Category Archives: Silicon Valley

How To Get Your Startup a Halo

How would you like your startup to win deals not only when you win a customer evaluation, but when you tie — and even sometimes when you lose?

That sounds great.  But is it even possible?  Amazingly, yes — but you need have a halo effect working to your advantage.  What is a halo effect?  Per Wikipedia,

The halo effect is a cognitive bias in which an observer’s overall impression of a person, company, brand, or product influences the observer’s feelings and thoughts about that entity’s character or properties

There’s a great, must-read book (The Halo Effect) on the how this and eight other related effects apply in business.  The book is primarily about how the business community makes incorrect attributions about “best practices” in culture, leadership, values, and process that are subsequent to — but were not necessarily drivers of — past performance.

I know two great soundbites that summarize the phenomenon of pseudo-science in business:

  • All great companies have buildings.” Which comes from the (partly discredited) Good To Great that begins with the observation that in their study cohort of top-performing companies that all of them had buildings — and thus that simply looking for commonalities among top-performing companies was not enough; you’d have to look for distinguishing factors between top and average performers.
  • “If Marc Benioff carried a rabbit’s foot, would you?”  Which comes from a this Kellblog post where I make the point that blindly copying the habits of successful people will not replicate their outcome and, with a little help from Theodore Levitt, that while successful practitioners are intimately familiar with their own beliefs and behaviors, that they are almost definitionally ignorant of which ones helped, hindered, or were irrelevant to their own success.

Now that’s all good stuff and if you stop reading right here, you’ll hopefully avoid falling for pseudo-science in business.  That’s important.  But it misses an even bigger point.

Has your company ever won (or lost) a deal because of:

  • Perceived momentum?
  • Analyst placement on a quadrant or other market map?
  • Perceived market leadership?
  • Word of mouth as the “everyone’s using it” or “next thing” choice?
  • Perceived hotness?
  • Vibe at your events or online?
  • A certain feeling or je ne sais quoi that you were more (or less) preferred?
  • Perceived vision?

If yes, you’re seeing halo effects at work.

Halo effects are real.  Halo effects are human nature.  Halo effects are cognitive biases that tip the scales in your favor.  So the smart entrepreneur should be thinking:  how do I get one for my company?  (And the smart customer, how can I avoid being over-influenced by them?  See bottom of post.)

In Silicon Valley, a number of factors drive the creation of halo effects around a company.  Some of these are more controllable than others.  But overall, you should be thinking about how you can best combine these factors into an advantage.

  • Lineage, typically in the form of previous success at a hot company (e.g., Reid Hoffman of PayPal into LinkedIn, Dave Duffield of PeopleSoft into Workday).  The implication here (and a key part of halo effects) is that past success will lead to future success, as it sometimes does.  This one’s hard to control, but ceteris paribus, co-founding (even somewhat ex post facto) a company with an established entrepreneur will definitely help in many ways, including halo effects.
  • Investors, in one of many forms:  (1) VC’s with a strong brand name (e.g., Andreessen Horowitz), (2) specific well known venture capitalists (e.g., Doug Leone), (3) well known individual investors (e.g., Peter Thiel), and to a somewhat lesser extent (4) visible and/or famous angels (e.g., Ashton Kutcher). The implication here is obvious, that the investor’s past success is an indication of your future success.  There’s no doubt that strong investors help build halo effects indirectly through reputation; in cases they can do so directly as well via staff marketing partners designed to promote portfolio companies.
  • Investment.  In recent years, simply raising a huge amount of money has been enough to build a significant halo effect around a company, the implication being that “if they can raise that much money, then there’s got to be a pony in there somewhere.” Think Domo’s $690M or Palantir’s $2.1B.   The media loves these “go big or go home” stories and both media and customers seem to overlook the increased risk associated with staggering burn rates, the waste that having too much capital can lead to, the possibility that the investors represent “dumb money,” and the simple fact that “at scale” these businesses are supposed to be profitable.  Nevertheless, if you have the stomach, the story, and the connections to raise a dumbfounding amount of capital, it can definitely build a halo around your company.  For now, at least.
  • Valuation.  Even as the age of the unicorn starts to wane, it’s undeniable that in recent years, valuation has been a key tool to generate halos around a company.  In days of yore, valuation was a private matter, but as companies discovered they could generate hype around valuation, they started to disclose it, and thus the unicorn phenomenon was born.  As unicorn status became increasingly de rigeur, things got upside-down and companies started trading bad terms (e.g., multiple liquidation preferences, redemption rights) in order to get $1B+ (unicorn) post-money valuations.  That multiplying the price of a preferred share with superior rights by a share count that includes the number of lesser preferred and common shares is a fallacious way to arrive at a company valuation didn’t matter.  While I think valuation as a hype driver may lose some luster as many unicorns are revealed as horses in party hats (e.g., down-round IPOs), it can still be a useful tool.  Just be careful about what you trade to get it.  Don’t sell $100M worth of preferred with a ratcheted 2 moving to 3x liquidation preference — but what if someone would buy just $5M worth on those terms.  Yes, that’s a total hack, but so is the whole idea of multiplying a preferred share price times the number of common shares.  And it’s far less harmful to the company and the common stock.  Find your own middle ground / peace on this issue.
  • Growth and vision.  You’d think that industry watchers would look at a strategy and independently evaluate its merits in terms of driving future growth.  But that’s not how it works.  A key part of halo effects is misattribution of practices and performance.  So if you’ve performed poorly and have an awesome strategy, it will overlooked — and conversely.  Sadly, go-forward strategy is almost always viewed through the lens of past performance, even if that performance were driven by a different strategy or affected positively or negatively by execution issues unrelated to strategy.  A great story isn’t enough if you want to generate a vision halo effect.  You’re going to need to talk about growth numbers to prove it.  (That this leads to a pattern of private companies reporting inflated or misleading numbers is sadly no surprise.)  But don’t show up expecting to wow folks with vision. Ultimately, you’ll need to wow them with growth — which then provokes interest in vision.
  • Network.  Some companies do a nice and often quiet job of cultivating friends of the company who are thought leaders in their areas.  Many do this through inviting specific people to invest as angels.  Some do this simply through communications.  For example, one day I received an email update from Vik Singh clearly written for friends of Infer. I wasn’t sure how I got on the list, but found the company interesting and over time I got to know Vik (who is quite impressive) and ended up, well, a friend of Infer.  Some do this through advisory boards, both formal and informal.  For example, I did a little bit of advising for Tableau early on and later discovered a number of folks in my network who’d done the same thing.  The company benefitted by getting broad input on various topics and each of us felt like we were friends of Tableau.  While sort of thing doesn’t generate the same mainstream media buzz as a $1B valuation, it is a smart influencer strategy that can generate fans and buzz among the cognoscenti who, in theory at least, are opinion leaders in their chosen areas.

Before finishing the first part of this post, I need to provide a warning that halo effects are both powerful and addictive.  I seem to have a knack for competing against companies pursuing halo-driven strategies and the pattern I see typically runs like this.

  • Company starts getting some hype off good results.
  • Company starts saying increasingly aggressive things to build off the hype.
  • Analysts and press reward the hype with strong quadrant placements and great stories and blogs.
  • Company puts itself under increasing pressure to produce numbers that support the hype.

And then one of three things happens:

  1. The company continues delivering strong results and all is good, though the rhetoric and vision gets more unrelated to the business with each cycle.
  2. The company stops delivering results and is downgraded from hot-list to shit-list in the minds of the industry.
  3. The company cuts the cord with reality and starts inflating results in order to sustain the hype cycle and avoid outcome #2 above.  The vision inflates as aggressively as the numbers.

I have repeatedly had to compete against companies where claims/results were inflated to “prove” the value of bad/ordinary strategies to impress industry analysts to get strong quadrant positions to support broader claims of vision and leadership to drive more sales to inflate to even greater claimed results.  Surprisingly, I think this is usually done more in the name of ego than financial gain, but either way the story ends the same way — in terminations, lawsuits and, in one case, a jail sentence for the CEO.

Look, there are valid halo-driven strategies out there and I encourage you to try and use them to your company’s advantage — just be very careful you don’t end up addicted to halo heroin.  If you find yourself wanting to do almost anything to sustain the hype bubble, then you’ll know you’re addicted and headed for trouble.

The Customer View

Thus far, I’ve written this post entirely from the vendor viewpoint, but wanted to conclude by switching sides and offering customers some advice on how to think about halo effects in choosing vendors.   Customers should:

  • Be aware of halo effects.  The first step in dealing with any problem is understanding it exists. While supposedly technical, rational, and left-brained, technology can be as arbitrary as apparel when it comes to fashion.  If you’re evaluating vendors with halos, realize that they exist for a reason and then go understand why.  Are those drivers relevant — e.g., buying HR from Dave Duffield seems a reasonable idea.  Or are they spurious —  e.g., does it really matter that one board member invested in Facebook?  Or are they actually negative — e.g., if the company has raised $300M how crazy is their burn rate, what risk does that put on the business, and how focused will they stay on you as a customer and your problem as a market?
  •  Stay focused on your problem.  I encourage anyone buying technology to write down their business problems and high-level technology requirements before reaching out to vendors.  Hyped vendors are skilled at “changing the playing field” and trained to turn their vision into your (new) requirements.  While there certainly are cases where vendors can point out valid new requirements, you should periodically step back and do a sanity check:  are you still focused on your problem or have you been incrementally moved to a different, or greatly expanded one.  Vision is nice, but you won’t be around solve tomorrow’s problems if you can’t solve today’s.
  • Understand that industry analysts are often followers, not leaders.  If a vendor is showing you analyst support for their strategy, you need to figure out if the analyst is endorsing the strategy because of the strategy’s merits or because of the vendor’s claimed prior performance.  The latter is the definition of a halo effect and in a world full of private startups where high-quality analysts are in short supply, it’s easy to find “research” that effectively says nothing more than “this vendor is a leader because they say they’re performing really well and/or they’ve raised a lot of money.” That doesn’t tell you anything you didn’t know already and isn’t actually an independent source of information.  They are often simply amplifiers of the hype you’re already hearing.
  • Enjoy the sizzle; buy the steak.  Hype king Domo paid Alec Baldwin to make some (pretty pathetic) would-be viral videos and had Billy Beane, Flo Rida, Ludacris, and Marshawn Lynch at their user conference.  As I often say, behind any “marketing genius” is an enormous marketing budget, and that’s all you’re seeing — venture capital being directly converted into hype.  Heck, let them buy you a ticket to the show and have a great time.  Just don’t buy the software because of it — or because of the ability to invest more money in hand-grooming a handful of big-name references.  Look to meet customers like you, who have spent what you want to spend, and see if they’re happy and successful.  Don’t get handled into meeting other customers only at pre-arranged meetings.  Walk the floor and talk to regular people.  Find out how many are there for the show, or because they’re actual successful users of the software.
  • Dive into detail on the proposed solution.  Hyped vendors will often try to gloss over solutions and sell you the hype (e.g., “of course we can solve your problem, we’ve got the most logos, Gartner says we’re the leader, there’s an app for that.”)  What you need is a vendor who will listen to your problem, discuss it with you intelligently, and provide realistic estimates on what it takes to solve it.  The more willing they are to do that, the better off you are.  The more they keep talking about the founder’s escape from communism, the pedigree of their investors, their recent press coverage, or the amount of capital they’ve raised, the more likely you are to end up high and dry.  People interested in solving your problem will want to talk about your problem.
  • Beware the second-worst outcome:  the backwater.  Because hyped vendors are actually serving Sand Hill Road and/or Wall Street more than their customers, they pitch broad visions and huge markets in order to sustain the halo.  For a customer, that can be disastrous because the vendor may view the customer’s problems as simply another lily pad to jump off on the path to success.  The second-worst outcome is when you buy a solution and then vendor takes your money and invests it in solving other problems.  As a customer, you don’t want to marry your vendor’s fling.  You want to marry their core.  For startups, the pattern is typically over-expansion into too many things, getting in trouble, and then retracting hard back into the core, abandoning customers of the new, broader initiatives.  The second-worst outcome is when you get this alignment wrong and end up in a backwater or formerly-strategic area of your supplier’s strategy.
  • Avoid the worst outcome:  no there there.  Once in awhile, there is no “there there” behind some very hyped companies despite great individual investors, great VCs, strategic alliances, and a previously experienced team.  Perhaps the technology vision doesn’t pan out, or the company switches strategies (“pivots”) too often.  Perhaps the company just got too focused on its hype and not on it customers.  But the worst outcome, while somewhat rare, is when a company doesn’t solve its advertised problem. They may have a great story, a sexy demo, and some smart people — but what they lack is a core of satisfied customers solving the problem the company talks about.  In EPM, with due respect and in my humble opinion, Tidemark fell into this category, prior to what it called a “growth investment” and what sure seemed to me like a (fire) sale, to Marlin Equity Partners.  Customers need to watch out for these no-there-there situations and the best way to do that is taking strong dose of caveat emptor with a nose for “if it sounds too good to be true, then it might well possibly be.”

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.

# # #

(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.

# # #

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.

# # #

* 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.