Customer lifetime value calculator

A SaaS business spends $12,000 a month on sales and marketing and acquires 40 new customers a month. CAC: $12,000 ÷ 40 = $300 per customer.

Each customer pays $49 a month, at a 75% gross margin: $36.75 in gross-margin revenue per customer per month. Monthly churn is 3%, implying an average lifetime of 1 ÷ 0.03 = 33.3 months. LTV: $36.75 × 33.3 = $1,224.

LTV : CAC ratio: $1,224 ÷ $300 = 4.1×, comfortably above the 3:1 healthy benchmark. CAC payback: $300 ÷ $36.75 = 8.2 months to recover the acquisition cost.

Blended CAC divides all sales and marketing spend by every new customer, including organic search, referral, and word-of-mouth arrivals that cost nothing directly to acquire. Paid CAC divides only paid spend by the customers attributed to paid channels specifically. Blended is always lower, since organic customers pull the average down without adding to the numerator.

A business acquiring 2,000 customers a month. 1,200 from paid channels, 800 from organic and referral, on $84,000 of paid spend has a blended CAC of $84,000 ÷ 2,000 = $42, but a paid CAC of $84,000 ÷ 1,200 = $70, a 67% gap between the two numbers describing the exact same month.

Both are legitimate; they answer different questions. Blended CAC is the honest company-wide number. What growth actually costs across every source. Paid CAC is the actionable one. Whether the ad spend itself is working, independent of how much organic traffic happens to be carrying the average. Reporting only the blended figure while organic growth is strong can mask paid channels that are quietly becoming unprofitable; comparing your blended number against someone else's paid number is one of the more common apples-to-oranges mistakes in this space.

A single blended LTV:CAC ratio can look perfectly healthy while hiding a wide spread underneath it. One channel performing excellently, another barely breaking even, averaged together into a number that doesn't point anywhere useful. Calculating LTV and CAC separately by channel, not just splitting CAC into blended versus paid, is what actually reveals where to spend more and where to pull back.

A business acquiring 60 customers a month through referral at $1,600 CAC with $8,000 LTV, a 5.0:1 ratio , and 40 customers through paid search at $2,400 CAC with $6,000 LTV (a 2.5:1 ratio) shows a blended ratio of 3.75:1 across the business as a whole. That blended number is comfortably healthy and would raise no alarms on its own, but it obscures a paid-search channel running at exactly the widely-cited minimum and a referral channel performing twice as well. Reallocating spend toward referral and away from paid search (impossible to see from the blended number alone) is the actual decision this breakdown enables.

Below 1:1: losing money on every customer, before accounting for fixed costs at all, not sustainable at any volume.

1:1 to 3:1: marginal to acceptable, common at early-stage companies still refining their acquisition channels.

3:1 or better: the widely cited healthy target, enough margin above acquisition cost to fund operations and growth. Worth treating this as a floor rather than an aspiration, though: recent industry data puts the median for private B2B SaaS companies closer to 3.6:1, meaning a ratio right at 3:1 is already somewhat below typical, not comfortably ahead of it.

Well above 5:1: worth a second look. This can mean excellent unit economics, but it can also mean under-investment in growth, if the ratio is this favorable, spending more on acquisition would likely still be profitable.

SMB SaaS: roughly $100-$300 per customer, reflecting shorter sales cycles and smaller deal sizes.

Mid-market SaaS: roughly $500-$1,500, as sales cycles lengthen and deals increasingly involve a sales team rather than self-serve signup.

Enterprise SaaS: often $2,000 and up, sometimes well beyond, given long sales cycles, multiple stakeholders, and significant sales team involvement per deal.

DTC e-commerce: commonly $50-$200, though this varies heavily by category and how competitive paid social and search are for that specific product.

Because average lifetime is 1 ÷ churn rate, small changes in churn move LTV dramatically. Dropping monthly churn from 5% to 3% doesn't just improve retention , it takes average lifetime from 20 months to 33.3 months, a 67% increase in LTV from the same ARPU and margin.

This is why retention work often has a larger effect on unit economics than acquisition efficiency does, a churn improvement compounds into every customer already acquired, while a CAC improvement only affects new customers going forward.

The churn-based formula this calculator uses is a predictive approach, it projects forward from a current churn rate to estimate what a customer will be worth. The alternative is historic LTV: summing what a specific cohort of customers has actually spent to date, no projection involved.

Historic LTV is simple and fully auditable. Every dollar in it already happened, but it understates future value for a growing business, since a cohort measured today hasn't finished generating revenue yet, and it's prone to survivorship bias: a cohort measured a year in only includes the customers who stuck around, quietly excluding everyone who already churned out. Predictive LTV, the churn-rate approach used throughout this page, corrects for that by projecting the full expected lifetime rather than measuring only what's happened so far, the right choice for forward-looking decisions like setting a CAC budget, while historic LTV remains useful for auditing what a past cohort actually delivered.