Why document model assumptions separately?

A spreadsheet shows numbers but not the reasoning behind them. An assumptions document records the inputs and the logic so reviewers can challenge the drivers, not just the outputs, and so future-you remembers why a growth rate was set where it was.

How is LTV calculated here?

Lifetime value is estimated as average revenue per account multiplied by gross margin, divided by the monthly churn rate. Dividing by churn approximates the expected customer lifetime, giving a gross-margin-adjusted LTV that is comparable to CAC.

What is a healthy LTV/CAC ratio?

A ratio around 3 to 1 or higher is generally considered healthy for a SaaS business, meaning each customer returns roughly three times what it cost to acquire them. Much higher can signal underinvestment in growth; below 1 means you lose money on every customer.

CAC payback is the number of months of gross-margin revenue needed to recover the cost of acquiring a customer. Shorter payback frees up cash to reinvest; many investors look for payback under 12 to 18 months.

Is my financial data stored?

No. All calculations run in your browser and nothing is sent to a server. Copy the document before closing the tab to keep your assumptions.

Financial Model Assumptions Builder

Email me this result

Get this tool's output sent to your inbox, plus one useful tool a week. No spam, unsubscribe any time.

Make the thinking behind your model explicit

A financial model is only as credible as its assumptions. Investors and finance reviewers spend most of their time interrogating the inputs — growth rate, churn, CAC, headcount — not re-checking your formulas. This builder turns your key inputs into a clean assumptions document and computes the derived SaaS ratios (LTV, LTV/CAC, CAC payback) so you can pressure-test the model before anyone else does.

How it works

You enter the core drivers and the tool computes the standard unit-economics outputs:

LTV = (ARPA × gross margin) ÷ monthly churn rate
LTV/CAC = LTV ÷ CAC
CAC payback (months) = CAC ÷ (ARPA × gross margin)

Dividing by the monthly churn rate approximates the average customer lifetime in months, and multiplying ARPA by gross margin keeps LTV on a contribution basis so it is directly comparable to CAC. The document also captures the assumptions that do not reduce to a single number — the headcount plan, infrastructure cost trajectory, and revenue recognition policy — because those drive the cost side of the model.

Tips and example

Use conservative churn. A small change in churn swings LTV dramatically because it sits in the denominator.
Keep ARPA and CAC in the same currency and period; mixing monthly and annual figures is the most common modelling error.
State your revenue recognition basis explicitly (e.g. recognise annual contracts ratably over 12 months) — it changes when revenue appears in the model.
Treat the computed ratios as directional sanity checks, not precise forecasts; pair them with cohort data once you have it.