saas-metrics-coach

# 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.

## Quick Start

```bash
# 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

| Tool | Purpose | Input | Output |
|------|---------|-------|--------|
| `mrr_calculator.py` | MRR, ARR, growth rate, churn | CSV with subscription data | Revenue metrics + trends |
| `cohort_analyzer.py` | Cohort retention analysis | CSV with user signup/activity data | Retention matrix + curves |
| `unit_economics.py` | LTV, CAC, LTV:CAC, payback | JSON with acquisition/revenue data | Unit economics dashboard |

## 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
```csv
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
```csv
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
```json
{
  "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

| Metric | Concerning | Acceptable | Strong |
|--------|-----------|------------|--------|
| Monthly Churn | > 5% | 2-5% | < 2% |
| Net Revenue Retention | < 90% | 90-110% | > 120% |
| LTV:CAC | < 1:1 | 1:1-3:1 | > 3:1 |
| CAC Payback | > 24 mo | 12-18 mo | < 12 mo |
| Gross Margin | < 60% | 60-75% | > 75% |