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.

What is the Financial Model Assumptions Builder?

Build a financial assumptions document for a startup model — ARR growth rate, churn, CAC, LTV, headcount plan, infrastructure costs, and revenue recognition notes — with the LTV/CAC ratio computed for you. It runs free in your browser on Gera Tools, with nothing uploaded.

Financial Model Assumptions Builder

Name: Financial Model Assumptions Builder
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

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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.

Worked example

Suppose a SaaS business has: ARPA of £200/month, 75% gross margin, monthly churn of 2%, CAC of £600.

LTV = (200 × 0.75) / 0.02 = £7,500
LTV/CAC = 7,500 / 600 = 12.5
CAC payback = 600 / (200 × 0.75) = 4 months

An LTV/CAC of 12.5 is well above the healthy 3:1 floor and signals room to invest more aggressively in acquisition. A CAC payback of 4 months is very short — the business recoups customer acquisition spend quickly. If churn rises to 3%, LTV drops to £5,000 and the ratio falls to 8.3, still healthy but showing how sensitive the model is to that one variable.

Why each input matters

Starting ARR and growth rate set the revenue trajectory; small changes in monthly growth compound dramatically over three to five years.

Churn sits in the denominator of LTV, so it has an outsized effect. A move from 2% to 4% monthly churn cuts LTV in half. Model churn conservatively — early customers often retain better than mature cohorts.

Gross margin converts topline revenue into the contribution that can actually fund the business. High-margin SaaS (80%+) can sustain aggressive growth spend; lower-margin businesses must be more careful about the CAC they tolerate.

Headcount and infrastructure are the primary cost drivers. Adding engineers or sales hires changes your burn rate immediately; this builder captures those step-functions so the model reflects real-world hiring timelines.

Revenue recognition policy matters for annual contracts billed upfront: you collect cash in month one but recognize the revenue ratably over twelve months. Mixing cash and recognized revenue in the same model creates misleading numbers.

Common modelling mistakes

Mixing monthly ARPA and annual CAC without converting to the same period
Using optimistic churn from a young cohort before retention patterns mature
Ignoring hosting and support costs that scale with customer count
Forgetting a headcount ramp — new salespeople typically take 3-6 months to reach full quota

Copy the generated document and paste it alongside your spreadsheet so anyone opening the file can understand the rationale without reverse-engineering the cells.