The Ultimate SaaS Metric: The Customer Lifetime Value to Customer Acquisition Cost Ratio (LTV/CAC)

I’m a big fan of software-as-a-service (SaaS) metrics.  I’ve authored very deep posts on SaaS renewals rates and customer acquisition costs.  I also routinely point readers to other great posts on the topic, including:

But in today’s post, I’m going to examine the question:  of the literally scores of SaaS metrics out there, if you could only pick one single metric, which one would it be?

Let’s consider some candidates:

  • Revenue is bad because it’s a lagging indicator in a SaaS business.
  • Bookings is good because it’s a leading indicator of both revenue and cash, but tells you nothing about the existing customer base.
  • ARR (annual recurring revenue) is good because it’s a leading indicator of revenue and includes the effects of both new sales and customer churn.  However, there are two ways to have slow ending ARR growth:  high sales and high churn or low sales and low churn — and they are very different.
  • Cashflow is good because it tends to net-out a lot of other effects, but can be misleading unless you understand the structure of a company’s bookings mix and payment terms.
  • Gross margin (GM) is nice because it gives you an indicator of how efficiently the service is run, but unfortunately tells you nothing else.
  • The churn rate is good because it helps you value the existing customer annuity, but tells you nothing about new sales.
  • Customer acquisition cost (CAC) is a great measure of sales and marketing efficiency, but by itself is not terribly meaningful because you don’t know what you’re buying:  are you paying, for example, $12K in sales and marketing (S&M) expense for a $1K/month customer who will renew for 3 months or 120?  There’s a big difference between the two.
  • Lifetime value (LTV) is good measure of the annuity value of your customer base, but says nothing about new sales.

Before revealing my single best-choice metric, let me make what might be an unfashionable and counter-intuitive statement.  While I love SaaS “unit economics” as much as anybody, to me there is nothing better than a realistic, four-statement, three-year financial model that factors everything into the mix.  I say this not only because my company makes tools to create such models, but more importantly because unit economics can be misleading in a complicated world of varying contract duration (e.g., 1 to 3+ years), payment terms (e.g., quarterly, annual, prepaid, non-prepaid), long sales cycles (typical CAC calculations assume prior-quarter S&M drives current-quarter sales), and renewals which may differ from the original contract in both duration and terms.

Remember that SaaS unit economics were born in an era of monthly recurring revenue (MRR), so the more your business runs monthly, the better those metrics work — and conversely.  For example, consider two companies:

  • Company A does month-to-month contracts charging $100/month and has a CAC ratio of 1.0.
  • Company B does annual contracts, does three-year prepaid deals, and has a CAC ratio of 2.0.

If both companies have 80% subscription gross margins (GM), then the CAC payback period is 15 months for company A and 30 months for company B.  (CAC payback period is months of subscription gross margin to recover CAC.)

This implies company B is much riskier than company A because company B’s payback period is twice as long and company B’s money is at risk for a full 30 months until it recovers payback.

But it’s completely wrong.  Note that because company B does pre-paid deals its actual, cash payback period is not 30 months, but 1 day.  Despite ostensibly having half the CAC payback period, company A is far riskier because it has to wait 15 months until recovering its S&M investment and each month presents an opportunity for non-renewal.  (Or, as I like to say, “is exposed to the churn rate.”)  Thus, while company B will recoup its S&M investment (and then some) every time, company A will only recoup it some percentage of the time as a function of its monthly churn rate.

Now this is not to say that three-year prepaid deals are a panacea and that everyone should do them.  From the vendor perspective, they are good for year 1 cashflow, but bad in years 2 and 3.  From the customer perspective, three-year deals make plenty of sense for “high consideration” purchases (where once you have completed your evaluation, you are pretty sure of your selection), but make almost no sense in try-and-buy scenarios.  So the point is not “long live the three-year deal,” but instead “examine unit economics, but do so with an awareness of both their origins and limitations.”

This is why I think nothing tells the story better than a full four-statement, three-year financial model.  Now I’m sure there are plenty of badly-built over-optimistic models out there.  But don’t throw the baby out with the bathwater.   It is just not that hard to model:

  • The mix of the different types of deals your company does by duration and prepayment terms — and how that changes over time.
  • The existing renewals base and the matrix of deals of one duration that renew as another.
  • The cashflow ramifications of prepaid and non-prepaid multi-year contracts.
  • The impact on ARR and cashflow of churn rates and renewals bookings.
  • The impact of upsell to the existing customer base

Now that I’ve disclaimed all that, let’s answer the central question posed by this post:  if you could know just one SaaS metric, which would it be?

The LTV/CAC ratio.

Why?  Because what you pay for something should be a function of what it’s worth.

Some people say, for example, that a CAC of 2.0 is bad.  Well, if you’re selling a month-to-month product where most customers discontinue by month 9, then a CAC of 2.0 is horrific.  However, if you’re selling sticky enterprise infrastructure, replacing systems that have been in place for a decade with applications that might well be in place for another decade, then a CAC is 2.0 is probably fine.  That’s the point:  there is no absolute right or wrong answer to what a company should be willing to pay for a customer.  What you are willing to pay for a customer should be a function of what they are worth.

The CAC ratio captures the cost of acquiring customers.  In plain English, the CAC ratio is the multiple you are willing to pay for $1 for annual recurring revenue (ARR).  With a CAC ratio of 1.5, you are paying $1.50 for a $1 of ARR, implying an 18 month payback period on a revenue basis and 18-months divided by subscription-GM on a gross margin basis.

Lifetime value (LTV) attempts to calculate what a customer is worth and is typically calculated using gross margin (the profit from a customer after paying the cost of operating the service) as opposed to simply revenue.  LTV is calculated first by inverting the annual churn rate (to get the average customer lifetime in years) and then multiplying by subscription-GM.

For example, with a churn rate is 10%, subscription GM of 75%, and a CAC ratio of 1.5, the LTV/CAC ratio is (1/10%) * 0.75 / 1.5 = 5.0.

The general rule of thumb is that LTV/CAC should be 3.0 or higher, with of course, the higher the better.

There are three limitations I am aware of in working with LTV/CAC as a metric.

  • Churn rate.  Picking the right churn rate isn’t easy and is made complicated in the presence of a mix of single- and multi-year deals.  All in, I think simple churn is the best rate to use as it reflects the “auto-renewal” of multi-year deals as well as the very real negative churn generated by upsell.
  • Statistics and distributions.  I’m not a hardcore stats geek, but I secretly worry that many different distributions can produce an average of 10%, and thus inverting a 10% churn rate to produce an average 10-year customer lifetime scares me a bit.  It’s the standard way to do things, but I do worry late at night that averages can be misleading.
  • Light from a distant star.  Remember that today’s churn rate is a function of yesterday’s deals.  The more you change who you sell to and how, the less reflective yesterday’s churn is of tomorrow’s.  It’s like light arriving from a star that’s three light-years away:  what you see today happened three years ago.  To the extent that LTV is a forward-looking metric, beware that it’s based on churn which is backward-looking.  In perfect world, you’d use predicted-churn in an LTV calculation but since calculating that would be difficult and controversial, we take the next best thing:  past churn.  But remember that the future doesn’t always look like the past.

 

You Can’t Analyze Churn by Analyzing Churn

One thing that amazes me is when I hear people talk about how they analyze churn in a cloud, software as a service (SaaS), or other recurring revenue business.

You hear things like:

  • “17% of our churn comes from emerging small business (ESB) segment, which is normal because small businesses are inherently unstable.”
  • “22% of our churn comes from companies in the $1B+ revenue range, indicating that we may have a problem meeting enterprise needs.”
  • “40% of the customers in the residential mortgage business churned, indicating there is something wrong our product for that vertical.”

There are three fallacies at work here.

The first is assumed causes.  If you that 17% of your churn comes from the ESB segment, you know one and only one thing:  that 17% of your churn comes from the ESB segment.  Asserting small business stability as the cause is pure speculation.  Maybe they did go out of business or get bought.  Or maybe they didn’t like your product.  Or maybe they did like your product, but decided it was overkill for their needs.  If you want to how much of your churn came from a given segment, ask a finance person.  If you want to know why a customer churned, ask them.  Companies with relatively small customer bases can do it via a phone.  Customers with big bases can use an online survey.  It’s not hard.  Use metrics to figure out where your churn comes from.  Use surveys to figure out why.

The second is not looking at propensities and the broader customer base. If I said that 22% of your annual recurring revenue (ARR) comes from $1B+ companies, then you shouldn’t be surprised that 22% of your churn comes from them as well.  If I said that 50% of your ARR comes from $1B+ companies (and they were your core target market), then you’d be thrilled that only 22% of your churn comes from them.  The point isn’t how much of your churn comes from a given segment:  it’s how much of your churn comes from a given segment relative to how much of your overall business comes from that segment.  Put differently, what is the propensity of someone to churn in one segment versus another.

And you can’t perform that analysis without getting a full data set — of both customers who did churn and customers who didn’t.  That’s why I say you can’t analyze churn by analyzing churn.  Too many people, when tasked with churn analysis:  say, “quick, get me a list of all the customers who churned in the past 6 months and we’ll look for patterns.”   At that instant you are doomed.  All you can do is decompose churn into buckets, but know nothing of propensities.

For example, if you noticed that in one country that a stunning 99% of churn came from customers with blue eyes, you might be prompted to launch an immediate inquiry into how your product UI somehow fails for blue-eyed customers.  Unless, of course, the country was Estonia where 99% of the population has blue eyes, and ergo 99% of your customers do.  Bucketing churn buys you nothing without knowing propensities.

The last is correlation vs. causation.  Knowing that a large percentage of customers in the residential mortgage segment churned (or even have higher propensity to churn) doesn’t tell you why they are churning.  Perhaps your product does lack functionality that is important in that segment.  Or perhaps it’s 2008, the real estate crisis is in full bloom, and those customers aren’t buying anything from anybody.  The root cause is the mortgage crisis, not your product.   Yes, there is a high correlation between customers in that vertical and their churn rate.  But the cause isn’t a poor product fit for that vertical, it’s that the vertical itself is imploding.

A better, and more fun, example comes from The Halo Effect, which tells the story that a famous statistician once showed a precise correlation between the increase in the number of Baptist preachers and the increase in arrests for public drunkenness during the 19th Century.  Do we assume that one caused the other?  No.  In fact, the underlying driver was the general increase in the population — with which both were correlated.

So, remember these two things before starting your next churn analysis

  • If you want to know why someone churned, ask them.
  • If you want to analyze churn, don’t just look at who churned — compare who churned to who didn’t

CEO Out at Adaptive Planning / Adaptive Insights

[See bottom for update / new information as well as disclaimer]

Although I don’t know the circumstances of the seemingly sudden CEO change at Adaptive Insights (formerly known as Adaptive Planning) I can share what appears to be known at this point along with a few observations.

Adaptive Insights CEO John Herr, appointed on 10/31/2011, is no longer listed on the management section of the company’s web page or listed a member of the company’s board of directors, and is instead listed as a company advisor.  In his biography as advisor, he is explicitly referred to as “former CEO.”

ap-herr2a

 

While I don’t have much to work with, I can make the following observations:

  • This appears to have happened rather hastily as there is no new CEO listed on the management page.  Were the board working on an organized plan to replace the CEO (whose tenure was about 2.5 years) they would have executed this as a “Adaptive Planning appoints New CEO” as opposed to simply removing the existing one.
  • Time will tell if this is part of an organized replacement that we are catching in the middle.  If this is the result of a planned CEO replacement, then we should expect to see a new CEO appointed next week.
  • Otherwise, in the absence of an imminent new CEO announcement, I would conclude that the separation decision was made suddenly, perhaps in response to operational challenges (see disclaimer), a board dispute, or a personal issue.

The fact is that, barring personal issues, the majority of all Silicon Valley startup CEOs — particularly those hired one once a company already has some scale — stay on until one of three things happens:

  1. The company gets to a “liquidity event.”
  2. The CEO is asked to leave because things are not fine for some reason from the board perspective.
  3. The CEO and the board hit “irreconcilable differences” and are able to work out an amicable agree-to-disagree transition.

Note that the notion of “just quitting because you are unhappy”  basically doesn’t exist for a CEO because the CEO is the captain of the ship and few future investors will invest a CEO who has previously abandoned ship.  This is why I say the CEO job is unique because you are truly marrying the company (and in a country where only the spouse’s parents can ask for a divorce.)

In this situation, we are not in case 1 as there no liquidity announcement.  This is probably not case 3 as the whole point of case 3 is to deliver a smooth transition despite a major disagreement.  Ergo, I’d say we are in case 2 though one can never be sure of either the case or the reason for it.

I suppose it could also be the curse of the new building striking again, since they recently announced a new headquarters in Palo Alto.

Whatever happened, I can say that I’ve met John Herr a few times, found him smart and personable, and wish him (if not his former company) all the best going forward.

Update 7/21, 2:07 PM

Adaptive Insights has made its short-term plans clear with this press release announcing:

  • The appointment of Keith Nealon, formerly of phone supplier ShoreTel, to a new position as president and chief revenue officer (CRO).  Nealon is based in Austin, Texas.
  • The re-appointment of founder Rob Hull to chairman of the board, but not to CEO.
  • The appointment of a new Audit Committee Chair and board member, Jim Kelliher, CFO of LogMeIn.

Note that the company did not appoint a CEO and is thus going CEO-less at this time.  Time will tell, but this implies the company was caught somewhat flat-footed and is quite possibly launching a CEO search as we speak.

###

Disclaimer:  Host Analytics competes with Adaptive Insights, primarily at the low-end of the market.  In our competition with them, we have sensed recent operational challenges on their part, but we are certainly not unbiased observers.

Why, as CEO, I Love Driver-Based Planning

While driver-based planning is a bit of an old buzzword (the first two Google hits date to 2009 and 2011 respectively), I am nevertheless a huge fan of driver-based planning not because the concept was sexy back in the day, but because it’s incredibly useful.  In this post, I’ll explain why.

When I talk to finance people, I tend to see two different definitions of driver-based planning:

  • Heavy in detail, one where you build a pretty complete bottom-up budget for an organization and play around with certain drivers, typically with a strong bias towards what they have historically been.  I would call this driver-based budgeting.
  • Light in detail where you struggle to find the minimum set of key drivers around which you can pretty accurately model the business and where drivers tend to be figures you can benchmark in the industry.  I call this driver-based modeling.

While driver-based budgeting can be an important step in building an operating plan, I am actually bigger fan of driver-based modeling.  Budgets are very important, no doubt.  We need them to run plan our business, align our team, hold ourselves accountable for spending, drive compensation, and make our targets for the year.  Yes, a good CEO cares about that as a sine qua non.

But a great CEO is really all about two things:

  • Financial outcomes (and how they create shareholder value)
  • The future (and not just next year, but the next few)

The ultimate purpose of driver-based models is to be able answer questions like what happens to key financial outcomes like revenue growth, operating margins, and cashflow given set of driver values.

I believe some CEOs are disappointed with driver-based planning because their finance team have been showing them driver-based budgets when they should have been showing them driver-based models.

The fun part of driver-based modeling is trying to figure out the minimum set of drivers you need to successfully build a complete P&L for a business.  As a concrete example I can build a complete, useful model of a SaaS software company off the following minimum set of drivers

  • Number and type of salesreps
  • Quota/productivity for each type
  • Hiring plans for each type
  • Deal bookings mix for each (e.g., duration, prepayments, services)
  • Intra-quarter bookings linearity
  • Services margins
  • Subscription margins
  • Sales employee types and ratios (e.g., 1 SE per 2 salesreps)
  • Marketing as % of sales or via a set of funnel conversion assumptions (e.g., responses, MQLs, oppties, win rate, ASP)
  • R&D as % of sales
  • G&A as % of sales
  • Renewal rate
  • AR and AP terms

With just those drivers, I believe I can model almost any SaaS company.  In fact, without the more detailed assumptions (rep types, marketing funnel), I can pretty accurately model most.

Finance types sometimes forget that the point of driver-based modeling is not to build a budget, so it doesn’t have to be perfect.  In fact, the more perfect you make it, the heavier and more complex it gets.  For example, intra-quarter bookings linearity (i.e., % of quarterly bookings by month) makes a model more accurate in terms of cash collections and monthly cash balances, but it also makes it heavier and more complex.

Like each link in Marley’s chains, each driver adds to the weight of the model, making it less suited to its ultimate purpose.  Thus, with the additional of each driver, you need to ask yourself — for the purposes of this model, does it add value?  If not, throw it out.

One of the most useful models I ever built assumed that all orders came in on the last day of quarter.  That made building the model much simpler and any sales before the last day of the quarter — of which we hope there are many – become upside to the conservative model.

Often you don’t know in advance how much impact a given driver will make.  For example, sticking with intra-quarter bookings linearity, it doesn’t actually change much when you’re looking at quarter granularity a few years out.  However, if your company has a low cash balance and you need to model months, then you should probably keep it in.  If not, throw it out.

This process makes model-building highly iterative.  Because the quest is not to build the most accurate model but the simplest, you should start out with a broad set of drivers, build the model, and then play with it.  If the financial outcomes with which you’re concerned (and it’s always a good idea to check with the CEO on which these are — you can be surprised) are relatively insensitive to a given driver, throw it out.

Finance people often hate this both because they tend to have “precision DNA” which runs counter to simplicity, and because they have to first write and then discard pieces of their model, which feels wasteful.  But if you remember the point — to find the minimum set of drivers that matter and to build the simplest possible model to show how those key drivers affect financial outcomes — then you should discard pieces of the model with joy, not regret.

The best driver-based models end up with drivers that are easily benchmarked in the industry.  Thus, the exercise becomes:  if we can converge to a value of X on industry benchmark Y over the next 3 years, what will it do to growth and margins?  And then you need to think about how realistic converging to X is — what about your specific business means you should converge to a value above or below the benchmark?

At Host Analytics we do a lot of driver-based modeling and planning internally.  I can say it helps me enormously as CEO think about industry benchmarks, future scenarios, and how we create value for the shareholders.  In fact, all my models don’t stop at P&L, they go onto implied valuation given growth/profit and ultimately calculate a range of share prices on the bottom line.

The other reason I love driver-based planning is more subtle.  Much as number theory helps you understand the guts of numbers in mathematics, so does driver-based modeling help you understand the guts of your business — which levers really matter, and how much.

And that knowledge is invaluable.

Ten Classic Business Books for Entrepreneurs / Startup Founders

I often get asked by technical founders what business / marketing / strategy books they should read.  While there are many excellent relatively new books (e.g., The Lean Startup), the primary purpose of this post is to list a set of classic business books that most (older) business people have read — and that I think every budding entrepreneur should read as part of their basic business education.

  • Ogilvy on Advertising by David Ogilvy.  It’s getting a bit dated at this point, but still well worth the read.  The media have changed, but the core ideas remain the same.
  • Positioning by Al Ries and Jack Trout.  They, well, wrote the book on positioning.  Very focused on the mind of the customer.
  • Public Relations by Edward Bernays.  Another classic which studies PR in both history and application.  (I’m told Autonomy’s Mike Lynch swore by Bernays and Propoganda.)
  • The Innovator’s Dilemma by Clayton Christensen.  A newer book than many of the above, but an instant classic on the theory of disruptive innovation.
  • Guerrilla Marketing by Jay Conrad Levinson.  Oldie but goodie reinforcing the important idea that marketing doesn’t have to be expensive.
  • Blue Ocean Strategy by Renee Mauborgne and W. Chan Kim.  Again, a newer book than many of those on the list, but still an instant classic in my mind.  I particularly like their strategic levers analysis as shown in, e.g., the Cirque du Soleil case study.
  • Solution Selling by Michael Bosworth.  There as almost as many books on sales as there are salespeople.  I’ve read dozens and this, while superseded by Bosworth himself, remains the classic in my mind.
  • The Art of War by Sun Tzu.  The oldest book on the list by a few thousand years, so you want to find a version that is adapted to business.  While I like military-business analogies, On War remains on my to-read list.

Note that I have deliberately omitted Good to Great for three reasons:  (1) the case studies have largely under-performed undermining the book’s core thesis, (2) the book has generally been discredited, and (3) in my experience it is the most abused business book I have seen in terms of misapplication.  Despite reasons 1 and 2,  it nevertheless remains a top-seller; so much for rationality in business.

As a supplement, here are some newer books of which I’m a big fan:

  • The Halo Effect by Phil Rosenzweig.  A must read for anyone who wants to understand the weaknesses of business books and the business press.
  • Trust Me, I’m Lying by Ryan Holiday.  A simply amazing book by a self-confessed media manipulator and how he worked the top blogs.
  • The Lean Startup by Eric Ries.  Quickly becoming a new classic, on the art of iterative innovative (and frugal) strategy.
  • Thinking, Fast and Slow by Daniel Kahneman.  Amazing book by a psychologist who won the  Nobel prize in economics on human rationality and irrationality.

And finally, here are some near classics that didn’t quite make my top ten list.

  • The Wisdom of Crowds by James Surowiecki.  A great book on groups and their functions and dysfunctions.
  • Permission Marketing by Seth Godin.  Godin is an amazing speaker and thinker, but I have trouble identifying his one classic; he’s written too many books so it’s hard to find one to recommend.  This is my best shot.
  • The Five Dysfunctions of a Team by Patrick Lencioni.  Lencioni has also written numerous strong books on leadership, teamwork, and organizational dynamics, but I think this was his best.

Product is Not a Four-Letter Word

“Customers buy 1/4″ holes, not 1/4″ bits.”
Theodore Levitt, Harvard Business School

At some point in every marketer’s career they produce a data sheet that looks like this:

Our product uses state-of-the-art technology including a MapReduce distributed backend processing engine with predictive analytics including multivariate adaptive regression splines, support vector machine classification, and naive Bayesean machine learning.

When the draft review comes back someone invariably says “Yo! We sell solutions to problems here, not products.”  The author then revises the copy to:

Our solution uses state-of-the-art technology including a MapReduce distributed backend processing engine with predictive analytics including multivariate adaptive regression splines, support vector machine classification, and naive Bayesean machine learning.

And then, in most companies, everyone would be happy.  “Way to sell solutions!”

This, of course, would be called missing the point.  Completely.

Nothing drives me crazier than marketers who “sell solutions” by doing a global replacement of “product” for “solution” in their work.

While I am big believer in Theodore Levitt’s quote, it is not tantamount to saying never discuss product.  If I run a machine shop, while I am indeed “buying holes” at the macro level, I might nevertheless care very much about your drill bits:  are they carbon or titanium?  What is their useful life?  Can they drill into concrete?

Saying don’t lose sight of the fact that customers buy solutions to problems is not equivalent to declaring product a four-letter word.  There are both appropriate and inappropriate times to talk about features or “feeds and speeds” when discussing your product.  The problem in high technology is many marketers are so in love with the technology that all they talk about is features and technology at the cost discussing benefits.

That is, they are so in love with the bit that they forget people are buying it to drill holes.

There are two basic frameworks for doing product marketing:  FFB and FAB.

  • Feature/function/benefit (FFB).  Discuss the feature, describe how it works, and the first-order positive result from using it.
  • Feature/advantage/benefit (FAB).  Discuss the feature, the first-order positive result from using it, and the second-order results that come from the first-order result.

Here is an example showing elements from both frameworks.

  • Feature:  the green spots in Cheer laundry detergent.
  • Function:  some amazing chemical process that removes stains
  • Benefit 1:  whiter towels (and if you like puffery, towels that are whiter than white.)
  • Benefit 2:  you receive compliments on your towels’ whiteness at your pool party.
  • Benefit 3:  you receive a kiss from your spouse for getting complimented by the neighbors

You can see that the benefits are in effect a stack that you can climb arbitrarily high.  Here’s a business example:

  • New programming tool.
  • Makes your programmers more productive.
  • Means you output more product than your predecessor.
  • Means you get promoted.
  • Means you get nicer office.
  • Means you get a raise.
  • Means you get a bigger house.

Benefit-oriented marketers spend their time talking about this stack.  They talk about positive consequences for both you personally (cited above) and your company (imagine forking a different company benefits stack off more productive programmers).  There’s nothing wrong with this.

Since most tech marketers tend to forget it, a lot of sales and business people spend a lot time telling marketing “stop talking feeds and speeds,” “stop all the bits and bytes,” “don’t forget the benefits,” and “remember, we sell solutions to problems.”

But that is not to say that product is a four-letter word.  There is a time and a place to talk about product and marketers who answer clear product-oriented questions with benefits-stack answers will be seen as stupid and quite possibly evasive.

Think:  “yes, I know if goes faster I can buy fewer computers that will save my company, but what I’m asking is what makes it go faster?”

This means three things for product marketers

  • Never, ever do the product/solution global substitution as it accomplishes nothing.
  • Always know whether you are working a on primarily feature/benefit piece (e.g., a data sheet) or a feature/function piece (e.g., a white paper)
  • Get very, very good at clearly articulating the function of a feature.

Here’s a concrete example from my past of the FFB and the before/after of the “function” description, for a database feature called group commit.

  • Feature:  group commit
  • Function:  groups the commit records from different users into a single I/O to the transaction log file.
  • Benefit:  enables system performance in the 100 TPS range by eliminating a potential logging system bottleneck at around 30 TPS.

I spent hours talking with the engineers trying to understand the function of group commit.  I heard all kinds of stuff that I needed to filter before I finally could distill it:

Well you know when we commit a transaction we have to flush a record to the transaction log file in case the system crashes so we can guarantee the atomicity of transactions, you know so that we can either rollback or commit the transaction to the system and, as you know, those same transactions logs can be used in the roll-forward process in recovery, where we restore the entire database from a checkpoint and then systematically roll-forward the transactions applied to it up to some point in time.

Well in order to make all that stuff happen we need to flush records at commit time into the transaction logs and — this is important — it’s not enough to write them to some cache because if there’s a power failure and we lose that cache then we’ll lose the commit records and particularly because we now also have fast commit, we are not guaranteed to write all the database changes to the database at commit time, so it’s absolutely critical that we write the log records and then flush them to the disk.

Now the trick with flushing log records is that there is only current one logfile in the system and that can live on only 1 disk at a time.  And since then-current technology mean the most I/Os per second you could do to a disk, then you’ve got a built-in bottleneck that will prevent the system from going faster than 30 TPS.  Now that’s not to say that if you eliminate that specific bottleneck that we won’t find other bottlenecks that limit system performance, or — heck — there may be other bottlenecks in the system that cause us not to even get up to this 30 TPS limit, but as long as you are flushing one transaction in an I/O then you are about 30 TPS-limited.

Now, in a high-transaction environment, if you could make a few transactions wait just a bit before flushing them, you could probably pick up a few more transactions seeking to commit in the same timeframe and then group those commit records together and write them all out in a single I/O.  Thus your new bottleneck becomes the 30 times the number of commit records flushable in a single I/O …

That is the kind of stream of consciousness you sometimes get from an engineer when discussing product details.  Sometimes you’re lucky and get handed a very precise, terse definition.  Sometimes you get the rambling stuff above and it’s up to you to distill it.

The great product marketer, both because they want to be articulate and because they want to free up time to talk about benefits, thus seeks to describe the function of the feature as clearly and succinctly as possible.

Remember, product and feature are not four-letter words.  But you do need to be careful to when to talk product, when to talk function, and when to talk benefits.

The Box S-1, Delayed IPO, and the Genius of Tien Tzuo

While I did my own post on the Box S-1, I also noticed that fellow CEO blogger, Tien Tzuo of Zuora, had done a post of his own with the catchy title These Numbers Show That Box CEO Aaron Levie is a Genius.  I saw the post, clipped it to Evernote, and I decided to read it on my next flight.

That trip was a few days ago and at 35,000 feet I decided that Tien Tzuo was also a genius.  Not because he did a nice post on Box, but because he is devising an new accounting for SaaS companies which reflects them more accurately than current GAAP, and – rather amazingly– I’m guessing he came up with this more than 5 years ago.

You see, being a natural cynic, I had tended to dismiss Zuora’s “subscription economy” mantra as part Silicon Valley narcissism (lots of businesses have been selling subscriptions for a long time —  just because it’s new to us doesn’t mean it’s new to the world) and part marketing pitch.  In hindsight, I think I dismissed it too quickly.

While I’d seen one of Tien’s presentations, the concepts didn’t resonate with me until I read his post on the Box S-1.

I’ve always believed two things about SaaS companies and GAAP:

  •  GAAP P&Ls are not particularly reflective of the state of a SaaS business.  (Because expenses are taken now, but revenue is amortized going forward.)
  • The faster a SaaS company is growing, the less reflective the GAAP P&L is.

Box provides an extreme example of the second point, so it’s a good one to study.

However, with the exception of the CAC ratio, I’d defaulted to using other existing metrics that I thought captured things better, such as bookings and cashflow.  What I’d never tried to do was invent a new set of metrics that actually capture a SaaS business better – and that’s exactly what Tien has done.

Here are Tien’s core SaaS metrics:

  •  ARR (annual recurring revenue).  Everybody uses this one.  Tien however makes the clever and basic observation that current quarter subscription revenues * 4 is a good proxy for starting-quarter ARR.
  •  Gross recurring margin (GRM).  ARR – annualized COGS.  Tien argues this is the true gross margin on the business, and is equivalent to the steady-state gross margin if the business shut down all sales and marketing and stopped growing.  By Tien’s math, Box has GRM of 79%, Workday 83%, ServiceNow 78%, and Salesforce 85%.
  • Recurring revenue margin (RRM).  ARR – annualized ( COGS + R&D + G&A).  Tien argues this is margin on the recurring part of the business, including the recurring costs of delivering the service, enhancing it (as SaaS customers expect) and operating the business.  It notably excludes S&M, which is seen as a discretionary expense driver by how fast you want to grow.  By Tien’s math, Box has an RRM of 20%, Workday 28%, ServiceNow 40%, and Salesforce 57%.
  •  Customer acquisition cost (CAC) ratio.   I’ve covered this ratio extensively already, so I won’t redefine it.  I will note that Tien calculates Box’s CAC at around 2.0, which is higher than my estimate of 1.6.  However, we define CAC slightly differently (mine is based on new ARR, his on net new ARR) so I would expect mine to be lower since it’s not offset by churn.

And when you look on Tien’s metrics, Box looks pretty good.

If Tien’s Right, Why has the Box IPO Been Delayed?

Because Wall Street doesn’t care right now.  I think there are a number of reasons for that:

  • The general shellacking that SaaS stocks have taken in the past few months.  Many are off around 50%.
  • The unsustainable cash burn.  You might think it’s easy to back off growth, but it’s not. Growing fast means hiring like crazy and hiring like crazy adds the annualized cost of the new staff to your run rate.  Last I checked, Box was burning $20M+ per quarter and unless cash comes from somewhere that hiring party will end abruptly and unpleasantly — in the short-term at least.
  • Lifetime value concerns.  Tien’s math is silently predicated on a 100% renewal rate, and thus a high customer lifetime value (LTV).

Let’s look at this in more detail.

Tien’s metrics assume that if you have $150M in ARR and you turn off growth sales and marketing that you stay $150M forever.  That’s not true.  You actually enter a decay curve where you shrink by your churn rate each year.

Upsell and price increases can more than offset churn resulting in the hallowed negative churn rate, in which case you would actually grow every year, even without sales and marketing.  This appears to be the case at Box which claims a 123% net customer expansion rate.

So if the future looks like the past, things look pretty good for most SaaS companies and for Box in particular.  But what driver underlies that assumption?

Switching costs:  the cost of switching from offering A to offering B.  High switching costs ensure a high renewal rate regardless of whether you are delighting customers.  (Think of all those folks who write big maintenance checks to SAP or Oracle; they’re usually not “delighted” in my experience.)

And low switching costs, in my opinion, are Box’s potential Achilles’ Heel.  As a customer, and a happy one, I intend to renew for a while.  But if something better came along, well, it’s just not that hard to switch.

Put differently, Box’s file sharing isn’t that “sticky” — compared to a CRM or ERP system (and all the work you do to configure it, write reports, et cetera).

Put differently once more, what Box sells is much more of a commodity than other enterprise software offerings.

Despite that, this issue isn’t obvious in my opinion:

  • Switching costs can take subtle forms.  You can argue that part of Amazon’s success has been of the switching costs associated with account setup.  It’s not a huge cost, per se, but seemingly enough to cause me to just buy off Amazon instead of using Google Shopping or another price comparison engine.  Electronic wallets were supposed to fix this, but they didn’t.
  • Brand/trust.  Switching costs can also include what you lose in brand/trust by moving off an existing known supplier.  Box will certainly try to argue that leadership, trust, and brand are a big part of their value, and a cost to those who move away from them.
  • Entry barriers.  Box and Dropbox have both raised huge amounts of money and will work hard to create barriers to entry.  Switching costs to new entrants are only relevant to the extent there actually are new entrants.  The fundraising Box and Dropbox have done have basically scared, for the time being, everybody else out of the category.

So is Box theoretically very sticky?  In my opinion, no.

Might Box end up sticky in practice?  Quite possibly yes.

In which case Tien is right, and he’s a genius.  Which in turn makes Aaron Levie one, too.

Woe is Media: Lessons from Tidemark’s PR

[Major revision 5/11/14 5:10 PM]

  • “All media exist to invest our lives with artificial perceptions and arbitrary values.”  – Marshall McLuhan, philosopher of communications theory and coiner of the phrase “the medium is the message.”
  • “Modern business must have its finger continuously on the public pulse. It must understand the changes in the public mind and be prepared to interpret itself fairly and eloquently to changing opinion.”  – Edward Bernays, widely known as the Father of Public Relations and author of Propoganda [1].
  • “No one ever went broke underestimating the taste of the American public.”  – H.L. Mencken
  • “Don’t hate the media, become the media.”  – Jello Biafra, spoken word artist, producer, and formerly lead singer of the Dead Kennedys.

In this post, I’ll take some inspiration from Jello Biafra, “become the media,” and do some analysis of Tidemark’s most recent PR hit, a story in Business Insider entitled This Guy Arrived in the US with $26, Sold a Startup for Half a Billion, and is Working on Another Cool Company.  Since Host Analytics competes with Tidemark, see the footer for a disclaimer [2].

I’m doing this mostly because I’m tired of seeing stories like this one, where it’s my perception that a publication takes a story wholesale, spin and all, from a skilled PR firm and sends it down the line, unchallenged, to us readers.  I’m going to challenge the story, piece by piece, and try not to throw too many competitive jabs in the process.

Let’s start by analyzing the headline.

“$26″

While this may be true, it strikes me as exactly the kind of specifics that PR people know journalists love and a number that actually sounds better than say $30 or $25.  Perhaps CG (see footnote [3]) actually had $26 exactly in his pocket on arrival, but did he really have no other resources whatsoever on which to to rely?   Let us beware that it is not only the specificity of the $26 that makes the claim interesting, but also — and more importantly — the implication that he had nothing or no one else on which to rely.  Arriving with $26, not knowing the language, and having no friends/relatives is certainly much tougher than showing up with $26, a brother in Brooklyn, and $2,000 in the bank.  Which was the case?  I don’t know.  Given the overall quality of the story, and the author’s general susceptibility to spin (which we will show), I’d certainly wonder.

“Sold a startup for Half a Billion.”

To me, this clearly implies that CG was either:

  • Founder/CEO of a startup that sold for half a billion dollars, or
  • CEO of a startup that sold for half a billion dollars (while he was CEO)

He was neither.

CG was not a founder of OutlookSoft, nor was he ever CEO. He was CTO.  CTO’s don’t sell startups; CEO’s do.  Phil Wilmington was OutlookSoft’s CEO.

CG had founded a company called Tian Software which, per CG’s own LinkedIn profile, was acquired (not “merged” as the story later says) by OutlookSoft in 2005.

Now let’s challenge the half-a-billion.

My sources say SAP acquired OutlookSoft for $350M plus a $50M earn-out, making the deal worth $400M — not $500M.  This is sort of confirmed in another Tidemark PR marvel, here, which says “short of $500M,” a very nicely PR-packaged way of saying $400M.  A few phone calls to SAP alums and deal-makers in the valley might well have confirmed the lower price.

Net/net:  we have blown the headline to bits.  The $26 claim is suspect (if quite possibly true) while the very impressive “sold a startup for half a billion” is simply false.  It wasn’t half a billion.  It wasn’t his startup.  He didn’t sell it.  QED.

I know that neither CG nor Tidemark wrote this headline.  Someone at Business Insider did — and quite possibly not the journalist who wrote the article.

So perhaps we’re just caught up in headline sensationalism.  The Horatio Alger message still sells well in America and the SEO people at Business Insider know it — the URL for the story is:    www.businessinsider.com/christian-gheoghre-rags-to-riches-story.

Before digging into the story itself, we should observe that this is basically the same story as this one that ran on CNET over a year ago:  Escaping the Iron Curtain for Silicon Valley.  This raises a question that is difficult for me to answer.  It’s a cool story, no doubt, but the tech blogs are news blogs and old stories aren’t news.  So why even write the same story that CNET did 15 months earlier?  Is it possible they didn’t even fact check enough to know?

Let’s dig into some of the lines from the story.

“Today’s he working on his fourth successful startup, having sold all of his previous ones, including his third one, OutlookSoft, to SAP for $500M.”

I count two:  Tian Software and Tidemark.

The story itself contradicts the idea that Saxe Marketing “was CG’s” in saying, “[Andrew] Saxe hired CG” — i.e., if CG was “hired” he was not a founder and ergo the company was not “his.”   The name of company itself — Saxe Marketing, as opposed to Saxe & CG Marketing — additionally reinforces that.

As discussed above, you can’t call OutlookSoft “his,” nor can you say he sold it.

If we said, “CG spent 10 years toiling on two startups, one that got sold to Experian for $32M and one that was acquired by a private company at an undisclosed valuation” — would it have the same impact?  Methinks not.

“Taught himself English by listening to Pink Floyd.”  

I have no doubt that CG listened to Pink Floyd in his home country and that he learned (probably quite strange) words from so doing.  From my experience with second-language songs, it’s actually quite difficult to learn words and much easier to learn pronunciation.  Many of my French friends can sing English songs, but only in a phonetic way.

So, to me, this rings partially true but it also rings as something a PR person would grab onto faster than swimming across the border.  “Wait, you learned English listening to Pink Floyd.  Oh!  We’ve got to use that.”

So, to have some fun with this one, let me imagine the conversation he had with the immigration officer on arriving at JFK:

INS:  “So why are you entering America?”

CG:  “We don’t need no education.”

INS:  “So you’re not on a student visa?”

CG:  “We’re just two lost souls swimming in a fish bowl, year after year.”

INS:  “So you’re coming to to get married, then?”

CG:  “You raise the blade, you make the change, you re-arrange me ’till I’m sane.”

INS:  “Ah, a medical visa, excellent.”

This spin-taking was harmless.

“He taught himself to code by hacking into video games on [a Commodore 64] machine.”  

Frankly, I’m not sure you could “hack into” video games on a Commodore 64, but I guess that sounds better than saying “wrote BASIC programs on a Commodore 64″ like the rest of us.  If I had to guess, you probably got the source code since BASIC wasn’t a compiled language so there was no “hacking” to get in.  You were in if you wanted to be.

The CNET story somewhat contradicts this account saying CG “played games on the C64″ but he later bought a “Sinclair ZX and taught himself some programming.”

Details, yes, somehow programming a C64 or ZX isn’t good enough for the narrative:  he had to “hack into” them.  All part of the journalist embellishing the (probably already embellished) details in order to make CG larger than life and get a lot of hits on the story.

“[He got] a masters [sic] degree in Romania in mechanical engineering with a minor in computer science. But the degree wasn’t recognized and accepted once he got here.”  

If there were ever a field in which people care about what you can do as opposed to your degree, it’s programming.

Recognized (by whom?) or not, CG was not a limo driver who knew nothing about programming and miraculously started a software company.  He had a master’s degree in engineering and computer science.

“Immigrant with master’s in computer science founds software company” would probably describe about half of all Silicon Valley companies.

Business Insider insists on the Man Bites Dog approach of “Limo Driver Founds Software Company” to the point of explaining away the master’s degree because it interferes with the narrative.

“He launched a second startup, TIAN, and merged it with a company called OutlookSoft.”

Tian was not “merged” with OutlookSoft; it was acquired by them, per CG’s own LinkedIn.  Why the spin?

“OutlookSoft did a form of big data known as business analytics.”

There was nothing whatsoever “big data” about OutlookSoft, which was a business performance management company that did planning, budgeting, consolidation, and analytics.  Gratuitous buzzword inclusion, and nothing more.  Presumably inserted by the PR firm and swallowed whole by the journalist.

“Tidemark also does business analytics/big data, but it’s designed for the modern age: it works on a tablet and runs in the cloud.”  

The Holy Grail of PR these days is social, mobile, cloud.  This sentence scores a 2 out of 3.  For what it’s worth, I actually think this is part of their strategy, so in this case it’s not buzz-wordy journalism, it’s the clear communication of a buzz-wordy strategy.

“More importantly, it is designed to be what CG calls a ‘revolution at the edge’ with a ‘Siri-like interface.’”  

Revolution at the edge is both buzz-wordy and meaningless.  Siri is definitionally not revolutionary because it was launched 4 years ago in 2010 and based upon natural language and speech recognition technology that was more than a decade old.  What was revolutionary about Siri was its inclusion in a mass-market, consumer product.

I’d say a Siri-like interface for BI has been discussed since the Natural Language Inc (NLI) was acquired by Microsoft in the late 1980s.  If nobody’s noticed, it hasn’t worked.  Turns out the specificity of human language is not precise enough to directly map to a database query — even with a semantic layer.   But, hey, let’s go pitch the idea because it sounds cool, the journalist probably has no idea of the history and doesn’t realize that no CFO wants to say “Hey Tiri, I want to hire 3 people next quarter and increase average salaries 3.5%.”

“It’s like Google mixed with Wolfram|Alpha.” 

That’s like saying it’s nuclear fusion mixed with a perpetual motion machine.

While it may indeed do voice recognition like Siri, I can assure you it is not like Wolfram|Alpha (press the link to see just one example).   This seems an easily challenged assertion, but it gets repeated as a sexy soundbite.  Great packaging of the message to just flow through the media channel.

The first rule of PR is to have good metaphors and that certainly a good one.  The first rule of journalism, however, should be to challenge what’s said.  How is it like Wolfram|Alpha exactly (and there’s a lot, lot more to Wolfram|Alpha than a question-style interface).

“In the first 18 months since his product became available, his company is on track to hit $45 million in revenue, CG told us, growing 300% year over year. It has about 45 customers so far, with, on average, 180 business people at each customer using the product.”

We’re going to need to analyze this last set of claims one at a time.

  • “In the first 18 months.”  Tidemark was founded in 2009, so it’s about 5 years old.  While PR is cleverly trying to reframe the age issue around product availability, you’d think a journalist would want to know what happened during the other 3.5 years.  As it turns out, a lot.  The company was originally founded as Proferi, with an integrated GRC and EPM vision.  When that failed, the company “pivoted” (a euphemism for re-started with a new strategy) to a new vision which I’ve frankly never quite understood because of the buzzword-Cuisinart messaging strategy they employ.
  • “On track to hit $45M in revenue.”  Frankly, I have a lot of trouble believing this, but it’s happily stated without a timeframe and thus impossible to analyze.   Normally, when you say $45M, it implies “this fiscal year.”  But it could be anything.   Is it simply “on track” for doing $45M in, say, 2016?  Or, maybe it’s a really misleading answer like $45M in cumulative revenue since inception?   To paraphrase an old friend, saying $45M without a timeframe is like offering a salary of 100,000 but not mentioning the currency.
  • “Growing 300% year over year.”  Most journalists and some PR people confuse tripling with growing 300% which is actually quadrupling.  But let’s assume both that the math is right and we are talking annual revenues:  this means they did $11.25 in 2013 and are on track to do $45M in 2014.  To do this in revenues means an even bigger number in bookings (due to amortization of SaaS revenues).  I banged out a quick model to show my point.

Tidemark analysis

  • “Growing 300% a year.”  The far easier way to grow 300% year, of course, is to do so off a small base.  If you do some basic math on private company numbers and it doesn’t make sense, you probably shouldn’t repeat them.  Net/net:  a journalist who hears 200% or 300% growth claims should first make sure the math is right, and second default-conclude it’s off a small base until proven otherwise.
  • “It has 45 customers so far with 180 [users at each customer].”  Some quick math says $45M/45 = $1M/customer, which is Workday-class large and ergo highly suspect.  Slightly better math (using my quarterly model) suggests $800K/customer in ARR, which is still huge — by my estimates $100-$200K ARR is a nice deal in EPM.   Combining this with 180 users/customer implies an average price of $4.5K/user/year — 150% of the list price of the most expensive edition of Salesforce.com.  ERP-sized deals, deals 4-10x the industry average, deals done at 150% of Salesforce’s list.  It doesn’t add up.

I should also note that LinkedIn says Tidemark has 51-200 employees which is generally not consistent with the numbers in my model.  Moreover, I can find searching for words like “account” [executive] or  “sales” [executive], only fewer than 10 people who appear to be in sales at Tidemark.

Overall, I conclude that the $45M is more like 2014 bookings or maybe cumulative bookings since inception than any annual revenue figure.  The numbers just don’t hang together.  If I had to pick a figure, I’d guess they are closer to $10M in revenues in 2014 than $45M.

But what is a journalist supposed to do in this situation?  I’d argue:  fact check.  Call VCs and get company size estimates.  Use Google to find similar/alternative stories. See Crunchbase for history. Do some basic triangulation off LinkedIn both in terms of numbers of sales reps and size of company.   Ask industry execs for industry averages.  And if the numbers don’t hang together, don’t publish them.

To wrap this up, yes, I dislike this kind of puff-piece, softball story.  Not because it’s friendly — not all news has to be challenging and analytical and the raw material of CG’s story is indeed impressive — but because it seems to take the PR-enhanced version of it, and swallow it hook, line, and sinker.

The media should do better.  The trade press was crushed by the tech blogs for lack of sufficient value add.  The tech blogs are quickly falling into the same trap.

Disclaimer / Footnotes
[1]  I’m told Autonomy’s Mike Lynch was a big fan of this book.

[2] Host Analytics theoretically competes with Tidemark.  Since we rarely see them in deals, I feel comfortable editorializing about their PR as I might not with a more direct competitor.  Nevertheless, I can certainly be said to have a horse in this race.

[3] I refer to Christian Gheorghe as CG both because his name is notoriously hard to spell, but more importantly because this post is not supposed to be an attack on him — to my knowledge he is a delightful and inspiring person — but rather instead a call-out of the publication that wrote this story and the system of which it is a part.

A Look at the Zendesk S-1 (IPO)

I thought I’d take a quick read of the Zendesk S-1 today, so here are my real-time notes on so doing.  Before diving in, let me provide a quick pointer to David Cummings’ summary of the same.

My notes:

  • 40,000 customers in 140 countries
  • 2012 revenues of $38.2M
  • 2013 revenues of $72.0M, 88% growth
  • 41% of revenues from international.  (High for a SaaS company at this size, but makes sense given their roots.)
  • Net loss of $24.4M and $22.6M in 2012 and 2013, -30% net loss in 2013
  • Zendesk approach:  beautifully simple, omni-channel, affordable, natively mobile, cloud-based, open, proactive, strategic.  They do this well.  (I’ve always viewed them as a very well run, low-end-up market entrant.)
  • Founded in Denmark in 2007.
  • 115M shares outstanding anticipated after the offering with seemingly another 40M in options under various options and ESOP plans.  (Seems like a lot of dilution looming.)
  • 65% gross margins.  (Though they don’t break out subscription vs. service which probably depresses things a tad.)
  • 20% of revenue spent on R&D.  (Normal.)
  • 52% of revenue on S&M.  (High, particularly for freemium which is notionally low-cost!)
  • 22% of revenue on G&A  (Normal to high, probably due to IPO itself.)
  • $53M in cash at 12/31/13
  • Headcount growth from 287 to 473 employees in year ended 12/31/13, up 68%
  • They have experienced security breaches:

“We have experienced significant breaches of our security measures and our customer service platform and live chat software are at risk for future breaches as a result of third-party action, employee, vendor, or contractor error, malfeasance, or other factors. For example, in February 2013, we experienced a security breach involving unauthorized access to three of our customers’ accounts and personal information of consumers maintained in those customer accounts.”

  • “[We are] highly dependent on free trials.”  (These guys define freemium model for enterprise software in my opinion.)
  • S&M org grew from 85 to 165 employees in period ending 12/31/13.
  • Owe $23.8M on a credit facility.  (Rare to see this much debt, but probably a smart way to reduce equity dilution.)
  • The three principles that drive the founders:  Have great products.  Care for your customers.  Attract a great team.  (Beats “Don’t Be Evil” any day in my book.)
  • Dollar-based “net expansion rate” (closest thing they discuss relative to renewals or churn):

    “We calculate our dollar-based net expansion rate by dividing our retained revenue net of contraction and churn by our base revenue. We define our base revenue as the aggregate monthly recurring revenue of our customer base as of the date one year prior to the date of calculation. We define our retained revenue net of contraction and churn as the aggregate monthly recurring revenue of the same customer base included in our measure of base revenue at the end of the annual period being measured. Our dollar-based net expansion rate is also adjusted to eliminate the effect of certain activities that we identify involving the transfer of agents between customer accounts, consolidation of customer accounts, or the split of a single customer account into multiple customer accounts. [...] Our dollar-based net expansion rate was 126% and 123% as of December 31, 2012 and 2013, respectively. We expect our dollar-based net expansion rate to decline over time as our aggregate monthly recurring revenue grows.”

  • $66M accumulated deficit
  • Have data centers in North America, Europe, and Asia
  • 4Q13/4Q12 growth rate = 83% compared to 2013/2012 growth rate = 88%.  (Suggests growth is gently decelerating.)
  • Cashflow from operations in 2013 = $4.0M.
  • But they had -$24.1M in cashflow from investing activities.  (This is confusing because it’s a mix of items but broken into $12.4M in “marketable securities, property and equipment,” $7.1M to build data centers, and $4.7M in capitalized software development.  I’m not an accountant but if you ask me if “the business” is cashflow positive, the answer is no despite the $4.0M positive cashflow from operations. Building data centers and developing software, regardless of accounting classification, are all part of running the business to me.)
  • I am surprised they capitalize R&D.  Most software companies, far as I know, don’t.

zendesk common fmv

 

The FMV of the common stock is depicted above, by my math an annual 68% appreciation rate.

  • Huge number of leads are organic:  “the quarter ended December 31, 2013, 70% of our qualified sales leads, which are largely comprised of prospects that commence a free trial of our customer service platform, came from organic search, customer referrals, and other unpaid sources.”
  • SVPs listed (CFO, R&D) earn $240K base + $40K bonus
  • Automatic 5% share expansion / “overhang” built into the stock option and incentive plan.  Pretty rich in my experience and haven’t noticed anyone else doing it automatically before.
  • Letting execs buy stock with promissory notes … hum, I thought that went out with leg warmers.  Both loans were paid off by 12/31/31 and maybe that’s why.
  • CEO will own 7.1% of shares after the offering, including 4.3M (of the 8.1M beneficially owned) granted as options at the 2/14 board meeting.  (Seems odd to me; a huge option grant right before the IPO.  Hum.)
  • Nice banker line-up:  Goldman Sachs, Morgan Stanley, Credit Suisse, Pacific Crest
  • Raised $71M in preferred equity / venture capital
  • They do monthly, quarterly, and annual invoicing.  (Surprised they offer the short terms, particularly monthly.)
  • $6.5M in advertising expense in 2013
  • $11.4M in capitalized “internal use” software on the balance sheet at 12/31/13
  • They paid $16M for the Zopim (live chat) acquisition
  • Ticker symbol:  ZEN

If Marc Benioff Carried a Rabbit’s Foot, Would You?

In business we have a sad tendency to copy success blindly.

I remember the first time I read about this I didn’t even understand what I was reading:

“Nothing in business is so remarkable as the conflicting variety of success formulas offered by its numerous practitioners and professors.  And if, in the case of practitioners they’re not exactly “formulas,” they are explanations of “how we did it” implying with firm control over any fleeting tendencies toward modesty that “that’s how you ought to do it.”  Practitioners filled with pride and money turn themselves into prescriptive philosophers, filled mostly with hot air.”

Through blind luck, I’d had the good fortune that Theodore Levitt’s The Marketing Imagination (1983) was the very first book I read on marketing.  That paragraph — the opening paragraph of the book — stuck with me in some odd way, but it would be years before I truly appreciated what it said.

I was business-educated in the In Search of Excellence (1982) era and, while I suppose the same approach had been happening for years, In Search of Excellence was about as unscientific as they come.  The authors, Tom Peters and Bob Waterman, started out with a list of 62 companies identified by asking their McKinsey partners and friends “who’s doing cool work,” cut the list rather arbitrarily to 43 (excluding, for example, GE — but retaining Wang, Atari, and Xerox), and then “derived” eight themes which they thought were responsible for their success.

That was the mentality of the time.  Arbitrarily identify a set of companies you deem “cool” and then arbitrarily come up with things they have in common.  (And that’s not to mention the allegations of “faked data.”)

So I was happy when Jim Collins came along in 2001 arguing that he was bringing a more scientific approach in Good to Great.  Arguing that seeking only common traits could you lead to discoveries such as “all great companies have buildings,” Collins strove to differentiate good companies from great ones.  Starting with 1,435 companies and examining their performance over 40 years, Collins’ team identified 11 companies that became great along with 11 comparison companies in the same markets that did not.

While Collins’ thinking may have been clearer than Peters’, his luck was no better. Seven years after the book was published, several “great” companies like Circuit City were in deep trouble, Fannie Mae required a Federal bailout, and only only one of the eleven companies, Nucor, had dramatically outperformed the stock market.  Amazingly, despite the poor to lackluster performance of the “great” companies, it remains a best-seller to this day, ranking #5 on Amazon in management at last check.

Even when trying to avoid it, fake science and, in particular, survivor bias had struck again.  Thank goodness Phil Rosenzweig came along in 2009 with The Halo Effect, describing it and eight other business delusions from which managers suffer.  Here’s a nice excerpt:

On the way up to a stock market value of half a trillion dollars, everything about Cisco seemed perfect. It had a perfect CEO. It could close its books in a day and make perfect financial forecasts. It was an acquisition machine, ingesting companies and their technologies with great aplomb. It was the leader of the new economy, selling gear to new-world telecom companies that would use it to supplant old-world carriers and make their old-world suppliers irrelevant. Over the past year, every one of those characterizations has proved to be false.

As I often said about running analyst relations at Business Objects: “when the stock was going up everything I said was genius, when we missed a quarter, everything I said was suspect.”  This is, in my estimation, the real reason why some bad-egg companies such as bubble-era MicroStrategyFast Search & Transfer, or Autonomy (not yet settled) are tempted to inflate results.  I think it’s less about inflating valuation, and more about inflating the company’s perception of success in order to “validate” their strategy going forward.

But, to Levitt’s point at the start of this post, we are swimming in advice from successful practitioners.  

We have advice from Sequoia billionaire Mike Moritz who says the best advice he ever received was to “follow his instincts” which, as it turns out, works swimmingly well if you happen to have his instincts.  (And perhaps less so well, if you don’t.)

We have advice from billionaire Peter Thiel, who sounds vaguely like Timothy Leary with the drop-out part of turn on, tune in, drop out.

We have advice from Steve Blank, one of the more reasonable and thoughtful sources out there, and someone, in my opinion, to be admired for his commitment to giving back intellectually to Silicon Valley.

We have a plethora of advice from Marc Benioff, for example, the 111 “plays” in Beyond the Cloud, including “make your own metaphors” and “cultivate select journalists.” 

Who knows, maybe “beware of billionaires bearing business advice” may become the new “beware of Greeks bearing gifts.”

Finally, we also have advice from, dare I say, Kellblog who, while not a billionaire (yet), has opinions as tempered by experience and as firmly held as any of the above — and often as unscientific.

Given this sea of advice, how do I recommend processing it?  In the end, as Rosenzweig reminds us, in the absence of real silver bullets and magic formulae, we need to think for ourselves.  So every time I hear a successful businessperson bearing business advice I remind myself of one key fact — the plural of anecdote is not data — and ask myself two key questions:

  • Do I believe that he/she was successful because of, in spite of, or completely independent of this advice?
  • If Marc Benioff carried a rabbit’s foot, would I?