Customer lifetime value (LTV)

The Lifetime value tab on your Dashboard groups your customers by the month they first bought from you, then follows each group forward to show what they went on to spend, what they cost to win, and what they were really worth.

Most reports tell you what happened last month. Lifetime value tells you what a customer is worth over their whole relationship with you, which is a different question and a more useful one. It's the number that decides how much you can afford to spend on acquisition.

You'll find it in the Lifetime value tab of your Dashboard. The tab has two views: Standard, the cohort grid, and Compare, which puts stores or SKUs side by side.

For the definition of every metric on the Lifetime value tab, see What each number on the Dashboard means.

Lifetime value is what a customer is worth across their whole relationship with you, not what they spent the first time they purchased.

Here's why the whole relationship matters, in numbers:

If you only look at that first order, you'll decide you can spend about $20 to win a customer. Look at the whole relationship and you can comfortably spend $70, and outspend every competitor who's still only counting the first order.

To measure lifetime value properly, we group your customers into cohorts: everyone whose first-ever purchase happened in the same month. Your January cohort, your February cohort, and so on. We then follow each cohort forward, month by month.

Grouping by first-purchase month is what makes the comparison fair. If you simply looked at revenue per customer across your whole customer base, a strong month of new customers would drag the average down, not because anything got worse, but because you've just added a lot of people who have only bought once so far. Cohorts remove that distortion.

It tells you what you can afford to pay for a customer, in money you actually keep. Revenue-based LTV overstates what a customer is worth, because it hasn't paid for the goods, the shipping or the returns yet. True LTV has. If a customer is worth $85 in profit over their life and costs you $40 to win, you have room to scale. If they're worth $30, you're buying revenue at a loss, and the faster you grow, the faster you run out of cash.

It shows you a trend long before your annual numbers do. A monthly report blends every customer you have. If the customers you started winning in March cost more to serve or return more often, that shows up in a cohort within weeks and in your annual numbers much later.

It tells you when marketing spend comes back as cash. Two channels with identical lifetime value are not equally affordable if one repays you in two months and the other in eleven. The fast one lets you spend the same cash several times a year. That difference is a working capital question, and it's usually the constraint that actually binds.

It separates a volume problem from a quality problem. Flat revenue can mean fewer new customers or worse ones. Those have opposite fixes, and reading down a cohort column tells you which you have.

Look at True LTV and CAC payback together. True LTV, your lifetime value after costs, is your ceiling; payback tells you how long your cash is tied up getting there.

Read down a column, not across a row. Every cohort has an M1, so comparing the M1 column compares every cohort at the same age.

If seasonality is a big part of your year, also compare the same points in the year: this January's cohort against last January's. A cohort won in the Black Friday rush is often full of gift buyers and deal hunters; one won in June is more likely to be buying for themselves at full price.

Compare Retention rate with Average order value. These two problems look identical in your revenue and have nothing in common as fixes.

Look at True LTV:CAC. Below 1x, your cohorts haven't earned back what you paid for them. Around 1x you're breaking even and funding growth out of working capital. At 3x or more, you have room, and the thing to check next is CAC payback, because that's what decides how quickly the cash comes back.

Compare Discount rate on the first order with Repurchase rate later on.

Six cards sit above the grid: First-order profitability, True LTV, True LTV:CAC, Customer contribution, CAC payback (in months) and Repurchase rate. They cover the customers who first bought in the selected Period, compared with the period before. Click a cohort's month in the grid to open the same cards for that one cohort.

Rows are cohorts based on the month they made their first purchase ①. Columns are ages, not dates ②. M0 is a cohort's own first month, M1 the month after. So the M0 column holds January for your January cohort and February for your February cohort, lined up so you can compare cohorts at the same point in their life.

① Cohort rows by First order at month, ② the M0 column.

That's why the comparison to make is down a column, never across a row. Reading across tells you about one cohort's history. Reading down tells you whether your business is improving.

Up to four reference columns sit before the ages:

Overall closes each row with the cohort's whole life so far, and what it does depends on which metric you're looking at. It either adds the months up, takes where a running total finished, or pools every month together. Hover any Overall cell and the tooltip tells you which.

Average is a weighted average, so larger cohorts count for more and the figure describes your business rather than treating a small month and a large one as equals. On running-total metrics it builds up month by month, adding the average gain in each month to what came before. That's why it can read higher than any single cohort on screen: it describes the path a typical cohort follows, not a figure any one cohort has banked yet. Hover any Average cell and the tooltip names the blend and how many cohorts it's based on.

Total appears only where adding a column up means something: Total sales, Total gross profit, Cumulative orders and Cumulative contribution profit.

Cells show a dash where there is nothing to report. Most often that is a month the cohort hasn't lived yet, which is why the bottom right of the grid is empty: your newest cohorts are only a month or two old. But a dash also appears inside a cohort's own lifetime where it simply had no activity that month: no orders means no figure rather than a zero.

Running totals are the exception: Gross LTV, True LTV, Cumulative orders and the payback metrics carry the previous month's total forward through a quiet month rather than breaking the series.

① Dashes where cohorts haven't lived those months yet, ② Overall.

With the Predictive switch on, months a cohort hasn't lived yet are filled with estimates instead of dashes. Each estimate extends what the cohort has spent so far using the pattern of your last 12 cohorts at the same age. Projected cells are tinted, and their tooltip says the figure is projected. Months that have already happened are never changed, and Retention rate is never projected.

Cells are shaded from pale to dark by rank within the view you're currently looking at. The scale always uses its full range, so shading is relative, never absolute: the same $8,296 can be pale in one period and dark in another. Compare shades inside one grid, never between two. A month that's 100% for every cohort by definition, like retention's month 0, is left unshaded.

Under the grid, Insights reads your True LTV:CAC for the Overall business and gives it a badge: Losing money below 1x, Below target from 1x to under 3x, and Healthy at 3x or more. It always uses your cohorts from the last 12 months and never includes projected figures. Click More about this metric above the grid for a plain explanation of the metric you've picked and what your own numbers show.

On Compare, choose Compare byStore or SKUs and select at least two. Each row is the average cohort for one store or SKU, blended across every monthly cohort it won in the period. To narrow it, use Filter by store (Optional) or Filter by SKUs (Optional).

① Standard and Compare, ② Metric, ③ Stores and SKUs, ④ Predictive.

There are 19 metrics in the Metric picker, in five groups. Pick one and the whole grid switches. Each metric's meaning and calculation is in What each number on the Dashboard means, under Lifetime value tab.

Where a cohort has no marketing recorded, the three ratios, Gross LTV:CAC, True LTV:CAC and Return on investment, stay blank rather than dividing by zero and reporting an infinite return. Cumulative contribution profit and CAC payback per customer subtract whatever marketing we found, so a cohort with none recorded is costed at zero and reads as pure profit. That is the right answer for a month you genuinely ran no ads, and a badly flattering one for a month whose spend simply hasn't been attributed.

Here's how the main figures build up for one January cohort: 100 customers who cost $4,000 to win ($40 each). They placed 100 orders worth $6,000 in January (M0), 25 orders worth $2,000 in February (M1) and 20 orders worth $1,500 in March (M2). Their gross profit was $3,000, $1,000 and $700. In February 12 of them ordered again, and in March 10 did, 8 of them for the first time since January. Figures are rounded.

A customer joins the cohort of the month their first purchase landed, and membership is decided once. Everything they buy afterwards counts, on any store, any product, however it ships.

Only your direct and marketplace sales are on the Lifetime value tab. Cohorts are built from your direct-to-consumer (DTC) and marketplace channels, your own storefronts and the marketplaces you sell on. Wholesale and other B2B sales, in-person and point-of-sale, and service income are all outside it, both for deciding who joins a cohort and for counting what they spend later. If a meaningful share of your revenue is wholesale or in-store, the Lifetime value tab is describing part of your business, not all of it.

Only buyers we can identify can join a cohort. A cohort is a promise to follow the same people forward, so an order that arrives without a buyer attached can't take part. That covers guest checkouts and any channel that doesn't hand back a customer identity. The clearest case is Amazon, which identifies the buyer on Fulfilled by Amazon (FBA) orders but not on Fulfilled by Merchant (FBM) ones, so a customer who has only ever bought through FBM is left out. Including any of these would drag every per-customer figure down, since they could never appear in a later month.

"First purchase" means the first one we can see. A customer counts as new when we hold no earlier order for them. At the very start of your history that isn't the same as their first-ever order: customers who were already buying from you before your data begins are counted as new in the first months we have. So your earliest cohorts are larger than they should be, and their retention and repurchase rates read low. Treat the first few cohorts on a long period as a warm-up rather than a result.

Everything is counted by when your customer ordered, not when the transaction reached your books. A cohort column is a statement about customer behavior, so an order belongs to the month it was placed.

The same method as the Channel performance card in the Profits section of your Dashboard Home tab:

Net sales − product cost − selling fees − fulfillment fees − payment processing fees

Returns are your actual returns rather than an estimate, landing against the order they reverse. Using an average return rate would erase exactly the differences between cohorts the Lifetime value tab exists to show you. Delivery rates are measured per store, per month, on a three-month window centered on the month in question, so a cohort's older months are costed at the rates that applied then rather than today's. If you've corrected your selling, affiliate, FBA or Fulfilled by TikTok (FBT) rates in Channel performance (its Settings → Cost estimates), those corrections apply here too. To learn more about how those costs are built, see How profit by sales channel is calculated.

Returns keep arriving after a month closes. About 60% of a month's returns are in by the time it ends and about 94% a month later, so costing a just-finished month on what's visible today would count all of its sales against only some of its returns, and your newest month would look like your most profitable one every single month.

For example, it's March 10 and your February cohort sold $10,000. So far we can see $400 of returns. Your settled months show this store ends up at a 10% return rate, so we charge $1,000 rather than $400, and February's gross profit reads $600 lower than the incomplete picture would show.

For months that haven't finished settling we apply what your settled months measured for that store. Once the month settles, the real figures take over.

Acquisition cost is ad spend split across your stores, affiliate commission at that store's recent rate, and any giveaway cost you've booked, all charged to the month you won the cohort. All of a month's marketing is charged to that month's new customers, with none set aside for retention, which makes your CAC slightly cautious if you run retention campaigns. Marketplace ad spend, and any spend we can't match to a store, is charged to nobody rather than spread across cohorts that didn't earn it. This is also why the figure agrees with Channel performance, which leaves out the same spend.

Ad spend isn't tracked per SKU. When you filter or compare by SKU, a SKU's CAC is your store's marketing as a percentage of first-order sales, applied to that SKU's first orders.

Stores and SKUs filter on how a customer was won, not on what they bought later. Pick a store and you get the customers whose first order was on it, then everything they went on to buy anywhere. Filtering their later orders too would make your retention look worse than it is.

The Period you choose (Last 12 months, Last 6 months, Last 3 months or Since a year) sets both which cohorts appear and how long each is tracked, and it always ends at the last completed month. A longer period gives your oldest cohorts more months to accumulate, so extending it doesn't just add rows, it makes the existing ones longer.

In Finaloop Core, Finaloop's automated bookkeeping and accounting product, these figures are built from your booked numbers, but they won't reconcile line-for-line to a Profit and Loss (P&L) period, and they aren't meant to. Four differences account for the gap. Margins doesn't keep your books, so in Margins you can skip this section.

Different clock. Lifetime value counts an order in the month your customer placed it. Your P&L recognizes revenue under Finaloop's revenue recognition rules, which is a different question with a different answer. A cohort month and a P&L month are not the same window.

Different depth. Gross profit here stops after product cost and delivery costs. It's before your overheads, salaries, software, rent and every other fixed cost. A cohort that looks healthy on True LTV is not necessarily a profitable business once everything else is paid. Only your P&L answers that.

Different treatment of a month that hasn't settled. For recent months, lifetime value applies your store's settled return rate rather than the incomplete returns visible so far. Your P&L records refunds as they're booked. So the newest month on the Lifetime value tab is deliberately more conservative than the same month on your P&L.

Different scope of marketing.CAC here excludes marketplace ad spend and any spend that can't be matched to a store. Your P&L includes all of it as an expense.

It shouldn't. Lifetime value counts orders by the date your customer placed them and stops at gross profit, while your P&L recognizes revenue on its own rules and carries every other cost. How lifetime value relates to your P&L lists the four differences.

Your P&L. Lifetime value is a decision tool for acquisition spend, not a reporting statement. Nothing on the Lifetime value tab is used to close your books.

Everything on the Lifetime value tab is counted by order date, covers only DTC and marketplace sales from buyers we can identify, and the profit metrics come after fees, returns, product cost and delivery costs. Your sales channel reports its own sales on its own timing and doesn't deduct your product cost.

A dash means there's nothing to show. Usually that's a month the cohort hasn't lived yet: everything to the right of a cohort's current age is blank for that reason (unless Predictive is on). But a dash also appears inside a cohort's lifetime for a month in which it placed no orders at all. Gross LTV, True LTV, Cumulative orders and the payback metrics are running totals, so they carry the previous month's figure through a quiet month instead. The three acquisition ratios also show a dash when a cohort has no marketing recorded against it. Cumulative contribution profit and CAC payback per customer don't: they treat missing marketing as zero spend. See The metrics.

On running-total metrics, Average credits a typical cohort with a full run of months, adding up the average gain in each month. Your newest cohorts haven't reached the later months yet, so no single cohort on screen has banked that full path.

Only at the same age. Overall covers a cohort's whole life so far, so a one-month-old cohort will always look worse than a mature one no matter how good it is. Compare the same age column, and if seasonality drives your year, compare the same season too.

That's expected. Repurchase rate counts everyone who has ever come back; Retention rate counts only who ordered in that specific month. Retention can never exceed repurchase, and a wide gap means your repeat buyers return rarely rather than regularly.

Amazon doesn't identify the buyer on Fulfilled by Merchant orders, so we can't follow those customers over time and they're left out of cohorts. If a customer bought through FBA at any point, they're included from that first FBA purchase.

It changes which customers count, not which of their orders. Picking a store gives you the customers whose first order was on that store, and then counts everything they bought afterwards, on any store.

If a cohort's figures don't look like what you'd expect, or you're not sure whether a difference between two cohorts is real, email us at [email protected] with the cohort month and the metric you're looking at.