Analytics
LTV to CAC ratio calculator for sales-led B2B companies
By Charles Summers · Updated · Free, no signup
Short answer
In a sales-led business the acquisition cost is mostly people, so a ratio built on programme spend alone is out by a multiple rather than a margin: quota-carrying compensation, development reps, sales engineering, management, operations, tooling and the ramp months before a new hire closes anything all belong in the numerator. On the other side, expansion has to be inside lifetime value or enterprise economics are understated, but a net retention rate above one hundred percent makes the standard formula diverge rather than merely mislead, so the horizon has to be capped at something you have contracted for.
Use the ltv to cac ratio calculator
Why b2b sales led need a different approach
Two structural facts separate a sales-led company from everything else this formula was designed around. The cost of winning a customer is dominated by salaries rather than by media, and the revenue from a won customer grows rather than staying flat. Both break the arithmetic in the same direction if handled carelessly: acquisition cost gets understated because most of it sits in payroll, and lifetime value gets understated because expansion is left out. Two errors pointing opposite ways do not cancel, they just make the result unfalsifiable.
The third fact is time. An enterprise deal closed in October was created by pipeline work done in March and marketing done before that, so a ratio built from a single quarter of spend against a single quarter of wins is measuring the shape of your budget rather than the efficiency of your motion. None of this makes the metric useless. It makes it a metric that has to be constructed deliberately, per segment, with the assembly visible to whoever reads the answer.
It runs entirely in your browser. Nothing you type is sent to a server, no account is required, and there is no usage limit, because there is no cost per run to control.
Assemble the acquisition cost line by line, including the months nobody closes anything
Build it from the deal outward rather than from the marketing budget inward. Take an account executive on 220,000 of on-target earnings; with employer taxes and benefits the true annual cost lands nearer 264,000. Give them a quota that, at a 45,600 dollar annual contract value, means around twenty closed deals in a year. That is 13,200 dollars of quota-carrying cost per deal before anyone else in the organisation is counted.
Then add the rest of the machine. A development rep allocated across the same territory might contribute 5,500 per closed deal. Sales engineering shared across three reps, first-line management, sales operations and the tooling stack, the customer relationship system, sequencing software, data and intent subscriptions, conversation intelligence, add perhaps another 7,300. Marketing programme spend and the demand generation headcount allocated to closed new business is frequently the largest single line, around 20,000 in this example. Those four lines already total 46,000 against a deal worth 45,600 a year.
The line almost everyone omits is ramp. A new account executive is paid for months before they close a quota-relevant deal, some never reach quota at all, and territory changes reset partially built pipeline. That unproductive cost is a genuine cost of the deals that do close, and spreading it across them adds around 6,000 per deal here, taking the fully loaded figure to roughly 52,000. A team reporting 20,000 for the same business has counted the programme spend and ignored the payroll, which is why sales-led companies so often report ratios that look better than their profit and loss.
Expansion belongs in the numerator, and above one hundred percent net retention the formula breaks
Enterprise accounts rarely pay in year three what they paid in year one. Seats grow, usage grows, modules get added, and contracted uplifts are written into renewals. Leaving that out understates lifetime value badly in exactly the segment where it matters most. The obvious fix is to feed net revenue retention into the model instead of logo churn, and for any business retaining below one hundred percent that works cleanly: the net rate is a decay rate and the geometric sum behaves.
Above one hundred percent it does not. If surviving revenue grows faster than it decays, the series being summed has terms that increase rather than shrink, and the sum of an increasing series does not converge to a finite number. Fifteen percent annual net expansion is about 1.17 percent monthly growth per surviving account, and an undiscounted lifetime value on that basis is infinite, not large. The mathematics is telling you something real: without a discount rate exceeding the growth rate, or a horizon, the model claims a customer is worth an unbounded amount, and any ratio built from it is meaningless rather than optimistic.
So cap it, and cap it at something contractual. For this example, at 1.1 percent monthly logo churn the uncapped figure is 255,636 dollars, giving 4.92 to 1 against a fully loaded 52,000. Capped at 36 months, the longest term most enterprise contracts run, the same inputs give 83,968 dollars and a ratio of 1.61 to 1. Two thirds of the headline number sits beyond any term you have signed, which is a statement about renewal risk rather than about customer quality. Both figures are correct; only one of them is contracted, and a board should be shown the pair.
A company-level ratio can match no segment inside it
Take a company with 900 self-serve customers acquired at 400 dollars each and worth 1,200 in lifetime gross profit, alongside 20 enterprise customers acquired at 52,000 and worth 250,000. The self-serve segment runs at exactly 3.0 to 1, the enterprise segment at 4.8 to 1. Blend them the way most reporting does, by dividing total acquisition spend by total customers and total lifetime value by total customers, and the company reports 4.34 to 1, which is not between the two in any meaningful sense and describes neither business.
The reason is that the two averages are weighted by different things. Twenty enterprise customers, two percent of the count, carry 82 percent of the lifetime value pool and 74 percent of the acquisition spend, so the blended numerator is essentially an enterprise number while the blended customer count is essentially a self-serve number. Any decision taken on the aggregate figure is being made on an artefact of that mismatch, and the aggregate moves whenever the mix moves even if both segments are perfectly stable.
It gets worse when the ratio is used to allocate. The blended 4.34 sits comfortably above the conventional target and suggests spending more. Applied to self-serve, which is at 3.0, more spend is defensible but marginal. Applied to enterprise, it depends entirely on whether the 250,000 figure survives a contracted-term cap, and on this example it does not. Compute the ratio per segment before you compute it at all, and if a single company figure is required, present it as a table with the segment weights beside it rather than as one number that has quietly averaged away the decision.
Long cycles make the timing of the denominator a real choice
When a deal takes six to nine months from first touch to signature, the deals closing in a quarter were bought with money spent two or three quarters earlier. Dividing current spend by current wins therefore reports the trajectory of the budget rather than the efficiency of the motion: a team that increased spend six months ago looks efficient today, and a team that cut spend looks efficient too, right up until the pipeline arrives empty.
The repair is to cohort by opportunity creation rather than by close date. Attribute the spend of the period in which an opportunity was created to the deals that opportunity eventually produced, and accept that the most recent two or three quarters are incomplete and should be reported as such. It is more work and it produces a number that lags, which is unpopular. It is also the only version that responds to changes in efficiency rather than to changes in spending.
Two related habits are worth adopting alongside it. Report win rate and average contract value next to the ratio, because a ratio that improved because deal sizes grew is a very different event from one that improved because the team got better at closing, and the ratio alone cannot distinguish them. And keep new business acquisition cost separate from the cost of the renewal and account management function; folding customer success into acquisition inflates the cost of a new customer while hiding what retention actually costs to buy, which is a number the expansion assumptions above depend on entirely.
Numbers worth knowing
| Metric | Typical | What it means |
|---|---|---|
| What quota-carrying cost is of the total | often well under half | On the worked example, 13,200 of a 52,000 fully loaded figure. Development reps, sales engineering, management, operations, tooling, marketing programme spend and ramp make up the rest. |
| Ramp | months of paid time before a quota-relevant close | Unproductive ramp, reps who never reach quota and territory resets are real costs of the deals that do close. Spread them across closed business rather than treating them as overhead. |
| Share of lifetime value beyond a three-year term | about two thirds at 1.1% monthly churn | Uncapped 255,636 against 83,968 capped at 36 months on the worked example. The gap is renewal risk, not customer quality, and it should be shown as a pair. |
| When net retention exceeds 100% | the undiscounted sum does not converge | Fifteen percent annual expansion is roughly 1.17 percent monthly growth per surviving account. Without a discount rate above that, or a capped horizon, lifetime value is unbounded rather than high. |
Mistakes that quietly cost you results
- Building acquisition cost from the marketing budget
- In a sales-led motion payroll is the larger half. Quota-carrying compensation, development reps, sales engineering, management, operations and the tooling stack all belong in the figure, and omitting them typically halves the number rather than trimming it.
- Excluding ramp because those reps had not closed anything yet
- The months before productivity are a cost of the deals that eventually close, as are the hires who never reach quota. Allocate them across closed business, or a company that is hiring quickly will always report a better ratio than one that is not.
- Feeding net revenue retention above one hundred percent into an uncapped model
- The series diverges, so the answer is not optimistic, it is undefined. Cap the horizon at the contracted term, or apply a discount rate greater than the expansion growth rate, and say which you did.
- Entering annual contract value in a field asking for monthly revenue
- It multiplies the lifetime figure by twelve and produces a ratio nobody sane would believe if it were stated plainly. Divide contract value by twelve, and if billing is annual and prepaid, keep that cash-timing point in a separate note rather than inside this ratio.
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 exactly belongs in a fully-loaded acquisition cost for a sales-led team?
Everything spent to win new business, not just what marketing invoiced. Quota-carrying compensation at true cost including employer taxes and benefits, development reps, sales engineering, first-line management, sales operations, the tooling stack from the customer relationship system through sequencing and intent data, travel and events, marketing programme spend, demand generation salaries, and the ramp cost of hires who were paid before they produced. Renewal and account management cost stays out, because that buys retention, not acquisition.
How should ramp time for new reps be handled?
Spread it across the deals those reps eventually close. A hire paid for several months before their first quota-relevant signature has incurred a real acquisition cost that the deals must carry, as have the hires who leave before reaching quota and the territory reshuffles that strand partly built pipeline. Excluding it produces the perverse result that a fast-hiring company reports a better ratio than a stable one, which is the opposite of what the metric is meant to detect.
Our net revenue retention is above one hundred percent. How do we put expansion into lifetime value?
Not by inverting the net rate, because a growing per-account revenue stream makes the geometric sum diverge rather than converge, and an infinite lifetime value is not a strong result, it is an undefined one. Cap the horizon at the contracted term and add contracted uplifts explicitly, or apply a discount rate greater than the monthly expansion growth rate. Fifteen percent annual expansion is about 1.17 percent monthly, so the discount rate has to clear that before any finite answer exists.
Our blended ratio is 4.34 but neither segment is anywhere near it. How does that happen?
Because the numerator and the denominator are weighted by different populations. In the worked example, twenty enterprise customers hold 82 percent of the lifetime value while 900 self-serve customers set the customer count, so the blended lifetime value is essentially an enterprise figure divided by a self-serve headcount. The segments come out at 3.0 and 4.8; the blend at 4.34 belongs to neither, and it moves whenever mix moves even when both segments are stable.
How does a nine-month sales cycle change the way the cost side should be computed?
It makes close-date attribution actively misleading. Deals signed this quarter were created by spend from two or three quarters ago, so dividing current spend by current wins tracks your budget trajectory rather than your efficiency, and it flatters whichever direction spending has recently moved. Cohort by opportunity creation date instead, report the most recent quarters as incomplete, and put win rate and average contract value next to the ratio so you can tell which of them moved it.
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