

# Calculate MRR, ARR, growth rate, and churn from subscription data
python scripts/mrr_calculator.py subscriptions.csv

# Run cohort retention analysis
python scripts/cohort_analyzer.py users.csv --cohort-period monthly

# Calculate LTV, CAC, LTV:CAC ratio, and payback period
python scripts/unit_economics.py metrics.json

Tools Overview

Workflows

Workflow 1: Monthly SaaS Health Check

1. Export subscription data as CSV (columns: customer_id, plan, mrr, start_date, end_date)
2. Run mrr_calculator.py to get current MRR, ARR, net new MRR, churn rate
3. Run cohort_analyzer.py on user activity data to identify retention trends
4. Run unit_economics.py to validate LTV:CAC ratio stays above 3:1
5. Review output for warning flags (churn > 5%, LTV:CAC < 3, payback > 18 months)

Workflow 2: Investor Deck Preparation

1. Run mrr_calculator.py --format json to get growth metrics for charts
2. Run cohort_analyzer.py --format json for retention curves
3. Run unit_economics.py --format json for unit economics summary
4. Use JSON output to populate investor deck data points

Workflow 3: Churn Investigation

1. Run mrr_calculator.py with --breakdown to see churn by plan tier
2. Run cohort_analyzer.py to identify which cohorts churn fastest
3. Cross-reference cohort drop-off periods with product changes
4. Identify if churn is concentrated in specific segments or time windows

Reference Documentation

Key SaaS Metrics Definitions

• MRR (Monthly Recurring Revenue): Sum of all active subscription revenue normalized to monthly
• ARR (Annual Recurring Revenue): MRR x 12
• Net New MRR: New MRR + Expansion MRR - Churned MRR - Contraction MRR
• Gross Churn Rate: Lost MRR / Beginning MRR for the period
• Net Revenue Retention (NRR): (Beginning MRR + Expansion - Churn - Contraction) / Beginning MRR
• LTV (Lifetime Value): ARPU / Monthly Churn Rate (simplified) or ARPU x Gross Margin / Churn
• CAC (Customer Acquisition Cost): Total Sales & Marketing Spend / New Customers Acquired
• LTV:CAC Ratio: Target 3:1 or higher for healthy SaaS
• CAC Payback Period: CAC / (ARPU x Gross Margin) in months

See references/saas-metrics-guide.md for comprehensive framework details.

Common Patterns

Pattern: Subscription CSV Format

customer_id,plan,mrr,start_date,end_date,status
C001,pro,99.00,2025-01-15,,active
C002,basic,29.00,2025-02-01,2025-08-15,churned
C003,enterprise,499.00,2025-03-10,,active

Pattern: User Activity CSV Format

user_id,signup_date,last_active_date,activity_month
U001,2025-01-05,2025-06-15,2025-06
U002,2025-01-12,2025-03-20,2025-03

Pattern: Unit Economics JSON Format

{
  "period": "2025-Q4",
  "total_customers": 1200,
  "new_customers": 150,
  "churned_customers": 45,
  "total_mrr": 89500.00,
  "arpu": 74.58,
  "gross_margin": 0.82,
  "sales_marketing_spend": 45000.00,
  "monthly_churn_rate": 0.0375
}

Healthy SaaS Benchmarks