Customer lifetime value for marketers: how to calculate, benchmark, and use it

Your conversion tracking says a new customer cost $40 to acquire and came in worth $45. On paper the campaign barely breaks even, so you pull back. If that customer actually stays two years and reorders, they were worth $16,000, and you just throttled your best source of revenue. Customer lifetime value (LTV) is the revenue, or better the gross margin, a customer generates over their entire relationship with you.

Transactional businesses compute it as average order value × purchase frequency × years retained; contractual ones as revenue per account ÷ churn. The marketing use is the whole point: LTV is what a customer is worth, so it's what you should bid toward, not what a conversion costs. Below: which formula fits you, how to compute it from data you already have, what a healthy LTV:CAC ratio really is, and how to feed the number back into bidding.

#The formulas: pick by business model, not by blog post

There are two LTV formulas because there are two revenue shapes, and the first mistake is using the wrong one:

Transactional (ecommerce, repeat purchases, no contract):

LTV = average order value × purchases per year × years retained × gross margin

Contractual (SaaS, subscriptions, retainers):

LTV = average revenue per account (ARPA) × gross margin ÷ churn rate

The contractual formula is the transactional one with retention made explicit: 1 ÷ monthly churn is the expected customer lifetime in months, so a $500/mo account at 80% gross margin and 2.5% monthly churn is worth $500 × 0.80 ÷ 0.025 = $16,000, not the $500 your conversion tracking reported the day it signed.

Two rules that hold for both formulas. Use margin, not revenue, whenever you'll compare LTV against acquisition cost. CAC is spent in real dollars, so the comparison only means something in real dollars. And compute it from your own data, not from industry averages: the inputs (AOV, frequency, churn) are all sitting in your order history or CRM already.

#Worked example: computing LTV from data you actually have

The calculation is a straightforward data pull, not a finance project. Three steps, each reproducible against your own CRM or order database:

Assemble the revenue ledger per customer: every order or subscription payment with a customer ID and date. (SaaS: MRR movements per account; ecommerce: order lines.)
Assign each customer to an acquisition cohort: the month of their first payment, plus (if your campaigns are labeled) the channel and campaign that acquired them. This is where naming discipline pays off; unlabeled acquisition history is exactly the problem utm naming conventions and Why your future MMM depends on the data you label today exist to prevent.
Cumulate revenue by months-since-acquisition per cohort, apply gross margin, and read LTV off the curve at the horizon you trust (12, 24, 36 months), rather than extrapolating a lifetime you haven't observed.

Run this per acquisition channel and the single blended LTV number decomposes into the version you can act on: what a customer from paid search is worth versus one from organic versus one from partnerships. The decomposition typically looks like this (illustrative numbers; the spread is what the cohort cut exists to reveal): a blended "$1,800 LTV" hiding paid-search cohorts at $1,400 by month 12, organic at $2,600, and partnerships at $900. Three different acquisition decisions wearing one average.

#LTV:CAC is a decision threshold, not a trophy

LTV:CAC divides what a customer is worth by what they cost to acquire, and its job is to gate spend decisions. The benchmark you'll find everywhere, roughly 3:1, comes from the SaaS-metrics canon (David Skok's forEntrepreneurs work popularized it), and it's a reasonable starting threshold, not a law: below ~3:1 your acquisition math is fragile once you add payroll and payback time; sustained well above ~5:1 usually means you're underspending on growth, not that you're brilliant.

The honest caveats that the recycled "3:1 is good" line drops:

It's margin LTV or it's meaningless. Revenue-LTV:CAC flatters low-margin businesses into overspending.
CAC payback is the tie-breaker. A 4:1 ratio that takes 36 months to pay back is a financing problem wearing a good ratio. Months-to-recover-CAC (CAC ÷ margin per month) is the second number to keep beside the ratio.
Compute it per channel. A blended 3.5:1 can hide a 6:1 channel you should scale and a 1.2:1 channel you should cut.

#Where averages lie

One blended LTV number misleads in three specific, predictable ways:

Cohort drift. LTV computed over all historical customers includes the loyal 2019 cohort that today's acquisition mix will never reproduce. Recent cohorts, tracked on their own curves, are the truth.
Mix shifts. Grow a cheap, high-churn segment and blended LTV falls while every segment's LTV stays constant. The average moved, the business didn't.
Revenue vs margin. Fine for trend lines, wrong for any decision that compares against spend.

The stance, plainly: average LTV without cohort cuts misleads bidding. If the number is going to drive money (and the whole point is that it should), it needs to be cohorted, margined, and per-channel.

#LTV in Google Analytics: what the GA4 report can and can't tell you

GA4 ships a User lifetime exploration technique (Google's doc) that reports lifetime value per user alongside lifetime engagement. Useful, with three hard limits: it only counts value since your GA4 property started collecting (no import of pre-GA4 history), its identity is only as good as your reporting identity (without User-ID, a "user" is a device), and its "lifetime revenue" is whatever your event tagging reports, which for most B2B stacks means it never sees the CRM deal at all. Treat it as a directional view of on-site value, not the LTV of record; the durable computation lives in your own combined revenue data, and cross-device identity is its own discipline (Identity resolution: how user stitching actually works).

#What to DO with it: LTV is a bidding input

Here is the step the calculator pages skip, and the reason a marketer should care about any of the above: LTV closes the loop back into acquisition. Three moves, in ascending order of ambition:

Set value-based bids from LTV, not first-purchase value. Feeding per-customer values into Google's value-based strategies is the mechanics of Value-based bidding needs values you can trust; what the algorithm does with them is Smart Bidding is a signals problem: what the algorithm actually runs on.
Send the values physically. CRM-truth businesses push deal values via offline imports (Offline conversion tracking: send CRM deals back to Google Ads); ecommerce passes cart and repeat-purchase values through tags.
Predict it before the CRM knows. Actual LTV arrives months late in long-cycle businesses; modeling it from early signals is Predictive LTV and value-based bidding for B2B SaaS, the top rung of the values ladder.

Filed as a board-slide metric, LTV is trivia. Fed back into bidding, it's the difference between buying conversions and buying customers.

The catch is that the version worth acting on, cohorted and margined and split by channel, goes stale the moment your acquisition mix shifts, which is why most teams rebuild it once a quarter and trust it for a week. Buron keeps it live: it stitches spend from the ad platforms to revenue from your CRM or store, per customer, so LTV by acquisition channel stays a standing view instead of a quarterly spreadsheet rebuild. Connect your sources and the number is there the moment a bidding decision needs it. *[Connect your sources →]

Frequently asked questions

How do you calculate customer lifetime value?

Pick the formula that matches your business model. Transactional (ecommerce): average order value × purchase frequency per year × years retained, times gross margin if you want margin LTV. Contractual (SaaS/subscriptions): average revenue per account × gross margin ÷ monthly churn rate. Compute it from your own real revenue data, not assumptions.

What is a good LTV:CAC ratio?

The commonly cited benchmark from the SaaS-metrics canon is around 3:1. Below that, acquisition is too expensive; far above (5:1+), you're likely underinvesting in growth. Treat it as a decision threshold, not a grade, and pair it with CAC payback time as the tie-breaker.

What is LTV in marketing?

LTV (lifetime value) is the total revenue (or better, gross margin) a customer generates over their whole relationship with you. In marketing it sets the ceiling on what you can pay to acquire a customer, and it becomes a bidding input when you feed per-customer values back into ad platforms.