Analytics
LTV to CAC ratio calculator for ecommerce and DTC brands
By Charles Summers · Updated · Free, no signup
Short answer
For a retail brand the ratio is only as good as the repeat behaviour underneath it, and repeat behaviour is described by two numbers this formula does not ask for: the share of first-time buyers who ever place a second order, and the median gap before they place it. Judge cohorts at equal age against those, never on a blended average, because repeat purchasing is heavily skewed toward a small group of buyers, which makes the mean a poor guide to what the next acquired customer is worth and a worse guide to who you should be acquiring.
Use the ltv to cac ratio calculator
Why ecommerce need a different approach
Retail lifetime value is a curve, not a constant. Every cohort of first-time buyers has a cumulative contribution per acquired customer that climbs steeply in the first weeks, flattens for a long stretch, and then rises again in a slow, uneven way as the small group of committed buyers keeps coming back. Compressing that curve into one figure loses the two features that actually decide what a cohort is worth: how many buyers come back at all, and how long they take.
The formula in this calculator will accept a monthly revenue rate and a decay rate and produce a ratio, and it is useful for comparing acquisition sources against each other on consistent assumptions. What it cannot see is the shape of the distribution behind the average, and in retail that distribution is not remotely symmetric. A minority of customers generate a large share of repeat revenue, so a mean lifetime value describes almost nobody, and a targeting decision made from it optimises for a customer who does not exist.
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The second order is the hinge, and the median gap to it sets your measurement window
If you keep only one number about repeat behaviour, keep the share of first-time buyers who ever place a second order. It predicts cohort value better than any other single figure a brand has, because the difference between a one-time and a two-time buyer is far larger than the difference between a two-time and a three-time buyer. Repurchase probability rises sharply with each additional order, so the second purchase is where a customer stops being a transaction and starts being a customer, and the acquisition sources that produce a high second-order rate are worth more than their headline cost per acquisition suggests.
The median gap to that second order is the companion measurement, and it decides when you are allowed to have an opinion about a cohort. If half your repeat buyers take eleven weeks to come back, then a cohort judged at 30 days is being assessed before most of its value has had a chance to appear, and the acquisition channel that looks worst on a 30-day read may be the one bringing in considered buyers with longer cycles. Use the median rather than the mean here, because a handful of buyers returning after a year drags the average out to a window nobody can wait for.
Those two numbers also tell you which lever to pull. A brand with a healthy second-order rate and a long gap has a timing problem and should work on triggered replenishment reminders and cadence. A brand with a short gap and a poor second-order rate has a product or expectation problem, and no amount of retention messaging fixes it. The ratio alone cannot distinguish those two situations, and they call for opposite responses.
Repeat purchasing is skewed, so the mean answers a question nobody asked
Sort any brand's customers by cumulative contribution and the shape is consistent: a long head of people who bought once, a thinning middle, and a small group at the top who account for a disproportionate share of everything. The mean sits far above the median in that distribution, which means the average lifetime value is being set by the top group while the typical acquired customer contributes much less. Both facts are true simultaneously and they support different decisions.
For deciding whether the business works in aggregate, the mean is the right statistic, because the total is what pays the bills. For deciding who to acquire and what to bid, it is misleading, since you are not buying the average, you are buying a probability distribution, and what you want is to raise the odds of landing in the upper part of it. That reframes acquisition around the entry point rather than the channel: which first product someone buys, at what discount depth, off which creative, tends to predict repeat behaviour better than which platform served the ad.
The practical version is to report both. Mean contribution per acquired customer at a fixed cohort age for the aggregate view, and the second-order rate plus median contribution for the targeting view. When those two move in opposite directions, which happens whenever a promotion pulls in a large group of one-time bargain buyers alongside a few good ones, the divergence is the finding. A single ratio would have shown the aggregate holding steady and told you nothing about the composition that changed underneath it.
Blended figures hide the three splits that decide the answer
The first split is entry product. Brands with a range routinely find that customers who arrive through one item come back at multiple times the rate of those who arrive through another, usually because one is a trial-sized or replenishable item and the other is a considered one-off or a gift. Averaged together, those two populations produce a ratio that describes neither and hides the most actionable thing in the dataset, which is that acquisition spend should be pointed at a particular entry point rather than at the brand.
The second is discount depth. A cohort acquired at forty percent off is a different population from one acquired at full price, not merely a less profitable version of it, because the discount selects for price sensitivity in the customers it attracts. They contribute less on the first order and frequently return at a lower rate, so their contribution appears twice in the ratio, once in a thinner margin and once in weaker repeat behaviour. Track cohorts by the discount they were acquired on and the two effects separate cleanly.
The third is seasonality, and gifting is its sharpest form. Buyers acquired in a peak gifting week are often purchasing for someone else, so a large share of them have no personal use for the product and no reason to return, and their repeat rate can look catastrophic without indicating anything wrong with the brand. Folding them into an annual blended figure drags the whole number down and, worse, makes the non-peak cohorts look better than they are once the peak is excluded from a comparison. Report peak cohorts on their own line, every year, and compare them to the same week last year rather than to the rest of this year.
Underneath all three sits the acquisition cost question. Total advertising divided by total orders is not a customer acquisition cost, because a substantial share of those orders came from customers already in the file. New-customer spend against genuinely new customers is the only denominator the ratio can carry, and the gap between the two widens as the repeat base grows, which means the sloppy version of the metric improves automatically as the brand matures.
Numbers worth knowing
| Metric | Typical | What it means |
|---|---|---|
| Most predictive single number | share of first-time buyers who place a second order | The gap between one and two orders is far larger than any gap further up the sequence. Repurchase probability climbs with each additional order, so the second one is where value concentrates. |
| When a cohort can be judged | after the median gap to second order | Measure your own median rather than adopting a category figure. A 30-day read on a brand whose repeat buyers return in eleven weeks is scoring channels before most of their value can appear. |
| Mean against median contribution | mean sits well above median in almost every brand | A small group of buyers carries a disproportionate share of repeat revenue. Use the mean for aggregate viability and the median plus second-order rate for targeting decisions. |
| Discount-acquired cohorts | a different population, not a discounted one | Depth of discount selects for price sensitivity, so it shows up twice: once in thinner first-order contribution and again in weaker repeat behaviour. Segment cohorts by the offer they arrived on. |
Mistakes that quietly cost you results
- Comparing cohorts of different ages against each other
- A four-month-old cohort has had four months to accumulate contribution and a one-month-old cohort has not. Fix an age, compare every cohort at that same age, and the channel and offer differences become readable instead of being swamped by elapsed time.
- Making bidding decisions from mean lifetime value
- You are not buying the mean, you are buying a skewed distribution. Optimise for the odds of acquiring a repeat buyer, which is driven by entry product and offer at least as much as by the platform serving the impression.
- Leaving peak and gifting cohorts in the annual average
- Buyers purchasing for someone else have no reason to return, so they depress the blended figure while telling you nothing about the brand. Report them on their own line and compare them to the same week last year.
- Dividing total advertising by total orders
- Orders from customers already in the file did not need to be acquired. The resulting figure gets better automatically as the repeat base grows, so it flatters maturing brands and misprices every acquisition decision made from it.
What does the output look like?
This is the exact output the tool produces from the example inputs. It is generated by the same code that runs when you click the button, so what you see here is what you get.
Frequently asked questions
What is the most predictive single number for a DTC brand's lifetime value?
The proportion of first-time buyers who ever place a second order. The jump in repurchase probability between the first and second purchase is far larger than any later step, so cohorts separate on that number more cleanly than on average order value, category or channel. Track it by acquisition source and by entry product, at a fixed cohort age, and most of what you need to know about where spend should go becomes visible without any modelling.
How long do we have to wait before judging a cohort?
At least the median gap between first and second order for your own brand, which you can measure directly from the order file. Judging earlier systematically penalises acquisition sources that bring in considered buyers with longer decision cycles and rewards those bringing impulse purchases that never repeat. Use the median rather than the mean gap, since a small number of buyers returning after many months stretches the average past any window you can actually wait for.
Should we use mean or median lifetime value in the ratio?
Mean for the question of whether the business works, because the total revenue is what covers the cost base. Median, alongside the second-order rate, for deciding who to acquire, because the distribution is skewed enough that the mean describes a customer who barely exists. When the two diverge, usually after a promotion brings in a wave of one-time bargain hunters, that divergence is the most useful signal in the report and a single ratio erases it.
Does the conventional three to one target apply to a retail brand?
Not directly, because the target was framed for businesses whose costs are largely fixed once the product exists. A retail brand pays variable cost on every order and commits cash to goods before the revenue arrives, so a ratio that would be comfortable for a software company can still leave a brand unable to fund its own growth. Treat the conventional band as a foreign benchmark and set your own threshold from what your cost base and cash cycle actually require.
Why does our ratio look better every year even though our advertising is getting less efficient?
Almost always because the acquisition cost is computed across all orders rather than new customers only. As the repeat base grows, a rising share of orders comes from customers who were paid for in earlier periods, so the denominator falls without any improvement in the advertising. Split new-customer spend from retention spend, divide by first-time buyers only, and the two trends stop cancelling each other out in a single number.
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