Skip to content
cccskills
FreePublished on GitHub

business-metrics-calculator

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.

The English summary is being prepared. Showing the original description.

Run score Not tested

See how to install

Files included (4)

  • SKILL.md2.6 KB
  • assets/metrics_report_template.md1.7 KB
  • references/metric_definitions.md3.2 KB
  • scripts/saas_metrics.py7.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.

The English summary is being prepared. Showing the original description.

nimrodfisher/data-analytics-skills495Updated Sep 25, 2026

Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.

The English summary is being prepared. Showing the original description.

nimrodfisher/data-analytics-skills495Updated Sep 25, 2026

Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.

The English summary is being prepared. Showing the original description.

nimrodfisher/data-analytics-skills495Updated Sep 25, 2026

Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.

The English summary is being prepared. Showing the original description.

nimrodfisher/data-analytics-skills495Updated Sep 25, 2026

Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.

The English summary is being prepared. Showing the original description.

nimrodfisher/data-analytics-skills495Updated Sep 25, 2026

Post-analysis learning and process improvement. Use when completing major analysis projects, documenting lessons learned, or improving team analytical practices.

The English summary is being prepared. Showing the original description.

nimrodfisher/data-analytics-skills495Updated Sep 25, 2026

See all skills by nimrodfisher

Report a problem with this skill