Customer lifetime value: three CLV formulas and how to use LTV:CAC

Customer lifetime value is the total margin a customer brings your business over the whole relationship, not just on the first order. You can estimate it three ways: a quick formula (average order value × purchase frequency × lifespan), a margin-based version that accounts for retention, or a cohort calculation built from what real customers actually spent. Compare the result with what it costs to acquire a customer (CAC) and you know how much you can afford to spend on ads.

Customer lifetime value (CLV or LTV) is the expected contribution, meaning revenue minus the variable costs of serving the customer, that one customer generates from their first purchase until they stop buying, usually reported over a fixed window such as 12 or 24 months.

Why does customer lifetime value matter?

Most ad platforms judge a campaign on the first conversion. That works if customers buy once. Most online stores, subscription products and service businesses depend on repeat purchases, though, and a first-order view underprices their customers. A campaign that loses money on the first order can be the most profitable one you run if its customers come back.

Academic work points the same way. Gupta, Lehmann and Stuart, who valued five public companies from their customer base, found that improving retention by 1% raised customer value by about 3–7%, while improving margin by 1% raised it by about 1%. They also found that a 1% improvement in retention had almost five times the impact of a 1% improvement in the discount rate. Their numbers are specific to the firms they studied, but the direction is useful: lifetime value is driven more by whether customers return than by squeezing a little more out of each order.

CLV answers three practical questions:

• How much can you pay to acquire a customer and still make money?
• Which acquisition channels bring customers who come back?
• How long will it take to earn back what you spent?

If you track a short list of marketing KPIs, LTV and CAC belong on it.

What is the simplest CLV formula?

The formula most people start with uses three averages you can pull from any order database:

CLV (revenue) = average order value × purchases per year × lifespan in years

Take a hypothetical online skincare store. Its average order value is $40, a typical customer orders 3 times a year, and customers stay for about 2 years.

CLV (revenue) = 40 × 3 × 2 = $240

This is quick, and it is useful for a first sense of scale. It has three problems you should know about before you put it in a deck.

1. It counts revenue, not profit. You cannot compare $240 of revenue with $18 of acquisition cost; you need the margin you keep.
2. “Purchases per year” is usually measured on active customers. People who bought once and disappeared pull the true average down, but they are often missing from the calculation. The result skews high.
3. Lifespan is a guess. Few young businesses know how long customers stay, so this input often comes from optimism.

How do you calculate a margin-based CLV?

The fix for the first problem is to multiply by contribution margin. Contribution margin is revenue minus the costs that grow with each order: cost of goods, shipping, payment fees, packaging and the expected cost of returns. Do not subtract fixed costs such as salaries or rent.

CLV (margin) = average order value × purchases per year × lifespan × contribution margin %

If the skincare store keeps 35% of each order after those costs:

CLV (margin) = 40 × 3 × 2 × 0.35 = $84

The figure dropped from $240 to $84. That is the number to compare with acquisition cost.

Adding retention and discounting

A fixed “2 years” treats every customer as if they stay exactly two years and then vanish. In reality some leave after one order and a few stay for years. Gupta, Lehmann and Stuart model this with a retention rate and a discount rate. With a constant margin per period, the value of a customer’s future margins is:

Future value = m × r / (1 + i − r)
CLV          = m + m × r / (1 + i − r)

Here m is the contribution margin per period (a year in this example), r is the share of customers who are still buying in the next period, and i is the discount rate, which reflects that money next year is worth less than money today. The second line adds the first year’s margin, which a new customer delivers right away.

For the store, annual margin per customer is 40 × 3 × 0.35 = $42. Say 60% of customers buy again the following year, and the discount rate is 10%:

Future value = 42 × 0.6 / (1 + 0.10 − 0.6) = 25.2 / 0.5 = $50.40
CLV          = 42 + 50.40 = $92.40

A 60% retention rate implies an average lifespan of 1 / (1 − 0.6) = 2.5 years. Multiplying $42 by 2.5 years without discounting gives $105, about 14% more than $92.40. The gap grows as the discount rate rises. Gupta, Lehmann and Stuart make the same point in their paper: turning retention into an expected lifespan and summing over it overstates lifetime value.

The weak spot of this formula is that it assumes retention stays constant. In e-commerce it usually does not: the drop after the first order is much steeper than the drop between the fourth and fifth. Probabilistic models such as the BG/NBD model of Fader, Hardie and Lee handle this for non-subscription businesses, and the review by Gupta and colleagues compares several such approaches. For most marketing teams, the next step is not a statistical model but cohorts.

How do you calculate LTV from cohorts?

A cohort is a group of customers who made their first purchase in the same period. Cohort-based LTV skips the assumptions and measures what happened: for each cohort, add up the margin those customers generated over time and divide by the number of customers in the cohort at the start.

1. Pick a cohort period, usually the month of first purchase.
2. Count the new customers in that cohort. This number never changes, even when customers leave.
3. For each month after acquisition, sum all revenue from those customers, including repeat orders.
4. Multiply by your contribution margin and divide by the starting customer count.
5. Accumulate month by month to get a curve of LTV over time.

Say the store acquired 500 new customers in January. Here is their cumulative value per customer (hypothetical):

The 12-month margin LTV is $30.80. Compare that with the simple formula, which implied $42 of margin in year one ($40 × 3 × 0.35). The cohort tells a less flattering story because it includes everyone who bought once and never returned.

Cohorts have two other advantages. You can compare January with April to see whether retention is improving, and you never have to guess a lifespan. The trade-off is time: a 12-month LTV needs 12 months of data, so for recent cohorts you read the early months of the curve and compare them with older cohorts at the same age.

What is a good LTV to CAC ratio?

The LTV to CAC ratio divides lifetime value by customer acquisition cost:

LTV:CAC = LTV (margin) / CAC
CAC     = acquisition spend / new customers acquired

Say the store spent $9,100 on acquisition in January and won those 500 customers. CAC is 9,100 / 500 = $18.20.

With the simple margin LTV the ratio is 84 / 18.20 = 4.6. With the 12-month cohort LTV it is 30.80 / 18.20 = 1.69. Same store, same month, very different conclusions. This is why you should always say which LTV you used.

You will often hear that 3:1 is the target. Treat it as a rule of thumb that circulates among investors and SaaS operators, not as a research-backed threshold. What the ratio has to cover depends on your business. A ratio of 1 on contribution margin means you break even on each customer before fixed costs. A high-margin business with cheap capital can grow profitably at 2:1; a business with expensive capital or uncertain retention may want more headroom. A very high ratio can also mean you are underspending and leaving growth on the table.

How long is your CAC payback period?

The ratio says nothing about timing. Payback period does: it is the number of months until a customer’s cumulative margin equals CAC. Read it straight from the cohort table. The January cohort’s cumulative margin reaches $18.20 at month 3, exactly the blended CAC, so payback is about 3 months.

Payback matters because you pay for acquisition today and collect margin over months. Two channels with the same LTV:CAC can have very different cash needs if one pays back in 2 months and the other in 10.

How does LTV change your target ROAS?

Return on ad spend usually counts only the revenue from the conversion window. The store’s break-even ROAS on a single order is 1 / 0.35 = 2.86: below that, the first order loses money. For January, first-order ROAS was 40 / 18.20 = 2.20, which looks like a loss. Over 12 months, revenue per customer was $88, so 12-month ROAS was 88 / 18.20 = 4.84, and margin per customer ($30.80) clearly beats CAC ($18.20).

That is how LTV informs target ROAS. If your cohorts reliably repeat, you can accept a first-order ROAS below break-even, as long as the payback period fits your cash. Our ROAS formula guide covers break-even ROAS in more detail.

Why does an average LTV mislead?

A blended LTV mixes customers who came from very different places. Split the January cohort by acquisition channel and the picture changes (hypothetical numbers again):

The blended 1.69 looks acceptable. Underneath, paid social, half of all new customers, barely clears 1.2, while referral customers are worth roughly twice as much as paid social customers and cost little more than half as much. If you set budgets from the blended number, you keep funding the weakest channel at the same rate.

Two cautions before you act on a table like this. First, the split depends on how you assign each customer to a channel; first-touch and last-touch can give different answers, which our guide to attribution models explains. Second, channel tagging must be consistent, or a large share of customers lands in “direct” or “unknown”. Clean UTM parameters on every paid link fix most of that.

Channel is the first split to make, but not the only one. First-order product, discount use and device often separate high-value customers from one-time buyers.

How do you estimate customer lifetime value in AdPix?

A useful LTV figure depends on your margin and your chosen window, which no analytics tool knows by default. What AdPix gives you are the inputs to build the cohort version yourself.

• Order analysis, available on every plan, gives you order counts over time. From the Starter plan up, the online sales and revenue reports add revenue by product and campaign, which gives you average order value per cohort and channel.
• Acquisition reports show which channel, source / medium and UTM-tagged campaign brought each user, so you can split customers by channel.
• One view of every user connects a person’s visits across devices and channels into one journey, so a repeat purchase on mobile is not counted as a new customer.
• Segments and cohorts let you group users by when they were acquired and follow how many come back over the following weeks and months. Segments are available from the Starter plan up.
• Data export lets you take orders and acquisition data into a spreadsheet, apply your own contribution margin and build the table above.

A practical routine: once a month, export the latest cohort, add its revenue to your cohort sheet, apply margin, and update LTV and payback for each channel. Compare each cohort at the same age (month 3 against month 3) instead of waiting a year for a full curve. If a channel’s 3-month value keeps falling below older cohorts, you see it within a quarter, not after a year of spend. The steps that come before a purchase, where people drop off on the way to their first order, are covered in our guide to sales funnel analysis.

You can see what each plan includes on the AdPix pricing page. Start with the January-style table for your own store: one cohort, one channel split, one honest margin. Even three months of data will tell you more than a blended average.

ROAS calculator

Return on ad spend and your break-even point. The free plan covers 100,000 events a month, no credit card. Setup takes under five minutes.

Start freeTry it free