CAC/LTV & Payback Analysis

This use case describes how to use Model Reef to understand customer acquisition cost (CAC), lifetime value (LTV) and payback periods for a SaaS or subscription business.

Rather than computing these metrics in a separate spreadsheet, you will derive them from the same drivers and variables that power your MRR, ARR and three statement model.

When to use this pattern

Use this pattern when:

• You want to understand whether your go to market spending is efficient.

• You need CAC, LTV and payback metrics for board or investor discussions.

• You want to connect unit economics directly to your forecast, not treat them as stand alone calculations.

This pattern builds on ARR/MRR Forecasting and optionally Churn, Retention & Cohort Modelling.

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Architecture overview

CAC, LTV and payback analysis will use:

1. Acquisition drivers and costs

   • New customers per period.

   • Sales and marketing spend attributable to acquisition.

2. Lifetime value components

   • Revenue per customer over time.

   • Gross margin.

   • Churn and retention.

3. Derived unit economics

   • CAC per customer.

   • LTV per customer.

   • LTV to CAC ratio.

   • CAC payback period.

Some metrics can be approximated inside Model Reef using formulas and charts, while others may be calculated in external tools from exported series if you need more complex definitions.

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1

Set up acquisition and cost drivers

In the Data Library, define drivers for:

• New customers per period, ideally by segment or channel.

• Sales and marketing spend per period, broken down enough that you can decide which costs to include in CAC (for example paid media, sales salaries, marketing tools).

In your variables:

• Use Opex and Staff variables to represent acquisition costs by category.

• Tag or classify these variables so that you can distinguish acquisition costs from retention or support costs.

This separation is important when computing CAC.

2

Derive CAC per customer

For a given period and segment, CAC can be approximated as:

• CAC = Acquisition Costs / New Customers

Implementation options:

• Use a custom chart or report that pulls:

  • The sum of selected Opex and Staff variables for acquisition.

  • The number of new customers from your drivers.

• Apply a simple division in the charting layer or outside the engine.

If you have multiple channels or segments, compute CAC separately for each, using the relevant cost and customer drivers.

3

Define LTV assumptions

LTV is conceptually:

• LTV = Lifetime Gross Profit per Customer

You will need assumptions for:

• Gross margin percentage for your SaaS product or segment.

• Average lifetime or retention behaviour:

  • Either a simple expected lifetime in months or years.

  • Or more detailed retention curves from cohort modelling.

In the Data Library, create drivers for:

• Gross Margin Percentage by segment.

• Expected Lifetime (months) or equivalent.

Use cohort outputs if you prefer a more detailed view of lifetime revenue.

4

Compute LTV per customer

There are two common approaches:

• Simple approximation

  • LTV = ARPA × Gross Margin Percentage × Expected Lifetime

• Cohort based

  • Sum discounted gross profit per customer across future periods until churn, using cohort based revenue and gross margin.

The simple version can generally be implemented via custom chart formulas using:

• Revenue per customer or ARPA driver.

• Gross margin percentage driver.

• Expected lifetime driver.

For cohort based LTV, you may export cohort revenue and margin series to a separate tool if you require more complex discounting than the chart engine provides.

5

Calculate LTV to CAC ratio

Once you have CAC and LTV per customer for a segment:

• LTV to CAC ratio = LTV / CAC

Implement this as:

• A KPI card on a SaaS dashboard using custom formulas, or

• An external calculation based on model exports if you prefer to preserve flexibility.

This ratio is one of the primary indicators of whether your acquisition strategy is sustainable.

6

Estimate CAC payback period

CAC payback period is the time it takes for gross profit from a new customer to cover the acquisition cost.

Approximate calculation:

• Payback Period (months) = CAC / Monthly Gross Profit per Customer

Where:

• Monthly Gross Profit per Customer = ARPA × Gross Margin Percentage

You can represent this in Model Reef using:

• Drivers and formulas for ARPA and gross margin.

• A KPI card or chart that shows how payback changes over time as assumptions evolve.

For more complex models where gross profit per customer changes over time, you may choose to do a more detailed payback calculation externally using exported cashflows.

7

Surface metrics on a unit economics dashboard

Create a dedicated Unit Economics or SaaS Metrics dashboard that shows:

• CAC per segment or channel.

• LTV per segment.

• LTV to CAC ratio.

• CAC payback period.

• Supporting charts such as MRR growth and gross margin.

Use consistent definitions across models so that comparisons between scenarios or across time are meaningful.

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Check your work

• Acquisition costs used in CAC are clearly defined and not mixed with retention or general overhead costs.

• LTV calculations use realistic gross margin and lifetime assumptions.

• LTV to CAC ratios and payback periods are in ranges that make sense for your sector and maturity stage.

• Changes in these metrics can be traced back to changes in underlying drivers.

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Troubleshooting

CAC appears extremely volatile

Ensure that new customer counts are not too small in the periods you are analysing, or use a rolling or aggregated view.

LTV seems unreasonably high

Check that churn and retention assumptions are not overly optimistic and that margin assumptions are realistic.

Stakeholders are confused by multiple CAC definitions

Agree a firm or team standard for what counts as acquisition cost and stick to it across your models.

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Related guides

• Build a Sensitivity Analysis Pack

• Build a Variance Analysis Model

• Attachments

• Cloning Scenarios