Category Archives: VC

Congratulations to Nuxeo on its Acquisition by Hyland

It feels like the just the other day when I met a passionate French entrepreneur in the bar on the 15th floor of the Hilton Times Square to discuss Nuxeo.  I remember being interested in the space, which I then viewed as next-generation content management (which, by the way, seemed extraordinarily in need of a next generation) and today what we’d call a content services platform (CSP) — in Nuxeo’s case, with a strong digital asset management angle.

I remember being impressed with the guy, Eric Barroca, as well.  If I could check my notebook from that evening, I’m sure I’d see written:  “smart, goes fast, no BS.”  Eric remains one of the few people who — when he interrupts me saying “got it” — that I’m quite sure that he does.

To me, Nuxeo is a tale of technology leadership combined with market focus, teamwork, and leadership.  All to produce a great result.

Congrats to Eric, the entire team, and the key folks I worked with most closely during my tenure on the board:  CMO/CPO Chris McGlaughlin, CFO James Colquhoun, and CTO Thierry Delprat.

Thanks to the board for having me, including Christian Resch and Nishi Somaiya from Goldman Sachs, Michael Elias from Kennet, and Steve King.  It’s been a true pleasure working with you.

My Two Appearances on the SaaShimi Podcast: Comprehensive SaaS Metrics Overview and Differences between PE and VC

The SaaShimi podcast just dropped the first two episodes of its second season and I’m back speaking with PNC Technology Finance banker Aznaur Midov, this time discussing some of the key difference between private equity (PE) and venture capital (VC) when it comes to philosophy, business model, portfolio company engagement, diligence,  and exit processes.  You can check out the entire podcast on the web here or this episode on Spotify or Apple podcasts.

I’ve also embedded it below:

Dave Kellogg on SaaShimi Discussing Differences between Private Equity and Venture Capital.

 

If you missed it and/or you’re otherwise interested, on my prior appearance we did a pretty darn comprehensive overview of SaaS metrics, available here on Apple podcasts and here on Spotify.

I’ve embedded this episode as well, below:

Dave Kellogg on SaaShimi with a Comprehensive Overview of SaaS Metrics.

 

Thanks Aznaur for having me.  I think he’s created a high quality, focused series on SaaS.

Are We Due for a SaaSacre?

I was playing around on the enterprise comps [1] section of Meritech‘s website today and a few of the charts I found caught my attention.  Here’s the first one, which shows the progression of the EV/NTM revenue multiple [2] for a set of 50+ high-growth SaaS companies over the past 15 or so years [3].

meritech saas multiples

While the green line (equity-value-weighted [4]) is the most dramatic, the one I gravitate to is the blue line:  the median EV/NTM revenue multiple.  Looking at the blue line, you can see that while it’s pretty volatile, eyeballing it, I’d say it normally runs in the range between 5x and 10x.  Sometimes (e.g., 2008) it can get well below 5x.  Sometimes (e.g., in 2013) it can get well above 10x.  As of the last data point in this series (7/14/20) it stood at 13.8x, down from an all-time high of 14.9x.  Only in 2013 did it get close to these levels.

If you believe in regression to the mean [5], that means you believe the multiples are due to drop back to the 5-10 range over time.  Since mean reversion can come with over-correction (e.g., 2008, 2015) it’s not outrageous to think that multiples could drop towards the middle or bottom of that range, i.e., closer to 5 than 10 [6].

Ceteris paribus, that means the potential for a 33% to 66% downside in these stocks. It also suggests that — barring structural change [7] that moves baseline multiples to a different level — the primary source of potential upside in these stocks is not continued multiple expansion, but positive NTM revenue surprises [8].

I always love Rule of 40 charts, so the next fun chart that caught my eye was this one.  meritech r40 score While this chart doesn’t speak to valuations over time, it does speak to the relationship between a company’s Rule of 40 Score and its EV/NTM revenue multiple.  Higher valuations primarily just shift the Y axis, as they have done here, uplifting the maximum Y-value by nearly three times since I last blogged about such a chart [9].  The explanatory power of the Rule of 40 in explaining valuation multiple is down since I last looked, by about half from an R-squared of 0.58 to 0.29.  Implied ARR growth alone has a higher explanatory power (0.39) than the Rule of 40.

To me, this all suggests that in these frothy times, the balance of growth and profit (which is what Rule of 40 measures) matters less than other factors, such as growth, leadership, scarcity value and hype, among others.

Finally, to come back to valuation multiples, let’s look at a metric that’s new to me, growth-adjusted EV/R multiples.

meritech r40 growth adjusted

I’ve seen growth-adjusted price/earnings ratios (i.e., PEG ratios) before, but I’ve not seen someone do the same thing with EV/R multiples.  The basic idea is to normalize for growth in looking at a multiple, such as P/E or — why not — EV/R.  For example, Coupa, trading at (a lofty) 40.8x EV/R is growing at 21%, so divide 40.8 by 21 to get 1.98x.  Zoom, by comparison looks to be similarly expensive at 38.3x EV/R but is growing at 139%, so divide 38.3 by 139 to get 0.28x, making Zoom a relative bargain when examined in this light [10].

This is a cool metric.  I like financial metrics that normalize things [11].  I’m surprised I’ve not seen someone do it to EV/R ratios before.  Here’s an interesting observation I just made using it:

  • To the extent a “cheap” PE firm might pay 4x revenues for a company growing 20%, they are buying in at a 0.2 growth-adjusted EV/R ratio.
  • To the extent a “crazy” VC firm might pay 15x revenues for a company growing at 75%, they are buying in at a 0.2 growth-adjusted EV/R ratio.
  • The observant reader may notice they are both paying the same ratio for growth-adjusted EV/R. Given this, perhaps the real difference isn’t that one is cheap and the other free-spending, but that they pay the same for growth while taking on very different risk profiles.

The other thing the observant reader will notice is that in both those pseudo-random yet nevertheless realistic examples, the professionals were paying 0.2.  The public market median today is 0.7.

See here for the original charts and data on the Meritech site.

Disclaimer:  I am not a financial analyst and do not make buy/sell recommendations.  I own positions in a wide range of public and private technology companies.  See complete disclaimers in my FAQ.

# # #

Notes 
[1] Comps = comparables.

[2] EV/NTM Revenue = enterprise value / next twelve months revenue, a so-called “forward” multiple.

[3] Per the footer, since Salesforce’s June, 2004 IPO.

[4] As are most stock indexes. See here for more.

[5] And not everybody does.  People often believe “this time it’s different” based on irrational folly, but sometimes this time really is different (e.g., structural change).  For example, software multiples have structurally increased over the past 20 years because the underlying business model changed from one-shot to recurring, ergo increasing the value of the revenue.

[6] And that’s not to mention external risk factors such as pandemic or election uncertainty.  Presumably these are already priced into the market in some way, but changes to how they are priced in could result in swings either direction.

[7] You might argue a scarcity premium for such leaders constitutes a form of structural change. I’m sure there are other arguments as well.

[8] To the extent a stock price is determined by some metric * some multiple, the price goes up either due to increasing the multiple (aka, multiple expansion) or increasing the metric (or both).

[9] While not a scientific way to look at this, the last time I blogged on a Rule of 40 chart, the Y axis topped out at 18x, with the highest data point at nearly 16x.  Here the Y axis tops out at 60x, with the highest data point just above 50x.

[10] In English, to the extent you’re paying for EV/R multiple in order to buy growth, Zoom buys you 7x more growth per EV/R point than Coupa.

[11] As an operator, I don’t like compound operational metrics because you need to un-tangle them to figure out what to fix (e.g., is a broken LTV/CAC due to LTV or CAC?), but as investor I like compound metrics as much as the next person.

 

The Pipeline Chicken or Egg Problem

The other day I heard a startup executive say, “we will start to accelerate sales hiring — hiring reps beyond the current staffing levels and the current plan — once we start to see the pipeline to support it.”

What comes first: the pipeline or the egg?  Or, to unmix metaphors, what comes first:  the pipeline or the reps to prosecute it?  Unlike the chicken or the egg problem, I think this one has a clear answer: the reps.

My answer comes part from experience and part from math.

First, the experience part:  long ago I noticed that the number of opportunities in the pipeline of a software company tends to be a linear function of the number of reps, with a slope in the 12-18 range as a function of business model [1].  That is, in my 12 years of being a startup CEO, my all-quarters, scrubbed [2] pipeline usually had somewhere between 12 and 18 opportunities per rep and the primary way it went up was not by doing more marketing, but by hiring more reps.

Put differently, I see pipeline as a lagging indicator driven by your capacity and not a leading indicator driven by opportunity creation in your marketing funnel.

Why?  Because of the human factor:  whether they realize it or not, reps and their managers tend to apply a floating bar on opportunity acceptance that keeps them operating around their opportunity-handling capacity.  Why’s that?  It’s partially due to the self-fulfilling 3x pipeline prophecy:  if you’re not carrying enough pipeline, someone’s going to yell at you until you do, which will tend to drop your bar on opportunity acceptance.  On the flip side, if you’re carrying more opportunities than your capacity — and anyone is paying attention — your manager might take opportunities away from you, or worse yet hire another rep and split your territory.  These factors tends to raise the bar, so reps cherry pick the best opportunities and reject lesser ones that they’d might otherwise accept in a tougher environment.

So unless you’re running a real machine with air-tight definitions and little/no discretion (which I wouldn’t advise), the number of opportunities in your pipeline is going to be some constant times the number of reps.

Second, the math part.  If you’re running a reasonably tight ship, you have a financial model and an inverted funnel model that goes along with it.  You’re using historical costs and conversion rates along with future ARR targets to say, roughly, “if we need $4.0M in New ARR in 3 quarters, and we insert a bunch of math, then we’re going to need to generate 400 SALs this quarter and $X of marketing budget to do it.”  So unless there’s some discontinuity in your business, your pipeline generation doesn’t reflect market demand; it reflects your financial and demandgen funnel models.

To paraphrase Chester Karrass, you don’t get the pipeline you deserve, you get the one you plan for.  Sure, if your execution is bad you might fall significantly short on achieving your pipeline generation goal.  But it’s quite rare to come in way over it.

So what should be your trigger for hiring more reps?  That’s probably the subject of another post, but I’d look first externally at market share (are you gaining or losing, and how fast) and then internally at the CAC ratio.

CAC is the ultimate measure of your sales & marketing efficiency and looking at it should eliminate the need to look more deeply at quota attainment percentages, close rates, opportunity cost generation, etc.  If one or more of those things are badly out of whack, it will show up in your CAC.

So I’d say my quick rule is if your CAC is normal (1.5 or less in enterprise), your churn is normal (<10% gross), and your net dollar expansion rate is good enough (105%+), then you should probably hire more reps.  But we’ll dive more into that in another post.

# # #

Notes

[1]  It’s a broad range, but it gets tighter when you break it down by business model.  In my experience, roughly speaking in:

  • Classic enterprise on-premises ($350K ASP with elephants over $1M), it runs closer to 8-10
  • Medium ARR SaaS ($75K ASP), it runs from 12-15
  • Corporate ARR SaaS ($25K ASP) where it ran 16-20

[2] The scrubbed part is super important.  I’ve seen companies with 100x pipeline coverage and 1% conversation rates. That just means a total lack of pipeline discipline and ergo meaningless metrics.  You should have written definitions of how to manage pipeline and enforce them through periodic scrubs.  Otherwise you’re building analytic castles in the sand.

Ten Pearls Of Enterprise Software Startup Wisdom From My Friend Mark Tice

I was talking with my old friend, Mark Tice, the other day and he referred to a startup mistake as, “on his top ten list.”  Ever the blogger, I replied, “what are the other nine?”

Mark’s been a startup CEO twice, selling two companies in strategic acquisitions, and he’s run worldwide sales and channels a few times.  I first met Mark at BusinessObjects, where he ran our alliances, we worked together for a while at MarkLogic, and we’ve stayed in touch ever since.  Mark’s a seasoned startup executive, he’s go-to-market oriented, and he has some large-company chops that he developed earlier in his career.

Here’s an edited version of Mark’s top ten enterprise software startup mistakes list, along with a few comments prefaced by DK.

1. Thinking that your first VP of Sales will take you from $0 to $100M.  Startups should hire the right person for the next 18-24 months; anything beyond that is a bonus.  (DK:  Boards will often push you to hire someone “bigger” and that’s often a mistake.) 

2. Expecting the sales leader to figure out positioning and pricing.  They should  have input, but startups should hire a VP of Marketing with strong product marketing skills at the same time as the first VP of Sales. (DK:  I think the highest-risk job in Silicon Valley is first VP of Sales at a startup and this is one reason why.)

3. Hiring the wrong VP Sales due to incomplete vetting and then giving them too much runway to perform.  Candidates should give a presentation to your team and run through their pipeline with little to no preparation (and you should see if they pay attention to stage, last step, next step, keys to winning).  You should leverage backdoor references.  Finally, you should hire fast and fire faster — i.e., you’ll know after 3 months; don’t wait for more proof or think that time is going to make things better.  (DK:  a lot of CEOs and boards wait too long in denial on a bad VP of Sales hire.  Yes, starting over is difficult to ponder, but the only thing worse is the damage the wrong person does in the meantime.)

4. Marketing and selling a platform as a vertical application.  Having a platform is good to the extent it means there is a potentially large TAM, but marketing and selling it as an application is bad because the product is not complete enough to deliver on the value proposition of an application.  Align the product, its positioning, and its sales team — because the rep who can sell an analytic platform is very different from the rep who can sell a solution to streamline clinical trials.  (DK:  I think this happens when a company is founded around the idea of a platform, but it doesn’t get traction so they then fall back into a vertical strategy without deeply embracing the vertical.  That embrace needs to be deeper than just go-to-market; it has to include product in some way.)

5. Ignoring churn greater than 15%.  If your churn is greater than 15%, you have a problem with product, market, or most likely both. Don’t ignore it — fix it ASAP at all costs.  It’s easy to say it will get better with the next release, but it will probably just get a bit less bad.  It will be harder to fix than you think. (DK:  if your SaaS bucket is too leaky, you can’t build value.  Finding the root cause problem here is key and you’ll need a lot of intellectual honesty to do so.)

6. Waiting too long to create Customer Success and give it renewals.  After you have five customers, you need to implement Customer Success for renewals and upsells so Sales can focus on new logos. Make it work. (DK:  Truer words have never been spoken; so many startups avoid doing this.  While the upsell model can be a little tricky, one thing is crystal clear:  Customer Success needs to focus on renewals so sales can focus on new ARR.)

7. Pricing that doesn’t match the sales channel.  Subscriptions under $50K should only be sold direct if it’s a pilot leading to a much larger deployment.  Customers should become profitable during year two of their subscription. Having a bunch of customers paying $10K/year (or less) might make you feel good, but you’ll get crushed if you have a direct sales team acquiring them. (DK:  Yes, you need to match price point to distribution channel. That means your actual street price, not the price you’re hoping one day to get.)

8. Believing that share ownership automatically aligns interests.  You and your investors both own material stakes in your company.  But that doesn’t automatically align your interests.  All other things being equal, your investors want your company to succeed, but they also have other interests, like their own careers and driving a return for their investors.  Moreover, wanting you to succeed and being able to offer truly helpful advice are two different things.  Most dangerous are the investors who are very smart, very opinionated, and very convincing, but who lack operating experience.  Thinking that all of their advice is good is a bit like believing that a person who reads a lot will be a good author — they’ll be able to tell you if your go-to-market plan is good, but they won’t write it for you. (DK:  See my posts on interest mis-alignments in Silicon Valley startups and taking advice from successful people.)

9. Making decisions to please your investors/board rather than doing what’s best for your company. This is like believing that lying to your spouse is good for your marriage. It leads to a bad outcome in most cases.  (DK: There is a temptation to do this, especially over the long term, for fear of some mental tally that you need to keep in balance.  While you need to manage this, and the people on your board, you must always do what you think is right for company.  Perversely at times, it’s what they (should, at least) want you to do, too.)

10. Not hiring a sales/go-to-market advisor because they’re too expensive.  A go-to-market mistake will cost you $500K+ and a year of time. Hire an advisor for $50K to make sure you don’t make obvious mistakes.  It’s money well spent.  (DK:  And now for a word from our sponsor.)

Thanks Mark.  It’s a great list.