Category Archives: SaaS

A Fresh Look at How to Measure SaaS Churn Rates

It’s been nearly three years since my original post on calculating SaaS renewal rates and I’ve learned a lot and seen a lot of new situations since then.  In this post, I’ll provide a from-scratch overhaul on how to calculate churn in an enterprise SaaS company [1].

While we are going to need to “get dirty” in the detail here, I continue to believe that too many people are too macro and too sloppy in calculating these metrics.  The details matter because these rates compound over time, so the difference between a 10% and 20% churn rate turns into a 100% difference in cohort value after 7 years [2].  Don’t be too busy to figure out how to calculate them properly.

The Leaky Bucket Full of ARR

I conceptualize SaaS companies as leaky buckets full of annual recurring revenue (ARR).  Every time period, the sales organization pours more ARR into the bucket and the customer success (CS) organization tries to prevent water from leaking out [3].

This drives the leaky bucket equation, which I believe should always be the first four lines of any SaaS company’s financial statements:

Starting ARR + new ARR – churn ARR = ending ARR

Here’s an example, where I start with those four lines, and added two extra (one to show a year over year growth rate and another to show “net new ARR” which offsets new vs. churn ARR):

leaky

For more on how to present summary SaaS startup financials, go here.

Half-Full or Half-Empty:  Renewals or Churn?

Since the renewal rate is simply one minus the churn rate, the question is which we should calculate?  In the past, I favored splitting the difference [4], whereas I now believe it’s simpler just to talk about churn.  While this may be the half-empty perspective, it’s more consistent with what most people talk about and is more directly applicable, because a common use of a churn rate is as a discount rate in a net present value (NPV) formula.

Thus, I now define the world in terms of churn and churn rates, as opposed to renewals and renewal rates.

Terminology: Shrinkage and Expansion

For simplicity, I define the following two terms:

  • Shrinkage = anything that makes ARR decrease. For example, if the customer dropped seats or was given a discount in return for signing a multi-year renewal [5].
  • Expansion = anything that makes ARR increase, such as price increases, seat additions, upselling from a bronze to a gold edition, or cross-selling new products.

Key Questions to Consider

The good news is that any churn rate calculation is going to be some numerator over some denominator.  We can then start thinking about each in more detail.

Here are the key questions to consider for the numerator:

  • What should we count? Number of accounts, annual recurring revenue (ARR), or something else like renewal bookings?
  • If we’re counting ARR should we think at the product-level or account-level?
  • To what extent should we offset shrinkage with expansion in calculating churn ARR? [6]
  • When should we count what? What about early and late renewals?  What about along-the-way expansion?  What about churn notices or non-payment?

Here are the key questions to consider for the denominator:

  • Should we use the entire ARR pool, that portion of the ARR pool that is available to renew (ATR) in any given time period, or something else?
  • If using the ATR pool, for any given renewing contract, should we use its original value or its current value (e.g., if there has been upsell along the way)?

What Should We Count?  Logos and ARR

I believe the two metrics we should count in churn rates are

  • Logos (i.e., number of customers). This provides a gross indication of customer satisfaction [7] unweighted by ARR, so you can answer the question:  what percent of our customer base is turning over?
  • This provides a very important indication on the value of our SaaS annuity.  What is happening to our ARR pool?

I would stay completely away from any SaaS metrics based on bookings (e.g., a bookings CAC, TCV, or bookings-based renewals rate).  These run counter to the point of SaaS unit economics.

Gross, Net, and Account-Level Churn

Let’s look at a quick example to demonstrate how I now define gross, net, and account-level churn [8].

gross-and-net-churn

Gross churn is the sum of all the shrinkage. In the example, 80 units.

Net churn is the sum of the shrinkage minus the sum of the expansion. In the example, 80-70 = 10 units.

To calculate account-level churn, we proceed, account by account, and look at the change in contract value, separating upsell from the churn.  The idea is that while it’s OK to offset shrinkage with expansion within an account that we should not do so across accounts when working at the account level [9].  This has the effect of splitting expansion into offset (used to offset shrinkage within an account) and upsell (leftover expansion after all account-level shrinkage has been offset).  In the example, account-level churn is 30 units.

Make the important note here that how we calculate you churn – and specifically how we use expansion ARR to offset shrinkage—not only affects our churn rates, but our reported upsell rates as well.  Should we proudly claim 70 units of upsell (and less proudly 80 units of churn), 30 units of churn and 20 of upsell, or simply 10 units of churn?  I vote for the second.

While working at the account-level may seem odd, it is how most SaaS companies work operationally.  First, because they charter customer success managers (CSMs) to think at the account level, working account by account doing everything they can to preserve and/or increase the value of the account.  Second, because most systems work at and finance people think at the account level – e.g., “we had a customer worth 100 units last year, and they are worth 110 units this year so that means upsell of 10 units.  I don’t care how much is price increase vs. swapping some of product A for product B.” [11]

So, when a SaaS company reports “churn ARR,” in its leaky bucket analysis, I believe they should report neither gross churn nor net churn, but account-level churn ARR.

Timing Issues and the Available to Renew (ATR) Concept

Churn calculations bring some interesting challenges such as early/late renewals, churn notices, non-payment, and along-the-way expansion.

A renewals booking should always be taken in the period in which it is received.  If a contract expires on 6/30 and the renewal is received in on 6/15 it should show up in 2Q and if received on 7/15 it should up in 3Q.

For churn rate calculations, however, the customer success team needs to forecast what is going to happen for a late renewal.  For example, if we have a board meeting on 7/12 and a $150K ARR renewal due 6/30 has not yet been happened, we need to proceed based on what the customer has said.  If the customer is actively using the software, the CFO has promised a renewal but is tied up on a European vacation, I would mark the numbers “preliminary” and count the contract as renewed.  If, however, the customer has not used the software in months and will not return our phone calls, I would count the contract as churned.

Suppose we receive a churn notice on 5/1 for a contract that renews on 6/30.  When should we count the churn?  A Bessemer SaaS fanatic would point to their definition of committed monthly recurring revenue (CMRR) [12] and say we should remove the contact from the MRR base on 5/1.  While I agree with Bessemer’s views in general — and specifically on things like on preferring ARR/MRR to ACV and TCV — I get off the bus on the whole notion of “committed” ARR/MRR and the ensuing need to remove the contract on 5/1.  Why?

  • In point of fact the customer has licensed and paid for the service through 6/30.
  • The company will recognize revenue through 6/30 and it’s much easier to do so correctly when the ARR is still in the ARR base.
  • Operationally, it’s defeatist. I don’t want our company to give up and say “it’s over, take them out of the ARR base.” I want our reaction to be, “so they think they don’t want to renew – we’ve got 60 days to change their mind and keep them in.” [13]

We should use the churn notice (and, for that matter, every other communication with the customer) as a way of improving our quarterly churn forecast, but we should not count churn until the contract period has ended, the customer has not renewed, and the customer has maintained their intent not to renew in coming weeks.

Non-payment, while hopefully infrequent, is another tricky issue.  What do we do if a customer gives us a renewal order on 6/30, payable in 30 days, but hasn’t paid after 120?  While the idealist in me wants to match the churn ARR to the period in which the contract was available to renew, I would probably just show it as churn in the period in which we gave up hope on the receivable.

Expansion Along the Way (ATW)

Non-payment starts to introduce the idea of timing mismatches between ARR-changing events and renewals cohorts.  Let’s consider a hopefully more frequent case:  ARR expansion along the way (ATW).  Consider this example.

expansion

To decide how to handle this, let’s think operationally, both about how our finance team works and, more importantly, about how we want our customer success managers (CSMs) to think.  Remember we want CSMs to each own a set of customers, we want them to not only protect the ARR of each customer but to expand it over time.  If we credit along-the-way upsell in our rate calculations at renewal time, we shooting ourselves in the foot.  Look at customer Charlie.  He started out with 100 units and bought 20 more in 4Q15, so as we approach renewal time, Charlie actually has 120 units available to renew (ATR), not 100 [14].  We want our CSMs basing their success on the 120, not the 100.  So the simple rule is to base everything not on the original cohort but on the available to renew (ATR) entering the period.

This begs two questions:

  • When do we count the along-the-way upsell bookings?
  • How can we reflect those 40 units in some sort of rate?

The answer to the first question is, as your finance team will invariably conclude, to count them as they happen (e.g., in 4Q15 in the above example).

The answer to the second question is to use a retention rate, not a churn rate.  Retention rates are cohort-based, so to calculate the net retention rate for the 2Q15 cohort, we divide its present value of 535 by its original value of 500 and get 107%.

Never, ever calculate a retention rate in reverse – i.e., starting a group of current customers and looking backwards at their ARR one year ago.  You will produce a survivor biased answer which, stunningly, I have seen some public companies publish.  Always run cohort analyses forwards to eliminate survivor bias.

Off-Cycle Activity

Finally, we need to consider how to address off-cycle (or extra-cohort) activity in calculating churn and related rates.  Let’s do this by using a big picture example that includes everything we’ve discussed thus far, plus off-cycle activity from two customers who are not in the 2Q16 ATR cohort:  (1) Foxtrot, who purchased in 3Q14, renewed in 3Q15, and who has not paid, and (2) George, who purchased in 3Q15, who is not yet up for renewal, but who purchased 50 units of upsell in 2Q16.

bigpic

Foxtrot should count as churn in 2Q16, the period in which we either lost hope of collection (or our collections policy dictated that collection we needed to de-book the deal). [15]

George should count as expansion in 2Q16, the period in which the expansion booking was taken.

The trick is that neither Foxtrot nor George is on a 2Q renewal cycle, so neither is included in the 2Q16 ATR cohort.  I believe the correct way to handle this is:

  • Both should be factored into gross, net, account-level churn, and upsell.
  • For rates where we include them in the numerator, for consistency’s sake we must also include them in the denominator. That means putting the shrinkage in the numerator and adding the ATR of a shrinking (or lost) account in denominator of a rate calculation.  I’ll call this the “+” concept, and define ATR+ as inclusive of such additional logos or ARR resulting from off-cycle accounts [16].

Rate Calculations

We are now in the position to define and calculate the churn rates that I use and track:

  • Simple churn = net churn / starting period ARR * 4.  Or, in English, the net change in ARR from existing customers divided by starting period ARR (multiplied by 4 to annualize the rate which is measured against the entire ARR base). As the name implies, this is the simplest churn rate to calculate. This rate will be negative whenever expansion is greater than shrinkage. Starting period ARR includes both ATR and non-ATR contracts (including potentially multi-year contracts) so this rate takes into account the positive effects of the non-cancellability of multi-year deals.  Because it takes literally everything into account, I think this is the best rate for valuing the annuity of your ARR base.
  • Logo churn = number of discontinuing logos / number of ATR+ logos. This rate tells us the percent of customers who, given the chance, chose to discontinue doing business with us.  As such, it provides an ARR-unweighted churn rate, providing the best sense of “how happy” our customers are, knowing that there is a somewhat loose correlation between happiness and renewal [16].  Remember that ATR+ means to include any discontinuing off-cycle logos, so the calculation is 1/16 = 6.3% in our example.
  • Retention = current ARR [time cohort] / time-ago ARR [time cohort]. In English, the current ARR from some time-based cohort (e.g., 2Q15) divided by the year-ago ARR from that same cohort.  Typically we do this for the one-year-ago or two-years-ago cohorts, but many companies track each quarter’s new customers as a cohort which they measure over time.  Like simple churn, this is a great macro metric that values the ARR annuity, all in.
  • Net churn = account-level churn / ATR+. This churn rate foots to the reported churn ARR in our leaky bucket analysis (which is account-level churn), which partially offsets shrinkage with expansion at an account-level, and is how most SaaS companies actually calculate churn.  While perhaps counter-intuitive, it reflects a philosophy of examining, at an account basis, what happens to value of our each of our customers when we allow shrinkage to be offset by expansion (which is what we want our CSM reps doing) leaving any excess as upsell.  This should be our primary churn metric.
  • Gross churn = shrinkage / ATR+. This churn rate is important because it reveals the difference between companies that have high shrinkage offset by high expansion and companies which simply have low shrinkage.  While net churn is powerful because it’s “all in,” any metric that enables offset can allow one thing to mask another.  Gross churn is a great metric because it simply shows the glass half-empty view:  at what rate is ARR leaking out of your bucket before offset it with refills in the form of expansion ARR.

# # #

Notes

[1] Replacing these posts in the process.

[2] The 10% churn group decays from 100 units to 53 in value after 7 years, while the 20% group decays to 26.

[3] We’ll sidestep the question of who is responsible for installed-based expansion in this post because companies answer it differently (e.g., sales, customer success, account management) and the good news is we don’t need to know who gets credited for expansion to calculate churn rates.

[4] Discussing churn in dollars and renewals in rates.

[5] For example, if a customer signed a one-year contract for 100 units and then was offered a 5% discount to sign a three-year renewal, you would generate 5 units of ARR churn.

[6] Or, as I said in a prior post, should I net first or sum first?

[7] And yes, sometimes unhappy customers do renew (e.g., if they’ve been too busy to replace you) and happy customers don’t (e.g., if they get a new key executive with different preferences) but counting logos still gives you a nice overall indication.

[8] Note that I have capitulated to the norm of saying “gross” churn means before offset and thus “net” churn means after netting out shrinkage and expansion.  (Beware confusion as this is the opposite of my prior position where I defined “net” to mean “net of expansion,” i.e., what I’d now call “gross.”)

[9] Otherwise, you just end up with a different way of calculating net churn.  The idea of account-level churn is to restrict the ability to offset shrinkage with expansion across accounts, in effect, telling your customer success reps that their job is to, contract by contract, minimize shrinkage and ensure expansion.

[10] “Offset” meaning ARR used to offset shrinkage that ends up neither churn nor upsell.

[11] While this approach works fine for most (inherently single-product) SaaS startups it does not work as well for large multi-product SaaS vendors where the failure of product A might be totally or partially masked by the success of product B.  (In our example, I deliberately had all the shrinkage coming from downsell of product A to make that point.  The product or general manager for product A should own the churn number that product and be trying to find out why it churned 80 units.)

[12] MRR = monthly recurring revenue = 1/12th of ARR.  Because enterprise SaaS companies typically run on an annual business rhythm, I prefer ARR to MRR.

[13] Worse yet, if I churn them out on 5/1 and do succeed in changing their mind, I might need to recognize it as “new ARR” on 6/30, which would also be wrong.

[14] The more popular way of handling this would have been to try and extend the original contract and co-terminate with the upsell in 4Q16, but that doesn’t affect the underlying logic, so let’s just pretend we tried that and it didn’t work for the customer.

[15] Whether you call it a de-booking or bad receivable, Foxtrot was in the ARR base and needs to come out.  Unlike the case where the customer has paid for the period but is not using the software (where we should churn it at the end of the contract), in this case the 3Q15 renewal was effectively invalid and we need to remove Foxtrot from the ARR base at some defined number of days past due (e.g., 90) or when we lose hope of collection (e.g., bankruptcy).

[16] I think the smaller you are the more important this correction is to ensure the quality of your numbers.  As a company gets bigger, I’d just drop the “+” concept whenever it’s only changing things by a rounding error.

[17] Use NPS surveys for another, more precise, way of measuring happiness.  See [7] as well.

SaaS Startup One-Slide Financials Dashboard

In the course of my board and advisory work, I get to look at a lot of software as a service (SaaS) startups financials and I’m often surprised how people choose to present their companies.

Because people — e.g., venture capital (VC) investors — judge you by the metrics you track, the order in which you track them, and how clearly you present them, I think it’s very important to put real thought into how you want to present your company’s one-slide financial and key operating metrics.

As both an author and analytics enthusiast, I also believe in minimalism and reader empathy.  We should neither bury the reader in facts nor force them to perform basic calculations that answer easily anticipated questions.

I always try to remember this Blaise Pascal quote (which is often misattributed to Mark Twain):

I would have written you a shorter letter, but I did not have time to do so.

So, in this spirit, let me offer my one-slide SaaS startup financials and key operating metrics dashboard, which captures all the key high-level questions I’d have about any enterprise SaaS company.

saas-one-slide-financial-dashboard

While this is certainly not a complete set of SaaS metrics, it provides a great summary of the state of your annual recurring revenue (ARR), your trajectory, your forecast, and your performance against plan.  Most important, perhaps, it shows that you are focused on the right thing by starting with 5 lines dedicated not to TCV, bookings, or GAAP revenue, but the key value driver for any SaaS business:  ARR.

If you like it, you can download the spreadsheet here.

Aligned to Achieve: A B2B Marketing Classic

Tracy Eiler and Andrea Austin’s Aligned to Achieve came out today and it’s a great book on an important and all too often overlooked topic:  how to align sales and marketing.

I’m adding it to my modern SaaS executive must-read book list, which is now:

So, what do I like about Aligned to Achieve?

The book puts a dead moose issue squarely on the table:  sales and marketing are not aligned in too many organizations.  The book does a great job of showing some examples of what misalignment looks like.  My favorites were the one where the sales VP wouldn’t shake the new CMO’s hand (“you’ll be gone soon, no need to get to know you”) and the one where sales waived off marketing from touching any opportunities once they got in the pipeline.  Ouch.  #TrustFail.

Aligned to Achieve makes great statements like this one:  “We believe that pipeline is absolutely the most important metric for sales and marketing alignment, and that’s a major cultural shift for most companies.”  Boom, nothing more to say about that.

The book includes fun charts like the one below.  I’ve always loved tension-surveys where you ask two sides for a view on the same issue and show the gap – and this gap’s a doozy.

sm gap

Aligned to Achieve includes the word “transparency” twenty times.  Transparency is required in the culture, in collaboration, in definitions, in planning, in the reasons for plans, in process and metrics, in data, in assessing results, in engaging customers, and in objectives and performance against them.  Communication is the lubricant in the sales/marketing relationship and transparency the key ingredient.

The book includes a nice chapter on the leadership traits required to work in the aligned environment:  collaborative, transparent, analytical, tech savvy, customer focused, and inspirational.  Having been a CMO fifteen years ago, I’d say that transparent, analytical, and tech savvy and now more important than ever before.

Aligned to Achieve includes a derivative of my favorite mantra (marketing exists to make sales easier) in the form of:

Sales can’t do it alone and marketing exists to make sales easier

The back half of that mantra (which I borrowed from CTP co-founder Chris Greendale) served me well in my combined 12 years as a CMO.  I love the insertion of the front half, which is now more true than ever:  sales has never been more codependent with marketing.

The book includes a fun, practical suggestion to have a bi-monthly “smarketing” meeting which brings sales and marketing together to discuss:

  • The rolling six-week marketing campaign calendar
  • Detailed review of the most recently completed campaigns
  • Update on immediately pending campaigns
  • Bigger picture items (e.g., upcoming events that impact sales and/or marketing)
  • Open discussion and brainstorming to cover challenges and process hiccups

Such meetings are a great idea.

Back in the day when Tracy and I worked together at Business Objects, I always loved Tracy’s habit of “crashing” meetings.  She was so committed to sales and marketing alignment – even back then – that if sales were having an important meeting, invited or not, she’d just show up.  (It always reminded me of the Woody Allen quote, 80% of success is showing up.)  In her aligned organization today, the CEO makes sure she doesn’t have to do that, but by hook or by crook the sales/marketing discussion must happen.

Aligned to Achieve has a nice discussion of the good old sales velocity model which, like my Four Levers of SaaS, is a good way to think about and simplify a business and the levers that drive it.

Unsurprisingly, for a book co-authored by the CMO of a company that sells market data and insights, Aligned to Achieve includes a healthy chapter on the importance of data, including a marketing-adapted version of the DIKW pyramid featuring data, insights, and connections as the three layers.  The nice part is that the chapter remains objective and factual – it doesn’t devolve into an infomercial by any means.

The book moves on to discuss the CIO’s role in a sales/marketing-aligned organization and provides a chapter reviewing the results of a survey of 1000 sales and marketing professionals on alignment, uncovering common sources of misalignment and some of the practices used by sales/marketing alignment leaders.

Aligned to Achieve ends with a series of 7 alignment-related predictions which I won’t scoop here.  I will say that #4 (“academia catches up”) and #6 (“account-based everything is a top priority”) are my two favorites.

Congratulations to my long-time friend and colleague Tracy Eiler on co-authoring the book and to her colleague Andrea Austin.

The Four Levers of SaaS

There are a lot of SaaS posts out there with some pretty fancy math in them.  I’m a math guy, so I like to geek on SaaS metrics myself.  But, in the heat of battle running a SaaS company, sometimes you just need to keep it simple.

Here’s the picture I keep on my wall to help me do that.

It reminds me that new ARR in any given period is the product of four levers.

  • The MQL to stage 2 opportunity conversion rate (MTS2CR), the rate at which MQLs convert to stage 2, or sales-accepted, opportunities.  Typically they pass through a stage 1 phase first when a sales development rep (SDR) believes there is a real opportunity, but a salesperson has not yet agreed.
  • The stage 2 to close rate (S2TCR), the rate at which stage 2 opportunities close into deals, and avoid being lost to a competitor or derailed (e.g., having the evaluation project cancelled).
  • The annual recurring revenue average sales price (ARR ASP), the average deal size, expressed in ARR.

That’s it.  Those four levers will predict your quarterly new ARR every time.

Aside:  before diving into each of the four levers, let me note that sales velocity is omitted from this model.  That keeps it simple, but it does overlook a potentially important lever.  So if you think you have a sales velocity (i.e., sales cycle length) problem, go look at a different model that includes this lever and suggests ways to decrease it.

So now that we have identified the four levers, let’s focus on what we can do about them in order to increase our quarterly new ARR.

Marketing Qualified Leads (MQLs)

Getting MQLs is the domain of marketing, which should be constantly measuring the cost effectiveness of various marketing programs in terms of generating MQLs (cost/MQL).  This isn’t easy because most leads will require numerous touches over time in order to graduate to MQL status, but marketing needs to stay atop that complexity (e.g., by assigning credits to various programs as MQL-threshold points accumulate).

The best marketers understand the demand is variable and have designed their programs mix so they can scale spending quickly in response to increased needs.  Nothing is worse than an MQL shortage and a marketing department that’s not ready to spend incremental money to address it.

The general rule is to constantly A/B test your programs and nurture streams and do more of what’s working and less of what isn’t.

MQL to Stage 2 Opportunity Conversion Rate

Increasing the MQL to stage 2 opportunity conversion rate (MTS2CR) requires either generating better MQLs or doing a better job handling them so that they convert into stage 2 opportunities.

Generating better MQLs can be accomplished by analyzing past programs to determine which generated the best-converting MQLs and increasing them, putting a higher gate on what you pass over to sales (using predictive or behavioral scoring), or using buyer personas to optimize what you say to buyers, when, and through which channels.

Do a better job handling your existing MQLs comes down ensuring your operational processes work and you don’t let leads fall between the cracks.  Basic activity and aging reports are a start.  Establishing a formal service-level agreement between sales and marketing is a common next step.

Moving up a level and checking that your whole process fits well with the customer’s buying journey is also key.  While each step of your process might individually make sense, when assembled the process may not — e.g., are you irritating customers by triple-qualifying them with an SDR, a salesrep, and a solution consultant each doing basic discovery?

The Stage 2 to Close Rate

Once created, one of three things can happen to a stage 2 opportunity:  you can win it, you can lose it, or it can derail (i.e., anything else, such as project cancellation or “slips” to the distant future).

Increasing your win rate can be accomplished through better product positioning, sales tools, and sales training, improved competitive intelligence, improved buzz/aura, improved case studies and customer references, and better pricing and discounting strategy.  That’s not to mention more strategic approaches via improved sales methodology and process or product improvements, in terms of functionality, non-functional requirements, and product design.

Decreasing your loss rate can be accomplished through better up-front sales qualification, better sales tools and training, improved competitive strategy and tactics, and better pricing and discounting.  Improved sales management can also play a key role in catching in-trouble deals early and escalating to get the necessary resources deployed to win.

Reducing your derail rate is hard because project slips or cancellations seem mostly out of your control.  What’s the best way to reduce your derail rate?  Focus on velocity — take deals off the table before the company has a chance to prioritize another project, do a reorganization, or hire a new executive that kills it.  The longer a deal hangs around, the more likely something bad happens to it.  As the adage goes, time kills all deals.

ARR ASP

The easiest way to increase ARR ASP is to not shrink it through last-minute discounting.  Adopt a formal discount policy with approvals so that, in the words of one famous sales leader, “your rep is more afraid of his/her sales manager than the customer” when it comes to speaking about discounts.

Selling value and product differentiation are two other discount reduction strategies.  The more customers see real value and a concrete return for their business the less they will focus on price.  Additionally, the more they see your offering as unique, the less price pressure you will face from the competition.  Conversely, the more they see your product as a cost and your company as one of several suppliers from whom they can buy the same capabilities, the more discount pressure you will face.

Up-selling to a higher edition or cross selling (“fries with your burger?”) are both ways to increase your ASP as well.  Just be careful to avoid customers feeling nickled and dimed in the process.

For SaaS businesses, remember that multi-year deals typically do not help your ARR ASP (though, if prepaid, they do help with year-one cash).  In fact, it’s usually the opposite — a small ARR discount is typically traded for the multi-year commitment.  My general rule of thumb is to offer a multi-year discount that’s less than your churn rate and everybody wins.

Conclusion

Hopefully this framework will make it easier for you to diagnose and act upon the problems that can impede achieving your company’s new ARR goals.  Always remember that any new ARR problem can be broken down into some combination of an MQL problem, an MQL to stage 2 conversion rate problem, a stage 2 to close rate problem, or an average sales price problem.  By focusing on these four levers, you should be able to optimize the productivity of your SaaS sales model.

 

 

CAC Payback Period:  The Most Misunderstood SaaS Metric

The single most misunderstood software-as-a-service (SaaS) metric I’ve encountered is the CAC Payback Period (CPP), a compound metric that is generally defined as the months of contribution margin to pay back the cost of acquiring a customer.   Bessemer defines the CPP as:

bess cac

I quibble with some of the Bessemerisms in the definition.  For example, (1) most enterprise SaaS companies should use annual recurring revenue (ARR), not monthly recurring revenue (MRR), because most enterprise companies are doing annual, not monthly, contracts, (2) the “committed” MRR concept is an overreach because it includes “anticipated” churn which is basically impossible to measure and often unknown, and (3) I don’t know why they use the prior period for both S&M costs and new ARR – almost everybody else uses prior-period S&M divided by current-period ARR in customer acquisition cost (CAC) calculations on the theory that last quarter’s S&M generated this quarter’s new ARR.

Switching to ARR nomenclature, and with a quick sleight of mathematical hand for simplification, I define the CAC Payback Period (CPP) as follows:

kell cac

Let’s run some numbers.

  • If your company has a CAC ratio of 1.5 and subscription gross margins of 75%, then your CPP = 24 months.
  • If your company has a CAC ratio of 1.2 and subscription gross margins of 80%, then your CPP = 18 months.
  • If you company has a CAC ratio of 0.8 and subscription gross margins of 80%, then your CPP = 12 months.

All seems pretty simple, right?  Not so fast.  There are two things that constantly confound people when looking at CAC Payback Period (CPP).

  • They forget payback metrics are risk metrics, not return metrics
  • They fail to correctly interpret the impact of annual or multi-year contracts

Payback Metrics are for Risk, Not Return

Quick, basic MBA question:  you have two projects, both require an investment of 100 units, and you have only 100 units to invest.  Which do you pick?

  • Project A: which has a payback period of 12 months
  • Project B: which has a payback period of 6 months

Quick, which do you pick?  Well, project B.  Duh.  But wait — now I tell you this:

  • Project A has a net present value (NPV) of 500 units
  • Project B has an NPV of 110 units

Well, don’t you feel silly for picking project B?

Payback is all about how long your money is committed (so it can’t be used for other projects) and at risk (meaning you might not get it back).  Payback doesn’t tell you anything about return.  In capital budgeting, NPV tells you about return.  In a SaaS business, customer lifetime value (LTV) tells you about return.

There are situations where it makes a lot of sense to look at CPP.  For example, if you’re running a monthly SaaS service with a high churn rate then you need to look closely how long you’re putting your money at risk because there is a very real chance you won’t recoup your CAC investment, let alone get any return on it.  Consider a monthly SaaS company with a $3500 customer acquisition cost, subscription gross margin of 70%, a monthly fee of $150, and 3% monthly churn.  I’ll calculate the ratios and examine the CAC recovery of a 100 customer cohort.

saas fail

While the CPP formula outputs a long 33.3 month CAC Payback Period, reality is far, far worse.  One problem with the CPP formula is that it does not factor in churn and how exposed a cohort is to it — the more chances customers have to not renew during the payback period, the more you need to consider the possibility of non-renewal in your math [1].  In this example, when you properly account for churn, you still have $6 worth of CAC to recover after 30 years!  You literally never get back your CAC.

Soapbox:  this is another case where using a model is infinitely preferable to back-of-the-envelope (BOTE) analysis using SaaS metrics.  If you want to understand the financials of a SaaS company, then build a driver-based model and vary the drivers.  In this case and many others, BOTE analysis fails due to subtle complexity, whereas a well-built model will always produce correct answers, even if they are counter-intuitive.

Such cases aside, the real problem with being too focused on CAC Payback Period is that CPP is a risk metric that tells you nothing about returns.  Companies are in business to get returns, not simply to minimize risk, so to properly analyze a SaaS business we need to look at both.

The Impact of Annual and Multi-Year Prepaid Contracts on CAC Payback Period

The CPP formula outputs a payback period in months, but most enterprise SaaS businesses today run on an annual rhythm.  Despite pricing that is sometimes still stated per-user, per-month, SaaS companies realized years ago that enterprise customers preferred annual contracts and actually disliked monthly invoicing.  Just as MRR is a bit of a relic from the old SaaS days, so is a CAC Payback Period stated in months.

In a one-hundred-percent annual prepaid contract world, the CPP formula should output in multiples of 12, rounding up for all values greater than 12.  For example, if a company’s CAC Payback Period is notionally 13 months, in reality it is 24 months because the leftover 1/13 of the cost isn’t collected until the a customer’s second payment at month 24.  (And that’s only if the customer chooses to renew — see above discussion of churn.)

In an annual prepaid world, if your CAC Payback Period is less than or equal to 12 months, then it should be rounded down to one day because you are invoicing the entire year up-front and at-once.  Even if the formula says the CPP is notionally 12.0 months, in an annual prepaid world your CAC investment money is at risk for just one day.

So, wait a minute.  What is the actual CAC Payback Period in this case?  12.0 months or 1 day?  It’s 1 day.

Anyone who argues 12.0 months is forgetting the point of the metric.  Payback periods are risk metrics and measured by the amount of time it takes to get your investment back [2].  If you want to look at S&M efficiency, look at the CAC ratio.  If you want to know about the efficiency of running the SaaS service, look at subscription gross margins.  If you want to talk about lifetime value, then look at LTV/CAC.  CAC Payback Period is a risk metric that measures how long your CAC investment is “on the table” before getting paid back.  In this instance the 12 months generated by the standard formula is incorrect because the formula misses the prepayment and the correct answer is 1 day.

A lot of very smart people get stuck here.  They say, “yes, sure, it’s 1 day – but really, it’s not.  It’s 12 months.”  No.  It’s 1 day.

If you want to look at something other than payback, then pick another metric.  But the CPP is 1 day.  You asked how long it takes for the company to recoup the money it spends to acquire a customer.  For CPPs less than or equal to 12 in a one-hundred percent annual prepaid world, the answer is one day.

It gets harder.  Imagine a company that sells in a sticky category (e.g., where typical lifetimes may be 10 years) and thus is a high-consideration purchase where prospective customers do deep evaluations before making a decision (e.g., ERP).  As a result of all that homework, customers are happy to sign long contracts and thus the company does only 3-year prepaid contracts.  Now, let’s look at CAC Payback Period.  Adapting our rules above, any output from the formula greater than 36 months should be rounded up in multiples of 36 months and, similarly, any output less than or equal to 36 months should be rounded down to 1 day.

Here we go again.  Say the CAC Payback Period formula outputs 33 months.  Is the real CPP 33 months or 1 day?  Same argument.  It’s 1 day.  But the formula outputs 33 months.  Yes, but the CAC recovery time is 1 day.  If you want to look at something else, then pick another metric.

It gets even harder.  Now imagine a company that does half 1-year deals and half 3-year deals (on an ARR-weighted basis).  Let’s assume it has a CAC ratio of 1.5, 75% subscription gross margins, and thus a notional CAC Payback Period of 24 months.  Let’s see what really happens using a model:

50-50

Using this model, you can see that the actual CAC Payback Period is 1 day. Why?  We need to recoup $1.5M in CAC.  On day 1 we invoice $2.0M, resulting in $1.5M in contribution margin, and thus leaving $0 in CAC that needs to be recovered.

While I have not yet devised general rounding rules for this situation, the model again demonstrates the key point – that the mix of 1-year and 3-year payment structure confounds the CPP formula resulting in a notional CPP of 24 months, when in reality it is again 1 day.  If you want to make rounding rules beware the temptation to treat the average contract duration (ACD) as a rounding multiple because it’s incorrect — while the ACD is 2 years in the above example, not a single customer is paying you at two-year intervals:  half are paying you every year while half are paying you every three.  That complexity, combined with the reality that the mix is pretty unlikely to be 50/50, suggests it’s just easier to use a model than devise a generalized rounding formula.

But pulling back up, let’s make sure we drive the key point home.  The CAC Payback Period is the single most often misunderstood SaaS metric because people forget that payback metrics are about risk, not return, and because the basic formulas – like those for many SaaS metrics – assume a monthly model that simply does not apply in today’s enterprise SaaS world, and fail to handle common cases like annual or multi-year prepaid contracts.

# # #

Notes

[1] This is a huge omission for a metric that was defined in terms of MRR and which thus assumes a monthly business model.  As the example shows, the formula (which fails to account for churn) outputs a CAC payback of 33 months, but in reality it’s never.  Quite a difference!

[2] If I wanted to be even more rigorous, I would argue that you should not include subscription gross margin in the calculation of CAC Payback Period.  If your CAC ratio is 1.0 and you do annual prepaid contracts, then you immediately recoup 100% of your CAC investment on day 1.  Yes, a new customer comes with a future liability attached (you need to bear the costs of running the service for them for one year), but if you’re looking at a payback metric that shouldn’t matter.  You got your money back.  Yes, going forward, you need to spend about 30% (a typical subscription COGS figure) of that money over the next year to pay for operating the service, but you got your money back in one day.  Payback is 1 day, not 1/0.7 = 17 months as the formula calculates.

Book Review:  From Impossible to Inevitable

This post reviews Aaron Ross and Jason Lemkin’s new book, From Impossible to Inevitable, which is being launched at the SaaStr Conference this week.  The book is a sequel of sorts to Ross’s first book, Predictable Revenue, published in 2011, and which was loaded with great ideas about how to build out your sales machine.

From Impossible to Inevitable is built around what they call The Seven Ingredients of Hypergrowth:

  1. Nail a niche, which is about defining your focus and ensuring you are ready to grow. (Or, as some say “nail it, then scale it.)  Far too many companies try to scale it without first nailing it, and that typically results in frustration and wasted capital.
  2. Create predictable pipeline, which about “seeds” (using existing successful customers), “nets” (classical inbound marketing programs), and “spears” (targeted outbound prospecting) campaigns to create the opportunities sales needs to drive growth.
  3. Make sales scalable, which argues convincingly that specialization is the key to scalable sales. Separate these four functions into discrete jobs:  inbound lead handing, outbound prospecting, selling (i.e., closing new business), and post-sales roles (e.g., customer success manager).  In this section they include a nice headcount analysis of a typical 100-person SaaS company.
  4. Double your deal size, which discusses your customer mix and how to build a balanced business built off a run-rate business of average deals topped up with a lumpier enterprise business of larger deals, along with specific tactics for increasing deal sizes.
  5. Do the time, which provides a nice reality check on just how long it takes to create a $100M ARR SaaS company (e.g., in a great case, 8 years, and often longer), along with the wise expectations management that somewhere along the way you’ll encounter a “Year of Hell.”
  6. Embrace employee ownership, which reminds founders and executives that employees are “renting, not owning, their jobs” and how to treat them accordingly so they can act more like owners than renters.
  7. Define your destiny, which concludes the book with thoughts for employees on how to take responsibility for managing their careers and maximizing the opportunities in front of them.

The book is chock full of practice advice and real-world stories.  What it’s not is theoretical.  If Crossing the Chasm offered a new way of thinking about product lifecycle strategy that earned it a place on the top shelf of the strategy bookcase, From Impossible to Inevitable is a cookbook that you keep in the middle of the kitchen prep table, with Post-It’s sticking out the pages and oil stains on the cover.  This is not a book that offers one big idea with a handful of chapters on how to apply it.  It’s a book full of recipes and tactics for how to improve each piece of your go-to-market machine.

This book — like Predictable Revenue, The Lean Startup, Zero to One, and SalesHood — belongs on your startup executive’s bookshelf.  Read it!  And keep up with Jason’s and Aaron’s great tweetstreams and the awesome SaaStr blog.

The SaaSacre Part II: Time for the Rebound?

In response to my post, SaaS Stocks:  How Much Punishment is in Store, a few of my banker friends have sent me over some charts and data which shine more light on the points I was trying to make about SaaS forward twelve month (FTM) enterprise value (EV) revenue multiples, normal trading ranges, and the apparent “floor” value for this metric.

This chart comes from the folks at Pacific Crest:

paccrest saas multiples

In English, it says that SaaS stocks are trading at an EV/FTM revenue multiple of 3.2, 35% below the average since 2005, and down 66% since the peak in Jan 2014.  It also shows the apparent floor at around 2.0x, which they dipped below only once in the past decade during the crisis of 2008.

This is not to say that Wall Street doesn’t over-correct, that a new floor value could not be established, or that cuts in revenue forecasts due to macroeconomics couldn’t cause significant valuation drops at a constant, in-range EV/FTM ratio.

It is to say that, given historical norms, if you believe in reversion to the mean and that FTM revenue forecasts will not be materially reduced, that we are in “buying opportunity” territory.   The question is then which sentiment will win out in the market.

  • Fear of a potential 30% drop before hitting the floor value, breaking through the floor value, or cuts in FTM revenue forecasts.
  • Greed and the opportunity to get a nearly 50% return in a simple reversion to the mean.

My quick guess is more fear short-term, followed by some healthy greed winning out after that.

Might we see a temporary dead cat bounce before a further sell-off?  Maybe.  Should we remember the Wall Street maxim about catching falling knives?  Yes.

But at the same time remember that mixed in among the inflated, private, unicorn wreckage, that we have some high-quality, public, recurring-revenue companies trading at what’s starting to approach decades-low multiples.  At some point, that will become a real opportunity.

Disclaimers
See my FAQ for disclaimers and more background information.  I am not a financial analyst and I do not make stock recommendations.  I am simply a CEO sharing his experience and opinion which, as my wife will happily attest, is often incorrect.