Category Archives: Financial

Bookings vs. Billings in a SaaS Company

Financial analysts speak a lot about “billings” in a public SaaS companies, but in private VC-backed SaaS companies, you rarely hear discussion of this metric.  In this post, we’ll use a model of a private SaaS company (where we know all the internal metrics), to show how financial analysts use rules of thumb to estimate and/or impute internal SaaS metrics using external ones – and to see what can go wrong in that process.

For reference, here’s an example of sell-side financial analyst research on a public SaaS company that talks about billings [1].

saas1-zen

Let’s start with a quick model that builds up a SaaS company from scratch [1].  To simplify the model we assume all deals (both new and renewal) are for one year only and are booked on the last day of the quarter (so zero revenue is ever recognized in-quarter from a deal).  This also means next-quarter’s revenue is this-quarter’s ending annual recurring revenue (ARR) divided by 4.

saas13

Available to renew (ATR) is total subscription bookings (new and renewal) from four quarters prior.  Renew bookings are ATR * (1 – churn rate).  The trickiest part of this model is the deferred revenue (DR) waterfall where we need to remember that the total deferred revenue balance is the sum of DR leftover from the current and the prior three quarters.

If you’re not convinced the model is working and/or want to play with it, you can download it, then see how things work by setting some drivers to boundary conditions (e.g., churn to 0%, QoQ sales growth to 0, or setting starting ARR to some fixed number [2]).

 The Fun Part:  Imputing Internal Metrics from External Ones

Now that we know what’s going on the inside, let’s look in from the outside [3]:

  • All public SaaS companies release subscription revenues [4]
  • All public SaaS companies release deferred revenues (i.e., on the balance sheet)
  • Few SaaS companies directly release ARR
  • Few SaaS companies release ATR churn rates, favoring cohort retention rates where upsell both masks and typically exceeds churn [5]

It wasn’t that long ago when a key reason for moving towards the SaaS business model was that SaaS smoothed revenues relative to the all-up-front, lumpy on-premises model.  If we could smooth out some of that volatility then we could present better software companies to Wall Street.  So the industry did [6], and the result?  Wall Street immediately sought a way to look through the smoothing and see what’s really going on in the inherently lumpy, backloaded world of enterprise software sales.

Enter billings, the best answer they could find to do this.  Billings is defined as revenue plus change in deferred revenue for a period.  Conceptually, when a SaaS order with a one-year prepayment term is signed, 100% of it goes to deferred revenue and is burned down 1/12th every month after that.  To make it simple, imagine a SaaS company sells nothing in a quarter:  revenue will burn down by 1/4th of starting deferred revenue [7] and the change in deferred revenue will equal revenue – thus revenue plus change in deferred revenue equals zero.  Now imagine the company took an order for $50K on the last day of the quarter.  Revenue from that order will be $0, change in deferred will be +$50K, implying new sales of $50K [8].

Eureka!  We can see inside the SaaS machine.  But we can’t.

Limitations of Billings as a SaaS Metric

If you want to know what investors really care about when it comes to SaaS metrics, ask the VC and PE folks who get to see everything and don’t have to impute outside-in.  They care about

Of those, public company investors only get a clear look at subscription gross margins and the customer acquisition cost (CAC) ratio.  So, looking outside-in, you can figure out how efficiency a company runs its SaaS service and how efficiently it acquires customers [9].

But you typically can’t get a handle on churn, so you can’t calculate LTV/CAC or CAC Payback Period.  Published cohort growth, however, can give you comfort around potential churn issues.

But you can’t get a precise handle on sales growth – and that’s a huge issue as sales growth is the number one driver of SaaS company valuation [10].  That’s where billings comes into play.  Billings isn’t perfect because it shows what I call “total subscription bookings” (new ARR bookings plus renewal bookings) [11], so we can’t tell the difference between a good sales and weak renewals quarter and a bad sales and a good renewals quarter.

Moreover, billings has two other key weaknesses as a metric:

  • Billings is dependent on prepaid contract duration
  • Companies can defer processing orders (e.g., during crunch time at quarter’s end, particularly if they are already at plan) thus making them invisible even from a billings perspective [12]

Let’s examine how billings depends on contract duration.  Imagine it’s the last day of new SaaS company’s first quarter.  The customer offers to pay the company:

  • 100 units for a prepaid one-year subscription
  • 200 units for a prepaid two-year subscription
  • 300 units for a prepaid three-year subscription

From an ARR perspective, each of the three possible structures represents 100 units of ARR [13].  However, from a deferred revenue perspective, they look like 100, 200, 300 units, respectively.  Worse yet, looking solely at deferred revenue at the end of the quarter, you can’t know if 300 units represents three 100-unit one-year prepay customers or a single 100-unit ARR customer who’s done a three-year prepay.

In fact, when I was at Salesforce we had the opposite thing happen.  Small and medium businesses were having a tough time in 2012 and many customers who’d historically renewed on one-year payment cycles started asking for bi-annual payments.  Lacking enough controls around a rarely-used payment option, a small wave of customers asked for and got these terms.  They were happy customers.  They were renewing their contracts, but from a deferred revenue perspective, suddenly someone who looked like 100 units of deferred revenue for an end-of-quarter renewal suddenly looked 50.  When Wall St. saw the resultant less-than-expected deferred revenue (and ergo less-than-expected billings), they assumed it meant slower new sales.  In fact, it meant easier payment terms on renewals – a misread on the business situation made possible by the limitations of the metric.

This issue only gets more complex when a company is enabling some varying mix of one through five year deals combined with partial up-front payments (e.g., a five-year contract with years 1-3 paid up front, but years 4 and 5 paid annually).  This starts to make it really hard to know what’s in deferred revenue and to try and use billings as a metric.

Let’s close with an excerpt from the Zuora S-1 on billings that echoes many of the points I’ve made above.

saas3

Notes

[1] Source:  William Blair, Inc., Zendesk Strong Start to 2018 by Bhavan Suri.

[2] Even though it’s not labelled as a driver and will break the renewals calculations, implicitly assuming all of it renews one year later (and is not spread over quarters in anyway).

[3] I’m not a financial analyst so I’m not the best person to declare which metrics are most typically released by public companies, so my data is somewhat anecdotal.  Since I do try to read interesting S-1s as they go by, I’m probably biased towards companies that have recently filed to go public.

[4] As distinct from services revenues.

[5] Sometimes, however, those rates are survivor biased.

[6] And it worked to the extent that from a valuation perspective, a dollar of SaaS revenue is equivalent to $2 to $4 of on-premises revenue.  Because it’s less volatile, SaaS revenue is more valuable than on-premises revenue.

[7] Provided no customers expire before the last day of the quarter

[8] Now imagine that order happens on some day other than the last day of the quarter.  Some piece, X, will be taken as revenue during the quarter and 50 – X will show up in deferred revenue.  So revenue plus change in deferred revenue = it’s baseline + X + 50 – X = baseline + 50.

[9] Though not with the same clarity VCs can see it — VCs can see composition of new ARR (upsell vs. new business) and sales customers (new customer acquisition vs. customer success) and thus can create more precise metrics.  For example, a company that has a solid overall CAC ratio may be revealed to have expensive new business acquisition costs offset by high, low-cost upsell.

[10] You can see subscription revenue growth, but that is smoothed/damped, and we want a faster way to get the equivalent of New ARR growth – what has sales done for us lately?

[11] It is useful from a cash forecasting perspective because all those subscription billings should be collectible within 30-60 days.

[12] Moving the deferred revenue impact of one or more orders from Q(n) to Q(n+1) in what we might have called “backlogging” back in the day.  While revenue is unaffected in the SaaS case, the DR picture looks different as a backlogged order won’t appear in DR until the end of Q(n+1) and then at 75, not 100, units.

[13] Normally, in real life, they would ask a small discount in return for the prepay, e.g., offer 190 for two years or 270 for three years.  I’ll ignore that for now to keep it simple.

Regulating Venture Capital? Methinks Not.

What if you went to the doctor’s office with a sore wrist and she proposed bandaging your ankle?

That’s how I feel about the government’s proposal that venture capital be regulated along with other private capital pools including hedge and private equity funds. See this Mercury News story, Venture Capital Needs Transparency Not Regulation, for background.

I’m no financial expert, but far as I can tell, the root causes of our current financial crisis are:

  • Leverage. Investment banks and hedge funds building 30:1 levered portfolios (and somehow managing to only get 8-10% returns on them). Kind of reminds you of buying on margin as a root cause for the crash of 1929.
  • Financial system interlocking and the too-big-to-fail problem. Like it or not, as a citizen and taxpayer, it does seem to me that many of these firms/funds are indeed too big to fail and the government was correct to use my/our money to stop the collapse.
  • Agency problems and excess compensation. Basically, you had very smart people who could make $10M+ per year by taking excess risk. When viewed from their perspective, put undiplomatically, who cares what happens to their employers? It doesn’t take many years (e.g., one) of $10M income to become permanently wealthy so senior managers had huge agency issues (where the interests of the owner and the agent diverge) which seemingly were left unchecked both by the companies’ own boards and by government regulators.
  • The housing bubble and the conflicts of interests among loan-originating banks, assessors, developers, and mortgage brokers. Arguably, the root cause here is the securitization of mortgages combined with the next point.
  • Conflicts of interest in the ratings system. I never knew this before, but the people who pay ratings agencies are the issuers of debt, not the buyers. This would be like Sony paying Consumer Reports to rate their new television sets. Perhaps this is how a portfolio of zero-down, floating-rate mortgages on overpriced houses in Stockton gets rated AAA.*
  • The lets-insure-each-other problem associated with credit default swaps. In a tightly interlinked system where each player is too big to fail, this strikes me as a mathematical hallucination designed to make it look like each player is taking less risk. But, in reality, it seems like a bunch of people living on the same street in Florida insuring each other against hurricanes. The question isn’t when will the insurance system fail, but indeed will it ever work — i.e., will there ever be a hurricane that wipes out only a few of the houses in the pool?
  • Lack of regulation to control / keep in check the amount of risk, leverage, ratings, and agency issues.

I’m sure I missed some and if you think I got anything wrong in this laundry list, feel free to comment. Because my primary point is that nowhere on this long list will you find anything related to venture capital.

In fact, as I’ve previously argued, venture capital looks quaint by comparison. VCs buy and hold the shares of start-up companies on 5-year, plus or minus, timeframes. No leverage. No ratings agencies. Investment professionals (e.g., foundation managers) are typically the only investors, so there’s no duping of John Q. Public.

Yes, venture returns are down over the past decade. Yes, there are probably too many VC firms and a shake-out is imminent. Yes, VCs make lots of bad investments. Yes, VC is increasingly a “hits business” where the biggest winners in the portfolio account for a disproportionate share of the returns.

Yes, VCs can make a lot of money. (And yes, I view carried interest as income and not capital gains.) And yes, there is probably an element of Fooled-by-Randomness / increasing returns inherent in the VC pecking order.

Sure, there are lots of flaws, but overall, I believe the VC system works. Much as you might say democracy is the least bad form of government, VC strikes me as the least bad way of driving innovation in the economy.

It wasn’t part of the problem, so let’s leave it out as part of the solution.


* I know the ratings problem is more subtle and involves mixing loans of various quality to stay just-within the bounds of a given creditworthiness level. Nevertheless, I’d argue a “good” rating system would differentiate between a basket of all-solid loans and a basket which mixes solid, semi-solid, and wobbly ones. As an aside and largely from a position of pure ignorance, I’m amazed that someone hasn’t raised some venture capital and tried to challenge the ratings industry with a new consumer-focused model.

Goldman Sachs Smacks Software Stocks

See this story on SeekingAlpha (which might consider renaming itself SeekingShelter), entitled Goldman Slaps Most Software Stocks.

Excerpt on aggregate spending:

The worst of the IT-spending slowdown likely remains in front of us, as we start the clock on slashed 2009 budgets. We forecast 0 percent revenue growth for our group, below consensus at 5 percent, and 1 percent earnings growth, below Street at 2 percent.

The most interesting point addressed is whether the downturn will drive consumers to open source (i.e., nominally “free”) software:

There has been much discussion in the blogosphere about open source software and how it will see a surge of adoption do to its lower cost. Goldman quite rightly says this will not be the case. I have written that CIOs will hunker down and stick with the tried and true (which is not open source in most large-sized enterprises) and Goldman is in agreement, seeing a consolidation of functionality with big, established vendors and a moving away from the concept of seeking best-of-breed point solutions regardless of vendor.

On sectors:

So in terms of non-defense technology companies we are batting two for two: Neither hardware not software will be spared over the next several quarters as the outlook remains dim for both.

Happily for Mark Logic we have a large defense / intelligence business, which I believe will offer shelter from the storm. And, as I’ve argued before, for non-advertising-driven media companies, I believe that GDP growth (or lack thereof) is a second-order effect relative to seismic changes driven by the Internet and Google to which MarkLogic helps them respond.

New York Times on Risk Mismanagement

Those:

Should very much enjoy a story in today’s New York Times magazine entitled Risk Mismanagement by Joe Nocera.

The story discusses risk management and introduces the concept of value at risk (VaR), an easy-to-understand measure of the risk of a portfolio of assets, pioneered by JP Morgan. The story touches on two key questions:

  • Did sophisticated risk models help avoid or rather help enable the financial meltdown?
  • To what extent should people worry about the probable 99% or the improbable 1% in assessing risk?

A few quick thoughts:

  • I found the backward-looking nature of historical standard deviation to measure the risk of a portfolio of stocks so counter-intuitive that I didn’t actually understand it in b-school until about the 3rd time it was explained to me. That is, innately, I’ve always understood that risk is about the future and standard deviation is about the past (and, in particular, the past period you are using to calculate it.) So the ideas in the story easily resonate with me.
  • The question is not whether “the math works.” The math always works. It’s about whether people understand that 1% of the time … happens, well … about 1% of the time. To me, the issue is never whether the math works, it’s about what probabilities are built into the models and what boundary conditions cause the models to become invalid. In my (semi-educated) opinion, these are always the sources of the “math problems” in finance.
  • Finally, I’ve always believed that people problems dominate the math problems. For example, in the failure of Long Term Capital Management, the root problem was that other traders started copying the arbitrage strategies they were using, effectively picking the low-hanging fruit from the risk tree. That, plus increasing hubris on the part of the firm’s principals, caused them to take bigger and bigger risks, increasingly deviating from their original strategy, and eventually leading to the collapse of the firm.

Excerpt:

Which brings me back to David Viniar and Goldman Sachs. “VaR is a useful tool,” he said as our interview was nearing its end. “The more liquid the asset, the better the tool. The more history, the better the tool. The less of both, the worse it is. It helps you understand what you should expect to happen on a daily basis in an environment that is roughly the same. We had a trade last week in the mortgage universe where the VaR was $1 million. The same trade a week later had a VaR of $6 million. If you tell me my risk hasn’t changed — I say yes it has!” Two years ago, VaR worked for Goldman Sachs the way it once worked for Dennis Weatherstone — it gave the firm a signal that allowed it to make a judgment about risk. It wasn’t the only signal, but it helped. It wasn’t just the math that helped Goldman sidestep the early decline of mortgage-backed instruments. But it wasn’t just judgment either. It was both. The problem on Wall Street at the end of the housing bubble is that all judgment was cast aside. The math alone was never going to be enough.

The full 7500-word story is here. Enjoy!

[Addendum: a critique of the article is here on the naked capitalism blog.]

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Michael Lewis Portfolio Article: The End

A friend recently posted a link to this superb article in Portfolio by Michael Lewis (author of Liar’s Poker and Moneyball) entitled simply: The End.

The article is about the end of an era on Wall Street, an era that lasted about 20 years longer than Lewis thought it would when he quit his job at Salomon Brothers in 1988. The article is long (nearly 10,000 words, i.e., 20 pages) so I’d recommend printing the article and reading it when you have some quiet time.

I’ve read two of Lewis’s books and seen him speak once. He’s a delightful writer and a dynamic speaker, high on energy and low on ego. I highly recommend Moneyball, particularly for those in the BI community, focused on business metrics.

For those who follow Wall Street, The End is a must read. Rather than writing a review, I’ll just try to hook you with this excerpt, the first two paragraphs of the article:

To this day, the willingness of a Wall Street investment bank to pay me hundreds of thousands of dollars to dispense investment advice to grownups remains a mystery to me. I was 24 years old, with no experience of, or particular interest in, guessing which stocks and bonds would rise and which would fall. The essential function of Wall Street is to allocate capital—to decide who should get it and who should not. Believe me when I tell you that I hadn’t the first clue.

I’d never taken an accounting course, never run a business, never even had savings of my own to manage. I stumbled into a job at Salomon Brothers in 1985 and stumbled out much richer three years later, and even though I wrote a book about the experience, the whole thing still strikes me as preposterous—which is one of the reasons the money was so easy to walk away from. I figured the situation was unsustainable. Sooner rather than later, someone was going to identify me, along with a lot of people more or less like me, as a fraud. Sooner rather than later, there would come a Great Reckoning when Wall Street would wake up and hundreds if not thousands of young people like me, who had no business making huge bets with other people’s money, would be expelled from finance.

Should We Rename the 401K the 201K?

This quip popped into my mind the other day and, hoping to coin a phrase, I decided to blog on it. Sadly however, a few others seem to have beaten me to the punch. (Nothing like a bit of gallows humor to cheers us up.)

Quote of the Week

“The only thing that goes up in bear markets is the correlation between asset classes.”
— Richard Davis, Needham & Co

I’m sure it’s a bona fide “ism” in the finance industry, but I’d not heard it before. And boy does it seem true. Consider this headline: “Stocks, Oil, Gold Tank on Growing Recession Fears.

Isn’t gold supposed to inversely correlated to stocks? Aren’t commodities only partially correlated? And, worst of all, aren’t hedge funds supposed to be uncorrelated or inversely correlated?

If you believe the “ism,” it’s a great argument to suggest that a diversified portfolio is a fair weather friend, an illusion that appears to work in smooth seas but that sinks in rough ones. Is real diversification possible — i.e., diversification where the correlation doesn’t head to 1 in rough times? I’m not so sure.