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business-metrics-calculator

business-metrics-calculator

github.com/nimrodfisherData & databases504237 installsUpdated 5w ago
Repository

About business-metrics-calculator

A business-metrics skill for calculating and validating metrics across SaaS, e-commerce, and other business models. It combines metric definitions, benchmark thresholds, unit economics, and a reporting template for teams preparing or reviewing performance reports.

Key features

  1. 01Select metrics by business model
  2. 02Validate source data
  3. 03Calculate SaaS and e-commerce metrics
  4. 04Compute LTV, CAC, and payback
  5. 05Compare results with benchmarks
  6. 06Assemble a metrics report

Use cases

  1. 01Prepare board or investor metrics
  2. 02Resolve disagreements over metric definitions
  3. 03Benchmark business performance
  4. 04Validate existing metric calculations

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill business-metrics-calculator

After installing, type/business-metrics-calculatorin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
metrics_report_template.md
references/
metric_definitions.md
scripts/
saas_metrics.py

Details

From the project README

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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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/CAC
  • references/metric_definitions.md — canonical definitions and benchmark thresholds by model type
  • assets/metrics_report_template.md — structured report: revenue metrics, customer metrics, unit economics, benchmark comparison, insights

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