While I’ve blogged about this presentation before, I only recently stumbled into this full-length video of this very popular session — a 30-minute blaze through some subtle SaaS basics. Enjoy!
I look forward to seeing everyone again at SaaStr Annual 2019.
Posted in Churn, Enterprise, Entrepreneurship, Metrics, SaaS, SaaStr, Scaling, Silicon Valley, Startups, VC
Last week I sat down with interviewers Doug Henschen, Vala Afshar, and a bit of Ray Wang (live from a 777 taxiing en route to Tokyo) to participate in Episode 100 of DisrupTV along with fellow guests DataStax CEO Billy Bosworth and big data / science recruiter Virginia Backaitis.
We covered a full gamut of topics, including:
DisrupTV Episode 100, Featuring Dave Kellogg, Billy Bosworth, Virginia Backaitis from Constellation Research on Vimeo.
Posted in BI, Cloud, Data Science, Enterprise, Leadership, Management, SaaS, Uncategorized
New Year’s means three things in my world: (1) time to thank our customers and team at Host Analytics for another great year, (2) time to finish up all the 2017 planning items and approvals that we need to get done before the sales kickoff (including the one most important thing to do before kickoff), and time to make some predictions for the coming year.
Before looking at 2017, let’s see how I did with my 2016 predictions.
2016 Predictions Review
Now, let’s move on to my predictions for 2017 which – as a sign of the times – will include more macro and political content than usual.
Dance like no one’s watching; E-mail like it will be read in a deposition
And, as usual, I must add the bonus prediction that 2017 proves to be a strong year for Host Analytics. We are entering the year with positive momentum, the category is strong, cloud adoption in finance continues to increase, and the megavendors generally lack sufficient focus on the category. We continue to be the most customer-focused vendor in EPM, our new Modeling product gained strong momentum in 2016, and our strategy has worked very well for both our company and the customers who have chosen to put their faith in us.
I thank our customers, our partners, and our team and wish everyone a great 2017.
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Posted in BI, Bubbles, Data Science, Decision Making, Disruption, Enterprise, IPO, Predictions, Silicon Valley, Startups, VC
The single most misunderstood software-as-a-service (SaaS) metric I’ve encountered is the CAC Payback Period (CPP), a compound metric that is generally defined as the months of contribution margin to pay back the cost of acquiring a customer. Bessemer defines the CPP as:
I quibble with some of the Bessemerisms in the definition. For example, (1) most enterprise SaaS companies should use annual recurring revenue (ARR), not monthly recurring revenue (MRR), because most enterprise companies are doing annual, not monthly, contracts, (2) the “committed” MRR concept is an overreach because it includes “anticipated” churn which is basically impossible to measure and often unknown, and (3) I don’t know why they use the prior period for both S&M costs and new ARR – almost everybody else uses prior-period S&M divided by current-period ARR in customer acquisition cost (CAC) calculations on the theory that last quarter’s S&M generated this quarter’s new ARR.
Switching to ARR nomenclature, and with a quick sleight of mathematical hand for simplification, I define the CAC Payback Period (CPP) as follows:
Let’s run some numbers.
All seems pretty simple, right? Not so fast. There are two things that constantly confound people when looking at CAC Payback Period (CPP).
Payback Metrics are for Risk, Not Return
Quick, basic MBA question: you have two projects, both require an investment of 100 units, and you have only 100 units to invest. Which do you pick?
Quick, which do you pick? Well, project B. Duh. But wait — now I tell you this:
Well, don’t you feel silly for picking project B?
Payback is all about how long your money is committed (so it can’t be used for other projects) and at risk (meaning you might not get it back). Payback doesn’t tell you anything about return. In capital budgeting, NPV tells you about return. In a SaaS business, customer lifetime value (LTV) tells you about return.
There are situations where it makes a lot of sense to look at CPP. For example, if you’re running a monthly SaaS service with a high churn rate then you need to look closely how long you’re putting your money at risk because there is a very real chance you won’t recoup your CAC investment, let alone get any return on it. Consider a monthly SaaS company with a $3500 customer acquisition cost, subscription gross margin of 70%, a monthly fee of $150, and 3% monthly churn. I’ll calculate the ratios and examine the CAC recovery of a 100 customer cohort.
While the CPP formula outputs a long 33.3 month CAC Payback Period, reality is far, far worse. One problem with the CPP formula is that it does not factor in churn and how exposed a cohort is to it — the more chances customers have to not renew during the payback period, the more you need to consider the possibility of non-renewal in your math [1]. In this example, when you properly account for churn, you still have $6 worth of CAC to recover after 30 years! You literally never get back your CAC.
Soapbox: this is another case where using a model is infinitely preferable to back-of-the-envelope (BOTE) analysis using SaaS metrics. If you want to understand the financials of a SaaS company, then build a driver-based model and vary the drivers. In this case and many others, BOTE analysis fails due to subtle complexity, whereas a well-built model will always produce correct answers, even if they are counter-intuitive.
Such cases aside, the real problem with being too focused on CAC Payback Period is that CPP is a risk metric that tells you nothing about returns. Companies are in business to get returns, not simply to minimize risk, so to properly analyze a SaaS business we need to look at both.
The Impact of Annual and Multi-Year Prepaid Contracts on CAC Payback Period
The CPP formula outputs a payback period in months, but most enterprise SaaS businesses today run on an annual rhythm. Despite pricing that is sometimes still stated per-user, per-month, SaaS companies realized years ago that enterprise customers preferred annual contracts and actually disliked monthly invoicing. Just as MRR is a bit of a relic from the old SaaS days, so is a CAC Payback Period stated in months.
In a one-hundred-percent annual prepaid contract world, the CPP formula should output in multiples of 12, rounding up for all values greater than 12. For example, if a company’s CAC Payback Period is notionally 13 months, in reality it is 24 months because the leftover 1/13 of the cost isn’t collected until the a customer’s second payment at month 24. (And that’s only if the customer chooses to renew — see above discussion of churn.)
In an annual prepaid world, if your CAC Payback Period is less than or equal to 12 months, then it should be rounded down to one day because you are invoicing the entire year up-front and at-once. Even if the formula says the CPP is notionally 12.0 months, in an annual prepaid world your CAC investment money is at risk for just one day.
So, wait a minute. What is the actual CAC Payback Period in this case? 12.0 months or 1 day? It’s 1 day.
Anyone who argues 12.0 months is forgetting the point of the metric. Payback periods are risk metrics and measured by the amount of time it takes to get your investment back [2]. If you want to look at S&M efficiency, look at the CAC ratio. If you want to know about the efficiency of running the SaaS service, look at subscription gross margins. If you want to talk about lifetime value, then look at LTV/CAC. CAC Payback Period is a risk metric that measures how long your CAC investment is “on the table” before getting paid back. In this instance the 12 months generated by the standard formula is incorrect because the formula misses the prepayment and the correct answer is 1 day.
A lot of very smart people get stuck here. They say, “yes, sure, it’s 1 day – but really, it’s not. It’s 12 months.” No. It’s 1 day.
If you want to look at something other than payback, then pick another metric. But the CPP is 1 day. You asked how long it takes for the company to recoup the money it spends to acquire a customer. For CPPs less than or equal to 12 in a one-hundred percent annual prepaid world, the answer is one day.
It gets harder. Imagine a company that sells in a sticky category (e.g., where typical lifetimes may be 10 years) and thus is a high-consideration purchase where prospective customers do deep evaluations before making a decision (e.g., ERP). As a result of all that homework, customers are happy to sign long contracts and thus the company does only 3-year prepaid contracts. Now, let’s look at CAC Payback Period. Adapting our rules above, any output from the formula greater than 36 months should be rounded up in multiples of 36 months and, similarly, any output less than or equal to 36 months should be rounded down to 1 day.
Here we go again. Say the CAC Payback Period formula outputs 33 months. Is the real CPP 33 months or 1 day? Same argument. It’s 1 day. But the formula outputs 33 months. Yes, but the CAC recovery time is 1 day. If you want to look at something else, then pick another metric.
It gets even harder. Now imagine a company that does half 1-year deals and half 3-year deals (on an ARR-weighted basis). Let’s assume it has a CAC ratio of 1.5, 75% subscription gross margins, and thus a notional CAC Payback Period of 24 months. Let’s see what really happens using a model:
Using this model, you can see that the actual CAC Payback Period is 1 day. Why? We need to recoup $1.5M in CAC. On day 1 we invoice $2.0M, resulting in $1.5M in contribution margin, and thus leaving $0 in CAC that needs to be recovered.
While I have not yet devised general rounding rules for this situation, the model again demonstrates the key point – that the mix of 1-year and 3-year payment structure confounds the CPP formula resulting in a notional CPP of 24 months, when in reality it is again 1 day. If you want to make rounding rules beware the temptation to treat the average contract duration (ACD) as a rounding multiple because it’s incorrect — while the ACD is 2 years in the above example, not a single customer is paying you at two-year intervals: half are paying you every year while half are paying you every three. That complexity, combined with the reality that the mix is pretty unlikely to be 50/50, suggests it’s just easier to use a model than devise a generalized rounding formula.
But pulling back up, let’s make sure we drive the key point home. The CAC Payback Period is the single most often misunderstood SaaS metric because people forget that payback metrics are about risk, not return, and because the basic formulas – like those for many SaaS metrics – assume a monthly model that simply does not apply in today’s enterprise SaaS world, and fail to handle common cases like annual or multi-year prepaid contracts.
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Notes
[1] This is a huge omission for a metric that was defined in terms of MRR and which thus assumes a monthly business model. As the example shows, the formula (which fails to account for churn) outputs a CAC payback of 33 months, but in reality it’s never. Quite a difference!
[2] If I wanted to be even more rigorous, I would argue that you should not include subscription gross margin in the calculation of CAC Payback Period. If your CAC ratio is 1.0 and you do annual prepaid contracts, then you immediately recoup 100% of your CAC investment on day 1. Yes, a new customer comes with a future liability attached (you need to bear the costs of running the service for them for one year), but if you’re looking at a payback metric that shouldn’t matter. You got your money back. Yes, going forward, you need to spend about 30% (a typical subscription COGS figure) of that money over the next year to pay for operating the service, but you got your money back in one day. Payback is 1 day, not 1/0.7 = 17 months as the formula calculates.
Posted in Enterprise, Entrepreneurship, Finance, FP&A, Metrics, SaaS, Silicon Valley, Startups, VC
As the new year approaches, it’s time for another set of predictions, but before diving into my list for 2016, let’s review and assess the predictions I made for 2015.
Kellblog’s 2015 Predictions Review
So, let’s it call it 7.5 out of 10. Not bad, when you recall my favorite quote from Yogi Berra: “predictions are hard, especially about the future.”
Kellblog’s Top Predictions for 2016
Before diving into these predictions, please see the footnote for a reminder of the spirit in which they are offered.
1. The great reckoning begins. I view this as more good than bad because it will bring a return to commonsense business practices and values. The irrationality that came will bubble 2.0 will disperse. It took 7 years to get into this situation so expect it to take a few years to get out. Moreover, since most of the bubble is in illiquid securities held by illiquid partnerships, there’s not going to be any flash crash — it’s all going to proceed in slow motion, expect for those companies addicted to huge burn rates that will need to shape up quickly. Quality, well run businesses will continue attract funding and capital will be available for them. Overall, while there will be some turbulence, I think this will be more good than bad.
2. Silicon Valley cools off a bit. As a result of the previous prediction, Silicon Valley will calm a bit in 2016: it will get a bit easier to hire, traffic will modestly improve, and average burn rates will drop. You’ll see fewer corporate buses on 101. Rents will come down a bit, so I’d wait before signing a five-year lease on your next building.
3. Porter’s Five Forces comes back in style. I always feel that during bubbles the first thing to go is Porter five force analysis. What are there barriers to entry on a daily deal or on a check-in feature? What are the switching costs of going from Feedly to Flipboard? What are the substitutes for home-delivered meal service? In saner times, people take a hard look at these questions and don’t simply assume that every market is a greenfield market share grab and that market share itself constitutes a switching cost (as it does only in companies with real network effects).
4. Cyber-cash makes a rise. As the world becomes increasingly cashless (e.g., Sweden), governments will prosper as law enforcement and taxation bodies benefit, but citizens will increasingly start to sometimes want the anonymity of cash. (Recall with irony that anonymity helped make pornography the first “killer app” of the Internet. I suspect today’s closet porn fans would prefer the anonymity of cash in a bookshop to the permanent history they’d leave behind on Netflix or other sites — and this is not to mention the blackmailing that followed the data release in the Ashley Madison hack.) For these reasons and others, I think people will increasingly realize that in a world where everything is tracked by default, that the anonymity of some form of cyber-cash will sometimes be desired. Bitcoin currently fails the grade because people don’t want a floating (highly volatile) currency; they simply want an anonymous, digital form of cash.
5. The Internet of Things (IoT) starts its descent into what Gartner calls the Trough of Disillusionment. This is not to say that IoT is a bad thing in any way — it will transform many industries including agriculture, manufacturing, energy, healthcare, and transportation. It is simply to say that Silicon Valley follows a predictable hype cycle and that IoT hit the peak in 2015 and will move from the over-hyped yet very real phase and slide down to the trough of disillusionment. Drones are following along right behind.
6. Data science continues to rise as a profession. 23 schools now offer a master’s program in data science. As a hot new field, a formal degree won’t be required as long as you have the requisite chops, so many people will enter data science they way I entered computer science — with skills, but not a formal degree. See this post about a UC Berkeley data science drop-out who describes why he dropped the program and how he’s acquiring requisite knowledge through alternative means, including the Khan Academy. Galvanize (which acquired data-science bootcamp provider Zipfian Academy) has now graduated over 200 students. Apologies for covering this trend literally every year, but I continue to believe that “data science” is the new “plastics” for those who recall the scene from The Graduate.
7. SAP realizes it’s an complex, enterprise applications company. Over the past half decade, SAP has put a lot of energy into what I consider strategic distractions, like (1) entering the DBMS market via the Sybase acquisition, (2) putting a huge emphasis on their column-oriented, in-memory database, Hana, (3) running a product branding strategy that conflates Hana with cloud, and (4) running a corporate branding strategy that attempts to synonymize SAP with simple.
Some of these initiatives are interesting and featured advanced technology (e.g., Hana). Some of them are confusing (e.g., having Hana mean in-memory, column-oriented database and cloud platform at the same time). Some of them are downright silly. SAP. Simple. Really?
While I admire SAP for their execution commitment — SAP is clearly a company that knows how to put wood behind an arrow — I think their choice of strategies has been weak, in cases backwards looking (e.g., Hana as opposed to just using a NoSQL store), and out of touch with the reality of their products and their customers.
The world’s leader in enterprise software applications that deal with immense complexity should focus on building upon that strength. SAP’s customers bought enterprise applications to handle very complex problems. SAP should embrace this. The message should be: We Master the Complex, not Run Simple. I believe SAP will wake up to this in 2016.
Aside: see the Oracle ad below for the backfire potential inherent in messaging too far afield from your reality.
8. Oracle’s cloud strategy gets revealed: we’ll sell you any deployment model you like (regardless of whether we have it) as long as your yearly bill goes up. I saw a cartoon recently circulated on Twitter which depicted the org charts of various tech megavendors and, quite tellingly, depicted Oracle’s as this:
Oracle is increasingly becoming a compliance company more than anything else. What’s more, despite their size and power, Oracle is not doing particularly well financially. Per a 12/17/15 research note from JMP,
While Oracle’s cloud marketing has been strong, the reality is that cloud represents only 7% of Oracle’s total revenue and that is after Oracle has presumably done everything they can to “juice” it, for example, by bundling cloud into deals where, I’ve heard, customers don’t even necessarily know they’ve purchased it.
So while Oracle does a good job of bluffing cloud, the reality is that Oracle is very much trapped in the Innovator’s Dilemma, addicted to a huge stream of maintenance revenue which they are afraid to cannibalize, and denying customers one of the key benefits of cloud computing: lower total cost of ownership. That’s not to mention they are stuck with a bad hardware business (which again missed revenues) and are under attack by cloud application and platform vendors, new competitors like Amazon, and at their very core by next-generation NoSQL database systems. It almost makes you feel bad for Larry Ellison. Almost.
8. Accounting irregularities are discovered at one or more unicorns. In 2015 many people started to think of late-stage megarounds as “private IPOs.” In one sense that was the correct: the size of the rounds and the valuations were very much in line with previous IPO norms. However, there was one big difference: they were like private IPOs — but without all the scrutiny. Put differently, they were like an IPO, but without a few million dollars in extra accounting work and without more people pouring over the numbers. Bill Gurley did a great post on this: Investors Beware: Today’s $100M+ Late-Stage Private Rounds are Very Different from an IPO. I believe this lack of scrutiny, combined with some people’s hubris and an overall frothy environment, will lead to the discovery of one or more major accounting irregularity episodes at unicorn companies in 2016. Turns out the world was better off with a lower IPO bar after all.
9. Startup workers get disappointed on exits, resulting in lawsuits. Many startup employees work long hours predicated on making big money from a possible downstream IPO. This has been the model in Silicon Valley for a long time: give up the paycheck and the perks of a big company in exchange for sleeves-up work and a chance to make big money on stock options at a startup. However, two things have changed: (1) dilution has increased because companies are raising more capital than ever and (2) “vanity rounds” are being done that maximize valuation at the expense of terms that are bad for the common shareholder (e.g., ratchets, multiple liquidation preferences).
In extreme cases this can wipe out the value of the common stock. In other cases it can turn “house money” into “car money” upon what appears to be a successful exit. Bloomberg recently covered this in a story called Big IPO, Tiny Payout about Box and the New York Times in a story about Good Technology’s sale to BlackBerry, where the preferred stock ended up 7x more valuable than the common. When such large disparities occur between the common and the preferred, lawsuits are a likely result.
Many employees will find themselves wondering why they celebrated those unicorn rounds in the first place.
10. The first cloud EPM S-1 gets filed. I won’t say here who I think will file first, why they might do so, and what the pros and cons of filing first may be, but I will predict that in 2016 the first S-1 gets filed for a cloud EPM vendor. I have always believed that cloud EPM is a great category and one that will result in multiple IPOs — so I don’t believe the first filing will be the last. It will be fun to watch this trend and get a look at real numbers, as opposed to some of the hype that gets circulated.
11. Bonus: 2016 proves to be a great year for Host Analytics. Finally, I feel great about the future for Host Analytics and believe that 2016 will be a wonderful year for the company. We have strong products. We have amazing customers. We have built the best team in EPM. We have built a strong partner network. We have great core applications and exciting, powerful new capabilities in modeling. I believe we have, overall, the best, most complete offering in cloud EPM.
Thanks for your support in 2015 and I look forward to delivering a great 2016 for our customers, our partners, our investors, and our team.
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Footnotes
[1] These predictions are offered in the spirit of fun and I have no liability to anyone acting or not acting on the content herein. I am not an oracle, soothsayer, or prophet and make no claim to be. Please enjoy these predictions, please let them provoke your thoughts, but do not use them as investing or business consulting advice. See my FAQ for additional disclaimers.
Posted in Bubbles, Enterprise, Predictions, Silicon Valley, VC
I’m Dave Kellogg, advisor, director, blogger, and podcaster. I am an EIR at Balderton Capital and principal of my own eponymous consulting business.
I bring an uncommon perspective to enterprise software, having more than ten years’ experience in each of the CEO, CMO, and independent director roles in companies from zero to over $1B in revenues.
From 2012 to 2018, I was CEO of Host Analytics, where we quintupled ARR while halving customer acquisition costs, ultimately selling the company in a private equity transaction.
Previously, I was SVP/GM of the $500M Service Cloud business at Salesforce; CEO of MarkLogic, which we grew from zero to $80M over six years; and CMO at Business Objects for nearly a decade as we grew from $30M to over $1B in revenues.
I love disruptive startups and and have had the pleasure of working in varied capacities with companies including Bluecore, FloQast, Gainsight, Hex, Logikcull, MongoDB, Pigment, Recorded Future, Tableau, and Unaric.
I currently serve on the boards of Cyber Guru, Light, Scoro, TechWolf, and Vic.ai. I have previously served on boards including Alation, Aster Data, Granular, Nuxeo, Profisee, and SMA Technologies.
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Kellblog by Dave Kellogg is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.