LTV:CAC Ratio & SaaS Unit Economics Calculator

SaaS Financial Metrics: The 3x LTV:CAC Golden Standard

Venture capital firms and growth-stage tech companies use the LTV:CAC Ratio as the core barometer of business model viability and capital efficiency.

Why Gross Margin Must Be Factored into LTV

A common beginner mistake is calculating LTV as Revenue / Churn. If your gross margin is 50%, every 1,000 in top-line revenue costs you 500 in hosting and customer support. True LTV must multiply ARPU by Gross Margin % to reflect actual gross profit contributions.

How to Use This Calculator

Enter your Input Mode — choose from Calculate from ARPU, Churn, Margin & Acquisition Spend, Direct Input: Enter LTV and CAC Directly, Customer Lifetime Value (LTV in ) [Direct mode] (e.g. 3600), Customer Acquisition Cost (CAC in ) [Direct mode] (e.g. 900), Monthly Average Revenue Per User (ARPU in ) (e.g. 150 (Subscription price / mo)), Gross Margin (%) (e.g. 80 (SaaS avg ~75-85%)), Monthly Customer Churn Rate (%) (e.g. 3.0), Total Monthly Sales & Marketing Spend () (e.g. 45000 (Ads + team salaries)), and New Customers Acquired in Month (e.g. 50). Results update instantly as you type — adjust any value to compare scenarios.

The Formula

SaaS & subscription unit economics equations: Lifetime Value (LTV) = (ARPU × Gross Margin (\%)) / (Monthly Churn Rate (\%)) Acquisition Cost (CAC) = (Total Sales \& Marketing Spend) / (New Customers Acquired):CAC Ratio = (LTV) / (CAC) Payback Period (Months) = (CAC) / (ARPU × Gross Margin (\%)) Customer Lifespan = (1) / (Monthly Churn Rate)

Worked Example

For example, a B2B SaaS company with 150/month ARPU, 80% gross margin, 3.0% monthly churn rate, spending 45,000 to acquire 50 new customers (900 CAC): . Customer lifespan is 33.3 months. The LTV:CAC Ratio is with a CAC Payback Period of 7.5 months. This falls squarely within the ideal 3x-5x SaaS golden standard.

Understanding Your Result

Marketing metrics are most useful as trends over time rather than isolated snapshots. Benchmark your result against your own historical performance first, then against industry averages. Attribution models (last-click vs. multi-touch) can produce dramatically different channel ROI figures — ensure consistent attribution methodology before making channel budget decisions. Statistical significance requires sufficient sample size: most A/B test decisions need a minimum of 1,000 conversions per variant for reliable results.