Ecommerce LTV:CAC and Payback Calculator: Use Contribution-Aware Value

At $72 AOV, 2.4 purchases a year, 48% gross margin, $7 variable fulfillment, a 6% return allowance, 40% annual retention, a three-year horizon, and $45 CAC, modeled contribution LTV is $87.01, LTV:CAC is 1.93×, and simple payback is 9.7 months.

These figures are a planning model built from the supplied assumptions, not a forecast, guarantee, or report of observed merchant results. Use the model to make the cost boundary visible, then test how much the decision changes when retention, returns, or margin changes.

Table of contents

Use this map to move from the answer to the math, cohort interpretation, sensitivity check, and next actions.

• What the calculator measures
• Formula and inputs
• Worked example
• Retention and cohort interpretation
• Cohort sensitivity
• Edge cases and limitations
• Decision workflow

What the calculator measures

This calculator estimates customer value after selected variable costs, then compares that value with customer acquisition cost. Shopify describes customer lifetime value with a basic structure built from average order value, purchase frequency, and customer lifespan in its CLV overview.

The model here makes that structure more useful for ecommerce decisions by replacing revenue-only value with contribution value. Contribution LTV is the amount left after the supplied gross-margin effect, other variable cost per order, and returns allowance are applied. LTV:CAC divides that modeled value by CAC. Simple payback estimates how many months of year-one contribution are needed to recover CAC.

Revenue LTV can overstate spend capacity because revenue still has to cover product economics, fulfillment, returns, and other variable costs. The relationship between CAC, LTV, payback, and contribution margin is also part of the unit-economics context covered in Shopify's ecommerce acquisition guide.

Formula and inputs

This model uses five linked calculations, with each one retaining a visible connection to the inputs.

Contribution per order = AOV × gross margin − other variable cost/order − AOV × returns allowance

Expected active-year factor = sum of retention^year from year 0 through horizon − 1

Contribution LTV = contribution/order × purchases/year × expected active-year factor

LTV:CAC = contribution LTV ÷ CAC

Simple payback months = CAC ÷ (year-one contribution ÷ 12)

Year 0 represents the first active year, so the factor includes 1 before later retention terms. The payback calculation uses year-one contribution, calculated as contribution per order multiplied by purchases per year. It assumes that year-one contribution accrues evenly enough for a simple monthly screening calculation.

Each input has one defined role:

Keep the cost boundary consistent. If fulfillment is already included in another input, do not subtract it again; if it is not included, place it in other variable cost per order.

Worked example

The supplied example applies the defaults directly, so every output can be reproduced from the displayed formulas.

The returns allowance is $4.32 per order because $72 × 6% equals $4.32. That leaves $23.24 after the $34.56 gross-margin contribution and the $7 variable cost are accounted for. Year-one contribution is $55.78, or about $4.65 per month, which produces the 9.68-month payback result.

Retention and cohort interpretation

Retention compounds through the active-year factor, so later years contribute less when the annual retention assumption is below 100%. At 40% retention and a three-year horizon, the model counts one first-year active unit, 0.40 of a second-year unit, and 0.16 of a third-year unit.

Annual retention is a compact planning input, not an observed retention curve. A cohort that has not reached the full horizon should not be treated as if its eventual value has already been realized. Use mature observation windows when comparing cohorts, and keep the same start date, horizon, refund treatment, and cost boundary across the comparison.

Cohort segmentation prevents unlike customers from being blended into one average. You can run the model separately for acquisition source, product group, geography, or another business-defined cohort, provided the same definitions are used for each row. The measurement layer should also distinguish purchases and refunds; Google's GA4 ecommerce documentation describes purchase and refund measurement events that can support that discipline.

Cohort sensitivity

The table below isolates annual retention while holding every other supplied input constant. It shows why retention deserves a stress test before a CAC decision is made.

Payback stays at 9.7 months in this table because the formula uses year-one contribution and does not apply retention to that first year. LTV and LTV:CAC do change because later active-year terms are added through compounding.

After this cohort table, turn the assumptions into operating follow-through. Review SellerTrove's email-marketing tools and customer-service tools for retention and service actions, then carry the same cohort definitions into the SellerTrove report before documenting the related stack-builder workflow.

Edge cases and limitations

Treat the model as a transparent planning screen with clear boundaries, not as a complete financial forecast.

• If retention is 0%, the active-year factor is 1 because only year 0 contributes. If the horizon is 1 year, the factor is also 1. If retention is 100%, the factor equals the number of modeled years.
• If contribution per order is zero or negative, contribution LTV is zero or negative and simple payback is not a meaningful recovery period. The issue is unit economics, not a formatting problem.
• If CAC is zero, LTV:CAC cannot be interpreted as a normal ratio. Enter percentages as decimals in the formula, such as 6% as 0.06.
• The model uses annual retention and annual purchase frequency. It does not model the exact timing of repeat purchases, seasonality, order spacing, or changing AOV over time.
• No discount rate or cohort maturation adjustment is included. A dollar of modeled contribution in a later year is treated without a present-value adjustment.
• Returns are represented as a revenue allowance. If refunds, exchanges, or other adjustments are tracked differently, align the allowance with the measurement definition before comparing cohorts.

These boundaries mean the outputs are modeled scenario results from the supplied inputs. They are not a promise that a customer will generate $87.01, that a campaign will reach a 1.93× ratio, or that CAC will be recovered in exactly 9.7 months.

Decision workflow

Use the calculator as a repeatable decision sequence rather than as a single headline ratio.

1. Define the decision. Set the CAC ceiling, payback window, or growth gate that matters for the business.
2. Lock the definitions. Record what AOV includes, which costs sit inside gross margin, how returns are handled, and what counts as an active year.
3. Run the base case. Calculate contribution per order, the active-year factor, contribution LTV, LTV:CAC, and simple payback.
4. Stress the assumptions. Test lower and higher retention, then test any material change to margin, variable cost, returns, or CAC.
5. Compare mature cohorts. Use the same horizon and measurement rules, then decide whether to scale, revise the cost boundary, improve retention, or gather more observation time.

The most useful action is often to find which input would change the decision first. If retention drives the result, focus on repeat-purchase and service work. If contribution per order is the constraint, review margin, fulfillment, returns, and other variable costs before increasing acquisition spend.

Bottom line

The base case supports a clear planning read: $87.01 of contribution LTV against $45 CAC produces a 1.93× LTV:CAC ratio and 9.7-month simple payback. The result is only as reliable as the definitions behind its inputs, so keep cohorts comparable, use mature observation windows, and re-run the model whenever cost or retention assumptions change.