Category Archives: IPO

Why has Standalone Cloud BI been such a Tough Slog?

I remember when I left Business Objects back in 2004 that it was early days in the cloud.  We were using Salesforce internally (and one of their larger customers at the time) so I was familiar with and a proponent of cloud-based applications, but never felt great about BI in the cloud.  Despite that, Business Objects and others were aggressively ramping on-demand offerings all of which amounted to pretty much nothing a few years later.

Startups were launched, too.  Specifically, I remember:

  • Birst, née Success Metrics, and founded in 2004 by Siebel BI veterans Brad Peters and Paul Staelin, which was originally supposed to be vertical industry analytic applications.
  • LucidEra, founded in 2005 by Salesforce and Siebel veteran Ken Rudin (et alia) whose original mission was to be to BI what Salesforce was to CRM.
  • PivotLink, which did their series A in 2007 (but was founded in 1998), positioned as on-demand BI and later moved into more vertically focused apps in retail.
  • GoodData, founded in 2007 by serial entrepreneur Roman Stanek, which early on focused on SaaS embedded BI and later moved to more of a high-end enterprise positioning.

These were great people — Brad, Ken, Roman, and others were brilliant, well educated veterans who knew the software business and their market space.

These were great investors — names like Andreessen Horowitz, Benchmark, Emergence, Matrix, Sequoia, StarVest, and Tenaya invested over $300M in those four companies alone.

This was theoretically a great, straightforward cloud-transformation play of a $10B+ market, a la Siebel to Salesforce.

But of the four companies named above only GoodData is doing well and still in the fight (with a high-end enterprise platform strategy that bears little resemblance to a straight cloud transformation play) and the three others all came to uneventful exits:

So, what the hell happened?

Meantime, recall that Tableau, founded in 2003, and armed in its early years with a measly $15M in venture capital, and with an exclusively on-premises business model, literally blew by all the cloud BI vendors, going public in May 2013 and despite the stock being cut by more than half since its July 2015 peak is still worth $4.2B today.

I can’t claim to have the definitive answer to the question I’ve posed in the title.  In the early days I thought it was related to technical issues like trust/security, trust/scale, and the complexities of cloud-based data integration.  But those aren’t issues today.  For a while back in the day I thought maybe the cloud was great for applications, but perhaps not for platforms or infrastructure.  While SaaS was the first cloud category to take off, we’ve obviously seen enormous success with both platforms (PaaS) and infrastructure (IaaS) in the cloud, so that can’t be it.

While some analysts lump EPM under BI, cloud-based EPM has not had similar troubles.  At Host, and our top competitors, we have never struggled with focus or positioning and we are all basically running slightly different variations on the standard cloud transformation play.  I’ve always believed that lumping EPM under BI is a mistake because while they use similar technologies, they are sold to different buyers (IT vs. finance) and the value proposition is totally different (tool vs. application).  While there’s plenty of technology in EPM, it is an applications play — you can’t sell it or implement it without domain knowledge in finance, sales, marketing or whatever domain for which you’re building the planning system.  So I’m not troubled to explain why cloud EPM hasn’t been a slog while cloud BI absolutely has been.

My latest belief is that the business model wasn’t the problem in BI.  The technology was.  Cloud transformation plays are all about business model transformation.  On-premises applications business models were badly broken:  the software cost $10s of millions to buy and $10s of millions more to implement (for large customers).  SMBs were often locked out of the market because they couldn’t afford the ante.  ERP and CRM were exposed because of this and the market wanted and needed a business model transformation.

With BI, I believe, the business model just wasn’t the problem.  By comparison to ERP and CRM, it was fraction of the cost to buy and implement.  A modest BusinessObjects license might have cost $150K and less than that to implement.  That problem was not that BI business model was broken, it was that the technology never delivered on the democratization promise that it made.  Despite shouting “BI for the masses” in 1995, BI never really made it beyond the analyst’s desk.

Just as RDBMS themselves failed to deliver information democracy with SQL (which, believe it or not, was part of the original pitch — end users could write SQL to answer their own queries!), BI tools — while they helped enable analysts — largely failed to help Joe User.  They weren’t easy enough to use.  They lacked information discovery.  They lacked, importantly, easy-yet-powerful visualization.

That’s why Tableau, and to a lesser extent Qlik, prospered while the cloud BI vendors struggled.  (It’s also why I find it profoundly ironic that Tableau is now in a massive rush to “go cloud” today.)  It’s also one reason why the world now needs companies like Alation — the information democracy brought by Tableau has turned into information anarchy and companies like Alation help rein that back in (see disclaimers).

So, I think that cloud BI proved to be such a slog because the cloud BI vendors solved the wrong problem. They fixed a business model that wasn’t fundamentally broken, all while missing the ease of use, data discovery, and visualization power that both required the horsepower of on-premises software and solved the real problems the users faced.

I suspect it’s simply another great, if simple, lesson is solving your customer’s problem.

Feel free to weigh in on this one as I know we have a lot of BI experts in the readership.

The Evolution of Marketing Thanks to SaaS

I was talking with my friend Tracy Eiler, author of Aligned to Achieve, the other day and she showed me a chart that they were using at InsideView to segment customers.  The chart was a quadrant that mapped customers on two dimensions:  renewal rate and retention rate.  The idea was to use the chart to plot customers and then identify patterns (e.g., industries) so marketing could identify the best overall customers in terms of lifetime value as the mechanism for deciding marketing segmentation and targeting.

Here’s what it looked like:

saas-strategic-value

While I think it’s a great chart, what really struck me was the thinking behind it and how that thinking reflects a dramatic evolution in the role of marketing across my career.

  • Back two decades ago when marketing was measured by leads, they focused on how to cost-effectively generate leads, looking at response rates for various campaigns.
  • Back a decade ago when marketing was measured by opportunities (or pipeline), they focused on how to cost-effectively generate opportunities, looking at response and opportunity conversion rates.
  • Today, as more and more marketers are measured by marketing-sourced New ARR, they are focused on cost-effectively generating not just opportunities, but opportunities-that-close, looking all the way through the funnel to close rates.
  • Tomorrow, as more marketers will be measured on the health of the overall ARR pool, they will be focused on cost-effectively generating not just opportunities-that-close but opportunities that turn into the best long-term customers. (This quadrant helps you do just that.)

As a company makes this progression, marketing becomes increasingly strategic, evolving in mentality with each step.

  • Starting with, “what sign will attract the most people?” (Including “Free Beer Here” which has been used at more than one conference.)
  • To “what messages aimed at which targets will attract the kind of people who end up evaluating?”
  • To “who are we really looking to sell to — which people end up buying the most and the most easily – and what messages aimed at which targets will attract them?”
  • To “what are the characteristics of our most successful customers and how can we find more people like them?”

The whole pattern reminds me of the famous Hubspot story where the marketing team was a key part forcing the company to focus on either “Owner Ollie” (the owner of a <10 person business) or “Manager Mary” (a marketer at a 10 to 1000 person business).  For years they had been serving both masters poorly and by focusing on Manager Mary they were able to drive a huge increase in their numbers that enabled cost-effectively scaling the business and propelling them onto a successful IPO.

hubspot

What kind of CMO does any CEO want on their team?  That kind.  The kind worried about the whole business and looking at it holistically and analytically.

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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Thoughts on the Coupa S-1

It’s been a while since I dove into an S-1 and while I almost never get all the way through, here we go with another quick romp through a recent S-1.

Coupa, a ten-year old company that sells cloud-based spend management software and who pitches “value as a service” (ugh) recently filed its S-1.  Before diving in, I wonder if I should mention the potential irony in a company that sells “spend management” software running with a 40% operating loss.

But, remember the average SaaS business, per research from my friends at JMP, has negative operating margins at IPO time:  the median is -21% and the mean -36%.   So cheap jabs aside, Coupa is running a bit on the high side and, more importantly, in a time where I thought the markets were demanding better profitability than in the past.  That’s the interesting part.  From an operating margin perspective, Coupa is looking like a typical IPO in a market that was supposedly setting a higher bar.

Coupa’s most recent $80M private round put it in unicorn status (i.e., meaning that it was raised at a $1B+ valuation).

Estimating the shares outstanding after the offering is frankly quite confusing (e.g., share counts in the summary P&L seem to not include conversion of the preferred) and after spending 20 minutes trying to figure this out, I think there will be something like 180M shares outstanding after the offering.

Normally that might suggest a reverse split prior to IPO (as Talend recently did, an eight-for-one) but since I can’t find any evidence to suggest that, I’ll have to assume that Coupa and its bankers are bullish on valuation.  Otherwise, if I’ve got the right share count, any valuation less than about $1.8B will put them in single-digit stock price territory (which is the condition companies do reverse splits to avoid).

Highlights from the first pages of the S-1:

  • They connect 460 organizations (customers) with over 2M suppliers, globally
  • They estimate they have saved their customers $8B to date, on a cumulative basis
  • Fiscal year (FY) ends 1/31
  • FY15 revenues of $50.8M, FY16 of $83.7M, 65% YoY growth
  • FY15 net losses of $27.3M, FY16 of $46.2M, 68% YoY growth
  • 1H16 revenues of $34.5M, 1H17 of $60.3M, 75% YoY growth – accelerating

Now, let’s look at the income statement, which I’ve cleaned up and color-highlighted.

coupa1

Income statement comments:

  • Approximately 90%/10% mix of subscription to services, generally good
  • $83.7M revenues in last full FY is appropriate IPO scale by recent historical standards
  • 75% accelerating YoY growth in 1H17 over 1H16 is pretty strong
  • Subscription gross margins running 77% to 80%, pretty standard
  • Services gross margins of -89% in FY16 and -59% in 1H17 are horrific.  Happily it’s only 10% of the business.
  • Overall gross margins run around 60%, which strikes me as a bit low, but according to my JMP data, is roughly on target
  • 1H17 R&D of 25% of revenues, at the mean
  • 1H17 S&M of 58% of revenues, 7% above the mean
  • 1H17 G&A of 17%, 4% below the mean – but after running at a shocking 32% in 1H16
  • 1H17 total opex of 100% of revenues, about 3% above the mean
  • 1H17 operating margin of -39%, about 3% below the mean

They also present a non-GAAP operating loss which I can’t easily benchmark. They define it as:  operating loss before stock-based compensation, litigation-related costs and amortization of acquired intangible assets.  There was about a $12.9M delta between GAAP and non-GAAP operating income in FY16, which reduces to only $3.8M in 1H17.

Back to highlights from the S-1 body copy:

  • They typically do 3-year contracts
  • They say “we rely heavily on Amazon Web Services (AWS)” as a risk in the risk factors
  • 29% of revenues from international in 1H17
  • They had a “material weakness” in their FY14 audit, unusual
  • 25 pages of risk factors in total, normal
  • They’ve raised $165M in venture capital, and have $80M in cash
  • Almost $7M in litigation costs in FY15
  • They claim an estimated LTV/CAC that exceeds 6.0 in each of the past 3 years
  • They do an interesting analysis of their 2013 customer cohort concluding that its contribution margin was -249% in FY13, but 75% in FY14-16. (Page 53.)
  • Average ARR/customer up from $138K in FY15 to $183K in FY16

Here are the quarterly numbers; things look pretty consistent except for 2QF15, where among other things, they had $7.5M in stock-based compensation expense.

coupa2

More highlights

  • Operating cash burn of about $4M/quarter (page 67)
  • The CEO made $660K in cash comp in FY16, $320K and $340K bonus
  • The EVP of sales made $497K in cash comp in F16, $250K base and $247K commissions

Let’s take a closer look at the unicorn round:

  • It raised $80M at $4.18/share (page 119)
  • The beneficial ownership analysis (page 121) is based on 162.8M shares outstanding as of 7/31/16, but I believe excludes 61M shares associated with granted and un-granted stock options (page 43)
  • 162.8M + 61M = 223.8M shares on a fully diluted basis x $4.18/share = $935M
  • Not a unicorn you cry! ($935M < $1B)
  • But remember these claims are usually based on post-money valuation
  • $935M + 80M = $1.015B
  • So the math appears to hold up, but it’s also pretty clear Coupa was holding out for a valuation that squeaked them into the club

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Be sure to read my disclaimers.

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.

The Great Reckoning: Thoughts on the Deflation of Technology Bubble 2.0

This post shares a collection of thoughts on what I’ve variously heard referred to as “the tightening,” “the unwinding,” “the unraveling,” or “the great reckoning” — the already-in-process but largely still-coming deflation of technology-oriented stock valuations, particularly in consumer-oriented companies and particularly in those that took large, late-stage private financings.

The Four Horsemen

Here are four key signs that trouble has already arrived:

  • The IPO as last resort.  Box is the best example of this, and while I can’t find any articles, I have heard numerous stories of companies deciding to go public because they are unable to raise high-valuation, late-stage private money.
  • The markdowns.  Fortune ran a series of articles on Fidelity and other mutual funds marking down companies like Snapchat (25%), Zenefits (48%), MongoDB (54%), or Dataminr (35%).  A unique feature of Bubble 2.0 is publicly-traded mutual funds investing in private, VC-backed companies resulting in some CEOs feeling, “it’s like we went public without even knowing it.”
  • The denial.  No bubble would be complete without strong community leaders arguing there is no bubble.  Marc Andreessen seems to have taken point in this regard, and has argued repeatedly that we’re not in a technology bubble and his firm has built a great data-rich deck to support that argument.

The Unicorn Phenom

If those aren’t sufficient signs of bubbledom, consider that mainstream media like Vanity Fair were writing about unicorns  and describing San Francisco as the “city by the froth” back in September.

It’s hard to talk about Bubble 2.0 without mentioning the public fascination with unicorns — private tech companies with valuations at $1B+.  The Google search “technology unicorn” returns 1.6M hits, complete with two unicorn trackers, one from Fortune and the other from CBInsights.  The inherent oxymoron that unicorns were so named because they were supposed to be exceptionally rare can only be lost in Silicon Valley.  (“Look, there’s something rare but we’re so special, we’ve got 130 of them.”)  My favorite post on the unicorn phenom comes from Mark Suster and is entitled:  Why I Effing Hate Unicorns and the Culture They Breed.

As the bubble has started to deflate, we now hear terms like formercorns, onceacorns, unicorpses, or just plain old ponies (with birthday hats on) to describe the downfallen.  Rumors of Gilt Groupe, once valued at $1.1B, possibly selling to The Hudson’s Bay Company for $250M stokes the fire.

What Lies Ahead?

While this time it’s different is often said and rarely true, I do believe we are in case when the unwinding will happen differently for two reasons:  (1) the bubble is in illiquid assets (private company preferred shares) that don’t trade freely on any market and (2) the owners of these illiquid shares are themselves illiquid, typically structured as ten-year limited partnerships like most hedge, private equity growth/equity, or venture capital funds.

All this illiquidity suggests not a bubble bursting overnight but a steady deflation when it comes to asset prices.  As one Wall Street analyst friend put it, “if it took 7 years to get into this situation, expect it to take at least 3.5 years to get out.”

Within companies, particularly those addicted to cheap cash and high burn, change will be more dramatic as management teams will quickly shift gears from maximizing growth to preserving cash, once and when they realize that the supply of cheap fuel is finite.

So what’s coming?

  • Management changes.  As I wrote in The Curse of the Megaround, big rounds at $1B+ valuations come wrapped in high expectations (e.g., typically a 3x valuation increase in 3 years).  Executives will be expected to deliver against those expectations, and those who do not may develop sudden urges to “spend more time with the family.”  Some CEOs will discover that they are not in the same protected class as founders when these expectations go unmet.
  • Layoffs.  Many unicorns are burning $10M or more each quarter.  At a $10M quarterly burn, a company will need to layoff somewhere between 200 and 400 people to get to cashflow breakeven.  Layoffs of this size can be highly destabilizing, particularly when the team was putting in long hours, predicated on the company’s unprecedented success and hypergrowth, all of which presumably lead to a great exit.  Now that the exit looks less probable — and maybe not so great — enthusiasm for 70-hour weeks may vanish.
  • Lawsuits from common stockholders.  Only recently has the valuation-obsessed media noticed that many of those super valuations were achieved via the use of special terms, such as ratchets or multiple liquidation preferences.   For example, if a $100M company has a $300M preference stack and the last $100M went in with a 3x preference, then the common stock would be be worthless in a $500M sale of the company.  In this case, an executive with a 0.5% nominal ownership stake discovers his effective ownership is 0.0% because the first $500M of the sale price (i.e., all of it) goes to the preferred shareholders.  When people find they’re making either “no money” or “car money” when they expected “house money,” disappointment, anger, and lawsuits can result.  This New York Times story about the sale of formercorn Good Technology provides a real example of what I’m talking about, complete with the lawsuits.
  • Focus will be the new fashion.  Newly-hired replacement executive teams will credit the core technology of their businesses, but trash their predecessors for their lack of focus on core markets and products.  Customers unlucky enough to be outside the new core business will be abandoned — so they should be careful to ask themselves and their vendors whether their application is central to the company’s business, even in a downturn or refocus scenario.
  • Attention to customer success.  Investors are going to focus back on customer success in assessing the real lifetime value of a customer or contract.  People will remember that the operative word in SaaS is not software, but service, and that customers don’t pay for services that aren’t delivering.  Companies that emphasized TCV over ARR will be shown to have been swimming naked when the tide goes out, and much of that TCV is proven theoretical as opposed to collectible.
  • Attention to switching costs.  There is a tendency in Silicon Valley to assume all markets have high switching costs.  While this is certainly true in many categories (e.g., DBMS, ERP), investors are going to start to question just how hard it is to move from one service to another when companies are investing heavily in customer acquisition on potentially invalid assumptions about long-term relationships and high pricing power.

Despite considerable turmoil some great companies will be born from the wreckage.  And overall, it will be a great period for Silicon Valley with a convergence to the mean around basics like focus, customer success, and sustainable business models.  The real beauty of the system is not that it never goes out of kilter, but that it always returns to it, and that great companies continue to be produced both by, and in cases despite, the ever-evolving Silicon Valley process.

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Footnotes

This post was inadvertently published on 12/23/15 with an incomplete ending and various notes-to-self at the bottom.  While I realized my mistake immediately (hitting PUBLISH instead of SAVE) and did my best to pull back the post (e.g., deleted the post and the auto-generated tweet to it, created a draft with a new name/URL), as the movie Sex Tape portrays, once something gets out in the cloud, it can be hard to get it back.

Kellblog Ten Predictions for 2015

As we move into the third week of January, I figured it was “now or never” in terms of getting a set of predictions out for 2015.  Before jumping into that, let’s take a quick review of how I did with my 2014 predictions and do some self-grading.

  1. 2014 to be a good year in Silicon Valley.  Correct.
  2. Cloud computing will continue to explode.  Correct.
  3. Big data hype will peak. Gartner seems to agree, placing it in August midway past peak on the way to trough of disillusionment. Correct.
  4. The market will be unable to supply enough data science talent. Mashable is now calling data scientist 2015’s hottest professionPer McKinsey, this is a problem that’s going to continue for the next several years. Correct.
  5. Privacy will remain center stage.  Correct.
  6. Mobile will continue to drive both consumer and (select) enterprise. I got the spirit correct on this one, but I think the core problem is probably better thought of as multi-device access to cloud data than mobile per se.  That is, it’s not about using Evernote on my phone, but instead about uniform access to my cloud-based notes from all my mobile (and non-mobile) devices. Basically, correct.
  7. Social becomes a feature, not an app. Correct again.  The struggles of companies like Jive only validate that (enterprise) social should be a feature of virtually all apps, and not a category unto itself.
  8. SAP’s HANA strategy actually works. Well SAP didn’t seem to agree with this one, when Hasso Plattner wrote a post blasting customers for not understanding its business benefits.  But my angle was more – the merits of the strategy aside – when a company the size of SAP shows total commitment to a strategy it’s going to get results.  And it has.  And SAP continues to drive it.  Mostly correct.
  9. Good Data goes public. While this didn’t happen, I continue to believe that Good Data has a smart strategy and a solid product.  They raised $25M in September.  Maybe this year they will make me an honest man.
  10. Adaptive Planning (now, Adaptive Insights) gets acquired by NetSuite. This didn’t happen, either.  The prediction was based on the fairly well known play of OEM-ing something before acquiring it.  Time may well prove me right on this one, but a swing-and-a-miss for 2014.

Our “bonus” prediction last year was that my company, Host Analytics, would have a great year and indeed we did.  We grew new subscriptions well in excess of 100%, making us, I believe, the fastest growing company in the category.  We launched a new sales planning solution as part of our vision to unite financial and operational planning.  We hired scores of great new people to join us on our mission to create a great EPM company, one that transforms how enterprises manage their financial performance.  And we raised $25M in venture capital to boot.

So, all in all, for the 2014 predictions, let’s call it 8.5 out of 11.

Here are my predictions for 2015.

  1. The good times continue to roll in Silicon Valley. If you feel “bubble,” remember that unlike in the dot-com days that most companies experiencing great success today have real, often recurring, revenue and real customers.   From a cycle perspective, to the extent there is a bubble coming, I’d say we’re in 1999 not 2001.
  1. The IPO as a down-round trend continues. One of the odder things about this time period is that I’m repeatedly hearing that successful IPO companies are pricing at down-rounds relative to their last private financings.  This doesn’t spell danger in general – because the public market valuations are both healthy and supportable – it just suggests a highly competitive later-stage private financing market is overbidding prices.  I suspect that will calm down in 2015 but down-round IPOs will continue in 2015.
  1. The curse of the megaround will strike many companies and CEOs. As part of the prior bullet companies are now often able to raise unprecedented amounts of capital at high valuations.  While those companies today may celebrate their $100M, $150M or $200M+ financing rounds, tomorrow they will wake up with a hangover that looks like:  huge pressure to invest that money for growth, even in dubious growth opportunities; anxious board members who need a 3x return in three years atop already stratospheric valuations; companies missing plan when the dubious growth opportunities don’t deliver; and CEOs who get replaced for missing plans that were unrealistic in the first place.  Before you take a megaround, be careful what you wish for — you sometimes get it.
  1. Cloud disruption continues. Megavendors will continue to wrestle cloud disruption and their cloud strategies.    They will continue to talk about success and high growth in the 10% or less of their business that is cloud, while asking investors to ignore the lack of health in the 90% that is non-cloud.  As part of a general Innovator’s Dilemma problem, they will be forced to explain and defend cloud strategies that will hopefully help them long term but depress results in the short term (as SAP had to do last week.)
  1. Privacy becomes a huge issue. People who were once too busy to care when Facebook changed their security setting are now asking who can access what and how.  The Internet of Things will only exacerbate this focus as more data than ever will be available.  In the past, you could see my pictures and status updates.  Now you can know where I am, when, how many hours I sleep at night, when I exercised, what temperature I set my thermostat to, and when I’m home.  The more data that becomes available, and the more readily you can be de-anonymized, the more you will start monitoring your privacy settings and previously unread site terms and conditions.
  1. Next-generation apps continue to explode. Apps like Slack and Zenefits will continue to redefine enterprise software.  While Slack is a technology, design, and integration play in the collaboration space, Zenefits is more of a business-model disruption play (i.e., give us the rather large commissions you rather invisibly paid your health insurance broker and we’ll give you free, high-quality HR software).  Either way, consumerization, design, and the search for new business models / revenue opportunities will continue.
  1. IBM software rebounds. IBM used to be a stronger player in software than it is today (e.g., recall that they invented the relational database). Watson aside, things have been pretty quiet on the IBM software front. Cloud-wise, while they claim to have a $7B business, it’s pretty invisible to me, and it does seem that Amazon has beaten them in low-level categories like IaaS.  While I’m not sure what happened – I don’t track them that closely – they do seem to have just faded away.  Once thing’s for sure – it can’t continue.  While there are contradicting stories in recent press, IBM does appear to be in the midst of a large re-organization, and I’m going to bet that, as a result, they come to market with a stronger software and cloud story.
  1. Angel investing slows. Much has been written about the financing chokepoint where tens of thousands of angels are funding companies that then need to get in line to get funded by the approximately 100 or so VCs who do A rounds.  The first-order result is that many companies think “wow this is easy” on raising a angel round only to die 12-18 month later when they fail to raise VC.  The second-order result, which I think will start kicking in this year, is that angel money will be harder to come by as the system corrects back to a balanced state.
  1. The data scientist shortage continues. With more “big data” and a huge supply of analytic tools and computing power, the limiting factor on analysis-driven business is neither data nor technology.  It’s our ability to find people who can correctly leverage it.  Tell every college kid you know to take lots of stats, analytics, and computing classes.  Or better yet, to go get a degree in data science.
  1. The unification of planning becomes the top meme in enterprise performance management (EPM). EPM has a long history of helping finance departments prepare annual operating budgets and financial reports, but increasingly—in recent years – planning has quietly decentralized to the various departments and divisions within the enterprise.  For example, sales ops increasingly builds the sales plan, marketing ops the marketing plan, and services ops the consulting and professional services plan.   (This is why I sometimes call this trend the “rise of the ops person” as they are increasingly acting as stealth FP&A.)  What’s needed is to unite all these plans and put them on a common planning framework so the CFO and CEO can do what-if analysis and scenario planning holistically across the organization.