Category Archives: Enterprise Software

Using “Win Themes” to Improve Your Sales Management and Increase Win Rates

At most sales review meetings what do you hear sales management asking the reps?  Questions like these:

  • What stage is this opportunity in?
  • What value do you have it at in the pipeline?
  • Is there upside to that value?
  • What forecast category is it in?
  • In what quarter will it close?
  • What competitors are in the deal?
  • What products will they be buying?
  • Do they have budget for the purchase?
  • How do we meet their primary requirements for a solution?
  • How have we demonstrated that we can meet those requirements?
  • What are the impacts of not solving those problems?
  • How did they attempt to solve those problems before?
  • Who is impacted by the consequences of those impacts?
  • Who is the primary decision maker?
  • What is the decision-making process?
  • Who else is involved in the decision and in what roles?
  • Who have you developed relationships with in the account?
  • What risk is there of a goal-post move?

And on and on.

Some of these questions are about systems and process.  Some are about forecasting.  Ideally, most are about the problem the customer is trying to solve, the impacts of not solving it, how they tried to solve it before, the ideal solution to the problem, and the benefits of solving it.  But in our collective hurry to be process-oriented, methodology-driven, systems-compliant, and solutions-oriented, all too often something critical gets lost:

Why are we going to win?

What?  Oh shoot.  Yep, forgot to ask that one.  And, of course, that’s the most important one.  As I sometimes need to remind sales managers, while the process is great, let’s not forget the purpose of the process is to win.

(I’ve even met a few sales managers so wedded to process and discipline that I’ve wondered if they’d rather crash while flying in perfect formation than win flying out of it.)

Process is great.  I love process.  But let’s not forget the point.  How can we do that?  With win themes — two to three simple, short, plain-English reasons why you are going to win the deal.  Here’s an example.  We are going to win because:

  • Joe the CFO saw first-hand how Adaptive didn’t scale in his last job and is committed to purchasing a system he can grow with.
  • Our partner, CFO Experts, has worked with Joe in the past, has a great relationship with him, and firmly believes that Host is the best fit with the requirements.

Build win themes into your systems and process.  Don’t add win themes to the bottom of your Salesforce opportunity screen; put them right up top so the first conversation about any deal — before you dive into the rabbit hole — is “why are we going to win?”   Two to three win themes should provide a proposed answer and a healthy platform for strategic discussion.

(And, as my friend Kate pointed out, in case it didn’t come up in the win theme conversation, don’t forget to ask “why might we lose?”)

Video of my SaaStr 2018 Presentation: Ten Non-Obvious Things About Scaling SaaS

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.

My Appearance on DisrupTV Episode 100

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:

  • The impact of artificial intelligence (AI) and machine learning (ML) on the enterprise performance management (EPM) market.
  • Why I joined Host Analytics some 5 years ago.
  • What it’s like competing with Oracle … for basically your entire career.
  • What it’s like selling enterprise software both upwind and downwind.
  • How I ended up on the board of Alation and what I like about data catalogs.
  • What I learned working at Salesforce (hint:  shoshin)
  • Other lessons from BusinessObjects, MarkLogic, and even Ingres.

DisrupTV Episode 100, Featuring Dave Kellogg, Billy Bosworth, Virginia Backaitis from Constellation Research on Vimeo.

 

Kellblog’s 2017 Predictions  

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

  1. The great reckoning begins. Correct/nailed.  As predicted, since most of the bubble was tied up in private companies owned by private funds, the unwind would happen in slow motion.  But it’s happening.
  2. Silicon Valley cools off a bit. Partial.  While IPOs were down, you couldn’t see the cooling in anecdotal data, like my favorite metric, traffic on highway101.
  3. Porter’s five forces analysis makes a comeback. Partial.  So-called “momentum investing” did cool off, implying more rational situation analysis, but you didn’t hear people talking about Porter per se.
  4. Cyber-cash makes a rise. CorrectBitcoin more doubled on the year (and Ethereum was up 8x) which perversely reinforced my view that these crypto-currencies are too volatile — people want the anonymity of cash without a highly variable exchange rate.  The underlying technology for Bitcoin, blockchain, took off big time.
  5. Internet of Things goes into trough of disillusionment. Partial.  I think I may have been a little early on this one.  Seems like it’s still hovering at the peak of inflated expectations.
  6. Data science rises as profession. Correct/easy.  This continues inexorably.
  7. SAP realizes they are a complex enterprise application company. Incorrect.  They’re still “running simple” and talking too much about enabling technology.  The stock was up 9% on the year in line with revenues up around 8% thus far.
  8. Oracle’s cloud strategy gets revealed – “we’ll sell you any deployment model you want as long as your annual bill goes up.”  Partial.  I should have said “we’ll sell you any deployment model you want as long as we can call it cloud to Wall St.”
  9. Accounting irregularities discovered at one or more unicorns. Correct/nailed.  During these bubbles the pattern always repeats itself – some people always start breaking the rules in order to stand out, get famous, or get rich.  Fortune just ran an amazing story that talks about the “fake it till you make it” culture of some diseased startups.
  10. Startup workers get disappointed on exits. Partial.  I’m not aware of any lawsuits here but workers at many high flyers have been disappointed and there is a new awareness that the “unicorn party” may be a good thing for founders and VCs, but maybe not such a good thing for rank-and-file employees (and executive management).
  11. The first cloud EPM S-1 gets filed. Incorrect.  Not yet, at least.  While it’s always possible someone did the private filing process with the SEC, I’m guessing that didn’t happen either.
  12. 2016 will be a great year for Host Analytics. Correct.  We had a strong finish to the year and emerged stronger than we started with over 600 great customers, great partners, and a great team.

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.

  1. The United States will see a level of divisiveness and social discord not seen since the 1960s. Social media echo chambers will reinforce divisions.  To combat this, I encourage everyone to sign up for two publications/blogs they agree with and two they don’t lest they never again hear both sides of an issue. (See map below, coutesy of Ninja Economics, for help in choosing.)  On an optimistic note, per UCSD professor Lane Kenworthy people aren’t getting more polarized, political parties are.

news

  1. Social media companies finally step up and do something about fake news. While per a former Facebook designer, “it turns out that bullshit is highly engaging,” these sites will need to do something to filter, rate, or classify fake news (let alone stopping to recommend it).  Otherwise they will both lose credibility and readership – as well as fail to act in a responsible way commensurate with their information dissemination power.
  1. Gut feel makes a comeback. After a decade of Google-inspired heavily data-driven and A/B-tested management, the new US administration will increasingly be less data-driven and more gut-feel-driven in making decisions.  Riding against both common sense and the big data / analytics / data science trends, people will be increasingly skeptical of purely data-driven decisions and anti-data people will publicize data-driven failures to popularize their arguments.  This “war on data” will build during the year, fueled by Trump, and some of it will spill over into business.  Morale in the Intelligence Community will plummet.
  1. Under a volatile leader, who seems to exhibit all nine of the symptoms of narcissistic personality disorder, we can expect sharp reactions and knee-jerk decisions that rattle markets, drive a high rate of staff turnover in the Executive branch, and fuel an ongoing war with the media.  Whether you like his policies or not, Trump will bring a high level of volatility the country, to business, and to the markets.
  1. With the new administration’s promises of $1T in infrastructure spending, you can expect interest rates to raise and inflation to accelerate. Providing such a stimulus to already strong economy might well overheat it.  One smart move could be buying a house to lock in historic low interest rates for the next 30 years.  (See my FAQ for disclaimers, including that I am not a financial advisor.)
  1. Huge emphasis on security and privacy. Election-related hacking, including the spearfishing attack on John Podesta’s email, will serve as a major wake-up call to both government and the private sector to get their security act together.  Leaks will fuel major concerns about privacy.  Two-factor authentication using verification codes (e.g., Google Authenticator) will continue to take off as will encrypted communications.  Fear of leaks will also change how people use email and other written electronic communications; more people will follow the sage advice in this quip:

Dance like no one’s watching; E-mail like it will be read in a deposition

  1. In 2015, if you were flirting on Ashley Madison you were more likely talking to a fembot than a person.  In 2016, the same could be said of troll bots.  Bots are now capable of passing the Turing Test.  In 2017, we will see more bots for both good uses (e.g., customer service) and bad (e.g., trolling social media).  Left unchecked by the social media powerhouses, bots could damage social media usage.
  1. Artificial intelligence hits the peak of inflated expectations. If you view Salesforce as the bellwether for hyped enterprise technology (e.g., cloud, social), then the next few years are going to be dominated by artificial intelligence.  I’ve always believed that advanced analytics is not a standalone category, but instead fodder that vendors will build into smart applications.  They key is typically not the technology, but the problem to which to apply it.  As Infer founder Vik Singh said of Jim Gray, “he was really good at finding great problems,” the key is figuring out the best problems to solve with a given technology or modeling engine.  Application by application we will see people searching for the best problems to solve using AI technology.
  1. The IPO market comes back. After a year in which we saw only 13 VC-backed technology IPOs, I believe the window will open and 2017 will be a strong year for technology IPOs.  The usual big-name suspects include firms like Snap, Uber, AirBnB, and SpotifyCB Insights has identified 369 companies as strong 2017 IPO prospects.
  1. Megavendors mix up EPM and ERP or BI. Workday, which has had a confused history when it comes to planning, acquired struggling big data analytics vendor Platfora in July 2016, and seems to have combined analytics and EPM/planning into a single unit.  This is a mistake for several reasons:  (1) EPM and BI are sold to different buyers with different value propositions, (2) EPM is an applications sale, BI is a platform sale, and (3) Platfora’s technology stack, while appropriate for big data applications is not ideal for EPM/planning (ask Tidemark).  Combining the two together puts planning at risk.  Oracle combined their EPM and ERP go-to-market organizations and lost focus on EPM as a result.  While they will argue that they now have more EPM feet on the street, those feet know much less about EPM, leaving them exposed to specialist vendors who maintain a focus on EPM.  ERP is sold to the backward-looking part of finance; EPM is sold to the forward-looking part.  EPM is about 1/10th the market size of ERP.  ERP and EPM have different buyers and use different technologies.  In combining them, expect EPM to lose out.

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.

# # #

 

CAC Payback Period:  The Most Misunderstood SaaS Metric

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:

bess cac

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:

kell cac

Let’s run some numbers.

  • If your company has a CAC ratio of 1.5 and subscription gross margins of 75%, then your CPP = 24 months.
  • If your company has a CAC ratio of 1.2 and subscription gross margins of 80%, then your CPP = 18 months.
  • If you company has a CAC ratio of 0.8 and subscription gross margins of 80%, then your CPP = 12 months.

All seems pretty simple, right?  Not so fast.  There are two things that constantly confound people when looking at CAC Payback Period (CPP).

  • They forget payback metrics are risk metrics, not return metrics
  • They fail to correctly interpret the impact of annual or multi-year contracts

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?

  • Project A: which has a payback period of 12 months
  • Project B: which has a payback period of 6 months

Quick, which do you pick?  Well, project B.  Duh.  But wait — now I tell you this:

  • Project A has a net present value (NPV) of 500 units
  • Project B has an NPV of 110 units

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.

saas fail

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:

50-50

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.

# # #

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.

Kellblog Predictions for 2016

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

  1. The good times will continue to roll in Silicon Valley.  I asserted that even if you felt a bubble, that it was more 1999 than 2001.  While IPOs slowed on the year, private financing remained strong — traffic is up, rents are up and unemployment is down.  Correct.
  2. The IPO as down-round continues.  Correct.
  3. The curse of the mega-round strikes many companies and CEOs.  While I can definitely name some companies where this has occurred, I can think of many more where I still think it’s coming but yet to happen.  Partial / too early.
  4. Cloud disruption continues.  From startups to megavendors, the cloud and big data are almost all everyone talks about these days.  Correct.
  5. Privacy becomes a huge issue.  While I think privacy continues to move to center stage, it hasn’t become as big as I thought it would, yet.  Partial / too early.
  6. Next-generation apps like Slack and Zenefits continue to explode.  I’d say that despite some unicorn distortion that this call was right (and we’re happy to have signed on Slack as a Host Analytics customer in 2015 to boot).  Correct.
  7. IBM software rebounds.  At the time I made this prediction IBM was in the middle of a large reorganization and I was speculating (and kinda hoping) that the result would be a more dynamic IBM software business.  That was not to be.  Incorrect.
  8. Angel investing slows.  I couldn’t find any hard figures here, but did find a great article on why Tucker Max quit angel investing.  I’m going to give myself a partial here because I believe the bloom is coming off the angel investing rose.  Partial.
  9. The data scientist shortage continues. This one’s pretty easy.   Correct.
  10. The unification of planning becomes the top meme in EPM.  This was a correct call and supported, in part, through our own launch of Modeling Cloud, a cloud-based, multi-dimensional modeling engine that helps tie enterprise models both to each other and the corporate plan.  Correct.

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).

porters-five-forces

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.

the-graduate-plastics
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.
SAP_logo

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.

 

powered by oracle

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-org-chart-300x195

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,

  • Oracle has missed revenue estimates for four quarters in a row.
  • Oracle provided weak, below-expectations guidance on its most recent earnings call for EPS, cloud revenue, and total revenue.
  • “While the bull case is that the cloud business is accelerating dramatically, we remain concerned because the cloud represented only 7% of total revenue in F2Q16 and we worry the core database
    and middleware business (which represents about half of Oracle’s revenue) will face increasing competition from Amazon Web Services.”

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.

good

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.

# # #

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.

The Customer Acquisition Cost (CAC) Ratio: Another Subtle SaaS Metric

The software-as-a-service (SaaS) space is full of seemingly simple metrics that can quickly slip through your fingers when you try to grasp them.  For example, see Measuring SaaS Renewals Rates:  Way More Than Meets the Eye for a two-thousand-word post examining the many possible answers to the seemingly simple question, “what’s your renewal rate?”

In this post, I’ll do a similar examination to the slightly simpler question, “what’s your customer acquisition cost (CAC) ratio?”

I write these posts, by the way, not because I revel in the detail of calculating SaaS / cloud metrics, but rather because I cannot stand when groups of otherwise very intelligent people have long discussions based on ill-defined metrics.  The first rule of metrics is to understand what they are and what they mean before entertaining long discussions and/or making important decisions about them.  Otherwise you’re just counting angels on pinheads.

The intent of the CAC ratio is to determine the cost associated with acquiring a customer in a subscription business.  When trying to calculate it, however, there are six key issues to consider:

  • Months vs. years
  • Customers vs. dollars
  • Revenue on top vs. bottom
  • Revenue vs. gross margin
  • The cost of customer success
  • Time periods of S&M

Months vs. Years

The first question — which relates not only to CAC but also to many other SaaS metrics:  is your business inherently monthly or annual?

Since the SaaS movement started out with monthly pricing and monthly payments, many SaaS businesses conceptualized themselves as monthly and thus many of the early SaaS metrics were defined in monthly terms (e.g., monthly recurring revenue, or MRR).

While for some businesses this undoubtedly remains true, for many others – particularly in the enterprise space – the real rhythm of the business is annual.  Salesforce.com, the enterprise SaaS pioneer, figured this out early on as customers actually encouraged the company to move to an annual rhythm, for among other reasons, to avoid the hassle associated with monthly billing.

Hence, many SaaS companies today view themselves as in the business of selling annual subscriptions and talk not about MRR, but ARR (annual recurring revenue).

Customers vs. Dollars

If you ask some cloud companies their CAC ratio, they will respond with a dollar figure – e.g., “it costs us $12,500 to acquire a customer.”  Technically speaking, I’d call this customer acquisition cost, and not a cost ratio.

There is nothing wrong with using customer acquisition cost as a metric and, in fact, the more your business is generally consistent and the more your customers resemble each other, the more logical it is to say things like, “our average customer costs $2,400 to acquire and pays us $400/month, so we recoup our customer acquisition cost in six months.”

However, I believe that in most SaaS businesses:

  • The company is trying to run a “velocity” and an “enterprise” model in parallel.
  • The company may also be trying to run a freemium model (e.g., with a free and/or a low-price individual subscription) as well.

Ergo, your typical SaaS company might be running three business models in parallel, so wherever possible, I’d argue that you want to segment your CAC (and other metric) analysis.

In so doing, I offer a few generic cautions:

  • Remember to avoid the easy mistake of taking “averages of averages,” which is incorrect because it does not reflect weighting the size of the various businesses.
  • Remember that in a bi-modal business that the average of the two real businesses represents a fictional mathematical middle.

avg of avg

For example, the “weighted avg” column above is mathematically correct, but it contains relatively little information.  In the same sense that you’ll never find a family with 1.8 children, you won’t find a customer with $12.7K in revenue/month.  The reality is not that the company’s average months to recoup CAC is a seemingly healthy 10.8 – the reality is the company has one very nice business (SMB) where it takes only 6 months to recoup CAC and one very expensive one where it takes 30.  How you address the 30-month CAC recovery is quite different from how you might try to squeeze a month or two out the 10.8.

Because customers come in so many different sizes, I dislike presenting CAC as an average cost to acquire a customer and prefer to define CAC as an average cost to acquire a dollar of annual recurring revenue.

Revenue on Top vs. Bottom

When I first encountered the CAC ratio is was in a Bessemer white paper, and it looked like this.

cac picture

In English, Bessemer defined the 3Q08 CAC as the annualized amount of incremental gross margin in 3Q08 divided by total S&M expense in 2Q08 (the prior quarter).

Let’s put aside (for a while) the choice to use gross margin as opposed to revenue (e.g., ARR) in the numerator.  Instead let’s focus on whether revenue makes more sense in the numerator or the denominator.  Should we think of the CAC ratio as:

  • The amount of S&M we spend to generate $1 of revenue
  • The amount of revenue we get per $1 of S&M cost

To me, Bessemer defined the ratio upside down.  The customer acquisition cost ratio should be the amount of S&M spent to acquire a dollar of (annual recurring) revenue.

Scale Venture Partners evidently agreed  and published a metric they called the Magic Number:

Take the change in subscription revenue between two quarters, annualize it (multiply by four), and divide the result by the sales and marketing spend for the earlier of the two quarters.

This changes the Bessemer CAC to use subscription revenue, not gross margin, as well as inverts it.  I think this is very close to CAC should be calculated.  See below for more.

Bessemer later (kind of) conceded the inversion — while they side-stepped redefining the CAC, per se, they now emphasize a new metric called “CAC payback period” which puts S&M in the numerator.

Revenue vs. Gross Margin

While Bessemer has written some great papers on Cloud Computing (including their Top Ten Laws of Cloud Computing and Thirty Q&A that Every SaaS Revenue Leader Needs to Know) I think they have a tendency to over-think things and try to extract too much from a single metric in defining their CAC.  For example, I think their choice to use gross margin, as opposed to ARR, is a mistake.

One metric should be focused on measuring one specific item. To measure the overall business, you should create a great set of metrics that work together to show the overall state of affairs.

leaky

I think of a SaaS company as a leaky bucket.  The existing water level is a company’s starting ARR.  During a time period the company adds water to the bucket in form of sales (new ARR), and water leaks out of the bucket in the form of churn.

  • If you want to know how efficient a company is at adding water to the bucket, look at the CAC ratio.
  • If you want to know what happens to water once in the bucket, look at the renewal rates.
  • If you want to know how efficiently a company runs its SaaS service, look at the subscription gross margins.

There is no need to blend the efficiency of operating the SaaS service with the efficiency of customer acquisition into a single metric.  First, they are driven by different levers.  Second, to do so invariably means that being good at one of them can mask being bad at the other.  You are far better off, in my opinion, looking at these three important efficiencies independently.

The Cost of Customer Success

Most SaaS companies have “customer success” departments that are distinct from their customer support departments (which are accounted for in COGS).  The mission of the customer success team is to maximize the renewals rate – i.e., to prevent water from leaking out of the bucket – and towards this end they typically offer a form of proactive support and adoption monitoring to ferret out problems early, fix them, and keep customers happy so they will renew their subscriptions.

In addition, the customer success team often handles basic upsell and cross-sell, selling customers additional seats or complementary products.  Typically, when a sale to an existing customer crosses some size or difficultly threshold, it will be kicked back to sales.  For this reason, I think of customer success as handling incidental upsell and cross-sell.

The question with respect to the CAC is what to do with the customer success team.  They are “sales” to the extent that they are renewing, upselling, and cross-selling customers.  However, they are primarily about ARR preservation as opposed to new ARR.

My preferred solution is to exclude both the results from and the cost of the customer success team in calculating the CAC.  That is, my definition of the CAC is:

dk cac pic

I explicitly exclude the cost customer success in the numerator and exclude the effects of churn in the denominator by looking only at the new ARR added during the quarter.  This formula works on the assumption that the customer success team is selling a relatively immaterial amount of new ARR (and that their primary mission instead is ARR preservation).  If that is not true, then you will need to exclude both the new ARR from customer success as well as its cost.

I like this formula because it keeps you focused on what the ratio is called:  customer acquisition cost.  We use revenue instead of gross margin and we exclude the cost of customer success because we are trying to build a ratio to examine one thing:  how efficiently do I add new ARR to the bucket?  My CAC deliberately says nothing about:

  • What happens to the water once S&M pours it in the bucket.  A company might be tremendous at acquiring customers, but terrible at keeping them (e.g., offer a poor quality service).  If you look at net change in ARR across two periods then you are including both the effects of new sales and churn.  That is why I look only at new ARR.
  • The profitability of operating the service.  A company might be great at acquiring customers but unable to operate its service at a profit.  You can see that easily in subscription gross margins and don’t need to embed that in the CAC.

There is a problem, of course.  For public companies you will not be able to calculate my CAC because in all likelihood customer success has been included in S&M expense but not broken out and because you can typically only determine the net change in subscription revenues and not the amounts of new ARR and churn.  Hence, for public companies, the Magic Number is probably your best metric, but I’d just call it 1/CAC.

My definition is pretty close to that used by Pacific Crest in their annual survey, which uses yet another slightly different definition of the CAC:  how much do you spend in S&M for a dollar of annual contract value (ACV) from a new customer?

(Note that many vendors include first-year professional services in their definition of ACV which is why I prefer ARR.  Pacific Crest, however, defines ACV so it is equivalent to ARR.)

I think Pacific Crest’s definition has very much the same spirit as my own.  I am, by comparison, deliberately simpler (and sloppier) in assuming that customer success not providing a lot of new ARR (which is not to say that a company is not making significant sales to its customer base – but is to say that those opportunities are handed back to the sales function.)

Let’s see the distribution of CAC ratios reported in Pacific Crest’s recent, wonderful survey:

pac crest cac

Wow.  It seems like a whole lot of math and analysis to come back and say:  “the answer is 1.

But that’s what it is.  A healthy CAC ratio is around 1, which means that a company’s S&M investment in acquiring a new customer is repaid in about a year.  Given COGS associated with running the service and a company’s operating expenses, this implies that the company is not making money until at least year 3.  This is why higher CACs are undesirable and why SaaS businesses care so much about renewals.

Technically speaking, there is no absolute “right” answer to the CAC question in my mind.  Ultimately the amount you spend on anything should be related to what it’s worth, which means we need relate customer acquisition cost to customer lifetime value (LTV).

For example, a company whose typical customer lifetime is 3 years needs to have a CAC well less than 1, whereas a company with a 10 year typical customer lifetime can probably afford a CAC of more than 2.  (The NPV of a 10-year subscription increasing price at 3% with a 90% renewal rate and discount at 8% is nearly $7.)

Time Periods of S&M Expense

Let me end by taking a practical position on what could be a huge rat-hole if examined from first principles.  The one part of the CAC we’ve not yet challenged is the use of the prior quarter’s sales and marketing expense.  That basically assumes a 90-day sales cycle – i.e., that total S&M expense from the prior quarter is what creates ARR in the current quarter.  In most enterprise SaaS companies this isn’t true.  Customers may engage with a vendor over a period of a year before signing up.  Rather than creating some overlapped ramp to try and better model how S&M expense turns into ARR, I generally recommend simply using the prior quarter for two reasons:

  • Some blind faith in offsetting errors theory.  (e.g., if 10% of this quarter’s S&M won’t benefit us for a year than 10% of a year ago’s spend did the same thing, so unless we are growing very quickly this will sort of cancel out).
  • Comparability.  Regardless of its fundamental correctness, you will have nothing to compare to if you create your own “more accurate” ramp.

I hope you’ve enjoyed this journey of CAC discovery.  Please let me know if you have questions or comments.