Category Archives: Venture Capital

How To Get Your Startup a Halo

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

And then one of three things happens:

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

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

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

The Customer View

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

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

Don’t Start a Customer Relationship with a Lie

As a manager, I like to make sure that every quarter that each of my direct reports has written, agreed-to goals.  I collect these goals in a Word document, but since that neither scales nor cascades well, I’ve recently been looking for a simple SaaS application to manage our quarterly Objectves and Key Results (OKRs).

What I’ve found, frankly, is a bit shocking.

Look, this is not the world’s most advanced technical problem.  I want to enter a goal (e.g., improve sales productivity) and associate 1-3 key results with that goal (e.g., improve ARR per salesrep from $X to $Y).  I have about 10 direct reports and want to assign 3-5 OKRs per quarter.  So we’re talking 30-50 objectives with maybe 60-100 associated key results for my little test.

I’d like some progress tracking, scoring at the end of the quarter, and some basic reporting.  (I don’t need thumbs-ups, comments, and social features.)  If the app works for the executive team, then I’ll probably scale it across the company, cascading the OKRs throughout the organization, tracking maybe 1,200 to 1,500 objectives per quarter in total.

This is not rocket science.

Importantly, I figure that if I want to roll this out across the entire team, the app better be simple enough for me to just try it without any training, presentations, demos, or salescalls. So I decide to go online and start a trial going with some SaaS OKR management providers.

Based on some web searches, PPC ads, and website visits, I decide to try with three vendors (BetterWorks, 15Five, and 7Geese).  While I’m not aiming to do a product or company comparison here, I had roughly the same experience across all three:

  • I could not start a free trial online
  • I was directed to an sales development rep (SDR) or account exec (AE) before getting a trial
  • That SDR or AE tried to insist on a phone call with me before giving me the trial
  • The trial itself was quite limited — e.g., 15 or 30 days.

At BetterWorks, after getting stuck with the SDR, I InMailed the CEO asking for an SDR-bypass and got one (thanks!) — but I found the application not intuitive and too hard to use.  At 7Geese, I got directed to an AE who mailed me a link to his calendar and wanted to me to setup a meeting.  After grumbling about expectations set by the website, he agreed to give me a trial.  At 15Five, I got an SDR who eventually yielded after I yelled at him to let me “follow my own buyer journey.”

But the other thing I noticed is that all three companies started our relationship with a lie of sorts.  What lie?  In all three cases they implied that I’d have easy access to a free trial.  Let’s see.

If you put a Free Trial button on your website, when I press it I expect to start an online process to get a free trial — not get a form that, once filled, replies that someone will be in touch.  That button should be called Contact Us, not Free Trial.

7Geese was arguably more misleading.  While the Get Started button down below might imply that you’re starting the process of getting access to a trial, the Get Started Now button on the top right says, well, NOW.

Worse yet, if you press the Get Started Now button on 7Geese, you get this screen next.

Tailored tour?  I pressed a button called Get Started Now.  I don’t want to setup a demo.  I want to get started using their supposedly “simple” OKR tracking app.

15Five was arguably the most misleading.

When you write “14 days free. No credit card needed.” I am definintely thinking that when I press Get Started that I’ll be signing up for a free 14-day trial on the next screen.  Instead I get this.

I didn’t ask to see if 15Five was right for my company.  I pressed a button that advertised a 14-day free trial with no credit card required.

Why, in all three cases, did these companies start our relationship by lying to me?  Probably, because in all three cases their testing determined that the button would be clicked more if it said Get Started or Frial Trial than if it said something more honest like Contact Us or  Request Free Trial.

They do get more clicks, I’m sure.  But those clicks start the relationship on a negative note by setting an expectation and immediately failing to meet it.

I get that Free Trials aren’t always the best way to market enterprise software.  I understand that the more complicated the application problem, the less a Free Trial is effective or even relevant.  That’s all fine.  If you haven’t built a viral product or work in a consumer-esque cateogry, that’s fine.  Just don’t promise a Free Trial on your website.

But when you’re in a category where the problem is pretty simple and you promise a Free Trial on your website, then I expect to get one.  Don’t start our relationship with a lie.  Even if your testing says you’ll get more clicks — because all you’ll be doing is telling more lies and starting more customer relationships on the wrong foot.

Quick Thoughts on Tagetik Acquistion by Wolters Kluwer

Earlier today, the tax and accounting division of Dutch publishing giant Wolters Kluwer announced the acquistion of Italian enterprise performance management (EPM) vendor Tagetik for 300M Euros, or about $318M.

Founded in 1986, Tagetik was a strong regional European player in on-premises EPM and about 2.5 years ago had raised $37M in capital in order to attack the USA market and accelerate their transition from on-premises to cloud computing.

The press release said Tagetik was valued at 300M Euros off 57M Euros in 2016 revenues, of which 35% are “recurring in nature.”  At a hybrid on-premises / SaaS software company you have two types of revenue that’s recurring in nature:  (1) SaaS subscription fees and (2) on-premises annual maintenance fees.  Doing some back of the envelope math (detailed below), you end with Tagetik breaking into a roughly $13M SaaS business and a $47M on-premises business.

If you buy that analysis, then we can do some valuation guestimation.

While we know the overall multiple of 5.3x revenues, we need to estimate separate multiples paid for the estimated $13M SaaS business vs. the estimated $47M on-premises business.  While there is an infinite number of ways to weight the two pieces compromising the total valuation, my best guess is that Wolters Kluwer paid 10x revenues for the SaaS business and 3.9x revenues for the on-premises business, generally in line with the notion that $1 of SaaS revenue is worth about $2.5 to $3.0 of on-premises.

White Bridge, who led the investment in 2014, got about a 3x return on investment by my math (with one assumption) over about a 3 year period, for an IRR of around 45%.

Market-wise, this is not the first EPM vendor to acquired by an off-axis competitor.  Axiom was acquired by vertically oriented management consultancy Kaufman Hall in 2014 (and has since generally disappeared from the regular EPM market).  My belief is that Tagetik awaits a similar fate.

“The acquisition of Tagetik tightly aligns with our vision to expand our position in the faster growing areas of the corporate tax and accounting market,” said Tax & Accounting Division CEO Karen Abramson.

While Wolters Kluwer has a strong tax and accounting division, only one piece of EPM (consolidation) is generally sold to accounting.  Planning, in all its forms, represents about 65% of the EPM market and that is sold to FP&A, not tax and accounting.  Bridging that gap, both in terms of buyer and mentality, should not be easy for Wolters Kluwer.  I suspect this means Tagetik will play a dimishing role in the mainstream EPM market going forward.

But either way, congratulations to co-CEOs Marco Pierallini and Manuel Vellutini on a successful sale of their company.  Felicitazioni!

The Evolution of Marketing Thanks to SaaS

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

Here’s what it looked like:

saas-strategic-value

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

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

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

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

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

hubspot

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

In-Memory Analytics: The Other Kind – A Key Success Factor for Your Career

I’m not going to talk about columnar databases, compression, horizontal partitioning, SAP Hana, or real-time vs. pre-aggregated summarization in this post on in-memory analytics.  I’m going to talk about the other kind of in-memory analytics.  The kind that can make or break your career.

What do you mean, the other kind of in-memory analytics?  Quite simply, the kind you keep in your head (i.e., in human memory).  Or, better put, the kind you should be expected to keep in your head and be able to recite on demand in any business meeting.

I remember when I worked at Salesforce, I covered for my boss a few times at the executive staff meeting when he was traveling or such.  He told me:  “Marc expects everyone to know the numbers, so before you go in there, make sure you know them.”  And I did.  On the few times I attended in his place, I made a cheat sheet and studied it for an hour to ensure that I knew every possible number that could reasonably be asked.  I’d sit in the meeting, saying little, and listening to discussion not directly related to our area.  Then, boom, out of left field, Marc asked:  “what is the Service Cloud pipeline coverage ratio for this quarter in Europe?”

“3.4,” I replied succinctly.  If I hadn’t have known the number I’m sure it would been an exercise in plucking the wings off a butterfly.  But I did, so the conversation quickly shifted to another topic, and I lived to fight another day.

Frankly, I was happy to work in an organization where executives were expected to know — in their heads, in an instant — the values of the key metrics that drive their business.  I weak organizations you constantly hear “can I get back to you on that” or “I’m going to need to look that one up.”

If you want to run a business, or a piece of one,  and you want to be a credible leader — especially in a metrics-driven organization — you need to have “in-memory” the key metrics that your higher-ups and peers would expect you to know.

This is as true of CEO pitching a venture capitalist and being asked about CAC ratios and churn rates as it is of a marketing VP being asked about keywords, costs, and conversions in an online advertising program.  Or a sales manager being asked about their forecast.

In fact, as I’ve told my sales directors a time or two:  “I should be able to wake you up at 3:00 AM and ask your forecast, upside, and pipeline and you should be able to answer, right then, instantly.”

That’s an in-memory metric.  No “let me check on that.”  No “I’ll get back to you.”  No “I don’t know, let me ask my ops guy,” which always makes me think: who runs the department, you or the ops guy — and if you need to ask the ops guy all the numbers maybe he/she should be running the department and not you?

I have bolded the word “expect” four times above because this issue is indeed about expectations and expectations are not a precise science.  So, how can you figure out the expectations for which analytics you should hold in-memory?

  • Look at your department’s strategic goals and determine which metrics best measure progress on them.
  • Ask peers inside the company what key metrics they keep in-memory and design your set by analogy.
  • Ask peers who perform the same job at different companies what key metrics they track.
  • When in doubt, ask the boss or the higher-ups what metrics they expect you to know.

Finally, I should note that I’m not a big believer in the whole “cheat sheet” approach I described above.  Because that was a special situation (covering for the boss), I think the cheat sheet was smart, but the real way to burn these metrics into your memory is to track them every week at your staff meeting, watching how they change week by week and constantly comparing them to prior periods and to a plan/model if you have one.

The point here is not “fake it until you make it” by running your business in a non-metrics-focused way and memorizing figures before a big meeting, but instead to burn the metrics review into your own weekly team meeting and then, naturally, over time you will know these metrics so instinctively that someone can wake you up at 3:00 AM and you can recite them.

That’s the other kind of in-memory analytics.  And, much as I love technology, the more important kind for your career.

Kellblog’s 2017 Predictions  

New Year’s means three things in my world:  (1) time to thank our customers and team at Host Analytics for another great year, (2) time to finish up all the 2017 planning items and approvals that we need to get done before the sales kickoff (including the one most important thing to do before kickoff), and time to make some predictions for the coming year.

Before looking at 2017, let’s see how I did with my 2016 predictions.

2016 Predictions Review

  1. The great reckoning begins. Correct/nailed.  As predicted, since most of the bubble was tied up in private companies owned by private funds, the unwind would happen in slow motion.  But it’s happening.
  2. Silicon Valley cools off a bit. Partial.  While IPOs were down, you couldn’t see the cooling in anecdotal data, like my favorite metric, traffic on highway101.
  3. Porter’s five forces analysis makes a comeback. Partial.  So-called “momentum investing” did cool off, implying more rational situation analysis, but you didn’t hear people talking about Porter per se.
  4. Cyber-cash makes a rise. CorrectBitcoin more doubled on the year (and Ethereum was up 8x) which perversely reinforced my view that these crypto-currencies are too volatile — people want the anonymity of cash without a highly variable exchange rate.  The underlying technology for Bitcoin, blockchain, took off big time.
  5. Internet of Things goes into trough of disillusionment. Partial.  I think I may have been a little early on this one.  Seems like it’s still hovering at the peak of inflated expectations.
  6. Data science rises as profession. Correct/easy.  This continues inexorably.
  7. SAP realizes they are a complex enterprise application company. Incorrect.  They’re still “running simple” and talking too much about enabling technology.  The stock was up 9% on the year in line with revenues up around 8% thus far.
  8. Oracle’s cloud strategy gets revealed – “we’ll sell you any deployment model you want as long as your annual bill goes up.”  Partial.  I should have said “we’ll sell you any deployment model you want as long as we can call it cloud to Wall St.”
  9. Accounting irregularities discovered at one or more unicorns. Correct/nailed.  During these bubbles the pattern always repeats itself – some people always start breaking the rules in order to stand out, get famous, or get rich.  Fortune just ran an amazing story that talks about the “fake it till you make it” culture of some diseased startups.
  10. Startup workers get disappointed on exits. Partial.  I’m not aware of any lawsuits here but workers at many high flyers have been disappointed and there is a new awareness that the “unicorn party” may be a good thing for founders and VCs, but maybe not such a good thing for rank-and-file employees (and executive management).
  11. The first cloud EPM S-1 gets filed. Incorrect.  Not yet, at least.  While it’s always possible someone did the private filing process with the SEC, I’m guessing that didn’t happen either.
  12. 2016 will be a great year for Host Analytics. Correct.  We had a strong finish to the year and emerged stronger than we started with over 600 great customers, great partners, and a great team.

Now, let’s move on to my predictions for 2017 which – as a sign of the times – will include more macro and political content than usual.

  1. The United States will see a level of divisiveness and social discord not seen since the 1960s. Social media echo chambers will reinforce divisions.  To combat this, I encourage everyone to sign up for two publications/blogs they agree with and two they don’t lest they never again hear both sides of an issue. (See map below, coutesy of Ninja Economics, for help in choosing.)  On an optimistic note, per UCSD professor Lane Kenworthy people aren’t getting more polarized, political parties are.

news

  1. Social media companies finally step up and do something about fake news. While per a former Facebook designer, “it turns out that bullshit is highly engaging,” these sites will need to do something to filter, rate, or classify fake news (let alone stopping to recommend it).  Otherwise they will both lose credibility and readership – as well as fail to act in a responsible way commensurate with their information dissemination power.
  1. Gut feel makes a comeback. After a decade of Google-inspired heavily data-driven and A/B-tested management, the new US administration will increasingly be less data-driven and more gut-feel-driven in making decisions.  Riding against both common sense and the big data / analytics / data science trends, people will be increasingly skeptical of purely data-driven decisions and anti-data people will publicize data-driven failures to popularize their arguments.  This “war on data” will build during the year, fueled by Trump, and some of it will spill over into business.  Morale in the Intelligence Community will plummet.
  1. Under a volatile leader, who seems to exhibit all nine of the symptoms of narcissistic personality disorder, we can expect sharp reactions and knee-jerk decisions that rattle markets, drive a high rate of staff turnover in the Executive branch, and fuel an ongoing war with the media.  Whether you like his policies or not, Trump will bring a high level of volatility the country, to business, and to the markets.
  1. With the new administration’s promises of $1T in infrastructure spending, you can expect interest rates to raise and inflation to accelerate. Providing such a stimulus to already strong economy might well overheat it.  One smart move could be buying a house to lock in historic low interest rates for the next 30 years.  (See my FAQ for disclaimers, including that I am not a financial advisor.)
  1. Huge emphasis on security and privacy. Election-related hacking, including the spearfishing attack on John Podesta’s email, will serve as a major wake-up call to both government and the private sector to get their security act together.  Leaks will fuel major concerns about privacy.  Two-factor authentication using verification codes (e.g., Google Authenticator) will continue to take off as will encrypted communications.  Fear of leaks will also change how people use email and other written electronic communications; more people will follow the sage advice in this quip:

Dance like no one’s watching; E-mail like it will be read in a deposition

  1. In 2015, if you were flirting on Ashley Madison you were more likely talking to a fembot than a person.  In 2016, the same could be said of troll bots.  Bots are now capable of passing the Turing Test.  In 2017, we will see more bots for both good uses (e.g., customer service) and bad (e.g., trolling social media).  Left unchecked by the social media powerhouses, bots could damage social media usage.
  1. Artificial intelligence hits the peak of inflated expectations. If you view Salesforce as the bellwether for hyped enterprise technology (e.g., cloud, social), then the next few years are going to be dominated by artificial intelligence.  I’ve always believed that advanced analytics is not a standalone category, but instead fodder that vendors will build into smart applications.  They key is typically not the technology, but the problem to which to apply it.  As Infer founder Vik Singh said of Jim Gray, “he was really good at finding great problems,” the key is figuring out the best problems to solve with a given technology or modeling engine.  Application by application we will see people searching for the best problems to solve using AI technology.
  1. The IPO market comes back. After a year in which we saw only 13 VC-backed technology IPOs, I believe the window will open and 2017 will be a strong year for technology IPOs.  The usual big-name suspects include firms like Snap, Uber, AirBnB, and SpotifyCB Insights has identified 369 companies as strong 2017 IPO prospects.
  1. Megavendors mix up EPM and ERP or BI. Workday, which has had a confused history when it comes to planning, acquired struggling big data analytics vendor Platfora in July 2016, and seems to have combined analytics and EPM/planning into a single unit.  This is a mistake for several reasons:  (1) EPM and BI are sold to different buyers with different value propositions, (2) EPM is an applications sale, BI is a platform sale, and (3) Platfora’s technology stack, while appropriate for big data applications is not ideal for EPM/planning (ask Tidemark).  Combining the two together puts planning at risk.  Oracle combined their EPM and ERP go-to-market organizations and lost focus on EPM as a result.  While they will argue that they now have more EPM feet on the street, those feet know much less about EPM, leaving them exposed to specialist vendors who maintain a focus on EPM.  ERP is sold to the backward-looking part of finance; EPM is sold to the forward-looking part.  EPM is about 1/10th the market size of ERP.  ERP and EPM have different buyers and use different technologies.  In combining them, expect EPM to lose out.

And, as usual, I must add the bonus prediction that 2017 proves to be a strong year for Host Analytics.  We are entering the year with positive momentum, the category is strong, cloud adoption in finance continues to increase, and the megavendors generally lack sufficient focus on the category.  We continue to be the most customer-focused vendor in EPM, our new Modeling product gained strong momentum in 2016, and our strategy has worked very well for both our company and the customers who have chosen to put their faith in us.

I thank our customers, our partners, and our team and wish everyone a great 2017.

# # #

 

A Fresh Look at How to Measure SaaS Churn Rates

[Editor’s note:  revised 3/27/17 with changes to some definitions.]

It’s been nearly three years since my original post on calculating SaaS renewal rates and I’ve learned a lot and seen a lot of new situations since then.  In this post, I’ll provide a from-scratch overhaul on how to calculate churn in an enterprise SaaS company [1].

While we are going to need to “get dirty” in the detail here, I continue to believe that too many people are too macro and too sloppy in calculating these metrics.  The details matter because these rates compound over time, so the difference between a 10% and 20% churn rate turns into a 100% difference in cohort value after 7 years [2].  Don’t be too busy to figure out how to calculate them properly.

The Leaky Bucket Full of ARR

I conceptualize SaaS companies as leaky buckets full of annual recurring revenue (ARR).  Every time period, the sales organization pours more ARR into the bucket and the customer success (CS) organization tries to prevent water from leaking out [3].

This drives the leaky bucket equation, which I believe should always be the first four lines of any SaaS company’s financial statements:

Starting ARR + new ARR – churn ARR = ending ARR

Here’s an example, where I start with those four lines, and added two extra (one to show a year over year growth rate and another to show “net new ARR” which offsets new vs. churn ARR):

leaky

For more on how to present summary SaaS startup financials, go here.

Half-Full or Half-Empty:  Renewals or Churn?

Since the renewal rate is simply one minus the churn rate, the question is which we should calculate?  In the past, I favored splitting the difference [4], whereas I now believe it’s simpler just to talk about churn.  While this may be the half-empty perspective, it’s more consistent with what most people talk about and is more directly applicable, because a common use of a churn rate is as a discount rate in a net present value (NPV) formula.

Thus, I now define the world in terms of churn and churn rates, as opposed to renewals and renewal rates.

Terminology: Shrinkage and Expansion

For simplicity, I define the following two terms:

  • Shrinkage = anything that makes ARR decrease. For example, if the customer dropped seats or was given a discount in return for signing a multi-year renewal [5].
  • Expansion = anything that makes ARR increase, such as price increases, seat additions, upselling from a bronze to a gold edition, or cross-selling new products.

Key Questions to Consider

The good news is that any churn rate calculation is going to be some numerator over some denominator.  We can then start thinking about each in more detail.

Here are the key questions to consider for the numerator:

  • What should we count? Number of accounts, annual recurring revenue (ARR), or something else like renewal bookings?
  • If we’re counting ARR should we think at the product-level or account-level?
  • To what extent should we offset shrinkage with expansion in calculating churn ARR? [6]
  • When should we count what? What about early and late renewals?  What about along-the-way expansion?  What about churn notices or non-payment?

Here are the key questions to consider for the denominator:

  • Should we use the entire ARR pool, that portion of the ARR pool that is available to renew (ATR) in any given time period, or something else?
  • If using the ATR pool, for any given renewing contract, should we use its original value or its current value (e.g., if there has been upsell along the way)?

What Should We Count?  Logos and ARR

I believe the two metrics we should count in churn rates are

  • Logos (i.e., number of customers). This provides a gross indication of customer satisfaction [7] unweighted by ARR, so you can answer the question:  what percent of our customer base is turning over?
  • This provides a very important indication on the value of our SaaS annuity.  What is happening to our ARR pool?

I would stay completely away from any SaaS metrics based on bookings (e.g., a bookings CAC, TCV, or bookings-based renewals rate).  These run counter to the point of SaaS unit economics.

Gross and Net Shrinkage; Account-Level Churn

Let’s look at a quick example to demonstrate how I now define gross and net shrinkage as well as account-level churn [8].

gross and net shrinkage

Gross shrinkage is the sum of all the shrinkage. In the example, 80 units.

Net shrinkage is the sum of the shrinkage minus the sum of the expansion. In the example, 80-70 = 10 units.

To calculate account-level churn, we proceed, account by account, and look at the change in contract value, separating upsell from the churn.  The idea is that while it’s OK to offset shrinkage with expansion within an account that we should not do so across accounts when working at the account level [9].  This has the effect of splitting expansion into offset (used to offset shrinkage within an account) and upsell (leftover expansion after all account-level shrinkage has been offset).  In the example, account-level churn is 30 units.

Make the important note here that how we calculate you churn – and specifically how we use expansion ARR to offset shrinkage—not only affects our churn rates, but our reported upsell rates as well.  Should we proudly claim 70 units of upsell (and less proudly 80 units of churn), 30 units of churn and 20 of upsell, or simply 10 units of churn?  I vote for the second.

While working at the account-level may seem odd, it is how most SaaS companies work operationally.  First, because they charter customer success managers (CSMs) to think at the account level, working account by account doing everything they can to preserve and/or increase the value of the account.  Second, because most systems work at and finance people think at the account level – e.g., “we had a customer worth 100 units last year, and they are worth 110 units this year so that means upsell of 10 units.  I don’t care how much is price increase vs. swapping some of product A for product B.” [11]

So, when a SaaS company reports “churn ARR,” in its leaky bucket analysis, I believe they should report neither gross churn nor net churn, but account-level churn ARR.

Timing Issues and the Available to Renew (ATR) Concept

Churn calculations bring some interesting challenges such as early/late renewals, churn notices, non-payment, and along-the-way expansion.

A renewals booking should always be taken in the period in which it is received.  If a contract expires on 6/30 and the renewal is received in on 6/15 it should show up in 2Q and if received on 7/15 it should up in 3Q.

For churn rate calculations, however, the customer success team needs to forecast what is going to happen for a late renewal.  For example, if we have a board meeting on 7/12 and a $150K ARR renewal due 6/30 has not yet been happened, we need to proceed based on what the customer has said.  If the customer is actively using the software, the CFO has promised a renewal but is tied up on a European vacation, I would mark the numbers “preliminary” and count the contract as renewed.  If, however, the customer has not used the software in months and will not return our phone calls, I would count the contract as churned.

Suppose we receive a churn notice on 5/1 for a contract that renews on 6/30.  When should we count the churn?  A Bessemer SaaS fanatic would point to their definition of committed monthly recurring revenue (CMRR) [12] and say we should remove the contact from the MRR base on 5/1.  While I agree with Bessemer’s views in general — and specifically on things like on preferring ARR/MRR to ACV and TCV — I get off the bus on the whole notion of “committed” ARR/MRR and the ensuing need to remove the contract on 5/1.  Why?

  • In point of fact the customer has licensed and paid for the service through 6/30.
  • The company will recognize revenue through 6/30 and it’s much easier to do so correctly when the ARR is still in the ARR base.
  • Operationally, it’s defeatist. I don’t want our company to give up and say “it’s over, take them out of the ARR base.” I want our reaction to be, “so they think they don’t want to renew – we’ve got 60 days to change their mind and keep them in.” [13]

We should use the churn notice (and, for that matter, every other communication with the customer) as a way of improving our quarterly churn forecast, but we should not count churn until the contract period has ended, the customer has not renewed, and the customer has maintained their intent not to renew in coming weeks.

Non-payment, while hopefully infrequent, is another tricky issue.  What do we do if a customer gives us a renewal order on 6/30, payable in 30 days, but hasn’t paid after 120?  While the idealist in me wants to match the churn ARR to the period in which the contract was available to renew, I would probably just show it as churn in the period in which we gave up hope on the receivable.

Expansion Along the Way (ATW)

Non-payment starts to introduce the idea of timing mismatches between ARR-changing events and renewals cohorts.  Let’s consider a hopefully more frequent case:  ARR expansion along the way (ATW).  Consider this example.

ATW expansion

To decide how to handle this, let’s think operationally, both about how our finance team works and, more importantly, about how we want our customer success managers (CSMs) to think.  Remember we want CSMs to each own a set of customers, we want them to not only protect the ARR of each customer but to expand it over time.  If we credit along-the-way upsell in our rate calculations at renewal time, we shooting ourselves in the foot.  Look at customer Charlie.  He started out with 100 units and bought 20 more in 4Q15, so as we approach renewal time, Charlie actually has 120 units available to renew (ATR), not 100 [14].  We want our CSMs basing their success on the 120, not the 100.  So the simple rule is to base everything not on the original cohort but on the available to renew (ATR) entering the period.

This begs two questions:

  • When do we count the along-the-way upsell bookings?
  • How can we reflect those 40 units in some sort of rate?

The answer to the first question is, as your finance team will invariably conclude, to count them as they happen (e.g., in 4Q15 in the above example).

The answer to the second question is to use a retention rate, not a churn rate.  Retention rates are cohort-based, so to calculate the net retention rate for the 2Q15 cohort, we divide its present value of 535 by its original value of 500 and get 107%.

Never, ever calculate a retention rate in reverse – i.e., starting a group of current customers and looking backwards at their ARR one year ago.  You will produce a survivor biased answer which, stunningly, I have seen some public companies publish.  Always run cohort analyses forwards to eliminate survivor bias.

Off-Cycle Activity

Finally, we need to consider how to address off-cycle (or extra-cohort) activity in calculating churn and related rates.  Let’s do this by using a big picture example that includes everything we’ve discussed thus far, plus off-cycle activity from two customers who are not in the 2Q16 ATR cohort:  (1) Foxtrot, who purchased in 3Q14, renewed in 3Q15, and who has not paid, and (2) George, who purchased in 3Q15, who is not yet up for renewal, but who purchased 50 units of upsell in 2Q16.

big picture

Foxtrot should count as churn in 2Q16, the period in which we either lost hope of collection (or our collections policy dictated that collection we needed to de-book the deal). [15]

George should count as expansion in 2Q16, the period in which the expansion booking was taken.

The trick is that neither Foxtrot nor George is on a 2Q renewal cycle, so neither is included in the 2Q16 ATR cohort.  I believe the correct way to handle this is:

  • Both should be factored into gross, net, account-level churn, and upsell.
  • For rates where we include them in the numerator, for consistency’s sake we must also include them in the denominator. That means putting the shrinkage in the numerator and adding the ATR of a shrinking (or lost) account in denominator of a rate calculation.  I’ll call this the “+” concept, and define ATR+ as inclusive of such additional logos or ARR resulting from off-cycle accounts [16].

Rate Calculations

We are now in the position to define and calculate the churn rates that I use and track:

  • Simple churn rate = net shrinkage / starting period ARR * 4.  Or, in English, the net change in ARR from existing customers divided by starting period ARR (multiplied by 4 to annualize the rate which is measured against the entire ARR base). As the name implies, this is the simplest churn rate to calculate. This rate will be negative whenever expansion is greater than shrinkage. Starting period ARR includes both ATR and non-ATR contracts (including potentially multi-year contracts) so this rate takes into account the positive effects of the non-cancellability of multi-year deals.  Because it takes literally everything into account, I think this is the best rate for valuing the annuity of your ARR base.
  • Logo churn rate = number of discontinuing logos / number of ATR+ logos. This rate tells us the percent of customers who, given the chance, chose to discontinue doing business with us.  As such, it provides an ARR-unweighted churn rate, providing the best sense of “how happy” our customers are, knowing that there is a somewhat loose correlation between happiness and renewal [16].  Remember that ATR+ means to include any discontinuing off-cycle logos, so the calculation is 1/16 = 6.3% in our example.
  • Retention rate = current ARR [time cohort] / time-ago ARR [time cohort]. In English, the current ARR from some time-based cohort (e.g., 2Q15) divided by the year-ago ARR from that same cohort.  Typically we do this for the one-year-ago or two-years-ago cohorts, but many companies track each quarter’s new customers as a cohort which they measure over time.  Like simple churn, this is a great macro metric that values the ARR annuity, all in.
  • Gross churn rate = gross shrinkage / ATR+. This churn rate is important because it reveals the difference between companies that have high shrinkage offset by high expansion and companies which simply have low shrinkage.  Gross churn is a great metric because it simply shows the glass half-empty view:  at what rate is ARR leaking out of your bucket before offset it with refills in the form of expansion ARR.
  • Account-level churn rate = account-level churn / ATR+. This churn rate foots to the reported churn ARR in our leaky bucket analysis (which uses account-level churn), partially offsets shrinkage with expansion at an account-level, and is how most SaaS companies actually calculate churn.  While perhaps counter-intuitive, it reflects a philosophy of examining, at an account basis, what happens to value of our each of our customers when we allow shrinkage to be offset by expansion (which is what we want our CSM reps doing) leaving any excess as upsell.  This should be our primary churn metric.
  • Net churn rate = net shrinkage / ATR+.  This churn rate offsets shrinkage with expansion not at the account level, but overall.  This is similar to the simple churn rate but with the disadvantage of looking only at ATR and not factoring in the positive effects of non-cancellability of multi-year deals.    Ergo, I prefer using the simple churn rate to the net churn rate in valuing the SaaS annuity.

# # #

Notes

[1] Replacing these posts in the process.

[2] The 10% churn group decays from 100 units to 53 in value after 7 years, while the 20% group decays to 26.

[3] We’ll sidestep the question of who is responsible for installed-based expansion in this post because companies answer it differently (e.g., sales, customer success, account management) and the good news is we don’t need to know who gets credited for expansion to calculate churn rates.

[4] Discussing churn in dollars and renewals in rates.

[5] For example, if a customer signed a one-year contract for 100 units and then was offered a 5% discount to sign a three-year renewal, you would generate 5 units of ARR churn.

[6] Or, as I said in a prior post, should I net first or sum first?

[7] And yes, sometimes unhappy customers do renew (e.g., if they’ve been too busy to replace you) and happy customers don’t (e.g., if they get a new key executive with different preferences) but counting logos still gives you a nice overall indication.

[8] Note that I have capitulated to the norm of saying “gross” churn means before offset and thus “net” churn means after netting out shrinkage and expansion.  (Beware confusion as this is the opposite of my prior position where I defined “net” to mean “net of expansion,” i.e., what I’d now call “gross.”)

[9] Otherwise, you can just look at net shrinkage which offsets all shrinkage by all expansion.  The idea of account-level churn is to restrict the ability to offset shrinkage with expansion across accounts, in effect, telling your customer success reps that their job is to, contract by contract, minimize shrinkage and ensure expansion.

[10] “Offset” meaning ARR used to offset shrinkage that ends up neither churn nor upsell.

[11] While this approach works fine for most (inherently single-product) SaaS startups it does not work as well for large multi-product SaaS vendors where the failure of product A might be totally or partially masked by the success of product B.  (In our example, I deliberately had all the shrinkage coming from downsell of product A to make that point.  The product or general manager for product A should own the churn number that product and be trying to find out why it churned 80 units.)

[12] MRR = monthly recurring revenue = 1/12th of ARR.  Because enterprise SaaS companies typically run on an annual business rhythm, I prefer ARR to MRR.

[13] Worse yet, if I churn them out on 5/1 and do succeed in changing their mind, I might need to recognize it as “new ARR” on 6/30, which would also be wrong.

[14] The more popular way of handling this would have been to try and extend the original contract and co-terminate with the upsell in 4Q16, but that doesn’t affect the underlying logic, so let’s just pretend we tried that and it didn’t work for the customer.

[15] Whether you call it a de-booking or bad receivable, Foxtrot was in the ARR base and needs to come out.  Unlike the case where the customer has paid for the period but is not using the software (where we should churn it at the end of the contract), in this case the 3Q15 renewal was effectively invalid and we need to remove Foxtrot from the ARR base at some defined number of days past due (e.g., 90) or when we lose hope of collection (e.g., bankruptcy).

[16] I think the smaller you are the more important this correction is to ensure the quality of your numbers.  As a company gets bigger, I’d just drop the “+” concept whenever it’s only changing things by a rounding error.

[17] Use NPS surveys for another, more precise, way of measuring happiness.  See [7] as well.