Monthly Archives: July 2014

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

Posted on July 30, 2014 | 37 comments

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.

 

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Posted in Cloud, Management, Metrics, SaaS

You Can’t Analyze Churn by Analyzing Churn

Posted on July 26, 2014 | 5 comments

One thing that amazes me is when I hear people talk about how they analyze churn in a cloud, software as a service (SaaS), or other recurring revenue business.

You hear things like:

  • “17% of our churn comes from emerging small business (ESB) segment, which is normal because small businesses are inherently unstable.”
  • “22% of our churn comes from companies in the $1B+ revenue range, indicating that we may have a problem meeting enterprise needs.”
  • “40% of the customers in the residential mortgage business churned, indicating there is something wrong our product for that vertical.”

There are three fallacies at work here.

The first is assumed causes.  If you that 17% of your churn comes from the ESB segment, you know one and only one thing:  that 17% of your churn comes from the ESB segment.  Asserting small business stability as the cause is pure speculation.  Maybe they did go out of business or get bought.  Or maybe they didn’t like your product.  Or maybe they did like your product, but decided it was overkill for their needs.  If you want to how much of your churn came from a given segment, ask a finance person.  If you want to know why a customer churned, ask them.  Companies with relatively small customer bases can do it via a phone.  Customers with big bases can use an online survey.  It’s not hard.  Use metrics to figure out where your churn comes from.  Use surveys to figure out why.

The second is not looking at propensities and the broader customer base. If I said that 22% of your annual recurring revenue (ARR) comes from $1B+ companies, then you shouldn’t be surprised that 22% of your churn comes from them as well.  If I said that 50% of your ARR comes from $1B+ companies (and they were your core target market), then you’d be thrilled that only 22% of your churn comes from them.  The point isn’t how much of your churn comes from a given segment:  it’s how much of your churn comes from a given segment relative to how much of your overall business comes from that segment.  Put differently, what is the propensity of someone to churn in one segment versus another.

And you can’t perform that analysis without getting a full data set — of both customers who did churn and customers who didn’t.  That’s why I say you can’t analyze churn by analyzing churn.  Too many people, when tasked with churn analysis:  say, “quick, get me a list of all the customers who churned in the past 6 months and we’ll look for patterns.”   At that instant you are doomed.  All you can do is decompose churn into buckets, but know nothing of propensities.

For example, if you noticed that in one country a surprising 89% of churn came from customers with blue eyes, you might be prompted to launch an immediate inquiry into how your product UI somehow fails for blue-eyed customers.  Unless, of course, the country was Estonia where 89% of the population has blue eyes and ergo 89% of your customers do.  Bucketing churn buys you nothing without knowing propensities.

The last is correlation vs. causation.  Knowing that a large percentage of customers in the residential mortgage segment churned (or even have higher propensity to churn) doesn’t tell you why they are churning.  Perhaps your product does lack functionality that is important in that segment.  Or perhaps it’s 2008, the real estate crisis is in full bloom, and those customers aren’t buying anything from anybody.  The root cause is the mortgage crisis, not your product.   Yes, there is a high correlation between customers in that vertical and their churn rate.  But the cause isn’t a poor product fit for that vertical, it’s that the vertical itself is imploding.

A better, and more fun, example comes from The Halo Effect, which tells the story that a famous statistician once showed a precise correlation between the increase in the number of Baptist preachers and the increase in arrests for public drunkenness during the 19th Century.  Do we assume that one caused the other?  No.  In fact, the underlying driver was the general increase in the population — with which both were correlated.

So, remember these two things before starting your next churn analysis

  • If you want to know why someone churned, ask them.
  • If you want to analyze churn, don’t just look at who churned — compare who churned to who didn’t

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Posted in Cloud, Metrics, SaaS

Why, as CEO, I Love Driver-Based Planning

Posted on July 12, 2014 | 7 comments

While driver-based planning is a bit of an old buzzword (the first two Google hits date to 2009 and 2011 respectively), I am nevertheless a huge fan of driver-based planning not because the concept was sexy back in the day, but because it’s incredibly useful.  In this post, I’ll explain why.

When I talk to finance people, I tend to see two different definitions of driver-based planning:

  • Heavy in detail, one where you build a pretty complete bottom-up budget for an organization and play around with certain drivers, typically with a strong bias towards what they have historically been.  I would call this driver-based budgeting.
  • Light in detail where you struggle to find the minimum set of key drivers around which you can pretty accurately model the business and where drivers tend to be figures you can benchmark in the industry.  I call this driver-based modeling.

While driver-based budgeting can be an important step in building an operating plan, I am actually bigger fan of driver-based modeling.  Budgets are very important, no doubt.  We need them to run plan our business, align our team, hold ourselves accountable for spending, drive compensation, and make our targets for the year.  Yes, a good CEO cares about that as a sine qua non.

But a great CEO is really all about two things:

  • Financial outcomes (and how they create shareholder value)
  • The future (and not just next year, but the next few)

The ultimate purpose of driver-based models is to be able answer questions like what happens to key financial outcomes like revenue growth, operating margins, and cashflow given set of driver values.

I believe some CEOs are disappointed with driver-based planning because their finance team have been showing them driver-based budgets when they should have been showing them driver-based models.

The fun part of driver-based modeling is trying to figure out the minimum set of drivers you need to successfully build a complete P&L for a business.  As a concrete example I can build a complete, useful model of a SaaS software company off the following minimum set of drivers

  • Number and type of salesreps
  • Quota/productivity for each type
  • Hiring plans for each type
  • Deal bookings mix for each (e.g., duration, prepayments, services)
  • Intra-quarter bookings linearity
  • Services margins
  • Subscription margins
  • Sales employee types and ratios (e.g., 1 SE per 2 salesreps)
  • Marketing as % of sales or via a set of funnel conversion assumptions (e.g., responses, MQLs, oppties, win rate, ASP)
  • R&D as % of sales
  • G&A as % of sales
  • Renewal rate
  • AR and AP terms

With just those drivers, I believe I can model almost any SaaS company.  In fact, without the more detailed assumptions (rep types, marketing funnel), I can pretty accurately model most.

Finance types sometimes forget that the point of driver-based modeling is not to build a budget, so it doesn’t have to be perfect.  In fact, the more perfect you make it, the heavier and more complex it gets.  For example, intra-quarter bookings linearity (i.e., % of quarterly bookings by month) makes a model more accurate in terms of cash collections and monthly cash balances, but it also makes it heavier and more complex.

Like each link in Marley’s chains, each driver adds to the weight of the model, making it less suited to its ultimate purpose.  Thus, with the additional of each driver, you need to ask yourself — for the purposes of this model, does it add value?  If not, throw it out.

One of the most useful models I ever built assumed that all orders came in on the last day of quarter.  That made building the model much simpler and any sales before the last day of the quarter — of which we hope there are many — become upside to the conservative model.

Often you don’t know in advance how much impact a given driver will make.  For example, sticking with intra-quarter bookings linearity, it doesn’t actually change much when you’re looking at quarter granularity a few years out.  However, if your company has a low cash balance and you need to model months, then you should probably keep it in.  If not, throw it out.

This process makes model-building highly iterative.  Because the quest is not to build the most accurate model but the simplest, you should start out with a broad set of drivers, build the model, and then play with it.  If the financial outcomes with which you’re concerned (and it’s always a good idea to check with the CEO on which these are — you can be surprised) are relatively insensitive to a given driver, throw it out.

Finance people often hate this both because they tend to have “precision DNA” which runs counter to simplicity, and because they have to first write and then discard pieces of their model, which feels wasteful.  But if you remember the point — to find the minimum set of drivers that matter and to build the simplest possible model to show how those key drivers affect financial outcomes — then you should discard pieces of the model with joy, not regret.

The best driver-based models end up with drivers that are easily benchmarked in the industry.  Thus, the exercise becomes:  if we can converge to a value of X on industry benchmark Y over the next 3 years, what will it do to growth and margins?  And then you need to think about how realistic converging to X is — what about your specific business means you should converge to a value above or below the benchmark?

At Host Analytics we do a lot of driver-based modeling and planning internally.  I can say it helps me enormously as CEO think about industry benchmarks, future scenarios, and how we create value for the shareholders.  In fact, all my models don’t stop at P&L, they go onto implied valuation given growth/profit and ultimately calculate a range of share prices on the bottom line.

The other reason I love driver-based planning is more subtle.  Much as number theory helps you understand the guts of numbers in mathematics, so does driver-based modeling help you understand the guts of your business — which levers really matter, and how much.

And that knowledge is invaluable.

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Posted in CEO, Decision Making, Finance, FP&A, Management, Planning, Strategy