Category Archives: Startups

Bottom-Fishing Acquisitions and Catching Falling Knives

As mentioned in my recent Curse of the Megaround post, some companies that find themselves flush with cash and under heavy pressure to grow, decide to embark on dubious acquisitions to help shore up the growth story.

As one reader it put it, you can summarize your megaround post with the simple phrase “much money makes you stupid.”  And it can.  Thus, as the old saw goes, fools and their money are soon parted.

What separates good from bad acquisitions in this context?  As a general rule, I’d say that when high-growth venture-backed companies acquire firms that would otherwise best be acquired by private equity, it’s a bad thing.  Why?

Firms destined to be acquired by private equity follow a typical pattern.

  • They are old, typically 10+ years
  • They have tried multiple iterations on a strategy and none has worked
  • They have a deep stack of technology built over the years but most of which could be quickly replaced with modern, often open source, standard components
  • They tend to get strategically inverted — starting out with “what we have” as opposed to “what the market wants”
  • They have gone through several generations of management teams
  • Basically, they’re turnarounds

So private equity funds bottom-fish these opportunities, buy companies for a fraction of the total invested venture capital, scrap most of the original dream and either [1] double down on one core piece that’s working or [2] roll the company up with N adjacent companies all selling to the same buyer.

This is hard work.  This is dirty work.  This is “wet work” involving lots of headcount changes.  And private equity is good at it.   In one sense (and excluding private equity growth funds), it’s what they do.

High-flying VC backed startups are simply the wrong types of buyers to contemplate these acquisitions.  In the core business, it’s all about grow, grow, and grow.  In the acquired business, it’s all about cut, cut, cut and focus, focus, focus.  These are two very different mentalities to hold in your head at one time and the typical fail pattern is apply the grow-grow-grow mentality to the broken startup that repeatedly hasn’t-hasn’t-hasn’t.

The other failure pattern is what I call the worst-of-breed suite.  This happens when a player in space X acquires a two-bit player in space Y, hoping to “get a deal” on a cheap technology they can then sell to their customers.   The vendor is thinking “I can sell more stuff through my existing channel.”  However, the customer is thinking “I don’t want to use a worst-of-breed product just because you decided to acquire one on the cheap.”  Moreover, with easy of integration of cloud services, there is typically no real integration advantage between the cheaply acquired product and a third-party best-of-breed one.

On Wall Street, they say that bottom-fishing falling stocks is like catching falling knives.  For high-growth startups, trying to bottom-fish failed startups is pretty much the same thing.

The Curse of the Megaround

With what everyone seems reluctant to call a bubble in late-stage, private financing in full swing, I thought I’d do a quick post to drill into a concept I presented in my 2015 predictions post, something I call the curse of the megaround.

We will do that by examining the forces, and the winners and losers, surrounding a megaround.  Let’s start with a hypothetical example. Company X raises $200M at $1B pre-money, giving them a $1.2B post-money valuation.

Champagne is popped, the financing is celebrated, the tech press bows, and the company is added to many unicorn trackers.

Now what happens?

  • The CEO is under immediate pressure to invest the additional capital.  If you take the rule of thumb that most venture rounds are designed to last 18-24 months, then a $200M raise implies a cash burn rate of $8 to $10M/month or $25 to $30M/quarter.  That is an enormous burn rate and in many cases it is difficult or impossible to spend that much money wisely.
  • The CEO is under heavy pressure to triple the value of the company in 2-3 years.  The investors who do these rounds are typically looking for a 3x return in 2-3 years.  So the CEO is under huge pressure to make the company worth $3.6B in 2-3 years.
  • This, in turn, means the CEO will start investing the money not only in promising growth initiatives, but also dubious ones.   Product lines are over-extended.  Geographic over-expansion occurs.  Hiring quality drops — in an attempt to not fall behind the hiring plan and lose all hope of achieving the numbers.
  • In cases, money is waste en masse in the form of dubious acquisitions, in the hope of accelerating product, employee, and customer growth.  However, the worst time to take on tricky acquisitions is when a company is already falling behind its own hypergrowth plans.
  • All of this actions were done in the name of “well, we had no hope of making the plan if we didn’t open in 12 countries, hire 200 people, add 3 product lines, and buy those 2 companies.”  So we may as well have tried as we would have been fired anyway.  At least we gave it our best shot, right?
  • This often comes to a head in a Lone Ranger moment when the board turns on the CEO.  “Didn’t we agree to that hiring plan?  Didn’t we agree to those product line extensions?  Didn’t we agree to that acquisition?” the CEO thinks.  But the board thinks differently.  “Yes, we agreed to them, but you were accountable for their success.”

Yes, being CEO can be a lonely job.  This is why I call it the curse of the megaround — because it’s certainly a curse for the CEO.  But the situation isn’t necessarily a curse for everyone.  Let’s examine the winners and losers in these situations.

Winners

  • The founders.  They get the benefit of a large investment in their company at low dilution without the downside of increased expectations and the accountability for delivering against them.
  • The private equity fund managers.  Provided the turmoil itself doesn’t kill the company and new, more realistic plans are achieved, the PE fund managers still get their 2+20 type fee structure, earning 2% a year baseline and 20% of the eventual upside as carry.  In a “more normal” world where companies went public at $300M in market cap, there would be no way to earn such heavy fees in these investments.

Losers

  • The CEO who is typically taken out back and shot along with any of the operating managers also blamed for the situation.
  • The company’s customers who are typically ignored and under-served during the years of turmoil where the company’s focus is on chasing an unreachable growth plan and not on customer service.
  • In the event the company is sold at a flat or down valuation, the common stock holders (including founders and employees) who can see their effective ownership either slashed or wiped-out by the multiple liquidation preferences often attached to the megaround.  (People love to talk about the megaround valuation, but they never seem to talk about the terms that go with it!)
  • The private equity limited partners whose returns are diminished by the very turmoil their investment created and who are stuck paying a high 2+20 fee structure with decade-ly liquidity as opposed to the 1% fee structure and daily liquidity they’d have with mutual funds if the companies were all public (as they would have been pre-Sarbox.)
  • The private equity limited partners who ultimately might well end up with a down-round as IPO.

In some situations — e.g., huge greenfield markets which can adopt a new solution quickly and easily — a megaround may well be the right answer.  But for most companies these days, I believe they are more curse than blessing.

Survivor Bias in Churn Calculations: Say It’s Not So!

I was chatting with a fellow SaaS executive the other day and the conversation turned to churn and renewal rates.  I asked how he calculated them and he said:

Well, we take every customer who was also a customer 12 months ago and then add up their ARR 12 months ago and add up their ARR today, and then divide today’s ARR by year-ago ARR to get an overall retention or expansion rate.

Well, that sounds dandy until you think for a minute about survivor bias, the often inadvertent logical error in analyzing data from only the survivors of a given experiment or situation.  Survivor bias is subtle, but here are some common examples:

  • I first encountered survivor bias in mutual funds when I realized that look-back studies of prior 5- or 10-year performance include only the funds still in existence today.  If you eliminate my bogeys I’m actually an below-par golfer.
  • My favorite example is during World War II, analysts examined the pattern of anti-aircraft fire on returning bombers and argued to strengthen them  in the places that were most often hit.  This was exactly wrong — the places where returning bombers were hit were already strong enough.  You needed to reinforce them in the places that the downed bombers were hit.

So let’s turn back to churn rates.  If you’re going to calculate an overall expansion or retention rate, which way should you approach it?

  1. Start with a list of customers today, look at their total ARR, and then go compare that to their ARR one year ago, or
  2. Start with a list of customers from one year ago and look at their ARR today.

Number 2 is the obvious answer.  You should include the ARR from customers who choose to stop being customers in calculating an overall churn or expansion rate.  Calculating it the first way can be misleading because you are looking at the ARR expansion only from customers who chose to continue being customers.

Let’s make this real via an example.

survivor bias

The ARR today is contained in the boxed area.  The survivor bias question comes down to whether you include or exclude the orange rows from year-ago ARR.  The difference can be profound.  In this simple example, the survivor-biased expansion rate is a nice 111%.  However, the non-biased rate is only 71% which will get you a quick “don’t let the door hit your ass on the way out” at most VCs.  And while the example is contrived, the difference is simply one of calculation off identical data.

Do companies use survivor-biased calculations in real life?  Let’s look at my post on the Hortonworks S-1 where I quote how they calculate their net expansion rate:

We calculate dollar-based net expansion rate as of a given date as the aggregate annualized subscription contract value as of that date from those customers that were also customers as of the date 12 months prior, divided by the aggregate annualized subscription contract value from all customers as of the date 12 months prior.

When I did my original post on this, I didn’t even catch it.  But therein lies the subtle head of survivor bias.

# # #

Disclaimers:

  • I have not tracked the Hortonworks in the meantime so I don’t know if they still report this metric, at what frequency, how they currently calculate it, etc.
  • To the extent that “everyone calculates it this way” is true, then companies might report it this way for comparability, but people should be aware of the bias.  One approach is to create a present back-looking and a past forward-looking metric and show both.
  • See my FAQ for additional disclaimers, including that I am not a financial analyst and do not make recommendations on stocks.

Joining the Granular Board of Directors

I’m very happy to say that I’ve joined the Board of Directors of Granular.  In this post, I’ll provide some commentary that goes beyond the formal announcement.

I think all CEOs should sit on boards because it makes you a better CEO.  You get take remove the blinders that come from your own (generally all-consuming) company, you build the network of people you can rely upon for answering typical CEO questions, and most importantly, you get to turn the tables and better understand how things might look when seen from the board perspective of your own company.

Let’s share a bit about Granular.

  • Granular is a cloud computing company, specifically a vertical SaaS company, aimed at improving the efficiency of farms.
  • They have a world-class team with the usual assortment of highly intelligent overachievers and with an unusual number of physicists on the executive team, which is always a good thing in a big data company.  (While you might think data scientists are computer science or stats majors, a large number of them seem to come from physics.)

To get a sense of the team’s DNA, here’s a word cloud of the leadership page.

wordle 2

Finally, let’s share a bit about why I decided to join the board.

  • As mentioned, they have a world-class team and I love working with supersmart people.
  • I like vertical strategies.  At MarkLogic, we built the company using a highly vertical strategy.  At Versant, a decade earlier, we turned the company around with a vertical strategy.  At BusinessObjects, while we grew to $1B largely horizontally, as we began to hit scale we used verticals as “+1″ kickers to sustain growth.  As a marketeer by trade, I love getting into the mind of and focusing on the needs of the customer, and verticals are a great way to do that.
  • I love the transformational power of the cloud. (Wait, do I sound like too much like @Benioff?)  While cloud computing has many benefits, one of my favorites is that the cloud can bring software to markets and businesses where the technology was previously inaccessible.  This is particularly true with farming, which is a remote, fragmented, and “non-sexy” industry by Silicon Valley standards.
  • I like their angle.  While a lot of farming technology thus far has been focused on precision ag, Granular is taking more of financial and operations platform approach that is a layer up the stack.  Granular helps farmers make better operational decisions (e.g., which field to harvest when), tracks those decisions, and then as a by-product produces a bevy of data that can be used for big data analysis.
  • I love their opportunity.  Not only is this a huge, untapped market, but there is a two-fer opportunity:  [1] a software service that helps automate operations and [2] an information service opportunity derived from the collected big data.
  • Social good.  The best part is that all these amazing people and great technology comes packaged with a built-in social good.  Helping farmers be more productive not only helps feed the world but helps us maximize planetary resource efficiency in so doing.

I thank the Granular team for taking me on the board, and look forward to a bright, transformational future.

A Disney Parking Lot Attendant Gets More Training than Your Typical $250K Enterprise Sales Rep: Thoughts on Bootcamps

At Disney — a company that is truly focused on customer experience — every “cast member” (i.e., employee) gets six weeks of training before they see a “guest” (i.e., customer). “Face characters” (e.g., Snow White walking through the park) spend an additional 40 hours just watching and re-watching the movie to ensure they get every nuance right.

Oh, and how much training does your company give your $250K enterprise salesreps?

Anecdotally, I think the typical answer is a one-week bootcamp. Two weeks is on the long side. Once in a blue moon, you’ll hear 4 to 6 weeks, but that’s typically one to two weeks of corporate training followed by two to four weeks of deep technical training.

This is genuinely strange because a typical enterprise software or SaaS company freely spends between 40% and 100% of revenue on sales. Sales is typically the single biggest expense line in the firm. Sales runs 2-5x cost of goods sold.  It runs 2-5x R&D expense.  So, if we’re going to spend all this money on salespeople, then why don’t we want to train them?

I think there are a number of rationalizations:

  • “We hire experienced people so we don’t need to.” This is dangerous because your new people are experienced at someone else’s company and may have learned norms quite different from those you desire at yours.
  • “We train them on the job.” Either by throwing them in the pool and seeing if they sink or by building a conveyor-belt model where we hire folks as in-bound call-takers who we promote into outbound call-makers then into SMB reps then into mid-market reps. While there is nothing wrong with these models and they do very much help develop reps, it still doesn’t answer why we don’t give them deep training at the start.
  • “We never really developed it as a competency.” When you only have three reps you’re not going to create a six-week training program because — among other reasons — you don’t know what to teach. But as you scale your business that quickly becomes more excuse than reason.

I think the root answer is simple: most senior executives just don’t believe in training. (Think: “those who can, do; those who can’t, teach; and those who can’t teach do marketing.”)

Having competed against the output of some great internal training programs at Oracle and MicroStrategy, having created and run Business Objects University for several years, and then having gone through the outstanding on-boarding program at Salesforce, I’d like to share some perspective.

First, given my experience I would argue that by far the #1 key success criteria for these programs is a dictum from the CEO that they are important, they will be funded, and the organization will support them. Barring that, they get launched to lots of hype and then slowly erode into a self-fulfilling prophecy of mediocrity.

Here are some thoughts on how to run a great bootcamp.

  • Make it mandatory. Everybody goes. No one is too important to skip it from the new accountant to the new COO.
  • Make it long. Shoot for two weeks, minimum. Three is better. A double-dip is probably best of all (2 weeks initially followed by 3 months on the job followed by 2 weeks of reinforcement.)
  • Do it live. Some virtual pre-work and post-work is fine, but the core of your program should be live and in person. It shows commitment. It helps people build relationships. It enables better progress tracking and assessment.
  • Engage practitioners. Don’t learn how to sell from only a bootcamp trainer; hear from one of your top 5 reps on a rotating basis. (And pulling those top reps out of the field is an example of just one thing requires top-level support for the program.
  • Teach culture. Hit values. Train in how you define “The Your-Company Way.”
  • Be operational. Teach how the company wants deals entered in the pipeline, what your stage definitions are, and how to value deals. (These are critical items to maintaining a comparable set of pipeline metrics over time.)
  • Mix up the format. Have lectures, panels, individual exercises, group projects, videos, homework, reading, and team building exercises. Where applicable, do a volunteering session. (If volunteering is a key part of your culture, do some right from the get-go in the bootcamp – as Salesforce does.)
  • Keep it applied. Don’t just teach facts or theory (“Competitor A uses a proprietary, non-Excel formula language.”) Show them how to apply that fact in everyday life (e.g., suggest prospects to build some models to get a taste of what that feels like versus good-old Excel).
  • Everyone’s in sales. Teach everyone how the company sells, what problems it solves, and why customers buy from it.
  • Fire people who don’t take it seriously. The University head should be able to fire any employee during the training period. If you’re skipping sessions, not paying attention, late, disrupting, etc., then boom, you’re gone. It sends a message that won’t soon be forgotten.
  • Send home a report card. Build a culture where managers are embarrassed when their new hire gets a B- and the put people immediately on a performance plan when they get a C. List specific student strengths and development areas. Build the University program into the management process right from the start. Train managers on how work with fresh bootcamp graduates.
  • Try to use it for prediction. Give granular objective grades in different areas (e.g., delivery of corporate message, fluency in finance, consultative selling) along with an instructor success prediction and do regressions over time to see what really drives sales success as opposed to what you might think does. Try to answer the question: do people who do better in the University do better in real life?
  • Hire a consultant. My colleague Elay Cohen is a sales productivity expert, the author of Saleshood (Kellblog review here), and ran the outstanding program at Salesforce — I’m pretty sure he’d be happy to help you setup yours. You don’t have to invent this stuff anymore. Plenty of people know how to do it.

Finally, don’t stop with bootcamp. Build ongoing training programs that take care of your existing hires as much as your new ones. But that’s the subject of a different post.

Career Development:  What It Really Means to be a Manager, Director, or VP

It’s no secret that I’m not a fan of big company HR practices.  I’m more of the First Break all the Rules type.  Despite my general skepticism of many standard practices, we still do annual performance reviews at my company, though I’m thinking seriously of dropping them.  (See Get Rid of the Performance Review.)

Another practice I’m not hugely fond of is “leveling” which is the creation of a set of granular levels to classify jobs across the organization.  Leveling typically looks like this

level

While I am a huge fan of compensation benchmarking (i.e., figuring out what someone is worth in the market before they do by getting another job) for employee fairness and retention, I think classical leveling has a number of problems.

  • It is futile to level across functions. Yes, you might discover that a senior FPA analyst II earns the same as a product marketing director I, but why does that matter?  It’s a coincidence.  It’s like saying with $3.65 I can buy either a grande non-fat latte or a head of organic lettuce.  What matters is the fair price for each of those goods in the market, not they that happen to have the same price.  So I object to the whole notion of levels across the organization.  It’s not canonical; it’s coincidence.
  • Most leveling systems are too granular, with the levels separately by arbitrary characterizations. It’s makework.  It’s fake science.  It’s bureaucratic and encourages a non-thinking “climb the ladder” approach to career development.  (“Hey, let’s develop you to go from somewhat independent to rather independent this year.”)
  • It conflates career development and salary negotiation. It encourages a mindset of saying “what do I need to do make L10” when you want to say “I want a $10K raise.”  I can’t tell you the number of times I’ve had people ask for “development” or “leveling” conversations where I get excited and start talking about learning, skills gaps, and such and it’s clear all they wanted to talk about was salary.  #disappointing

That said, I do believe there are three meaningful levels in management and it’s important to understand the differences among them.  I can’t tell you the number of times someone has sincerely asked me “what does it take to be a director” or “how can I develop myself into a VP.”

It’s a hard question.  You can turn to the leveling system for an answer, but it’s not in there.  For years, in fact, I’ve struggled to deliver what I consider to be a good answer to the question.

I’m not talking about senior VP vs. executive VP or director vs. senior director.  I view such adjectives as window dressing or stripes:  important recognition along the way, but nothing that fundamentally changes one’s level.

I’m not talking about how many people you manage.  In call centers, a director might manage 500 people.  In startups, a VP might manage 0.

I’m talking about one of three levels at which people operate:  manager, director, and vice president.  Here are my definitions:

  • Managers are paid to drive results with some support. They have experience in the function, can take responsibility, but are still learning the job and will have questions and need support.  They can execute the tactical plan for a project, but typically can’t make it.
  • Directors are paid to drive results with little or no supervision (“set and forget”). Directors know how to do the job.  They can make a project’s tactical plan in their sleep.  They can work across the organization to get it done.  I love strong directors.  They get shit done.
  • VPs are paid to make the plan. Say you run marketing.  Your job is to understand the company’s business situation, make a plan to address it, build consensus and get approval of that plan, then go execute it.

The biggest single development issue I’ve seen over the years is that many VPs still think like directors. [1]

Say the plan didn’t work.   “But, we executed the plan we agreed to,” they might say, hoping to play a get-out-of-jail-free card with the CEO (which is about to boomerang on them).

Of course, the VP got approval to execute then plan.  Otherwise, you’d be having a different conversation, one about termination for insubordination.

But the plan didn’t work.  Because directors are primarily execution engines, they can successfully play this card.  Fair enough.  Good directors challenge their plans to make them better.  But they can still play the approval card successfully because their primary duty is to execute the plan, not make it.

VP’s, however, cannot play the approval card.  The VP’s job is to get the right answer.  They are the functional expert.  No one on the team knows their function better than they do.  And even if someone did, he or still is still playing the VP of function role and, as such, it’s their job – and no one else’s — to get the right answer.

Now, you might be thinking “glad I don’t work for Dave” right now — he’s putting failure for a plan he and the team agreed to on the back of the VP.  And I am.

But it’s the same standard to which the CEO is held.  If the CEO makes a plan, gets it approved by the board, and executes it well, but it doesn’t work, he/she cannot tell the board “but, but, it’s the plan we agreed to.”  Most CEOs wouldn’t even dream of saying that.  It’s because CEOs understand they are held accountable not for effort or activity, but results.

Part of truly operating at the VP level is to internalize this fact.  You are accountable for results.  Make a plan that you believe in.  Because if the plan doesn’t work, you can’t hide behind approval.  Your job was to make a plan that worked.  If the risk of dying on a hill is inevitable, you may as well die on your own hill, and not someone else’s.

Paraphrasing the ancient Fram oil filter commercial, I call this “you can fire me now, or fire me later” principle.  That is, an executive should never make or sign up for a plan they don’t believe in.  They should risk being fired now for refusing to sign up for the plan (e.g., challenging assumptions, delivering bad news) as opposed to halfheartedly executing a plan they don’t believe in, and almost certainly getting fired later for poor execution.  The former is a far better way to go than the latter.

This is important not only because it prepares the VP to be  CEO one day, but also because it empowers the VP in marking his/her plan.  If this my plan, if I am to be judged on its success or failure, if I am not able to use approval as a get-out-of-jail-free card, then is it the right plan?

That’s the thinking I want to stimulate.  That’s how great VP’s think.

# # #

Footnotes:

[1] Since big companies throw around the VP title pretty casually, this post is arguing that many of those VPs are actually directors in thinking and accountability.  This may be one reason why big company VPs have trouble adapting to the e-staff of startups.

Summary of the 4Q14 Fenwick & West VC Survey

Because I was reading it and had a minute, I thought I’d do a quick post summarizing the 4Q14 Fenwick & West Silicon Valley Venture Capital Survey (PDF).  As the name indicates, this is an ongoing quarterly survey  on the state of venture capital that pulls from many sources, integrating lots of data into a single picture.

Some highlights (glossary here):

  • Up rounds exceeded down rounds 79% to 6%, with 15% of rounds flat.
  • Average price up 115% in 4Q14, compared to 79% in 3Q14, and the highest value they’ve recorded since they started measuring this in 2005.  (Yes Virginia, prices are good.)
  • 50% of deals were in software companies
  • $14 B was invested in US VC-backed companies in 4Q14, the highest post-bubble amount yet.  (However, remember that during Bubble 1.0, the peak ran around $25B+/quarter.)
  • $49B was invested on the year.  (And you wonder why traffic so bad on 101.)
  • There were 21 VC-backed IPOs in 4Q14 which raised $3B, and 105 on the year.
  • There were 102 acquisitions of VC-backed companies for a total price of $32B in 4Q14 and 531 such deals on the year.
  • $33B was raised by VC funds in 2014, hitting 2005-2007 levels, but not coming close to the $106B raised in 2000.
  • China passed Europe in terms of VC funding raised, tripling from less than $5B in 2013 to more than $15B in 2014.  India more than doubled going from $2B to $5B.
  • Corporate venture capitalists invested $5B in 2014, the highest amount since 2000 (where it was $15B).
  • There are currently 225 accelerators worldwide which have assisted 4264 companies.  AngelList reported over $100M was raised in 2014 across 243 startups.  (This all contributes to a system imbalance where it’s relatively too easy to get angel money, resulting in a fairly large die-off rate between angel round and series A.)
  • When classifying VC deals by the university the CEO attended and then grouping by athletic conferences, the rankings go:  Pac 12, Ivy League, Big Ten, ACC, Big Tweleve, and SEC.  (I did my part for the Pac 12 in 4Q14 — Go Bears!)
  • The Silicon Valley Venture Capitalist Confidence Index published by USF reported confidence of 3.93 in 4Q14, up from 3.89 in 3Q14, and above the (eleven-year) average of 3.72.  Full report here.
  • 19% of rounds had a senior liquidation preference (to existing preferred, not just the common).  Reminder:  glossary here.
  • Only 5% of rounds had senior multiple liquidation preference.
  • 20% of rounds had participation in liquidation, down from a recent high of 34% in 2Q13.  53% of those that had participation, had it uncapped.
  • 5% of rounds had pay-to-play provisions.
  • 13% had redemption rights.