Category Archives: IPO

Bookings vs. Billings in a SaaS Company

Financial analysts speak a lot about “billings” in a public SaaS companies, but in private VC-backed SaaS companies, you rarely hear discussion of this metric.  In this post, we’ll use a model of a private SaaS company (where we know all the internal metrics), to show how financial analysts use rules of thumb to estimate and/or impute internal SaaS metrics using external ones – and to see what can go wrong in that process.

For reference, here’s an example of sell-side financial analyst research on a public SaaS company that talks about billings [1].

saas1-zen

Let’s start with a quick model that builds up a SaaS company from scratch [1].  To simplify the model we assume all deals (both new and renewal) are for one year only and are booked on the last day of the quarter (so zero revenue is ever recognized in-quarter from a deal).  This also means next-quarter’s revenue is this-quarter’s ending annual recurring revenue (ARR) divided by 4.

saas13

Available to renew (ATR) is total subscription bookings (new and renewal) from four quarters prior.  Renew bookings are ATR * (1 – churn rate).  The trickiest part of this model is the deferred revenue (DR) waterfall where we need to remember that the total deferred revenue balance is the sum of DR leftover from the current and the prior three quarters.

If you’re not convinced the model is working and/or want to play with it, you can download it, then see how things work by setting some drivers to boundary conditions (e.g., churn to 0%, QoQ sales growth to 0, or setting starting ARR to some fixed number [2]).

 The Fun Part:  Imputing Internal Metrics from External Ones

Now that we know what’s going on the inside, let’s look in from the outside [3]:

  • All public SaaS companies release subscription revenues [4]
  • All public SaaS companies release deferred revenues (i.e., on the balance sheet)
  • Few SaaS companies directly release ARR
  • Few SaaS companies release ATR churn rates, favoring cohort retention rates where upsell both masks and typically exceeds churn [5]

It wasn’t that long ago when a key reason for moving towards the SaaS business model was that SaaS smoothed revenues relative to the all-up-front, lumpy on-premises model.  If we could smooth out some of that volatility then we could present better software companies to Wall Street.  So the industry did [6], and the result?  Wall Street immediately sought a way to look through the smoothing and see what’s really going on in the inherently lumpy, backloaded world of enterprise software sales.

Enter billings, the best answer they could find to do this.  Billings is defined as revenue plus change in deferred revenue for a period.  Conceptually, when a SaaS order with a one-year prepayment term is signed, 100% of it goes to deferred revenue and is burned down 1/12th every month after that.  To make it simple, imagine a SaaS company sells nothing in a quarter:  revenue will burn down by 1/4th of starting deferred revenue [7] and the change in deferred revenue will equal revenue – thus revenue plus change in deferred revenue equals zero.  Now imagine the company took an order for $50K on the last day of the quarter.  Revenue from that order will be $0, change in deferred will be +$50K, implying new sales of $50K [8].

Eureka!  We can see inside the SaaS machine.  But we can’t.

Limitations of Billings as a SaaS Metric

If you want to know what investors really care about when it comes to SaaS metrics, ask the VC and PE folks who get to see everything and don’t have to impute outside-in.  They care about

Of those, public company investors only get a clear look at subscription gross margins and the customer acquisition cost (CAC) ratio.  So, looking outside-in, you can figure out how efficiency a company runs its SaaS service and how efficiently it acquires customers [9].

But you typically can’t get a handle on churn, so you can’t calculate LTV/CAC or CAC Payback Period.  Published cohort growth, however, can give you comfort around potential churn issues.

But you can’t get a precise handle on sales growth – and that’s a huge issue as sales growth is the number one driver of SaaS company valuation [10].  That’s where billings comes into play.  Billings isn’t perfect because it shows what I call “total subscription bookings” (new ARR bookings plus renewal bookings) [11], so we can’t tell the difference between a good sales and weak renewals quarter and a bad sales and a good renewals quarter.

Moreover, billings has two other key weaknesses as a metric:

  • Billings is dependent on prepaid contract duration
  • Companies can defer processing orders (e.g., during crunch time at quarter’s end, particularly if they are already at plan) thus making them invisible even from a billings perspective [12]

Let’s examine how billings depends on contract duration.  Imagine it’s the last day of new SaaS company’s first quarter.  The customer offers to pay the company:

  • 100 units for a prepaid one-year subscription
  • 200 units for a prepaid two-year subscription
  • 300 units for a prepaid three-year subscription

From an ARR perspective, each of the three possible structures represents 100 units of ARR [13].  However, from a deferred revenue (DR) perspective, they look like 100, 200, 300 units, respectively.  Worse yet, looking solely at DR at the end of the quarter, you can’t know if 300 units represents three 100-unit one-year prepay customers or a single 100-unit ARR customer who’s done a three-year prepay.

In fact, when I was at Salesforce we had the opposite thing happen.  Small and medium businesses were having a tough time in 2012 and many customers who’d historically renewed on one-year payment cycles started asking for bi-annual payments.  Lacking enough controls around a rarely-used payment option, a small wave of customers asked for and got these terms.  They were happy customers.  They were renewing their contracts, but from a deferred revenue perspective, suddenly someone who looked like 100 units of DR for an end-of-quarter renewal suddenly looked 50.  When Wall St. saw the resultant less-than-expected DR, they assumed it meant slower new sales.  In fact, it meant easier payment terms on renewals – a misread on the business situation made possible by the limitations of the metric.

This issue only gets more complex when a company is enabling some varying mix of one through five year deals combined with partial up-front payments (e.g., a five-year contract with years 1-3 paid up front, but years 4 and 5 paid annually).  This starts to make it really hard to know what’s in deferred revenue and to try and use billings as a metric.

Let’s close with an excerpt from the Zuora S-1 on billings that echoes many of the points I’ve made above.

saas3

Notes

[1] Source:  William Blair, Inc., Zendesk Strong Start to 2018 by Bhavan Suri.

[2] Even though it’s not labelled as a driver and will break the renewals calculations, implicitly assuming all of it renews one year later (and is not spread over quarters in anyway).

[3] I’m not a financial analyst so I’m not the best person to declare which metrics are most typically released by public companies, so my data is somewhat anecdotal.  Since I do try to read interesting S-1s as they go by, I’m probably biased towards companies that have recently filed to go public.

[4] As distinct from services revenues.

[5] Sometimes, however, those rates are survivor biased.

[6] And it worked to the extent that from a valuation perspective, a dollar of SaaS revenue is equivalent to $2 to $4 of on-premises revenue.  Because it’s less volatile, SaaS revenue is more valuable than on-premises revenue.

[7] Provided no customers expire before the last day of the quarter

[8] Now imagine that order happens on some day other than the last day of the quarter.  Some piece, X, will be taken as revenue during the quarter and 50 – X will show up in deferred revenue.  So revenue plus change in deferred revenue = it’s baseline + X + 50 – X = baseline + 50.

[9] Though not with the same clarity VCs can see it — VCs can see composition of new ARR (upsell vs. new business) and sales customers (new customer acquisition vs. customer success) and thus can create more precise metrics.  For example, a company that has a solid overall CAC ratio may be revealed to have expensive new business acquisition costs offset by high, low-cost upsell.

[10] You can see subscription revenue growth, but that is smoothed/damped, and we want a faster way to get the equivalent of New ARR growth – what has sales done for us lately?

[11] It is useful from a cash forecasting perspective because all those subscription billings should be collectible within 30-60 days.

[12] Moving the deferred revenue impact of one or more orders from Q(n) to Q(n+1) in what we might have called “backlogging” back in the day.  While revenue is unaffected in the SaaS case, the DR picture looks different as a backlogged order won’t appear in DR until the end of Q(n+1) and then at 75, not 100, units.

[13] Normally, in real life, they would ask a small discount in return for the prepay, e.g., offer 190 for two years or 270 for three years.  I’ll ignore that for now to keep it simple.

Kellblog Predictions for 2018

In continuing my tradition of offering predictions every year, let’s start with a review of my hits and misses on my 2017 predictions.

  1. The United States will see a level of divisiveness and social discord not seen since the 1960s.  HIT.
  2. Social media companies finally step up and do something about fake news. MISS, but ethical issues are starting to catch up with them.
  3. Gut feel makes a comeback. HIT, while I didn’t articulate it as such, I see this as the war on facts and expertise (e.g., it’s cold today ergo global warming isn’t real despite what “experts” say).
  4. Under a volatile leader, 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.  HIT.
  5. With the new administration’s promises of $1T in infrastructure spending, you can expect interest rates to raise and inflation to accelerate. MISS, turns out this program was never classical government investment in infrastructure, but a massive privatization plan that never happened.
  6. Huge emphasis on security and privacy. PARTIAL HIT, security remained a hot topic and despite numerous major breaches it’s still not really hit center stage.
  7. In 2017, we will see more bots for both good uses (e.g., customer service) and bad (e.g., trolling social media).  HIT.
  8. Artificial intelligence hits the peak of inflated expectations. HIT.
  9. The IPO market comes back. MISS, though according to some it “sucked less.”
  10. Megavendors mix up EPM and ERP or BI. PARTIAL HIT.  This prediction was really about Workday and was correct to the extent that they’ve seemingly not made much progress in EPM.

Kellblog’s Predictions for 2018

1.  We will again continue to see a level of divisiveness and social discord not seen since the 1960s. We have evolved from a state of having different opinions about policies based on common facts to a dangerous state based on different facts, even on easily disprovable claims, e.g., the White House nativity scene.  The media is advancing, not reducing, this divide.

2.  The war on facts and expertise will continue to escalate. Read The Death of Expertise for more.   This will extend to a war on college. While an attempted opening salvo on graduate student tuition waivers didn’t fire, in an environment where the President’s son says, “we’ll take $200,000 of your money; in exchange we’ll train your children to hate our country,” you can expect ongoing attacks on post-secondary education.  This spells trouble for Silicon Valley, where a large number of founders and entrepreneurs are former grad students as well as immigrants (which is a whole different area of potential trouble).

3.  Leading technology and social media companies finally step up to face ethical challenges. This means paying more attention to their own culture (e.g., sexual harassment, brogrammers).  This means taking responsibility for policing trolls, spreading fake news, building addictive content, and enabling foreign intelligence operations.  Thus far, they have tended to argue they are simply keepers of the town square, and not responsible for the content shared there.  This abdication of responsibility should start to stop in 2018, if only because people start to tune-out the services.  This leads to one of my favorite tweets of the year:

Capture

4.  AI will move from hype to action, meaning bigger budgets, more projects, and some high visibility failures. It will also mean more emphasis on voice and more conversational chatbots.  For finance departments, this means more of what Ventana’s Rob Kugel calls the age of robotic finance, which unites AI and machine learning, robotic process automation (RPA), natural language bots, and blockchain-based distributed ledgers.

5. AI will continue to generate lots of controversy about job displacement. While some remain optimistic, the consensus viewpoint seems to be that AI will suppress employment, most likely widening the wealth inequality gap.  A collapsing educational system combined with AI-driven pressure on low-skilled work seems a recipe for trouble.

6.  The bitcoin bubble bursts. As a reminder, at one point during the peak of tulip mania, the Dutch East India Company was worth more, on an inflated-adjusted basis, than twenty of today’s technology giants combined.

tulips

7.  The Internet of Things (IoT) will continue to build momentum.  IoT won’t hit in a massive horizontal way, instead B2B adoption will be lead by certain verticals such as healthcare, retail, and supply chain.

8.  The freelance / gig economy continues to gain momentum with freelance workers poised to pass traditional employees by 2027. While the gig economy brings advantages to high-skilled knowledge workers (e.g., freedom of location, freedom of work projects), this same trend threatens low-skilled workers via the continual decomposition of full-time jobs in a series of temp shifts.  This means someone working 60 hours a week across three 20-hour shifts wouldn’t be considered to be a full-time employee and thus not eligible for full-time benefits, further increasing wealth inequality.

freelancers

9.  M&A heats up due to repatriation of overseas cash. Apple alone, for example, has $252B in overseas cash.  With the new tax rate dropping from 35% to 15.5%, it will now be ~$50B less expensive for Apple to repatriate that cash.  Overall, US companies hold trillions of dollars overseas and making it cheaper for them to repatriate that cash suggests that they will be flush with dollars to invest in many areas, including M&A

10.  2018 will be a good year for cloud EPM vendors. The dynamic macro environment, the opportunities posed by cash repatriation, and the strong fundamentals in the economy will increase demand for EPM software that helps companies explore how to best exploit the right set of opportunities facing them.  Oracle will fail in pushing PBCS into the NetSuite base, creating a nice third-party opportunity.  SAP, Microsoft, and IBM will continue to put resources into other strategic investment areas (e.g., IBM and Watson, SAP and Hana) leaving fallow the EPM market adjacent to ERP.  And the greenfield opportunity to replace Excel for financial planning, budgeting, and even consolidations will continue drive strong growth.

Let me wish everyone, particularly the customers, partners, and employees of Host Analytics, a Happy New Year in 2018.

# # #

Disclaimer:  these predictions are offered in the spirit of fun.  See my FAQ for more on this and other usage terms.

A Look at the Tintri S-1

Every now and then I take a dive into an S-1 to see what clears the current, ever-changing bar for going public.  After a somewhat rocky IPO process, Tintri went public June 30 after cutting the IPO offering price and has traded flat thus far since then.

Let’s read an excerpt from this Business Insider story before taking a look at the numbers.

Before going public, Tintri had raised $260 million from venture investors and was valued at $800 million.

With the performance of this IPO, the company is now valued at about about $231 million, based on $7.50 a share and its roughly 31 million outstanding shares, (if the IPO’s bankers don’t buy their optional, additional roughly 1.3 million shares.)

In other words, this IPO killed a good $570 million of the company’s value.

In other words, Tintri looks like a “down-round IPO” (or an “IPO of last resort“) — something that frankly almost never happened before the recent mid/late stage private valuation bubble of the past 4 years.

Let’s look at some numbers.

tintri p+l

Of note:

  • $125M in FY2017 revenue.  (They have scale, but this is not a SaaS company so the revenue is mostly non-recurring, making it easier to get to grow quickly and making the revenue is worth less because only the support/maintenance component of it renews each year.)
  • 45% YoY total revenue growth.  (On the low side, especially given that they have a traditional license/maintenance model and recognize revenue on shipment.)
  • 65% gross margins  (Low, but they do seem to sell flash memory hardware as part of their storage solutions.)
  • 87% of revenue spent on S&M (High, again particularly for a non-SaaS company.)
  • 43% of revenue spent on R&D  (High, but usually seen as a good thing if you view the R&D money as well spent.)
  • -81% operating margins (Low, particularly for a non-SaaS company.)
  • -$70.4M in cashflow from operating activities in 2017 ($17M average quarterly cash burn from operations)
  • Incremental S&M / incremental product revenue = 73%, so they’re buying $1 worth of incremental (YoY) revenue for an incremental 73 cents in S&M.  Expensive but better than some.

Overall, my impression is of an on-premises (and to a lesser extent, hardware) company in SaaS clothing — i.e., Tintri’s metrics look like a SaaS company, but they aren’t so they should look better.  SaaS company metrics typically look worse than traditional software companies for two reasons:  (1) revenue growth is depressed by the need to amortize revenue over the course of the subscription and (2) subscriptions companies are willing to spend more on S&M to acquire a customer because of the recurring nature of a subscription.

Concretely, if you compare two 100-unit customers, the SaaS customer is worth twice the license/maintenance customer over 5 years.

saas compare

Moreover, even if Tintri were a SaaS company, it is quite out of compliance with the Rule of 40, that says growth rate + operating margin >= 40%.  In Tintri’s case, we get -35%, 45% growth plus -81% operating margin, so they’re 75 points off the rule.

Other Notes

  • 1250+ customers
  • 21 of the Fortune 100
  • 527 employees as of 1/31/17
  • CEO 2017 cash compensation $525K
  • CFO 2017 cash compensation $330K
  • Issued special retention stock grants in May 2017 that vest in the two years following an IPO
  • Did option repricing in May 2017 to $2.28/share down from weighted average exercise price of $4.05.
  • $260M in capital raised prior to IPO
  • Loans to CFO and CEO to exercise stock options at 1.6% to 1.9% interest in 2013
  • NEA 22.7% ownership prior to opening
  • Lightspeed 14.5% ownership
  • Insight Venture Partners 20.2% ownership
  • Silver Lake 20.4% ownership
  • CEO 3.8% ownership
  • CFO 0.7% ownership
  • $48.9M in long-term debt
  • $13.8M in 2017 stock-based compensation expense

Overall, and see my disclaimers, but this is one that I’ll be passing on.

 

Why has Standalone Cloud BI been such a Tough Slog?

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

Startups were launched, too.  Specifically, I remember:

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

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

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

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

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

So, what the hell happened?

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

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

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

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

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

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

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

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

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

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

The Evolution of Marketing Thanks to SaaS

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

Here’s what it looked like:

saas-strategic-value

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

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

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

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

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

hubspot

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

Kellblog’s 2017 Predictions  

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

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

2016 Predictions Review

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

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

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

news

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

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

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

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

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

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Thoughts on the Coupa S-1

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

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

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

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

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

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

Highlights from the first pages of the S-1:

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

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

coupa1

Income statement comments:

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

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

Back to highlights from the S-1 body copy:

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

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

coupa2

More highlights

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

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

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

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Be sure to read my disclaimers.