The Ultimate SaaS Metric: The Customer Lifetime Value to Customer Acquisition Cost Ratio (LTV/CAC)

I’m a big fan of software-as-a-service (SaaS) metrics.  I’ve authored very deep posts on SaaS renewals rates and customer acquisition costs.  I also routinely point readers to other great posts on the topic, including:

But in today’s post, I’m going to examine the question:  of the literally scores of SaaS metrics out there, if you could only pick one single metric, which one would it be?

Let’s consider some candidates:

  • Revenue is bad because it’s a lagging indicator in a SaaS business.
  • Bookings is good because it’s a leading indicator of both revenue and cash, but tells you nothing about the existing customer base.
  • ARR (annual recurring revenue) is good because it’s a leading indicator of revenue and includes the effects of both new sales and customer churn.  However, there are two ways to have slow ending ARR growth:  high sales and high churn or low sales and low churn — and they are very different.
  • Cashflow is good because it tends to net-out a lot of other effects, but can be misleading unless you understand the structure of a company’s bookings mix and payment terms.
  • Gross margin (GM) is nice because it gives you an indicator of how efficiently the service is run, but unfortunately tells you nothing else.
  • The churn rate is good because it helps you value the existing customer annuity, but tells you nothing about new sales.
  • Customer acquisition cost (CAC) is a great measure of sales and marketing efficiency, but by itself is not terribly meaningful because you don’t know what you’re buying:  are you paying, for example, $12K in sales and marketing (S&M) expense for a $1K/month customer who will renew for 3 months or 120?  There’s a big difference between the two.
  • Lifetime value (LTV) is good measure of the annuity value of your customer base, but says nothing about new sales.

Before revealing my single best-choice metric, let me make what might be an unfashionable and counter-intuitive statement.  While I love SaaS “unit economics” as much as anybody, to me there is nothing better than a realistic, four-statement, three-year financial model that factors everything into the mix.  I say this not only because my company makes tools to create such models, but more importantly because unit economics can be misleading in a complicated world of varying contract duration (e.g., 1 to 3+ years), payment terms (e.g., quarterly, annual, prepaid, non-prepaid), long sales cycles (typical CAC calculations assume prior-quarter S&M drives current-quarter sales), and renewals which may differ from the original contract in both duration and terms.

Remember that SaaS unit economics were born in an era of monthly recurring revenue (MRR), so the more your business runs monthly, the better those metrics work — and conversely.  For example, consider two companies:

  • Company A does month-to-month contracts charging $100/month and has a CAC ratio of 1.0.
  • Company B does annual contracts, does three-year prepaid deals, and has a CAC ratio of 2.0.

If both companies have 80% subscription gross margins (GM), then the CAC payback period is 15 months for company A and 30 months for company B.  (CAC payback period is months of subscription gross margin to recover CAC.)

This implies company B is much riskier than company A because company B’s payback period is twice as long and company B’s money is at risk for a full 30 months until it recovers payback.

But it’s completely wrong.  Note that because company B does pre-paid deals its actual, cash payback period is not 30 months, but 1 day.  Despite ostensibly having half the CAC payback period, company A is far riskier because it has to wait 15 months until recovering its S&M investment and each month presents an opportunity for non-renewal.  (Or, as I like to say, “is exposed to the churn rate.”)  Thus, while company B will recoup its S&M investment (and then some) every time, company A will only recoup it some percentage of the time as a function of its monthly churn rate.

Now this is not to say that three-year prepaid deals are a panacea and that everyone should do them.  From the vendor perspective, they are good for year 1 cashflow, but bad in years 2 and 3.  From the customer perspective, three-year deals make plenty of sense for “high consideration” purchases (where once you have completed your evaluation, you are pretty sure of your selection), but make almost no sense in try-and-buy scenarios.  So the point is not “long live the three-year deal,” but instead “examine unit economics, but do so with an awareness of both their origins and limitations.”

This is why I think nothing tells the story better than a full four-statement, three-year financial model.  Now I’m sure there are plenty of badly-built over-optimistic models out there.  But don’t throw the baby out with the bathwater.   It is just not that hard to model:

  • The mix of the different types of deals your company does by duration and prepayment terms — and how that changes over time.
  • The existing renewals base and the matrix of deals of one duration that renew as another.
  • The cashflow ramifications of prepaid and non-prepaid multi-year contracts.
  • The impact on ARR and cashflow of churn rates and renewals bookings.
  • The impact of upsell to the existing customer base

Now that I’ve disclaimed all that, let’s answer the central question posed by this post:  if you could know just one SaaS metric, which would it be?

The LTV/CAC ratio.

Why?  Because what you pay for something should be a function of what it’s worth.

Some people say, for example, that a CAC of 2.0 is bad.  Well, if you’re selling a month-to-month product where most customers discontinue by month 9, then a CAC of 2.0 is horrific.  However, if you’re selling sticky enterprise infrastructure, replacing systems that have been in place for a decade with applications that might well be in place for another decade, then a CAC is 2.0 is probably fine.  That’s the point:  there is no absolute right or wrong answer to what a company should be willing to pay for a customer.  What you are willing to pay for a customer should be a function of what they are worth.

The CAC ratio captures the cost of acquiring customers.  In plain English, the CAC ratio is the multiple you are willing to pay for $1 for annual recurring revenue (ARR).  With a CAC ratio of 1.5, you are paying $1.50 for a $1 of ARR, implying an 18 month payback period on a revenue basis and 18-months divided by subscription-GM on a gross margin basis.

Lifetime value (LTV) attempts to calculate what a customer is worth and is typically calculated using gross margin (the profit from a customer after paying the cost of operating the service) as opposed to simply revenue.  LTV is calculated first by inverting the annual churn rate (to get the average customer lifetime in years) and then multiplying by subscription-GM.

For example, with a churn rate is 10%, subscription GM of 75%, and a CAC ratio of 1.5, the LTV/CAC ratio is (1/10%) * 0.75 / 1.5 = 5.0.

The general rule of thumb is that LTV/CAC should be 3.0 or higher, with of course, the higher the better.

There are three limitations I am aware of in working with LTV/CAC as a metric.

  • Churn rate.  Picking the right churn rate isn’t easy and is made complicated in the presence of a mix of single- and multi-year deals.  All in, I think simple churn is the best rate to use as it reflects the “auto-renewal” of multi-year deals as well as the very real negative churn generated by upsell.
  • Statistics and distributions.  I’m not a hardcore stats geek, but I secretly worry that many different distributions can produce an average of 10%, and thus inverting a 10% churn rate to produce an average 10-year customer lifetime scares me a bit.  It’s the standard way to do things, but I do worry late at night that averages can be misleading.
  • Light from a distant star.  Remember that today’s churn rate is a function of yesterday’s deals.  The more you change who you sell to and how, the less reflective yesterday’s churn is of tomorrow’s.  It’s like light arriving from a star that’s three light-years away:  what you see today happened three years ago.  To the extent that LTV is a forward-looking metric, beware that it’s based on churn which is backward-looking.  In perfect world, you’d use predicted-churn in an LTV calculation but since calculating that would be difficult and controversial, we take the next best thing:  past churn.  But remember that the future doesn’t always look like the past.


35 responses to “The Ultimate SaaS Metric: The Customer Lifetime Value to Customer Acquisition Cost Ratio (LTV/CAC)

  1. There is still a metric that will somehow help you predict the future: NPS.
    You can define. Synthetic metric that includes it, as well, giving it a certain weight in the whole story.
    But! Thanks for the article, really good.

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  3. Dave,

    What are your thoughts on using a discount rate to adjust customer LTV? I suppose for benchmarking purposes, most SaaS companies have a similar cost of capital so it may just over-complicate matters.

    Also, is it correct to assume you are using the inverse of Bessemer’s CAC ratio calculation in your example?

    • You can discount for the time value of money (TVM) if you want. In reality the renewal rate is acting just like a discount rate but for odds of renewing as opposed to TVM. With such low interest rates now, I don’t use a discount rate for TVM, but there’s nothing to prevent you from doing so and I certainly would in a higher interest rate environment.

      Yes, I invert the Bessemer CAC and use ARR instead of gross margin.

  4. Great post. I was just thinking about the churn distribution problem when I hit that bullet point. I’m not a hardcore stats geek either, but I have some thoughts. The first thing I would do is take a look at the distribution of churn and see whether it follows a common pattern and whether it’s worth worrying about. For example, you might see two distinct populations: people who stay for a few months then leave; and people who stay for years; with very little in-between (use of open source projects often looks a lot like this). In this case, it might make sense to model those two populations separately, factoring in their relative sizes. But if you find a Normal distribution with a relatively low variance, you might as well use the simplest possible model. This doesn’t have to be an elaborate exercise. (As an aside, in general I am cautious about using the mean for anything without first knowing something about the distribution.)

  5. Customer bases are usually log normal distribution besides there are other aspects that need to be factored in like the impact of Enterprise deals or the huge discounts that skews the overall perceived stickiness!
    It’s also important to define customer Churn – for eg. do we use customer count, number of seats/ licenses, renewal amounts or something else?
    It would also be good to see how the diffusion of Innovation concept (Bass model) can be applied to SaaS model as it tries to apply the WOM, Viral effect of Marketing & helps push the try-and-buy approach. In B2C context, social circles, influence of innovators helps expand base via getting adopters/imitators

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  8. Dave, question on the use of gross margin in CAC ratio — most comments I see only talk to software gross margin. I expect that this is the case because SAAS companies typically lose money on implementation. What if you make a margin on implementation? Why not include those margin dollars in your CAC ratio? What support do you think there is for an enhanced CAC ratio that takes services margins into consideration?

    • Robert,

      I think companies should use software gross margin in CAC payback period calculation. For a CAC ratio, I think it’s about “how much you spend to acquire a new dollar of ARR” so don’t think gross margin is the appropriate measure when we are speaking of customer acquisition cost. However, as soon as you want to look at payback periods and such then I believe it makes sense use subscription gross profit because you are trying to calculate how long the subscription takes to pay for itself, in which you should exclude the cost of running the service. For services, I think the vast majority of people just see it as ‘separate’ and ergo should be excluded. The value of most SaaS businesses is 90%+ from the subscription annuity and services are only relevant to the extent that good one drive good implementations which drive happy customers which drives renewals which preserve ARR. (In fact, you can argue easily that most SaaS companies subsidize subscription a bit by deliberately losing a modest amount on services.)

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  10. Hi Dave.

    Great article. Our business has recently moved from an up-front heavy model (which was sold at an average discount of 35%) to a monthly model that is more user friendly for our suppliers.

    Is there any data or metrics you believe are more important to track now that we have moved to a predominantly monthly model?

    Thank you


    • James,

      I would probably look more towards the original SaaS metrics (e.g., the Bessemer white paper) which is more monthly oriented. Most enterprise stuff these days is trending annual, hence my ARR bias in my metrics and not MRR.

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