≈55 tokens always: the name and description. ≈611 when used: this file. ≈1.2k more on demand in 2 files.
Business Metrics Calculator
When to use
- Preparing a board or investor deck and need accurately defined metrics
- The team disagrees on how a key metric (e.g., churn) should be calculated
- Benchmarking performance against industry standards
- Building a metrics report for a new business or new metric set
- Validating that existing metric calculations match the standard definition
Process
- Identify the business model and period — confirm the model type (SaaS subscription, e-commerce, marketplace, product/app) and the calculation period (month, quarter, trailing 12M). Model type determines which metrics apply. See
references/metric_definitions.md.
- Load and validate the underlying data — check for expected row counts, missing values, and plausible date ranges. A metrics report is only as good as the data feeding it.
- Calculate primary metrics — for SaaS: MRR, ARR, new MRR, churned MRR, expansion MRR, customer churn rate, revenue churn rate. For e-commerce: GMV, AOV, conversion rate, ROAS. Use
scripts/saas_metrics.py or adapt for other models.
- Calculate unit economics — LTV (simple average and cohort-based), CAC, LTV:CAC ratio, payback period, and quick ratio. Document which assumptions were used for LTV lifetime.
- Compare to benchmarks — grade each metric against the industry benchmark thresholds in
references/metric_definitions.md (good / average / poor). Flag anything outside the acceptable range.
- Produce the metrics report — assemble results into
assets/metrics_report_template.md with trend charts, benchmark comparison, and 3–5 key insights. Document any definition choices that differ from industry standard.
Inputs the skill needs
- Subscription or transaction data with at minimum: customer ID, date, value, status
- Marketing spend data (for CAC calculation)
- Monthly targets or goals (for vs-target comparisons)
- The agreed-upon metric definitions (or default to industry standard)
- Time period and any segmentation required (by plan, region, cohort)
Output
scripts/saas_metrics.py — calculates standard SaaS metrics from a subscriptions CSV; includes MRR waterfall, churn, LTV/CACreferences/metric_definitions.md — canonical definitions and benchmark thresholds by model typeassets/metrics_report_template.md — structured report: revenue metrics, customer metrics, unit economics, benchmark comparison, insights
