# Business Metrics Calculator

Standard business metric calculation with industry benchmarks.

- Repository: https://github.com/nimrodfisher/data-analytics-skills (512 stars)
- Category: Business
- License: MIT
- Last updated: 2026-09-25
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add nimrodfisher/data-analytics-skills --skill business-metrics-calculator -g -a claude-code -y`
Codex: `npx skills add nimrodfisher/data-analytics-skills --skill business-metrics-calculator -g -a codex -y`
Cursor: `npx skills add nimrodfisher/data-analytics-skills --skill business-metrics-calculator -g -a cursor -y`
Gemini CLI: `npx skills add nimrodfisher/data-analytics-skills --skill business-metrics-calculator -g -a gemini-cli -y`

## Description

Standard business metric calculation with industry benchmarks. Use when calculating SaaS metrics (MRR, churn, LTV, CAC), e-commerce KPIs, or product analytics metrics with proper definitions.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.

The rest: https://github.com/nimrodfisher/data-analytics-skills/blob/HEAD/03-data-analysis-investigation/business-metrics-calculator/SKILL.md