Category Archives: SaaS

Insight Ventures Periodic Tables of SaaS Sales and Marketing Metrics

I just ran into these two tables of SaaS metrics published by Insight Venture Partners (or, more precisely, the Insight Onsite team) and they are too good not to share.

Along with Bessemer’s awkwardly titled 30 Questions and Answers That Every SaaS Revenue Leader Needs to Know, financial metrics from Opex Engine, and the wonderful Pacific Crest Annual SaaS Survey, SaaS leaders now have a great set of reference documents to benchmark their firms.

(And that’s not to mention David Skok’s great post on SaaS metrics or, for that matter, my own posts on the customer acquisition cost (CAC) ratio and renewals rates / churn.)

Here is Insight’s SaaS sales periodic table:

ivp saas sales

And here is Insight’s B2B digital marketing periodic table:

ivp saals mkting

The Customer Acquisition Cost (CAC) Ratio: Another Subtle SaaS Metric

The software-as-a-service (SaaS) space is full of seemingly simple metrics that can quickly slip through your fingers when you try to grasp them.  For example, see Measuring SaaS Renewals Rates:  Way More Than Meets the Eye for a two-thousand-word post examining the many possible answers to the seemingly simple question, “what’s your renewal rate?”

In this post, I’ll do a similar examination to the slightly simpler question, “what’s your customer acquisition cost (CAC) ratio?”

I write these posts, by the way, not because I revel in the detail of calculating SaaS / cloud metrics, but rather because I cannot stand when groups of otherwise very intelligent people have long discussions based on ill-defined metrics.  The first rule of metrics is to understand what they are and what they mean before entertaining long discussions and/or making important decisions about them.  Otherwise you’re just counting angels on pinheads.

The intent of the CAC ratio is to determine the cost associated with acquiring a customer in a subscription business.  When trying to calculate it, however, there are six key issues to consider:

  • Months vs. years
  • Customers vs. dollars
  • Revenue on top vs. bottom
  • Revenue vs. gross margin
  • The cost of customer success
  • Time periods of S&M

Months vs. Years

The first question — which relates not only to CAC but also to many other SaaS metrics:  is your business inherently monthly or annual?

Since the SaaS movement started out with monthly pricing and monthly payments, many SaaS businesses conceptualized themselves as monthly and thus many of the early SaaS metrics were defined in monthly terms (e.g., monthly recurring revenue, or MRR).

While for some businesses this undoubtedly remains true, for many others – particularly in the enterprise space – the real rhythm of the business is annual., the enterprise SaaS pioneer, figured this out early on as customers actually encouraged the company to move to an annual rhythm, for among other reasons, to avoid the hassle associated with monthly billing.

Hence, many SaaS companies today view themselves as in the business of selling annual subscriptions and talk not about MRR, but ARR (annual recurring revenue).

Customers vs. Dollars

If you ask some cloud companies their CAC ratio, they will respond with a dollar figure – e.g., “it costs us $12,500 to acquire a customer.”  Technically speaking, I’d call this customer acquisition cost, and not a cost ratio.

There is nothing wrong with using customer acquisition cost as a metric and, in fact, the more your business is generally consistent and the more your customers resemble each other, the more logical it is to say things like, “our average customer costs $2,400 to acquire and pays us $400/month, so we recoup our customer acquisition cost in six months.”

However, I believe that in most SaaS businesses:

  • The company is trying to run a “velocity” and an “enterprise” model in parallel.
  • The company may also be trying to run a freemium model (e.g., with a free and/or a low-price individual subscription) as well.

Ergo, your typical SaaS company might be running three business models in parallel, so wherever possible, I’d argue that you want to segment your CAC (and other metric) analysis.

In so doing, I offer a few generic cautions:

  • Remember to avoid the easy mistake of taking “averages of averages,” which is incorrect because it does not reflect weighting the size of the various businesses.
  • Remember that in a bi-modal business that the average of the two real businesses represents a fictional mathematical middle.

avg of avg

For example, the “weighted avg” column above is mathematically correct, but it contains relatively little information.  In the same sense that you’ll never find a family with 1.8 children, you won’t find a customer with $12.7K in revenue/month.  The reality is not that the company’s average months to recoup CAC is a seemingly healthy 10.8 – the reality is the company has one very nice business (SMB) where it takes only 6 months to recoup CAC and one very expensive one where it takes 30.  How you address the 30-month CAC recovery is quite different from how you might try to squeeze a month or two out the 10.8.

Because customers come in so many different sizes, I dislike presenting CAC as an average cost to acquire a customer and prefer to define CAC as an average cost to acquire a dollar of annual recurring revenue.

Revenue on Top vs. Bottom

When I first encountered the CAC ratio is was in a Bessemer white paper, and it looked like this.

cac picture

In English, Bessemer defined the 3Q08 CAC as the annualized amount of incremental gross margin in 3Q08 divided by total S&M expense in 2Q08 (the prior quarter).

Let’s put aside (for a while) the choice to use gross margin as opposed to revenue (e.g., ARR) in the numerator.  Instead let’s focus on whether revenue makes more sense in the numerator or the denominator.  Should we think of the CAC ratio as:

  • The amount of S&M we spend to generate $1 of revenue
  • The amount of revenue we get per $1 of S&M cost

To me, Bessemer defined the ratio upside down.  The customer acquisition cost ratio should be the amount of S&M spent to acquire a dollar of (annual recurring) revenue.

Scale Venture Partners evidently agreed  and published a metric they called the Magic Number:

Take the change in subscription revenue between two quarters, annualize it (multiply by four), and divide the result by the sales and marketing spend for the earlier of the two quarters.

This changes the Bessemer CAC to use subscription revenue, not gross margin, as well as inverts it.  I think this is very close to CAC should be calculated.  See below for more.

Bessemer later (kind of) conceded the inversion — while they side-stepped redefining the CAC, per se, they now emphasize a new metric called “CAC payback period” which puts S&M in the numerator.

Revenue vs. Gross Margin

While Bessemer has written some great papers on Cloud Computing (including their Top Ten Laws of Cloud Computing and Thirty Q&A that Every SaaS Revenue Leader Needs to Know) I think they have a tendency to over-think things and try to extract too much from a single metric in defining their CAC.  For example, I think their choice to use gross margin, as opposed to ARR, is a mistake.

One metric should be focused on measuring one specific item. To measure the overall business, you should create a great set of metrics that work together to show the overall state of affairs.


I think of a SaaS company as a leaky bucket.  The existing water level is a company’s starting ARR.  During a time period the company adds water to the bucket in form of sales (new ARR), and water leaks out of the bucket in the form of churn.

  • If you want to know how efficient a company is at adding water to the bucket, look at the CAC ratio.
  • If you want to know what happens to water once in the bucket, look at the renewal rates.
  • If you want to know how efficiently a company runs its SaaS service, look at the subscription gross margins.

There is no need to blend the efficiency of operating the SaaS service with the efficiency of customer acquisition into a single metric.  First, they are driven by different levers.  Second, to do so invariably means that being good at one of them can mask being bad at the other.  You are far better off, in my opinion, looking at these three important efficiencies independently.

The Cost of Customer Success

Most SaaS companies have “customer success” departments that are distinct from their customer support departments (which are accounted for in COGS).  The mission of the customer success team is to maximize the renewals rate – i.e., to prevent water from leaking out of the bucket – and towards this end they typically offer a form of proactive support and adoption monitoring to ferret out problems early, fix them, and keep customers happy so they will renew their subscriptions.

In addition, the customer success team often handles basic upsell and cross-sell, selling customers additional seats or complementary products.  Typically, when a sale to an existing customer crosses some size or difficultly threshold, it will be kicked back to sales.  For this reason, I think of customer success as handling incidental upsell and cross-sell.

The question with respect to the CAC is what to do with the customer success team.  They are “sales” to the extent that they are renewing, upselling, and cross-selling customers.  However, they are primarily about ARR preservation as opposed to new ARR.

My preferred solution is to exclude both the results from and the cost of the customer success team in calculating the CAC.  That is, my definition of the CAC is:

dk cac pic

I explicitly exclude the cost customer success in the numerator and exclude the effects of churn in the denominator by looking only at the new ARR added during the quarter.  This formula works on the assumption that the customer success team is selling a relatively immaterial amount of new ARR (and that their primary mission instead is ARR preservation).  If that is not true, then you will need to exclude both the new ARR from customer success as well as its cost.

I like this formula because it keeps you focused on what the ratio is called:  customer acquisition cost.  We use revenue instead of gross margin and we exclude the cost of customer success because we are trying to build a ratio to examine one thing:  how efficiently do I add new ARR to the bucket?  My CAC deliberately says nothing about:

  • What happens to the water once S&M pours it in the bucket.  A company might be tremendous at acquiring customers, but terrible at keeping them (e.g., offer a poor quality service).  If you look at net change in ARR across two periods then you are including both the effects of new sales and churn.  That is why I look only at new ARR.
  • The profitability of operating the service.  A company might be great at acquiring companies but unable to operate its service at a profit.  You can see that easily in subscription gross margins and don’t need to embed that in the CAC.

There is a problem, of course.  For public companies you will not be able to calculate my CAC because in all likelihood customer success has been included in S&M expense but not broken out and because you can typically only determine the net change in subscription revenues and not the amounts of new ARR and churn.  Hence, for public companies, the Magic Number is probably your best metric, but I’d just call it 1/CAC.

My definition is pretty close to that used by Pacific Crest in their annual survey, which uses yet another slightly different definition of the CAC:  how much do you spend in S&M for a dollar of annual contract value (ACV) from a new customer?

(Note that many vendors include first-year professional services in their definition of ACV which is why I prefer ARR.  Pacific Crest, however, defines ACV so it is equivalent to ARR.)

I think Pacific Crest’s definition has very much the same spirit as my own.  I am, by comparison, deliberately simpler (and sloppier) in assuming that customer success not providing a lot of new ARR (which is not to say that a company is not making significant sales to its customer base – but is to say that those opportunities are handed back to the sales function.)

Let’s see the distribution of CAC ratios reported in Pacific Crest’s recent, wonderful survey:

pac crest cac

Wow.  It seems like a whole lot of math and analysis to come back and say:  “the answer is 1.

But that’s what it is.  A healthy CAC ratio is around 1, which means that a company’s S&M investment in acquiring a new customer is repaid in about a year.  Given COGS associated with running the service and a company’s operating expenses, this implies that the company is not making money until at least year 3.  This is why higher CACs are undesirable and why SaaS businesses care so much about renewals.

Technically speaking, there is no absolute “right” answer to the CAC question in my mind.  Ultimately the amount you spend on anything should be related to what it’s worth, which means we need relate customer acquisition cost to customer lifetime value (LTV).

For example, a company whose typical customer lifetime is 3 years needs to have a CAC well less than 1, whereas a company with a 10 year typical customer lifetime can probably afford a CAC of more than 2.  (The NPV of a 10-year subscription increasing price at 3% with a 90% renewal rate and discount at 8% is nearly $7.)

Time Periods of S&M Expense

Let me end by taking a practical position on what could be a huge rat-hole if examined from first principles.  The one part of the CAC we’ve not yet challenged is the use of the prior quarter’s sales and marketing expense.  That basically assumes a 90-day sales cycle – i.e., that total S&M expense from the prior quarter is what creates ARR in the current quarter.  In most enterprise SaaS companies this isn’t true.  Customers may engage with a vendor over a period of a year before signing up.  Rather than creating some overlapped ramp to try and better model how S&M expense turns into ARR, I generally recommend simply using the prior quarter for two reasons:

  • Some blind faith in offsetting errors theory.  (e.g., if 10% of this quarter’s S&M won’t benefit us for a year than 10% of a year ago’s spend did the same thing, so unless we are growing very quickly this will sort of cancel out).
  • Comparability.  Regardless of its fundamental correctness, you will have nothing to compare to if you create your own “more accurate” ramp.

I hope you’ve enjoyed this journey of CAC discovery.  Please let me know if you have questions or comments.

Measuring SaaS Renewal Rates: Way More Than Meets the Eye

I love cloud computing. I love metrics. And I love renewals. So when I went looking on the Web for a great discussion of SaaS renewals and metrics I was surprised not to find much. Certainly, I found the two classics on SaaS metrics:

  • The Bessemer Venture Partners 10 Laws of Cloud Computing white paper, which I highly recommend despite its increasing pollution with portfolio-company marketing.

The Four Factors
While the above articles are all great, I was surprised that no one really dug into the nitty-gritty of renewals at an enterprise SaaS company, where I believe there are four independent factors at work:

  • Timing. When a contracted is renewed. For example, how to handle when a contract is renewed early or late.
  • Duration. The length of the renewed contract. For example, how to handle when a one-year customer renews for three years, and receives a multi-year discount in the process (for either pre-payment or the contractual commitment itself). [1]
  • Expansion/shrinkage. The expansion or shrinkage of the contract’s value compared to the original contract. For example, how to handle customers adding or dropping seats or products, and/or price increases or decreases.
  • The count metric. What do we wish to count (e.g., bookings, ARR, seats, or customers) and what does it mean when we count one thing versus another.

Particularly in a world where companies are increasingly marketing “negative churn” rates and renewal rates well in excess of 100%, I think it’s worth digging into this and offering some rigor.

A Simple Example
Let’s take a concrete example. Imagine a customer who buys 100 seats of product A at $1,200/seat/year on 7/30/12, with a contractual provision that says the price cannot increase by more than 3% per year [1a].

Imagine that customer renews on 6/30/13, buying 80 seats of product A for $1,225, and adding 40 seats of product B at $1,200/seat/year, and who receives a 15% discount for making a prepaid three-year commitment.

Hang on. While I know you want to run away right now, don’t. This is all real-life stuff in a SaaS company. Bear with me, and download the spreadsheet here (as an Excel file, not a PDF) that shows the supporting math.

A few questions are easy:

  • What were the bookings on the initial order? Answer: $120,000.
  • What was the annual recurring revenue (ARR) of the initial order? Answer: $120,000.
  • What were the bookings on the renewal order? Answer: $372,300.
  • What was the ARR of the renewal order? Answer: $124,100. [2]

Calculating Churn: Leaky Bucket Analysis
So far, so good. Now let’s talk about churn. Because, as you will see, renewal rates alone are complicated enough, I have adopted a convention where:

  • When it comes to renewals, I look only at rates
  • When it comes to churn, I look only at dollars/values

I know this is a completely arbitrary decision, but doing this lets me remember one set of formulas instead of two, reduces rat-hole conversations about definitions, and — most importantly – lets me look at one area in percentages and the other in dollars, helping me to avoid the “percent trap” where you can lose all perspective of absolute scale. [3]

I define churn with an equation that I call “leaky bucket analysis.” [4]

Starting ARR + new ARR – churn ARR = ending ARR

So, some questions:

  • Was there any churn associated with this renewal? Answer: Yes.
  • Why? Answer: Despite a small price increase on product A, there was a 15% multi-year discount and a loss of 20 seats which more than offset it.
  • How much ARR churned? Answer: $36,700. [5]
  • How much new ARR was added? Answer: $40,800. The after-discount value of the product B subscriptions.
  • What is ending ARR? 124,100 = 120,000 + 40,800 – 36,700.
  • How many customers churned? Answer: 0.
  • How many seats churned? Answer: 20.

Note that ARR, seats, and customers are all snapshot (or, point-in-time) metrics that lend themselves to leaky bucket analysis. Period-metrics, like bookings, do not. Bookings happen within a period. There is no concept of starting bookings + new bookings – churn bookings = ending bookings. That’s not how it works. So, when you define churn through leaky bucket analysis, measuring bookings churn doesn’t work.

We can, however, calculate bookings churn as the difference between what was up for renewal and what we renewed. In this case, $120,000 – $372,300 = ($252,300), showing one way to generate a negative churn number. The example makes somewhat more sense in the other direction: if we had a three-year $372,300 contract up for renewal and only renewed $120,000 them we might argue that $252,300 in bookings were churned. From a cash collections perspective, this makes sense [6].

But from a customer value perspective it does not. Unless the customer has plans to discontinue using the service, by dropping from a three-year to a one-year contract we will actually collect more money from them over the next 3 years if they continue to renew ($438,000 vs. $372,300) [7]. So the bookings churn that looks bad for year-one cash actually results in superior ARR and three-year cash collections.

The lesson here is that different metrics are suited for measuring different things. In this case, we can see that bookings churn is useful primarily for analyzing short-term cash collections and not, say, for customer lifetime value or customer satisfaction.

Renewal Rates and Timing
Now that we’re warmed up let’s have some fun. Let’s answer some questions on renewals:

  • From a bookings perspective, when should we count the renewal order? Answer: the order was received on 6/30/13 so it’s a 2Q13 booking.
  • From a renewal rate perspective, when should we count this order? Answer: while debatable, to me it’s a renewal of a 3Q contract, so I would count it in 3Q from a renewal rate perspective. [8]
  • When would we count the booking if it were late and arrived on 10/30/13? Answer: From a bookings perspective, it would be a 4Q13 booking. From a renewal rate perspective, it’s the renewal of a 3Q contract, so I would count it in 3Q. [9]
  • On a customer-count basis, how do we count this renewal? Answer: 100%. We had one logo before and we have one logo after, so 100%. [10]

Here it’s going to get a little dicey.

On an ARR basis, how do we measure this renewal? Answer: this begs the question of whether we should include expansion ARR due to new seats, new products, and price increases. Since I am worried that expansion may hide shrinkage, I want to see this both ways. Hence, I will define “gross” to mean including expansion and “net” to mean excluding expansion.

  • What is the gross ARR-based renewal rate? Answer: 103%. [11]
  • What is the net ARR-based renewal rate? Answer: 69%. Now you understand why I want to see it both ways. The net rate is showing that we lost real ARR on product A due to reduced seats and the multi-year discount. The upsell of product B hides shrinkage, producing an innocuous 103% number that might evoke a very different scenario in the mind’s eye (e.g., renewing the original deal for one year with a 3% price hike).
  • What is the gross bookings-based renewal rate? Answer: 310%. We took a $120,000 order and renewed it at $372,000. (But we transformed it greatly in the process.)
  • What is the net bookings-based renewal rate? 208%. We took a $120,000 order for product A and turned it into a $249,000 order for product A. But we dropped ARR about 33% in the process (from $120,000 to $83,300) through lost seats and the multi-year discount.
  • What is the gross seat-count renewal rate? 120%
  • What is the net seat-count renewal rate? 80%
  • What is the customer-count renewal rate? 100%

Identifying the Best Renewal-Related Metrics
So, what is the renewal rate then anyway?  69%, 80%, 100%, 103%, 120%, 208%, or 310%?

I’d say the answer depends on what you want to measure. Having nearly drowned you in the renewal-rate swamp, let me now drain it. Here are the metrics that I think matter most:

key renewals metrics

Here’s why:

  • Leaky bucket analysis is important because ARR growth is the single most important driver of value for a SaaS company.
  • Churn ARR shows you, viscerally, how much extra you had to sell just to make up for leaks [12].  Rates seem sterile by comparison.
  • The customer count-based renewal rate is the best indicator of overall customer satisfaction: what percent of your customers want to keep doing business with you, regardless of whether they change their configuration, product mix, seat mix, contract duration, etc.
  • The gross seat-based based renewal rate shows you how effective you are at driving adoption of your services. Think: land and expand (in terms of seats).
  • The gross ARR-based renewal rate shows you, overall, how effective you are at increasing your customers’ annual commitment. However, it says nothing about how you do that (i.e., which type of expansion ARR) or the extent to which expansion ARR in one area is offsetting shrinkage in another.
  • The net ARR-based renewal rate shows you how much of ARR you renew without relying on expansion. This is a very conservative metric designed to unmask problems that can be hidden by expansion ARR.
  • The gross bookings-based renewal rate is the best predictor of future cashflows. If we know that, on average, we take an order of 100 units and turn it into an order of 175 units – through whatever means – then we should use this metric to predict cashflows. Note that, as we’ve seen, there are trade-offs between ARR and bookings, but the consequences of those can be revealed by other metrics.

[1] Note that in a multi-year prepaid contract that bookings (order value) equals total contract value (TCV). When multi-year contracts are not prepaid, bookings are only the first-year portion of TCV.

[1a] Some purists would argue that having the right to raise the price 3% should set the denominator of subsequent renewal rate calculations to 1.03 * original-value.  While I get the idea, I nevertheless disagree.

[2] The renewal order is for three years, so to calculate the ARR we need to divide the bookings value by three.

[3] Saying our “churn rate was 10%” makes things sound OK, but saying we churned $2M in ARR is, to me, somehow more visceral. That is, we had to sell an extra $2M in ARR just to make up for existing business that we lost.

[4] A leaky bucket starts at one water level, during a period new water is added, some water leaks out, and the net change establish the ending water level. (Note that in leaky bucket analysis, definitionally, leaks are never negative.)

[5] Now might be a good time to download the spreadsheet accompanying this post so you can see my calculations. In this case, the churn is the difference between the total value from product A on the original order versus the renewals order.

[6] Subscription bookings typically turn into cash within 90 days.

[7] In reality, we should both uplift the price in years 2 and 3 and discount by the renewal rate to get a better expected cash collections figure. (There is nearly endless detail in analyzing this subject but I will make simplifying assumptions at times.)

[8] Otherwise, it would juice 2Q renewal rates and depress 3Q renewal rates, making both less meaningful.

[9] Bonus question:  how would you handle the late-renewal scenario at the 7/20/13 board meeting? Answer: I would publish provisional renewal rates that exclude the transaction, letting the board know we have an outstanding renewal in process. Then once it closed, I would revise the 3Q renewal rates accordingly.

[10] Which then begs the question of how you count customers. For example, while GE has one logo, they have numerous very independent divisions in a large number of countries.

[11] Note that purist might argue that since we had the right to raise prices up to 3% that we should put 103% of the ARR in denominator in this and all similar calculations, thus dropping the resulting renewal rate here to 100%.  While I believe annual increases are important, I still believe renewing someone to 103K in ARR who was at 100K in ARR is a 103% renewal.  Tab 3 of the supporting spreadsheet plays with some numbers in this regard.

[12] It is a good idea to divide churn into 3 buckets to describe the reason: owner change (including bankruptcy), leadership change, and customer dissatisfaction.

What Drives SaaS Company Valuation? Growth!

If you’ve ever wondered what drives the valuation of a SaaS vendor, then take a look at this chart that a banker showed me the other day.

saas valuations 2The answer, pretty clearly, is revenue growth.  The correlation is stunning.   Taking some points off the line:

  • 10% growth gets you an on-premises-like valuation of 2x (forward) revenues
  • 20% growth gets you 3x
  • 30% growth gets you 4x
  • 50% growth gets you nearly 6x

Basically (growth rate % / 10) + 1 = forward revenue multiple.

You might think that profitability played some role in the valuation equation, but if you did, you’re wrong.  Let’s demonstrate this by looking at CY13 EBITDA margins as reported by the same banker:

  • Marketo (MKTO) -44% with a ~4x revenue multiple
  • Marin Software (MRIN) -40% with a ~4x revenue multiple
  • Workday (WDAY) -22% with a ~11x revenue multiple
  • Bazaarvoice (BV) -6% with a ~5x revenue multiple
  • Cornerstone on Demand (CSOD) 0% with a ~8x revenue multiple
  • Qlik Technologies (QLIK) 13% with a ~3x revenue multiple
  • Tangoe (TNGO) 17% with a ~3x revenue multiple

As you can see, there’s basically no reward for profitability.  In real estate what matters is location, location, location.  In SaaS, it’s growth, growth, and growth.

The Best SaaS / Cloud White Paper: Bessemer’s Top 10 Laws of Cloud Computing and SaaS

After doing a lot of reading in recent days, I thought I’d take a few minutes to share what I think is one of the best resources I’ve discovered:  Bessemer’s Top 10 Laws of Cloud Computing and SaaS (PDF), co-authored by about ten people from Bessemer including Byron Deeter.

Here is a quick summary of their top 10 laws:

1.       Less is more!  Use the cloud where you can in your own business.  I think this is a great idea in the eat-your-own-dogfood (at a model level, at least) department.  While MarkLogic was not a SaaS company, we were nevertheless big SaaS users (e.g., sales automation, marketing automation, finance, time tracking, expense reporting) because I’m a big believer in the model.

2.       Trust the 6 C’s of cloud finance.  Your new key metrics should be (1) committed monthly recurring revenue (CMRR), (2) cash flow, (3) CMRR pipeline, (4) churn, (5) customer acquisition cost (CAC), and (6) customer lifetime value.  This is a different set of metrics from the traditional enterprise software business and one worth taking the time to understand.

3.       Study the sales learning curve (SLC) and only invest behind success.  The SLC is a creation of former Veritas CEO Mark Leslie and discussed in this HBR article (paid) or this presentation.  A simpler version of the principle is to hire reps in groups of threes and only expand when 2 of 3 become profitable in the first group.  This avoids prematurely scaling-up the sales force which, probably more than any other sinkhole, has wasted countless venture capital over the past few decades.

4.       Forget everything you learned about software channels.  Because cloud products, by their nature, are not services-intensive and this fundamentally changes the role, and reduces the importance, of service providers in the industry equation.  Put more simply:  SaaS businesses are generally direct, leverage the Internet as a direct channel, and are not indirect-channel friendly.

5.       Build employee software.  Employees are now powerful customers, not just their managers.  We’re witnessing “the consumerization of software,” so ease up.  This is a very clear trend, in fact, many SaaS/cloud businesses work their way into the enterprise by starting out with individual consumer managers at small and medium businesses.  In the past, you could sell executive management “a better return on information” and condemn clerks to horrific user interfaces.  Those days are gone.

6.       Savvy online marketing is a core competence (sometimes the only one) of every successful cloud business.  Among other things this foretells of the rise of analytical and quantitative marketing VPs, over the more traditionally product-strategy and/or communications-creative types.

7. The most important part of software-as-a-service isn’t “software,” it’s “service!” Support!  Support!  Support! Culturally, this runs dead opposite to the traditional enterprise software “drive-by sales” approach whereby, as one search-engine salesrep once told me:  “we sold the customer a Ferrari – but then we dumped the pieces in his driveway.”  This natural incentive alignment (which by the way was also a by-product of the vertical-focus strategy at MarkLogic) is one of my favorite features of the SaaS model.

8.       Leverage and monetize the data asset.  You can do this by leveraging your expertise to identify the metrics and dashboards of most analytic value and further by then selling industry benchmark data on them.  This, to me, is one of the more obvious SaaS opportunities, yet nevertheless to-date, in my experience, one of the most unexploited.  I expect to see much more progress in this area in the coming few years.

9.       Mind the GAAP.  Cloud accounting is all about matching revenue and costs to consumption … except when it’s not (i.e., professional services).    Taleo’s struggles have been well publicized, Bessemer’s paper provides a great overview of the issues, and for those who want to know more, here is an excellent paper (SaaS is Different, An Accounting Primer for SaaS Companies by Jay Howell of BDO) that discusses SaaS accounting differences which are primarily related to (1) recognizing revenue over the term during which the service is live/delivered and (2) pro-rating professional services over the full duration of the software-service contract and potentially the lifetime of the customer relationship.

10.   Cloudonomics requires that you plan your fuel stops very carefully.  SaaS companies are capital intensive and typically require at least 4 years before they are cash-flow positive.  NetSuite needed $126M before its IPO, DemandTec $66M, Salesforce $61M, and SuccessFactors $45M.

Fun Software Annuity Math: SaaS, Perpetual, and Open Source Models

As a follow-up to my previous post, Perpetual Money vs. Perpetual License, I thought I’d take a few minutes to further explore the math of software annuities.

Let’s start with some perpetual license software that costs 100 units and has an annual maintenance fee rate of 20%, or 20 units per year in perpetuity.  To avoid math and equations, I’m going to brute-force things in Excel, do a 40-year model, round it up, and consider that equivalent to perpetuity.  (You can download my spreadsheet here.)

Now what is this sequence of cashflows actually worth?  100 + 20 + 20 + …

Before calculating we need to remember two things:

  • Not everyone renews every year so we need to model a maintenance renewal rate (MRR) and use it as a probability of renewal each year
  • The time value of money becomes important in long time series so we’re going to need to pick a discount rate (say 8%) and model that as well

It turns out that 100 + 20 + 20 + … is worth 220 units.  Recall in the prior post that we said sales commissions typically run 10% and are paid on license and first-year maintenance only.  Thus, the company pays commissions on the 120 year-one units, which represent only about 54% of the value of the contract.

Now let’s see what a SaaS annuity is worth at 50 units per year.  The answer:  300 units.  More interestingly, let’s find the breakeven point between the SaaS and the perpetual model (i.e., where 100 + 20 + 20 + … = X + X + …).  The answer:  37 units.  That is, a SaaS annuity of 37 units is mathematically equivalent to a perpetual fee of 100 with a maintenance annuity of 20 in perpetuity.

Note that all these calculations have been based on a 90% renewal rate.  Let’s see what happens if the renewal rate drops to 80%.

  • The maintenance renewal stream’s value drops from 120 units to 77 (36%), so the total value drops from 220 to 177 units (20%)
  • The SaaS annuity stream drops in value from 300 units to 193 units (36%)

Conversely, if we increase the renewal rate from 90 to 95%:

  • The total value in the perpetual model jumps to 266 units (21%)
  • The total value in the SaaS model jumps to 413 units (38%)

So, unsurprisingly, the large up-front payment in the perpetual model acts as a keel on the total value, damping the volatility of the renewal stream’s value as a function of renewal rate.

But – back to plain English – you can see why SaaS companies are so focused on renewal rates:  increasing the renewal rate by 5% increases revenue by 38% over the long term.  That’s leverage.

Let’s conclude by looking at open source models where certified/enterprise releases and associated support services are sold on a subscription basis.  In some ways you can think of this as SaaS without the need to operate the software.  But I think it’s more accurate to think about about this as a perpetual model where the initial license fee is zero.  (Arguably, the difference is pure semantics.)

Let’s say a piece of enterprise software costs 100 units and comes with a 20 unit annual maintenance obligation.  We know that’s worth 220 units in total.  If you sell the open source support subscription for the same price as perpetual maintenance fee, then the value for the company is 120 units and the customer “saves” 46% — and all of it up-front – by avoiding the initial license payment.

If you could sell the open source subscription for 30 units, both sides still win.  The value is 180 units, still generating a savings for the customer and a revenue increase for the vendor.  The breakeven point is, as we found earlier, 37 units – at that price the customer should be indifferent to either a 100 + 20 + … stream or an annuity of 37 + 37 + …

Let’s say we decided to sell our open source subscriptions for 30 units and see what happens as we vary the renewal rate.

Now you can see why open source vendors are so focused on renewal rates.  What’s more, when a SaaS customer fails to renew they need to stop using the software.  When an open source customer fails to renew they simply downgrade to using the unsupported or community-supported open source version of the software, which is a far less dramatic alternative.  This is why open source vendors work so hard to justify the upgrade to their supported enterprise versions.  With a renewal rate of 95% the value is 248 units.  If that rate drops to 65% because many people feel they can get by with the community version, then the value drops to 75 units – a difference that could decimate a company.

As one friend in the open source business said:  “it’s hard work giving away your software.”  Remember that while MySQL was ubiquitous at the time of its $1B sale to Sun Microsystems, there were rumored to be doing only about $65M in annual revenue.  Such is the nature of disruption.

Perpetual Money vs. Perpetual License: Subscription, SaaS, and Perpetual Business Models

I had breakfast the other day with a software entrepreneur.  When I asked if his company was on a subscription or perpetual model he said:  “we should kill the guy who invented the perpetual license — I’m on the perpetual money model, subscription all the way.”

Having worked largely in perpetual license firms, I admit there are many downsides to the perpetual model.  Companies on perpetual models typically:

  • Have more volatile revenue performance due to a relatively smaller annuity “keel” on the business (in the form of maintenance renewals).
  • Are more exposed to end-of-quarter shocks driven by backend-loaded sales.  (Most software companies get 70%+ of their orders in the last month of the quarter and most of those in the last week.)
  • End up with “drive-by sales” cultures because sales reps are paid only on license sales and not on maintenance renewals.
  • Have less customer-success-focused cultures because sales reps care about customer success only to the extent they see potential follow-on license business in the short term.

That said, there are many ways to mitigate each of the above points and all of the world’s largest software companies, such as Oracle and SAP, still do most of their business on a perpetual license model.

Over the past decade companies like Salesforce, NetSuite, and SuccessFactors have pushed the software as a service (SaaS) model where the vendor both runs the software and bills on an annual subscription basis to use it.  While the SaaS model cut its teeth in applications like sales force automation, vendors are increasingly selling platform as a service (PaaS) offerings as well, such as Amazon Web Services, Google AppEngine, or

Clearly SaaS interest and hype remain strong.  Salesforce is trading at 100x FY11 earnings.  Bankers have told me that the IPO bar for SaaS companies is $75 to $100M in revenue, while for perpetual companies it might be 1.5 times higher than that.  A recent Software Equity Group report pegs the median enterprise value (EV) of of SaaS companies at 4.9x revenues, almost double the 2.7x revenues for perpetual companies.  On an EV/EBITDA basis, it’s even more dramatic with SaaS companies at 44x and perpetual ones at 13.6x.

Given all this, I thought it would be fun to make an Excel model that concretely demonstrates some of the differences between  perpetual and SaaS software companies.  To do so, I’ll first model a fictitious, red-hot software startup on a perpetual basis.  Then I’ll remodel the same company on a SaaS basis.  Then we’ll play around with the models and see what we find.  (For Excel geeks, my model is here; you’ll need to download it.)

To make my model, I started with bookings for the perpetual company and hard coded $5M in the first year on a reasonable ramp.  Then I made a set of reasonable assumptions (for a hot startup) that drove the rest of the model:  100% license bookings growth, a 20% maintenance rate, a 90% maintenance renewal rate, a 50% rate of professional services organization (PSO) services bookings relative to license, and a bookings-to-revenue conversion rate of 85% for PSO in the subsequent quarter.  To keep things simple, I didn’t model months, I didn’t model cash, I assume all bookings happen on the last day of the quarter, and I assume all license revenue is immediately recognizable.

Then I remodeled the company on a SaaS basis.  The most important assumption to make here is labeled “subscript as % of license” – i.e., if someone was ready to pay 100 units for a perpetual license to use something, presumably they want to pay some fraction of that for a one-year subscription to use it.  (I’ll call this F for fraction.)  For the initial model, I assumed F=50% which is arguably aggressive.  I kept the renewal rate at 90%.  I assumed that configuring a SaaS system requires less PSO than customizing a perpetual one, so I assumed a 50% PSO bookings rate relative to the subscription (or 25% of the total PSO required from the perpetual vendor).  I assumed subscriptions were one year and revenue was recognized ratably over the year and that all orders were received the last day of the quarter.

When you make these two models, here is what you find:

In year 4,

  • The perpetual company is 2.2 times larger than the SaaS company at $62M vs. $28M
  • The perpetual company is growing at 103% and the SaaS one at 115%
  • The perpetual company has an 8% “annuity keel” in the form of maintenance renewal bookings while the SaaS company has a 33% annuity keel in subscription renewal bookings.  (You can’t see this in the picture, but it’s in the model.)

Valuation and The Fallacy of Equivalence
Using the standard multiples above, let’s see what each of our companies is worth:

  • The $62M perpetual company is worth 2.7 x $62M = $167M
  • The $28M SaaS company is worth 4.9 x $28M = $137M

Simply put:  the stock market works.  With only a 20% difference in valuation between what ostensibly seem like two very different companies you can see that higher EV/R multiple for SaaS companies is almost completely offset by the increased difficulty of building a SaaS revenue stream.  Wall Street “sees through” the differences in the models and values the companies roughly equivalently.  Put differently, SaaS companies fetch 1.8x the revenue multiple of perpetual companies because they are worth 1.8x the revenue multiple of perpetual companies.

During the past few years I have spoken with several CEOs who transitioned their companies from perpetual to SaaS.  The standard word is that it takes 3 years to make the transition and the transition must be a top-three company goal for that entire period.  While there are many good reasons for perpetual companies to consider moving to SaaS models, valuation isn’t one of them.  Yes, you get roughly twice the EV/R multiple, but building the R (revenue) stream is just about twice as hard.

Max Schireson calls this the fallacy of equivalence.  If gold is worth twice silver and assume we have an equal amount of gold as we had silver then we are worth twice as much.  The fallacy is that gold is twice as hard to come by as silver so you can’t assume equal amounts — see the huge revenue delta which is largely driven by the SaaS company’s need to spread revenue over 4 quarters.

Taking a Bad Quarter
Let’s look at how each company takes a bad quarter by assuming that we hit 70% of our bookings target in 3Q13 — doing only $4M in perpetual license bookings (cell P8) and only $2.25M in new subscriptions (cell P27).

  • In the perpetual company 3Q11 revenue drops from $8.7M to $6.7M, the year/year growth rate drops from 105% to 58%, the stock is presumably crushed  by 80%, and the CEO summarily fired.
  • In the SaaS company 3Q11 revenue is unchanged. (Recall I modeled all bookings on the last day of the quarter.)  4Q11 revenue drops from $4.5M to $4.0M, 1Q12 drops from $5.8M to $5.6M, and the following two quarters also take ~$100K to $200K hits.  The stock drops 20% because 4Q11 guidance is dropped but the company appears in control of its business and no one is fired.

Hitting The Flat Part of the Market
Now let’s examine both companies assuming that the market goes flat in 2014 (i.e., that 2014 license bookings / new subscriptions do not grow over 2013, cells S8-V8 and S27-V27).

  • Our perpetual company sees 2014 revenue growth slow from 106% in 2013 to 17% in 2014.  Revenue drops from the plan of $62M to $35.9M.  The CEO is fired for flying the company off a cliff.
  • Our SaaS company sees 2014 revenue growth slow from 141% in 2013 to 76% in 2014.  Revenue drops from the plan of $27.9 to $22.9M.  The CEO is commended for successfully managing the company through a tough transition.

What going on here is simple:  volatility is being damped — for better and for worse — by the SaaS company’s need to spread revenue over the four quarters following the booking.  That makes it harder to grow the revenue stream quickly.  It also makes it harder to change once established.

Sales Compensation
One tricky issue in the SaaS model is sales compensation.  In a typical perpetual company total sales commissions (at all levels) add up to around 10%.  So, for 100 units of revenue, you pay 10 units in commissions.  Sales reps are usually not paid on the 20 unit annuity stream of maintenance renewals.

In SaaS model, we have a conflict.  If you assume the annual subscription fetches 50 units (i.e., if F=50%):

  • The company wants to pay 10% of 50 = 5 units in year 1 and then pay little or nothing on the renewals.
  • Sales want to argue either that [1] the deal is worth 150 units over three years and compensation should be 15 units or [2] (if they’re good at math) 300 units if you look at the stream’s terminal value (factored by renewal rates and discounted by 8%) and thus sales compensation should be 30 units.

So what do you pay:  5, 15, or 30 units?  I believe that most SaaS companies end up splitting the difference in the some way, perhaps paying on a declining scale over the first 3 years.  If you have good examples here, please share them in the comments.

While I didn’t model cash in the spreadsheets, one huge issue is the timing of commission payments.  For example, if a company were to adopt the 3-year 15-unit commission argument and foolishly pay those three years up front, it would have a big cash consumption issue because effective year 1 commission rates would be 15/50 = 30%, three times the industry norm of 10%.

I think the best answer is to pay commissions on an declining scale and timed close to the receipt of cash from the customer (e.g., on booking the annual renewal).

What if F>=1?
Recall earlier that we talked about the fraction, which I called F, that represented the fraction you would be willing to pay to use something for a year as opposed to license it forever.  Because of the big difference between “forever” and “1 year,” I led you easily to the assumption that F should be less than 1.

But should it be?  When you look at total cost of ownership, it’s not obvious.  In the perpetual  model you need to license the software, pay annual maintenance, pay typically 4x the license payment in total deployment costs, and buy the hardware on which the system will run.

In the SaaS model, you have the subscription cost each year and some modest year 1 costs to configure the application.  See this simple model:

With F at 50% the SaaS TCO is $200K vs. $610K for the perpetual model.  With F at 100% the SaaS TCO is $400K.  Even with F at 150% the SaaS TCO is $600K — still less expensive than the perpetual TCO at $610K.

And this, by the way, isn’t theory.  A friend who worked at Siebel told me that a typical Siebel sales perpetual license seat sold for about $1,500 back in the day.  A friend’s company recently renewed Salesforce at roughly $100/seat/month, that is $1,200/seat/year — not quite F=1, but in the same order of magnitude.

Let’s finish the post by seeing what happens to our model when we assume that F=1, i.e., that the SaaS vendor can get an annual subscription equivalent to the license fee a perpetual vendor would have charged.

In year 4, our our SaaS company is now $55.8M or 90% of our perpetual company, but with all the added benefits of being on a SaaS model.  In terms of valuation it is now worth $274M vs. $167M for the perpetual company.  This is clearly SaaS panacea.  The implicit assumption that an annual subscription to use a service should cost less than equivalent perpetual license is both invalid from a customer TCO viewpoint and suboptimal from a SaaS vendor viewpoint.

While this would seem to suggest that every software vendor should switch to a SaaS model, it is important to remember that many customers don’t want to buy — particularly development platforms — on a SaaS basis.  Why?  Some of it is about ownership and control.  But much of it is because many customers think on time horizons much longer than a 3-year TCO.   With F=100% in our TCO model (and ignoring TVM effects), the SaaS system becomes more expensive after year 6.

If you like playing with financial models, I encourage you to download the model spreadsheet that I built for this analysis, play with the assumptions, and share your own conclusions.  My plan is to do some open source analysis by setting F=35% and the license fee to zero.

To SaaS or Not To SaaS: That is the Question

[Revised, rewritten, and replacing a post from yesterday]

One question we encounter with our Information and Media customers is whether they should buy MarkLogic Server and build an application on top of it, or use a SaaS offering (which may or may not be based on MarkLogic) and effectively rent the use of an application to meet their online publishing needs.

The primary arguments in favor of the rent (SaaS) approach are:

  • You get up and running faster because you’re renting the use of an existing application
  • You have lower up-front fees because you need neither to build your application nor buy the hardware/software platform on which to run it
  • You can focus on what matters because you are liberated from the nitty-gritty of building and deploying production systems

The primary arguments in favor of the build approach are:

  • You create a unique offering which you can use to differentiate from your competition
  • Your costs are potentially lower over the mid-term (SaaS’s relatively high annual payments reverse the initial savings over a few years; if you don’t believe me, remember that Wall Street values a dollar of SaaS revenue at about 2-3x a dollar of perpetual revenue)
  • You create a strategic platform on which you build future applications, reducing the marginal cost of experimentation and new product development

To me, SaaS is not a religious issue; it’s a practical one.

While we typically sell our software on a perpetual license basis, we nevertheless are a big user of SaaS solutions at Mark Logic. We happily use Salesforce and somewhat less happily use Netsuite. I was also a champion of bringing Salesforce into Business Objects, where we became one of their earliest, large enterprise customers. (As I told IT at the time: if you won’t treat me as a customer, then I’ll go find someone who will.)

Turning back to the question of publishers and SaaS, like most questions in business, the answer should derive from strategy.

  • If you are trying to compete solely on the basis of your proprietary content, then you should consider a “rent” strategy.
  • If you are trying to compete on the basis of mixing content and its delivery mechanism, then should consider a “buy” strategy.
  • If you are in between, then you’ll need to figure out where you are on the continuum and what you’re willing to trade for what.

As I always say, there are two things that money can’t buy: love and competitive advantage. Applied here, if you can rent a solution then your competitor down the street can rent it, too, and no amount of application configuration is going to result in competitive advantage (or disadvantage) for either of you.

What does this mean? It means that SaaS is great for what Geoffrey Moore calls “context” and rotten for what he calls “core.” Excerpt from the referred page:

Core – See Core/context analysis
Any activity which creates sustainable differentiation in the target market resulting in premium prices or increased volume. Core management seeks to dramatically outperform all competitors within the domain of core.

Context – See Core/context analysis
Any activity which does not differentiate the company from the customers’ viewpoint in the target market. Context management seeks to meet (but not exceed) appropriate accepted standards in as productive a manner as possible.

That’s why we happily use Salesforce and Netsuite at Mark Logic — we aren’t trying to differentiate on the basis of our accounts receiveable or pipeline management systems. (We are trying to differentiate on technology, market focus, and services excellence.)

So, for publishers

  • The more your basis of competition is ownership of a proprietary content set, the more delivery becomes context, and the more you should consider SaaS
  • The more your basis of competition is (1) uniting your content with other content, (2) delivering content in unique in-context ways, and (3) rapid innovation in online product development, the more delivery is core, and the more you should build custom applications (i.e., new information products) on a standardized platform.

Highlights from the 2007 Software Industry Equity Report

On a recent flight from New York, I read the 2007 annual Software Industry Equity Report ($495) by the Software Equity Group. I think they do a great job with these reports and they’re a great value. I thought I’d share a few highlights here.

  • Among the “high flyers” in their public equity software index, the median trailing twelve month (TTM) revenue growth was 51% and the median enterprise value (EV) / revenue ratio was about 8x.
  • In 4Q07, the median EV/revenue was 2.3x, EV/EBITDA 15.2x, EBITDA margin 11.2%, and TTM revenue growth 14.4%. Put differently, while the median software company is worth only 2.3x sales, that company has only 11% EBITDA margins and is growing at only 14%.
  • My favorite arbitrage in software continues to exist: the median software company with revenues $1B is valued at 3.6x sales. This means that big software companies can buy revenue from little software companies all day long and make money at it. Example: Business Objects buys Cartesis and its $125M revenues for 2.4x ($300M) and then sells that revenue to SAP for 4.5x sales, effectively $562M. How’s that for a simple explanation of consolidation in enterprise software?
  • The median software as a service (SaaS) EV/revenue was 7.5x in 4Q07, with median revenue growth 42.5%, and median EBITDA margin 7.5%. These increased valuations — and more predictable revenue streams — help to explain the market’s continued enthusiasm for SaaS.
  • There were 26 total software initial public offerings (IPOs) in 2007. The median offering amount was $107M, enterprise value $689M, EV/revenues 9.2x, and EV/EBITDA 37.2x.
  • In the current US IPO pipeline, the median offering amount is $86M, annual revenues $56.4M, and net income -$4.8M. (This suggests to me that the current “IPO window” is set to 50/0/50 — $50M in revenue, 0 profit, and 50%+ growth.)
  • In 2007, $5.1B in venture capital was raised by software companies (a 3% increase compared to the prior year).
  • 408 software M&A transactions closed in 4Q07, representing $32.5B in value.

For more information, the executive summary of the report is available for free, here. The full report is $495, here.