← ALL FREE RESOURCESFree unit economics kit

Work out what a customer is worth before you spend on them

Four formulas, four inputs, and a cohort grid that makes you pull every one of them from your own store data instead of assuming them.

What it does

Writes out contribution per order, lifetime value, the ratio and payback in full, and makes you mark every input as measured or guessed.
Gives you four cohort tables, including the grid that shows whether the customers you are buying now behave like the ones you bought last year.
Values your own time inside the acquisition cost figure, which is the number most stores leave out to flatter the ratio.
Sorts the four levers in the order most stores should try them, and tells you what to do when your data is too thin for a defensible lifetime value.

Resource Teaser Preview

    contribution per order = average order value x gross margin percentage
    lifetime value = contribution per order x average orders per customer
    ratio = lifetime value / acquisition cost
    monthly contribution per customer = average orders per customer per month x contribution per order
    payback months = acquisition cost / monthly contribution per customer

To unlock the full kit, complete prompt set, and automated workflow, request access below.

What you need

An order export you can group, and at least one customer cohort old enough to have stopped adding orders.
Your ad spend, agency fees and tool costs for the same period you are measuring orders in.
Honestly, at least one cohort. New stores should read the thin data section in cohort-inputs.md first.

Setup

Open cohort-inputs.md and fill in table 1, acquisition cost, including your own hours valued at your own rate.
Fill table 2 from your order export, splitting customers by how many times they have ordered rather than averaging them together.
Fill table 3, the cohort grid, one row per month of first orders, and look at whether the newest cohort is buying less often than the oldest at the same age.
Open ltv-cac-worksheet.md and run the four formulas, marking each input M for measured or G for guessed.
Read the ratio and the payback together, then work the four levers in the order the worksheet gives them.

On a real store

A fictional skincare brand calculates a 2.2 ratio and 3.4 month payback, which reads fine on a spreadsheet, then finds in the cohort grid that each new month of customers is ordering less often than the last, reruns the model at 1.9 lifetime orders and gets 1.8. Invented store, invented numbers, shown so you can see which input to go and measure first.

Frequently Asked Questions

What ratio should I be aiming for?

The worksheet uses our own bands, 3 and above healthy, 1 to 3 thin, under 1 losing money, and they are there to describe a result rather than as a published benchmark. A business with 85 percent gross margin is comfortable at a lower ratio than one at 40 percent. Check the bands against your own overheads.

My store is new and I do not have lifetime data yet. What do I do?

Optimise payback instead, because you can measure it in weeks. Run the model with low, middle and high lifetime assumptions and only act on conclusions that survive all three, and treat your current repeat rate as a floor rather than an estimate.

Should my own time be in the acquisition cost?

Yes. Acquisition cost that counts ad spend but not the hours you spend finding customers flatters the ratio, usually by a lot. The kit has a column for it in table 1 of cohort-inputs.md.

Want this done for you instead?

We run the whole audit for you: a scored read of your store, what's costing you sales this week, and what to fix first. $500, most stores get it back in the first fix.

Get the $500 audit