How Unit Economics Calculator works

1. Scope

The Unit Economics Calculator turns ARPU, churn, gross margin, and CAC into three ratios that together describe per-customer viability: lifetime value (LTV), CAC payback, and the LTV:CAC ratio. It is the aggregate version of the model — for cohort-level unit economics you need timestamped purchase data the tool does not ingest.

2. Inputs and outputs

Inputs: ARPU (monthly average revenue per user), gross margin (%), monthly churn rate (%), CAC (blended acquisition cost per paying customer). Outputs: LTV, CAC payback months, LTV:CAC ratio, and a health band (poor / acceptable / healthy / exceptional) benchmarked against OpenView and Paddle SaaS data.

Engine source: src/lib/unit-economics/engine.ts.

3. Formula / scoring logic

# Gross-margin-adjusted LTV
LTV              = (ARPU * gross_margin) / monthly_churn

# Months to recover CAC from gross profit
CAC_payback      = CAC / (ARPU * gross_margin)

# Efficiency ratio
LTV_CAC_ratio    = LTV / CAC

The LTV = ARPU / churn form (without the gross-margin multiplier) reports gross-revenue lifetime and inflates LTV. Our form is the gross-profit version — the one you can actually reinvest into acquisition.

4. Assumptions

• Memoryless churn. The LTV formula assumes the probability of churn in any given month is constant. Real retention curves are often logarithmic — customers who stick past 90 days are much stickier than brand-new ones — so the aggregate LTV under-estimates the tail.
• Blended ARPU and blended CAC. Cohort-level variance is hidden. A business with 80% self-serve (low CAC) and 20% sales-led (high CAC) customers needs to compute two separate unit-economics profiles.
• Gross margin is customer-variable-cost margin. Include: inference, hosting, auth, payment-processing fees. Exclude: headquarters overhead, founder salary, general R&D.
• No expansion revenue. If NDR > 100%, the LTV formula under-states lifetime. Use the MRR/ARR Growth Calculator with NDR input for a more accurate figure.
• ARPU is MRR-equivalent. Usage-priced products need to be normalised to monthly.

5. Data sources

• OpenView SaaS Benchmarks 2024 — LTV:CAC percentiles by stage and GTM motion.
• Paddle SaaS Benchmarks 2024 — churn percentiles by B2B vs B2C and stage.
• SaaS Capital Annual Survey — CAC payback and gross-margin norms for bootstrapped and capital-efficient SaaS.

6. Known limitations

• Small-sample instability. Fewer than ~300 customer-months of data produces a churn rate with wide confidence intervals. A 3% measured churn can be 1–6% true churn; LTV scales by 6× between those extremes.
• Revenue-churn vs logo-churn. We use revenue-churn by default because LTV is a revenue metric. If your product has heavy seat expansion or contraction, logo and revenue churn diverge and aggregate LTV misleads.
• The 3:1 LTV:CAC rule of thumb is lore, not law. The widely-cited target comes from David Skok's 2009 blog series. OpenView 2024 data shows bootstrapped SaaS clusters at 5:1–10:1 at the median, while venture-stage SaaS routinely accepts 1:1–2:1 during growth. Use the band appropriate to your stage and GTM motion.
• No account for customer heterogeneity. The aggregate LTV hides the fact that 20% of customers may deliver 80% of revenue — a healthier insight for acquisition strategy than the aggregate number.

7. Reproducibility

8. Change log

• 2026-04-24methodology page first published.