Category Archives: Silicon Valley

Do You Want to be Judged on Intentions or Results?

It was early in my career, maybe 8 years in, and I was director of product marketing at a startup.  One day, my peer, the directof of marketing programs hit me with this in an ops review meeting:

You want to be judged on intentions, not results.

I recall being dumbfounded at the time.  Holy cow, I thought.  Is he right?  Am I standing up arguing about mitigating factors and how things might have been when all the other people in the room were thinking only about black-and-white results?

It was one of those rare phrases that really stuck with me because, among other reasons, he was so right.  I wasn’t debating whether things happened or not.  I wasn’t making excuses or being defensive.  But I was very much judging our performance in the theoretical, hermetically sealed context of what might have been.

Kind of like sales saying a deal slipped instead of did not close.   Or marketing saying we got all the MQLs but didn’t get the requisite pipeline.  Or alliances saying that we signed up the 4 new partners, but didn’t get the new opportunities that were supposed to come with them.

Which phrase of the following sentence matters more — the first part or the second?

We did what we were supposed to, but it didn’t have the desired effect.

We would have gotten the 30 MQLS from the event if it hadn’t snowed in Boston.  But who decided to tempt fate by doing a live event in Boston in February?  People who want to be judged on intentions think about the snowstorm; people who want to be judged on results think about the MQLs.

People who want to judged on intentions build in what they see as “reasons” (which others typically see as “excuses”) for results not being achieved.

I’m six months late hiring the PR manager, but that’s because it’s hard to find great PR people right now.  (And you don’t want me to hire a bad one, do you?)

No, I don’t want you to hire a bad one.  I want you to hire a great one and I wanted you to hire them 6 months ago.  Do you think every other PR manager search in the valley took 6 months more than plan?  I don’t.

Fine lines exist here, no doubt.  Sometimes reasons are reasons and sometimes they are actually excuses.  The question isn’t about any one case.  It’s about, deep down, are you judging yourself by intentions or results?

You’d be surprised how many otherwise very solid people get this one thing wrong — and end up career-limited as a result.

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.

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Can the Media Please Stop Referring to Company Size by Valuation?

The following tweet is the umpteenth time I’ve seen the media size a company by valuation, not revenue, in the past few years:

mktcap

Call me old school, but I was taught to size companies by revenue, not market capitalization (aka, valuation).

Calling Palantir a $20B company suggests they are doing $20B in revenues, which is certainly not the case.  (They say they did $1B in 2015 and that’s bookings, not revenue.)  So we’re not talking a small difference here.  Depending on the hype factor surrounding a company, we might be talking 20x.

Domo is another company the media loves to size by its market cap.

domo

I’ve heard revenue estimates of $50M to $100M for Domo, so here again, we’re not talking about a small difference.  Maybe 20x.

When my friend Max Schireson stepped down from MongoDB to spend more time with his family, the media did it again (see the first line of text below the picture)

mongodb

I love Max.  I love MongoDB.  While I don’t know what their revenues were when he left (I’d guess $50M to $100M), they certainly were not a “billion-dollar database company.”  But, hey, the article got 4,000 shares.  Inflation-wise, I’m again guessing 10-20x.

So why does the media do this?  Why do they want to mislead readers by a factor of 20?

  • Because if makes the numbers bigger
  • And makes the headlines cooler
  • And increases drama

In the end, because it (metaphorically) sells more newspapers.  “Wow, some guy just quit as CEO of a billion-dollar company to actually spend more time with his family” just sounds a whole lot better than the same line with a comparatively paltry $50M instead.  Man Bites Dog beats Dog Bites Man every time.

But it’s wrong, and the media should stop doing it.  Why?

  • It’s misleading, and not just a little.  Up to 20x as the above examples demonstrate.
  • It’s not verifiable.  For private companies, you can’t really know or verify the valuation.  It’s not in any public filing.  (While private companies don’t disclose revenue either, it’s much more easily triangulated.)
  • Private company valuations are misleading because VCs buy preferred stock and employees/founders have common stock. So you take a preferred share price and multiply it by the total number of outstanding shares, both preferred and common.  (This ignores the fact that the common is definitionally worth less than the preferred and basically assumes an IPO scenario, which happens only for the fortunate few, where the preferred converts into common.)
  • In the past few years, companies are increasingly taking late-stage money that often comes with “structure” that makes it non-comparable in rights to both the regular preferred and the common.  So just compound the prior problem with a new class of essentially super-preferred stock.  The valuation gets even more misleading.
  • Finally, compound the prior problem with a hyped environment where everyone wants to be a unicorn so they might deliberately take unfavorable terms/structure in order get a higher valuation and hopefully cross into unicorn-dom.  The valuation gets even-more-misleading squared.  See the following Tweet as my favorite example of this phenom.  (OH means overheard.)

ego

When was the last time I saw the media consistently size companies by valuation instead of revenue?  1997 to 2001.  Bubble 1.0.

Maybe we’ll soon be talking about eyeballs again.  Or, if you like Stance, the company that has raised $116in VC and has “ignited a movement of art and self-expression,” in socks (yes, socks) then maybe we’ll be talking about feet.

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(And while I’m not sure about the $116M, I do love the socks.)

 

Myths of the Headless Company

In the past year or so, two of our competitors have abruptly transitioned their CEOs and both have perpetuated a lot of mythology about what happens and/or will happen in such transitions.  As someone who’s run two startups as CEO for more than a combined ten years, been the “new guy” CEO twice after such transitions, sat on two startup boards as an independent director, and advised numerous startups, I thought I’d do a little myth-busting around some of the common things these companies say to employees and customers when these transitions happen.

“Everythings’s fine, there is no problem.”

If everything were fine, you would not have changed your CEO.  QED.

Houston, there is a problem.

“Uh, the actual problem is we’re doing too well, … so we need to change our the CEO for the next level of growth.”

This reminds me of the job interview response where you say your biggest weakness is perfectionism.

Look, while successful companies do periodically outgrow their executives, you can tell the difference between an organized scale-driven CEO swap out and something going wrong.  How?

Organized transitions are organized.  The CEO and the board agree that the company is scaling beyond the CEO’s abilities.  A search is started.  The new CEO is found.  The old CEO gracefully hands the reins over to the new CEO.  This can and does happen all the time in Silicon Valley because the problem is real and everyone — both the VCs and the outgoing CEO — are all big shareholders and want what’s best for the company, which is a smooth transition.

When a CEO is exited …

  • Abruptly, without notice, over a weekend, …
  • Without a replacement already identified
  • Without even a search firm hired
  • At an awkward time (e.g., a few days before the end of a quarter or a few weeks before the annual user conference)

You can be pretty sure that something went wrong.  What exactly went wrong you can never know.  But you can be sure of thing:  the conversation ended with either “I’m outta here” or “he’s (or she’s) outta here” depending on whether the person was “pushed’ or “jumped.”

“But we did need someone for the next level of growth.”

That’s quite possibly true and the board will undoubtedly use the transition as an attempt to find someone who’s done the next level of growth before.  But, don’t be confused, if the transition is abrupt and disorganized that’s not why the prior CEO was exited.  Something else is going on, and it typically falls into one of three areas:

  • Dispute with the board, including but not limited to disagreements about the executive team or company strategy.
  • Below-plan operating results.  Most CEOs are measured according to expectations set in fundraising and established in the operating plan.  At unicorns, I call this the curse of the megaround, because such rounds are often done on the back on unachievable expectations.
  • Improprieties — while hopefully rare — such as legal, accounting, or employment violations, can also result in abrupt transitions.

“Nothing’s going to change.”

This is a favorite myth perpetuated on customers.  Having been “the new guy” at both MarkLogic and Host Analytics, I can assure you that things did change and the precise reason I was hired was to change things.  I’ve seen dozens of CEO job specs and I’ve never a single one that said “we want to hire a new CEO but you are not supposed to change anything.”  Doesn’t happen.

But companies tell customers this — and maybe they convince themselves it’s true because they want to believe it — but it’s a myth.  You hire a new CEO precisely and exactly to change certain things.

When I joined MarkLogic I focused the company almost exclusively on media and government verticals.  When I joined Host, I focused us up-market (relative to Adaptive) and on core EPM (as opposed to BI).

Since most companies get in trouble due to lack of focus, one of the basic job descriptions of the new-person CEO is to identify the core areas on which to focus — and the ones to cut.  Particularly, as is the case at Anaplan where the board is on record saying that the burn rate is too high — that means cut things.  Will he or she cut the area or geography that most concerns customer X?  Nobody knows.

Nobody.  And that’s important.  The only person who knows will be the new CEO and he/she will only know after 30-90 days of assessment.  So if anyone tells you “they know” that nothing’s going to change, they are either lying or clueless.  Either way, they are flat wrong.  No one knows, by definition.

“But the founder says nothing’s going to change.”

Now that would be an interesting statement if the founder were CEO.  But, in these cases, the founder isn’t CEO and there is a reason for that — typically a lack of sufficient business experience.

So when the founder tells you “nothing is going to change” it’s simply the guy who lacks enough business experience to actually run the business telling you his/her opinion.

The reality is new CEOs are hired for a reason, they are hired to change things, that change typically involves a change in focus, and CEO changes are always risky.  Sometimes they work out great.  Sometimes the new person craters the company.  You can never know.

 

 

 

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.

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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.

Thoughts on Jeff Weiner’s “Pay No Attention to the Stock” Message

From everything I’ve heard for a long time, Jeff Weiner is a wonderful guy and great CEO.  In addition, LinkedIn is certainly a great company, so please don’t view this post as dissing either Jeff or the company.

I will say, however, that I found media coverage of Jeff’s now famous all-hands speech after the stock fell nearly 50% in a day (and the company lost $11B in market cap) to both be rather fawning and to miss one absolutely critical point.

Here’s the video:

 

What Jeff Got Right

  • He faced the issue directly.
  • He communicated quickly.  (Conveniently they seem to have biweekly all-hands meetings already in place which made that easier.)
  • He made good arguments (e.g., this happens in public markets; we are the same company we were yesterday, with the same vision and the same team; we are well positioned against macro trends)
  • He spoke with great delivery and articulation
  • He was authentic and sincere

What the Media Missed
Jeff’s basic message — when you strip to the core — is “ignore the stock price.”  This is absolutely the right message.  Markets are fickle, stocks go up and down seemingly without reason, markets over-correct punishing errors severely (particularly for companies price-for-perfection liked LinkedIn) — having worked at several public companies and often with insider status, I can assure you that (1) daily fluctuations are usually inexplicable from the inside and (2) employees will go crazy if they pin their emotions to the ups and downs of the stock market.  So the best advice is:  ignore it.

However, the place where most CEOs fail is that they only want to ignore the stock price when it goes down.  You can’t send emails celebrating* a big uptick, have a party when you break $50/share, or anything like that and then have an ounce of credibility when delivering the message that Jeff so successfully did.

I know Jeff Weiner is very smart, so I’m guessing that LinkedIn never put employee focus on the stock price on the way up, so Jeff’s message is credible on the way down.

But the question isn’t how beautifully your CEO can say “ignore the 50% drop in the stock price” the day after the stock goes down.  The question is what the CEO says and how he or she behaves on the way up.

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* I’m OK with celebrating IPOs as long as you celebrate liquidity and not the day-one stock uptick.  One way to see the day-one uptick is the amount of value left on the table that the company did not capture for itself in the IPO pricing process.  Some of that is normal and part of the process; too much of that is, well, nothing to celebrate.

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.

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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.