SaaS Valuations: Part 1

Long story short: The revenue multiples we typically use to describe SaaS company valuations obscure a lot of information, particularly growth. Replicating those multiples using simple discounted cash flow valuations shows that growth rates have a large impact on valuation. So let’s not react to multiples without giving equal, if not more, weight to the assumptions about growth rates and their persistence. IF you believe the high growth rates, you believe the multiple—that’s mechanical. 

Longer version:

The market isn’t crazy—at least not in the long-term. It tracks fundamentals (revenue, cash flows, capital invested, etc.).

Consider the following three perspectives:

• Since taking over Berkshire Hathaway in 1965, Warren Buffett has focused consistently on growing book value per share in the belief that the company’s market value per share will track accordingly. That belief has proven to be pretty true:

• In their popular book Valuation: Measuring and Managing the Value of Companies,  McKinsey & Company described an analysis they did in which they estimated the earnings multiple of the stock market at specific points in time over a forty year period based on a forward-looking cash flow model. They compared the predicted price-to-earnings (P/E) multiple to the actual P/E multiple, drawing the chart below. They concluded: “Over the long term, the stock market as a whole appears to follow the simple, fundamental economic laws described in Chapter 3: Value is driven by returns on capital, growth, and—via the cost of capital—interest rates.”
  

• A more recent study by McKinsey & Company tied different economic eras to stock returns. A nice (and deliberate) aspect of the descriptions is that they include eras long enough so that major drops in the stock market (e.g., the dot-com bubble and housing crash) are placed in proper context. Their conclusion was the same: 

Unlike the market for fine art or exotic cars, where value is determined by changing investor tastes and fads, the stock market is underpinned by companies that generate real profits and cash flows. Most of the time, its performance can be explained by those profits, cash flows, and the behavior of inflation and interest rates. Deviations from those linkages, as in the tech bubble in 1999–2000 or the panic in 2009, tend to be short-lived.

 

So for SaaS companies…?

I liked McKinsey’s approach of replicating the multiples with discounted cash flows. It squared the circle, so to say, for me by explicitly tying the shorthand language of multiples to the more meaningful underlying assumptions about fundamentals. 

I wondered: could you do the same for SaaS multiples? Could you describe a large part of the difference in multiples with simple assumptions?

Below are SaaS multiples for the most significant public SaaS companies:

These are all the SaaS companies from Pacific Crest’s weekly Software Company Valuations distribution as of May 3, 2013. (I’ve included the specific distribution here. See page 2 for the SaaS companies, and their detail.) 

The projected revenue growth rate from 2013 to 2014 is on the x axis, and the corresponding current enterprise value as a multiple of 2014 projected revenue is on the y axis. 

(Those outliers at the top are NetSuite, to the left, and Workday, to the right—I’ll dive into what’s going on there in a later post.)

There’s a pretty clear relationship:

The red point is RealPage (RP), which is projected in the Pacific Crest document to grow at 16 percent and is trading at 3.3x 2014 projected revenue. Specifically, it has an enterprise value of $1.5 billion and projected 2014 revenue of $454 million.

I valued RealPage using a simple discounted cash flow, with the following assumptions:

• Revenue decay rate of 10 percent (i.e., revenue growth of 30 percent this year, 27 percent next year)

• Unlevered free cash flow margins of 20 percent

• Forecast period of 10 years

• Share count increase of about 2 percent each year

• Discount rate of about 12 percent

• Perpetual growth rate at the end of the ten year period of 2.5 percent

The resulting valuation is pretty close (RP*):

If you take that discounted cash flow analysis and just change the growth rates to reflect assumed ‘13 to ‘14 revenue growth rates of 10 to 50 percent and keep everything else the same, you get the following points and trend line (orange):

In other words, if you’re reasonably confident a company will grow its revenue 50 percent in 2014 and that it will see no more than 10 percent decay in that growth rate for the next ten years while sustaining 20 percent unlevered free cash flow margins, you’d be willing to pay 7.5 times 2014 projected revenue for that company. 

And you’d only pay 2.8 times 2014 projected revenue for a similar company that would only grow revenue 20 percent in 2014. 

That’s mechanical. Those assumptions are certainly arguable, but that’s not the point. The point is that the multiples tell you nothing. They just incorporate deeper—more meaningful—assumptions that do tell you something. Those assumptions at least you can debate, test, and research. Those assumptions and their robustness drive the valuation. Not the multiples. The multiples are just a summary. 

In other words, a 7.5 times revenue valuation by itself doesn’t indicate a valuation that is expensive or cheap.

Now, let’s take those points and draw them again changing one assumption: revenue decay rate.

Let’s say, hypothetically, there were SaaS companies with very talented management, large revenue bases, high historic growth rates, great products, and proven development and sales capabilities attacking tremendously large markets with incumbents that were constrained by technology and legacy deployments from responding. And let’s say that for those reasons you believe those companies can sustain high growth rates for a long time horizon.

So let’s say instead of a 10 percent revenue decay rate you forecast a 5 percent revenue decay rate and keep the other assumptions above the same.

You get the red points and trend line:

Remove NetSuite and Workday, and you get this:

That’s a pretty good match. 

Conclusion

My goal with the above is to make clearer the language we should use to think about SaaS valuations. Revenue multiples obscure too many assumptions.

At the highest level, they obscure the impact growth has on valuations. If you’re an investor for the long-term, buying these companies for the future cash flows they give you in return for your investment, the higher valuations as a function of higher growth rates are completely justified. If you believe the growth rates and other assumptions, you believe the multiple—that’s mechanical.

At deeper levels—operating margins, capex, discount rates, perpetual growth rates—this gives you a framework to work with. We know roughly the assumptions the market is pricing into the valuation of these companies. We can then determine where we differ.

SaaS Valuations: Intro

I’ve had a few conversations recently that led me to dig deeper into SaaS company valuations. The conversations were along the lines of: 

• “Workday is trading at 15 times 2014 revenue—that’s crazy!”

• “Let’s hope SaaS company valuations hold up.”

• “I need to value [early stage SaaS company]—what are SaaS companies multiples looking like?”

These conversations bother me because embedded in them are a number of incorrect ideas.

In the first, it’s the idea that a 15 times revenue multiple is too high. It ignores the fact that Workday is forecast to grow revenue north of 50 percent in 2014—and that’s on 2013 projected revenue of $425 million. That’s tremendous growth by any standard, but on $425 million it’s incredible. So the number of 15 times revenue tells you nothing.

The second implies the valuations are unreasonable and that we are at the whim of the market in selling shares to the public. The reality is that, one, the valuations are reasonably supported by SaaS company fundamentals and that, two, the market overall is pretty good about aligning prices with fundamentals. At least, it is in the long-term. Not so much in the short-term. 

The third statement is, in part, a version of the first. Most private SaaS company are growing revenue at much faster rates than the public companies. Applying the same multiple ignores that fact.

It’s also flawed because the market for private SaaS companies is very different than that for public SaaS companies. Public SaaS companies have pretty definitively reached escape velocity. They are clear going concerns, whose near-term revenue growth is relatively known. Private SaaS companies are riskier affairs. I argue actually that they reach escape velocity earlier than people seem to think, justifying what many believe are unreasonable valuations. But given the risk at various stages of development, the right way to value them in my opinion is the probability-weighted forecast of an IPO at any given stage.

So I’m going to write three posts to address these and related points. Those posts are:

• Part 1: The stock market isn’t crazy. Stock market values in general track fundamentals, and SaaS company valuations do so as well. 

• Part 2: What are the chances? An approach I’ve been toying with builds on that idea. If the IPO values have a dependable logic to them, then the right approach to valuing earlier stage private SaaS companies is a valuation based on the probability that the company goes public. Having seen these valuations done, I know this is what investors in the private market do anyway. I’d just like to formalize this a bit. 

• Part 3: Embedded options in public SaaS companies. Finally, I’m going to explore a bit whether public SaaS valuations might be missing some key elements of SaaS companies: (1) low downside risk, given recurring revenues and high steady state cash flow margins and (2) dramatic upside potential given (i) their strong competitive positions in large markets and (ii) their R&D and sales/marketing capabilities that allow them to create or acquire complementary products with high growth potential. The low downside risk with upside potential is an option embedded in public SaaS companies that tends to be overlooked. 

Performance

I’m reading Creative Capital by Spencer Ante. It’s a biography of Georges Doriot, who in 1946 founded American Research and Development Corporation (ARD). It was one of the first venture capital firms, and it was the first to have an institutional base or to focus on technical ventures.

ARD’s biggest success was Digital Equipment Corporation in which it invested $70,000 in 1957. After the company’s IPO in 1968, ARD’s stake was worth $355 million.

But Doriot was a fascinating person in other ways. From 1946 to 1966, he was widely considered by many to be the most popular professor at Harvard Business School, not because his class was fun but because it was hard and taught students how to think. 

In a predecessor to the case method at HBS, he had students write two reports: a topic report and a company report.

The topic report asked students to write a report on “a subject of your own choosing which will be a contribution to the future of American business,” but there was a twist: they had to imagine the impact of the problem, product, or technology ten years in the future.

The company report made students learn the nuts and bolts of a business by actually working with local manufacturing companies. For half a year they’d study companies up close. 

Doriot on why he had them do that:

Up until that time when the students read that the price of copper went up, to them it was just statistical information which they might feed back to the teacher. In this case, I want them to say, “What does it mean?” And also very important, “What do I do about it?” I want them to learn to have pains in the stomach, you see what I mean?

This idea of really challenging people to help them grow is a theme throughout the book.

Georges Doriot was a thirteen-year-old boy in his home country of France when he placed second in his class. He was excited and raced home to show his parents the certificate he received. His mother, Camille, gave him a hug and baked him cookies. But when his father, Auguste, came home and saw the certificate, Ante describes a very different reaction:

In stark contrast to Camille, Auguste seemed unimpressed with his son’s award. He acknowledged the certificate with only a cursory glance, nodded perfunctorily, and then fixed his son with one of those chilling stares of appraisal. “And why not first?” asked Auguste.

The story goes on to describe how upset Georges was at this. He ran to his room bewildered and humiliated. 

Ante describes how Georges recounted the experience to a friend many years later:

His father…was not concerned that Georges had failed to achieve first place honors in his class at Ecole Communale. No, he was concerned that Georges was happy placing second. To Auguste, a famous automobile engineer who had raised his children to strive for excellence in everything they did, celebrating anything less than the best possible result smacked of contentment. And contentment, Auguste believed, is a state of mind that recognizes no need for improvement.

Georges apparently told that story often because many that knew him felt the experience played a large role in what he achieved later life. And the key point is worth repeating: 

Contentment is a state of mind that recognizes no need for improvement. 

This resonated with me for many reasons. My father was like this as well. It had a big impact on me, and I believe it’s the right approach. Yet, I’m seeing many indications that cultural norms are moving in the other direction. In both the parenting and professional realms, I see people giving less feedback, particularly negative feedback, and being less clear about what they expect. 

On parenting, I’ll make the perhaps controversial statement that I thought Battle Hymn of the Tiger Mother was a great book. It was well-written and entertaining. But more importantly, I respected Amy Chua’s approach. She expected the best from her children and pushed them to achieve it. She approached her interactions with her children assuming strength on their part, not weakness. She openly acknowledged throughout those years that the cultural norms around her were different. She actively and thoughtfully dismissed them, and she was happy to tell you why. (If you missed this controversy, read the WSJ article.)

In the professional world, I’m surprised how little I see people giving direct, timely, and thoughtful feedback, both good and bad. Early in Creative Capital, the book describes the career of Georges Doriot’s father, Camille. He was a brilliant automotive engineer at Peugeot, and he was actively mentored by Armand Peugeot on all aspects of the business, ultimately starting his own company.

Apprenticeship is the most natural way to learn, and yet it seems people are becoming loath to giving feedback. I just don’t hear it very often. I believe the right model has two interrelated pieces:

1. Frequent, even daily, feedback. These are immediate observations on what went well and what could have been better. For example: “You responded poorly to a question in that presentation. It sounded like you were ill-prepared, but I think if you had just paused a beat or two, you would have made a bigger impact.”

2. Periodic reviews. I think yearly reviews are too infrequent. In the quarterly reviews, you highlight common positive and negative themes (i.e., connect the dots from what should by then be a large amount of feedback) and review milestones and achievements (or the lack of them). The quarterly review could still be less involved than the more significant, but quarter reviews seem about the frequency to tie together trends, give higher level feedback, and evaluate (and potentially redirect) progress towards goals. 

In both of these worlds—parenting and professional—I believe these behaviors can have immense impact if there’s a genuine desire to see the person succeed. With parenting, I’d like to believe that’s obvious. You want to see your kids succeed. In professional settings, I see that vary much more. 

The other related element is expectations. People have to believe that mistakes are a necessary step to growth. Negative feedback shouldn’t be perceived as a knock. It isn’t personal. Every single person that has achieved something significant made missteps along the way. Those missteps are a necessary ingredient for growth. You need to at least learn from them on your own. But if someone else can help you do so with even more effectiveness, all the better. So if you can defuse the inherently defensive, painful nature of feedback, there’s tremendous opportunity for growth.

No silver bullets

I caught up with a good friend that is also in venture capital. We were chatting about a company that he had backed early and that is on track to be quite successful. 

When I asked him about some of the key decision points in the company’s history, he said, “You know, there really were no silver bullets.”

His point as he elaborated was that the company did nothing that isn’t commonly accepted wisdom in building successful technology companies. The only difference, he mused, was that many companies try to skip steps.

He mentioned that it’s a lot like doing well in school. You put in your time. You do the work. You get a base of knowledge. You build on that knowledge. You’ll get some things quickly. Other things you might need to work on more or ask for help. You progress forward doing all the right things.

That really resonated with me. Partly, and this is coincidental, it was an interesting analogy given Peter Thiel’s recent efforts to encourage bright students to skip college and pursue entrepreneurship (The 20 Under 20 Thiel Fellowship.) 

More so, however, his perspective jived with something I’d been wondering lately: there are no secrets. Wisdom is there for the taking. There’s more good writing on the web than one can digest.

Most people in and around the Valley ecosystem of entrepreneurship can articulate the key lessons of building a successful company: target a big market, build a great product, get a product in front of customers, learn what customers want, build a great team, find product-market fit, articulate benefits, establish a strong culture, work well with your teammates, find a revenue model, raise capital, scale distribution, scale marketing, scale recruiting, raise more capital, make the product better, etc. 

But it’s amazing how many companies I see that are quite far along (in terms of capital raised more so than revenue) that, for example, haven’t found a compelling product-market fit. 

There are no secrets. Just the conviction that you’re doing what needs to be done and the grit to do it day in and day out. 

Basketball

I read today about Muthu Alagappan. Two years ago, Alagappan was an undergraduate intern at the Palo Alto data analysis startup Ayasdi and, using their innovative technology, showed that basketball actually has thirteen positions—not five.

Ayasdi is a leader in topological data analysis, which uses a branch of mathematics called topology to yield insights from very large data sets.

The vast majority of tools for data analysis today allow you to ask questions of the data and get answers. Yet, as data sets grow ever larger, the number of possible questions you can ask grows exponentially larger, making it impossible to ask all the questions that matter.

Topology is a branch of mathematics that deals with shapes and spaces. Ayasdi’s insight was that complex data sets are about relationships and that those relationships can be represented mathematically and, through topology, spatially. The spatial representation can then suggest areas that may be interesting to explore. This allows you to discover relationships you may have never thought to explore. 

As the CEO Gurjeet Singh said to me: "The entire history of BI, despite dramatic advances in visualization and the ability to handle larger and larger sets, has seen the same fundamental query-answer structure. But as the size of data sets increases, there are more interesting questions than you can even ask. Ayasdi allows you to get answers to the questions you would never even think to ask."

Alagappan applied Ayasdi’s technology to basketball statistics. He entered seven statistics (points, rebounds, assists, steals, turnovers, fouls, and blocks) for every NBA player, adjusted for playing time. 

This is what he ultimately came up with:

Instead of seeing five clusters, he saw thirteen. (Later work narrowed the thirteen down to the ten you see above.)

According to the Mercury News article, two teams have a formal partnership with Alagappan: the Portland Trail Blazers and the Miami Heat. And apparently, throughout the current playoffs, a Miami Heat representative has been in touch with Alagappan before each series to create a data-driven scouting report.