Thanks to everyone who attended my SaaStr 2020 presentation and thanks to those who provided me with great feedback and questions on the content of the session. The slides from the presentation are available here. The purpose of this post is to share the video of the session, courtesy of the folks at SaaStr. Enjoy!
The podcast in question is an interview performed by Harry Stebbings of The Twenty Minute VC where we sat down to talk about the importance of the lifetime value to customer acquisition cost ratio (LTV/CAC) and why, if you could only know one SaaS metric about a company, that LTV/CAC would be it.
Of course with Harry it’s easy to end up in a wide-ranging conversation, as we did, and we thus discussed many other fun topics including:
How I got into enterprise software and SaaS.
The biggest challenge as a leader in a high-growth company (hanging on).
Why, for a public SaaS company, I’d probably take billings growth as the single metric, because LTV/CAC isn’t available.
LTV/CAC and the idea that it’s a powerful (if compound) metric that weights what you pay for something vs. what’s it worth.
Career Development: What It Really Means to be a Manager, Director, or VP. The number two post of 2018 was actually written in 2015! That says a lot about this very special post which appears to have simply nailed it in capturing the hard-to-describe but incredibly important differences between operating at the manager, director, or VP level. I must admit I love this post, too, because it was literally twenty years in the making. I’d been asked so many times “what does it really mean to operate at the director level” that it was cathartic when I finally found the words to express the answer.
The SaaS Rule of 40. No surprise here. Love it or not, understanding the rule of 40 is critical when running a SaaS business. Plenty of companies don’t obey the rule of 40 — it’s a very high bar. And it’s not appropriate in all circumstances. But something like 80% of public company SaaS market capitalization is captured by the companies that adhere to it. It’s the PEG ratio of modern SaaS.
The Role of Professional Services in a SaaS Company. I was surprised and happy to see that this post made the top five. In short, the mission of services in a SaaS company is “to maximize ARR while not losing money.” SaaS companies don’t need the 25-35% services margins of their on-premises counterparts. They need happy, renewing customers. Far better to forgo modest profits on services in favor of subsidizing ARR both in new customer acquisition and in existing customer success to drive renewals. Services are critical in a SaaS company, but you shouldn’t measure them by services margins.
The Customer Acquisition Cost Ratio: Another Subtle SaaS Metric. The number five post of 2018 actually dates back to 2013! The post covers all the basics of measuring your cost to acquire a customer or a $1 of ARR. In 2019 I intend to update my fundamentals posts on CAC and churn, but until then, this post stands strong in providing a comprehensive view of the CAC ratio and how to calculate it. Most SaaS companies lose money on customer acquisition (i.e., “sell dollars for 80 cents”) which in turn begs two critical questions: how much do they lose and how quickly do they get it back? I’m happy to see a “fun with fundamentals” type post still running in the top five.
 See disclaimer that I’m not a financial analyst and I don’t make buy/sell recommendations.
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.
My ears always perk up when I hear someone say “net new ARR” — because I’m trying to figure out which, of typically two, ways they are using the term:
To mean ARR from net new customers, in which case, I don’t know why they need the word “net” in there. I call this new business ARR (sometimes abbreviated to newbiz ARR), and we’ll discuss this more down below.
To mean net change in ARR during a period, meaning for example, if you sold $2,000K of new ARR and churned $400K during a given quarter, that net new ARR would be $1,600K. This is the correct way to use this term.
Let’s do a quick review of what I call leaky bucket analysis. Think of a SaaS company as a leaky bucket full of ARR.
Every quarter, sales dumps new ARR into the bucket.
Every quarter, customer success does its best to keep water from leaking out.
Net new ARR is the change in the water level of the bucket. Is it a useful metric? Yes and no. On the yes side:
Sometimes it’s all you get. For public companies that either release (or where analysts impute) ARR, it’s all you get. You can’t see the full leaky bucket analysis.
It’s useful for measuring overall growth efficiency with metrics like cash burn per dollar of net new ARR or S&M expense per dollar of net new ARR. Recall that customer acquisition cost (CAC) focuses only on sales efficiency and won’t detect the situation where it’s cheap to add new ARR only to have it immediately leak out.
If I were to define an overall SaaS growth efficiency index (GEI), I wouldn’t do it the way Zuora does (which is effectively an extra-loaded CAC), I would define it as:
Growth efficiency index = -1 * (cashflow from operations) / (net new ARR)
In English, how much cash are you burning to generate a dollar of net new ARR. I like this because it’s very macro. I don’t care if you’re burning cash as a result of inefficient sales, high churn, big professional services losses, or high R&D investment. I just want to know how much cash you’re burning to make the water level move up by one dollar.
So we can see already that net new ARR is already a useful metric, if a sometimes confused term. However, on the no side, here’s what I don’t like about it.
Like any compound metric, as they say at French railroad crossings, un train peut en cacher un autre (one train can hide another). This means that while net new ARR can highlight a problem you won’t immediately know where to go fix it — is weak net new ARR driven by a sales problem (poor new ARR), a product-driven churn problem, a customer-success-driven churn problem, or all three?
For example, above, you can quickly see that a massive 167% year-over-year increase in churn ARR was the cause for weak 1Q17 net new ARR. While this format is clear and simple, one disadvantage of this simpler format is that it hides the difference between new ARR from new customers (newbiz ARR) and new ARR from existing customers (upsell ARR). Since that can be an important distinction (as struggling sales teams often over-rely on sales to existing customers), this slightly more complex form breaks that out as well.
In addition to breaking out new ARR into its two sub-types, this format adds three rows of percentages, the most important of which is upsell % of new ARR, which shows to what extent your new ARR is coming from existing versus new customers. While the “correct” value will vary as a function of your market, your business model, and your evolutionary phase, I generally believe that figures below 20% indicate that you may be failing to adequately monetize your installed base and figures above 40% indicate that you are not getting enough new business and the sales force may be too huddled around existing customers.
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, Cyral, FloQast, GainSight, MongoDB, Recorded Future, and Tableau.
I previously sat on the boards of Granular (agtech, acquired by DuPont), Aster Data (big data, acquired by Teradata), and Nuxeo (content services, acquired by Hyland), and Profisee (MDM, exited to Pamlico).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).