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 .
Let’s start with a quick model that builds up a SaaS company from scratch . 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.
Available to renew (ATR) is total subscription bookings (new and renewal) from four quarters prior. Renewal 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 ).
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 :
All public SaaS companies release subscription revenues 
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 
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 , 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  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 .
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 .
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 . 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) , 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 
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 . 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.
 Source: William Blair, Inc., Zendesk Strong Start to 2018 by Bhavan Suri.
 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).
 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.
 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.
 Provided no customers expire before the last day of the quarter
 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.
 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.
 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?
 It is useful from a cash forecasting perspective because all those subscription billings should be collectible within 30-60 days.
 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.
 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.
After theSaaSacre of early 2016, investors generally backed off a growth-at-all-costs mindset and started to value SaaS companies using an “appropriate” balance of growth and profitability. The question then became, what’s appropriate? The answer was: the rule of 40 .
What’s the rule of 40? Growth rate + profit should be greater than or equal to 40%.
There are a number of options for deciding what to use to represent growth (e.g., ARR) and profit (e.g., EBITDA, operating margin). For public companies it usually translates to revenue growth rate and free cash flow margin.
It’s important to understand that such “rules” are not black and white. As we’ll see in a minute, lots of companies deviate from the rule of 40. The right way to think about these rules of thumb is as predictors. Back in the day, what best predicted the value of a SaaS company? Revenue growth — without regard for margin. (In fact, often inversely correlated to margin.) When that started to break down, people started looking for a better independent variable. The answer to that search was the rule of 40 score.
Let’s examine a few charts courtesy of the folks at Pacific Crest and as presented at the recent, stellar Zuora CFO Forum, a CFO gathering run alongside their Subscribed conference.
This scatter chart plots the two drivers of the rule of 40 score against each other, colors each dot with the company’s rule of 40 score, and adds a line that indicates the rule of 40 boundary. 42% of public SaaS companies, and 77% of public SaaS market cap, is above the rule of 40 line.
For those interested in company valuations, the more interesting chart is this one.
This chart plots rule of 40 score on the X axis, valuation multiple on the Y axis, and produces a pretty good regression line the shows the relationship between the two. In short, the rule of 40 alone explains nearly 50% of SaaS company valuation. I believe that outliers fall into one of two categories:
Companies in a strategic situation that explains the premium or discount relative to the model — e.g., the premium for Cloudera’s strong market position in the Hadoop space.
Companies whose valuations go non-linear at the high end due to scarcity — e.g., Veeva.
Executives and employees at startups should understand  the rule of 40 as it explains the general tendency of SaaS companies to focus on a balance of growth and profitability as opposed to a growth at all costs strategy that was more popular several years back. Ignore the rule of 40 at your peril.
 While the Rule of 40 concept preceded the SaaSacre, I do believe that the SaaSacre was the wake-up call that made more investors and companies pay attention to.
 Using operating margin here somewhat lazily as I don’t want to go find unlevered free cash flow margin, but I don’t think it materially changes the point.
While driver-based planning is a bit of an old buzzword (the first two Google hits date to 2009 and 2011 respectively), I am nevertheless a huge fan of driver-based planning not because the concept was sexy back in the day, but because it’s incredibly useful. In this post, I’ll explain why.
When I talk to finance people, I tend to see two different definitions of driver-based planning:
Heavy in detail, one where you build a pretty complete bottom-up budget for an organization and play around with certain drivers, typically with a strong bias towards what they have historically been. I would call this driver-based budgeting.
Light in detail where you struggle to find the minimum set of key drivers around which you can pretty accurately model the business and where drivers tend to be figures you can benchmark in the industry. I call this driver-based modeling.
While driver-based budgeting can be an important step in building an operating plan, I am actually bigger fan of driver-based modeling. Budgets are very important, no doubt. We need them to run plan our business, align our team, hold ourselves accountable for spending, drive compensation, and make our targets for the year. Yes, a good CEO cares about that as a sine qua non.
But a great CEO is really all about two things:
Financial outcomes (and how they create shareholder value)
The future (and not just next year, but the next few)
The ultimate purpose of driver-based models is to be able answer questions like what happens to key financial outcomes like revenue growth, operating margins, and cashflow given set of driver values.
I believe some CEOs are disappointed with driver-based planning because their finance team have been showing them driver-based budgets when they should have been showing them driver-based models.
The fun part of driver-based modeling is trying to figure out the minimum set of drivers you need to successfully build a complete P&L for a business. As a concrete example I can build a complete, useful model of a SaaS software company off the following minimum set of drivers
Number and type of salesreps
Quota/productivity for each type
Hiring plans for each type
Deal bookings mix for each (e.g., duration, prepayments, services)
Intra-quarter bookings linearity
Sales employee types and ratios (e.g., 1 SE per 2 salesreps)
Marketing as % of sales or via a set of funnel conversion assumptions (e.g., responses, MQLs, oppties, win rate, ASP)
R&D as % of sales
G&A as % of sales
AR and AP terms
With just those drivers, I believe I can model almost any SaaS company. In fact, without the more detailed assumptions (rep types, marketing funnel), I can pretty accurately model most.
Finance types sometimes forget that the point of driver-based modeling is not to build a budget, so it doesn’t have to be perfect. In fact, the more perfect you make it, the heavier and more complex it gets. For example, intra-quarter bookings linearity (i.e., % of quarterly bookings by month) makes a model more accurate in terms of cash collections and monthly cash balances, but it also makes it heavier and more complex.
Like each link in Marley’s chains, each driver adds to the weight of the model, making it less suited to its ultimate purpose. Thus, with the additional of each driver, you need to ask yourself — for the purposes of this model, does it add value? If not, throw it out.
One of the most useful models I ever built assumed that all orders came in on the last day of quarter. That made building the model much simpler and any sales before the last day of the quarter — of which we hope there are many — become upside to the conservative model.
Often you don’t know in advance how much impact a given driver will make. For example, sticking with intra-quarter bookings linearity, it doesn’t actually change much when you’re looking at quarter granularity a few years out. However, if your company has a low cash balance and you need to model months, then you should probably keep it in. If not, throw it out.
This process makes model-building highly iterative. Because the quest is not to build the most accurate model but the simplest, you should start out with a broad set of drivers, build the model, and then play with it. If the financial outcomes with which you’re concerned (and it’s always a good idea to check with the CEO on which these are — you can be surprised) are relatively insensitive to a given driver, throw it out.
Finance people often hate this both because they tend to have “precision DNA” which runs counter to simplicity, and because they have to first write and then discard pieces of their model, which feels wasteful. But if you remember the point — to find the minimum set of drivers that matter and to build the simplest possible model to show how those key drivers affect financial outcomes — then you should discard pieces of the model with joy, not regret.
The best driver-based models end up with drivers that are easily benchmarked in the industry. Thus, the exercise becomes: if we can converge to a value of X on industry benchmark Y over the next 3 years, what will it do to growth and margins? And then you need to think about how realistic converging to X is — what about your specific business means you should converge to a value above or below the benchmark?
At Host Analytics we do a lot of driver-based modeling and planning internally. I can say it helps me enormously as CEO think about industry benchmarks, future scenarios, and how we create value for the shareholders. In fact, all my models don’t stop at P&L, they go onto implied valuation given growth/profit and ultimately calculate a range of share prices on the bottom line.
The other reason I love driver-based planning is more subtle. Much as number theory helps you understand the guts of numbers in mathematics, so does driver-based modeling help you understand the guts of your business — which levers really matter, and how much.
I thought I’d take a quick read of the Zendesk S-1 today, so here are my real-time notes on so doing. Before diving in, let me provide a quick pointer to David Cummings’ summary of the same.
40,000 customers in 140 countries
2012 revenues of $38.2M
2013 revenues of $72.0M, 88% growth
41% of revenues from international. (High for a SaaS company at this size, but makes sense given their roots.)
Net loss of $24.4M and $22.6M in 2012 and 2013, -30% net loss in 2013
Zendesk approach: beautifully simple, omni-channel, affordable, natively mobile, cloud-based, open, proactive, strategic. They do this well. (I’ve always viewed them as a very well run, low-end-up market entrant.)
Founded in Denmark in 2007.
115M shares outstanding anticipated after the offering with seemingly another 40M in options under various options and ESOP plans. (Seems like a lot of dilution looming.)
65% gross margins. (Though they don’t break out subscription vs. service which probably depresses things a tad.)
20% of revenue spent on R&D. (Normal.)
52% of revenue on S&M. (High, particularly for freemium which is notionally low-cost!)
22% of revenue on G&A (Normal to high, probably due to IPO itself.)
$53M in cash at 12/31/13
Headcount growth from 287 to 473 employees in year ended 12/31/13, up 68%
They have experienced security breaches:
“We have experienced significant breaches of our security measures and our customer service platform and live chat software are at risk for future breaches as a result of third-party action, employee, vendor, or contractor error, malfeasance, or other factors. For example, in February 2013, we experienced a security breach involving unauthorized access to three of our customers’ accounts and personal information of consumers maintained in those customer accounts.”
“[We are] highly dependent on free trials.” (These guys define freemium model for enterprise software in my opinion.)
S&M org grew from 85 to 165 employees in period ending 12/31/13.
Owe $23.8M on a credit facility. (Rare to see this much debt, but probably a smart way to reduce equity dilution.)
The three principles that drive the founders: Have great products. Care for your customers. Attract a great team. (Beats “Don’t Be Evil” any day in my book.)
Dollar-based “net expansion rate” (closest thing they discuss relative to renewals or churn):
“We calculate our dollar-based net expansion rate by dividing our retained revenue net of contraction and churn by our base revenue. We define our base revenue as the aggregate monthly recurring revenue of our customer base as of the date one year prior to the date of calculation. We define our retained revenue net of contraction and churn as the aggregate monthly recurring revenue of the same customer base included in our measure of base revenue at the end of the annual period being measured. Our dollar-based net expansion rate is also adjusted to eliminate the effect of certain activities that we identify involving the transfer of agents between customer accounts, consolidation of customer accounts, or the split of a single customer account into multiple customer accounts. […] Our dollar-based net expansion rate was 126% and 123% as of December 31, 2012 and 2013, respectively. We expect our dollar-based net expansion rate to decline over time as our aggregate monthly recurring revenue grows.”
$66M accumulated deficit
Have data centers in North America, Europe, and Asia
4Q13/4Q12 growth rate = 83% compared to 2013/2012 growth rate = 88%. (Suggests growth is gently decelerating.)
Cashflow from operations in 2013 = $4.0M.
But they had -$24.1M in cashflow from investing activities. (This is confusing because it’s a mix of items but broken into $12.4M in “marketable securities, property and equipment,” $7.1M to build data centers, and $4.7M in capitalized software development. I’m not an accountant but if you ask me if “the business” is cashflow positive, the answer is no despite the $4.0M positive cashflow from operations. Building data centers and developing software, regardless of accounting classification, are all part of running the business to me.)
I am surprised they capitalize R&D. Most software companies, far as I know, don’t.
The FMV of the common stock is depicted above, by my math an annual 68% appreciation rate.
Huge number of leads are organic: “the quarter ended December 31, 2013, 70% of our qualified sales leads, which are largely comprised of prospects that commence a free trial of our customer service platform, came from organic search, customer referrals, and other unpaid sources.”
SVPs listed (CFO, R&D) earn $240K base + $40K bonus
Automatic 5% share expansion / “overhang” built into the stock option and incentive plan. Pretty rich in my experience and haven’t noticed anyone else doing it automatically before.
Letting execs buy stock with promissory notes … hum, I thought that went out with leg warmers. Both loans were paid off by 12/31/31 and maybe that’s why.
CEO will own 7.1% of shares after the offering, including 4.3M (of the 8.1M beneficially owned) granted as options at the 2/14 board meeting. (Seems odd to me; a huge option grant right before the IPO. Hum.)
I’m Dave Kellogg, advisor, director, consultant, angel investor, and blogger focused on enterprise software startups. I am an executive-in-residence (EIR) at Balderton Capital and principal of my own eponymous consulting business.
I bring an uncommon perspective to startup challenges having 10 years’ experience at each of the CEO, CMO, and independent director levels across 10+ companies ranging in size from zero to over $1B in revenues.
From 2012 to 2018, I was CEO of cloud EPM vendor Host Analytics, where we quintupled ARR while halving customer acquisition costs in a competitive market, ultimately selling the company in a private equity transaction.
Previously, I was SVP/GM of the $500M Service Cloud business at Salesforce; CEO of NoSQL database provider MarkLogic, which we grew from zero to $80M over 6 years; and CMO at Business Objects for nearly a decade as we grew from $30M to over $1B in revenues. I started my career in technical and product marketing positions at Ingres and Versant.
I love disruption, startups, and Silicon Valley and have had the pleasure of working in varied capacities with companies including Bluecore, FloQast, GainSight, Hex, MongoDB, Pigment, Recorded Future, and Tableau.
I currently serve on the boards of Cyber Guru (cybersecurity training), Jiminny (conversation intelligence), and Scoro (work management).
I previously served on the boards of Alation (data intelligence), Aster Data (big data), Granular (agtech), Nuxeo (content services), Profisee (MDM), and SMA Technologies (workload automation).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).