What does the Unit Economics Calculator compute?

It turns ARPU, churn, gross margin, and CAC into three ratios that describe per-customer viability: lifetime value (LTV), CAC payback, and the LTV:CAC ratio.

Can I verify it with a worked example?

Yes. With ARPU = $50, gross_margin = 80%, monthly_churn = 4%, CAC = $150: LTV = $1,000, CAC payback = 3.75 months, LTV:CAC ≈ 6.7×.

Methodology: Unit Economics Calculator

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

Input
ARPU = $50, gross_margin = 80%, monthly_churn = 4%, CAC = $150.

Expected output
LTV = $1,000 (= $50 × 0.80 / 0.04). CAC payback = 3.75 months (= $150 / ($50 × 0.80)). LTV:CAC ≈ 6.7× → healthy band for a bootstrapped SaaS, approaching the top-quartile threshold in OpenView 2024 benchmarks.

8. Change log

2026-04-24methodology page first published.

Worked example

Run live against the same engine this site ships (/engines/unit-economics-calculator.js). The inputs and outputs below are recomputed on every build and independently re-verified in CI — they are never hand-authored.

Input

tool: unit_economics
cac: 150
monthly_arpu: 49
gross_margin_percent: 75
avg_lifespan_months: 24
monthly_churn_percent: 3

Output

ltv: 882
ltvCacRatio: 5.88
cacPaybackMonths: 4.08
monthlyContribution: 36.75
unitProfit: 732
impliedLifespanFromChurn: 33.33
verdict: Excellent
verdictColor: blue
warnings[0]: Churn rate implies a lifespan of 33.3 months, but you entered 24 months. Your LTV projection may be optimistic.
assumptionsEcho.cac: 150
assumptionsEcho.monthly_arpu: 49
assumptionsEcho.gross_margin_percent: 75
assumptionsEcho.avg_lifespan_months: 24
assumptionsEcho.monthly_churn_percent: 3

Frequently asked questions

What does the Unit Economics Calculator compute?: It turns ARPU, churn, gross margin, and CAC into three ratios that describe per-customer viability: lifetime value (LTV), CAC payback, and the LTV:CAC ratio.
Does it do cohort-level analysis?: No. It is the aggregate version of the model — for cohort-level unit economics you need timestamped purchase data the tool does not ingest.
Can I verify it with a worked example?: Yes. With ARPU = $50, gross_margin = 80%, monthly_churn = 4%, CAC = $150: LTV = $1,000, CAC payback = 3.75 months, LTV:CAC ≈ 6.7×.