Category Archives: Venture Capital

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.

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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.”

To mix metaphors, what comes first: the pipeline or the egg?  To un-mix them, 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.

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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.

Why You Should Eliminate the Title “Implementation Consultant” from Your Startup

I’ve worked with several startups that fell into the following pattern:

  • Selling a SaaS application at a healthy price (e.g., $100K to $200K ARR)
  • With low, fixed-cost implementation packages (e.g., $25K)
  • But a product that actually takes maybe $50K to $75K to successfully deploy
  • Resulting in an unprofitable professional services business (and wrecking the market for partner services)
  • High adoption failure
  • And, depending on the initial contract duration, high customer churn [1]

For example, one company had a CAC of 4.0, churn of 25%, and services margins of negative 66% when I started working with them [2].  Ouch.

Before proceeding, let me say that if you have a low-touch, high-velocity, easy-adoption business model — and the product to go with it — then you don’t need to read this post [3].  If you don’t, and any of the above problems sound familiar, then let’s figure out what’s going on here and fix it.

The problem is the company is not charging the appropriate price for the services needed.  Perhaps this is because of a zero-sum fallacy between ARR and services.  Or perhaps they feel that customers “just won’t pay” that much for implementation services.  Or perhaps their product takes more work to deploy than the competition and they feel forced to match price on services [4].

This under-pricing usually triggers a number of other problems:

  • In order to work within the self-created, low-cost implementation services model, the company “hires cheap” when it comes to implementation consultants, preferring junior staff and/or staff in offshore locations.
  • The company’s “implementation consultants” are overloaded, working on too many projects in parallel, and are largely focused more on “getting onto the next one” than getting customers successfully implemented.
  • Once a certain number of hours are clocked on any given project, the consultants go from “in a hurry” to “in a big hurry” to finish up and move on.
  • Customers are left high-and-dry with failed or partial implementations that, if left unfinished, will likely lead to churn.
  • Customer success, whose job is to prevent churn, is left holding the bag and is pulled away from its primary mission of adoption, renewal, and expansion into the implementation-completion business, potentially changing its hiring profile from more sales-oriented to more product-oriented and/or complementing CSMs with customer success architects (CSAs) or technical account managers (TAMs) to try and fill the implementation void.

I sometimes consider fixing this corporate chiropractor work, because one maladjustment results in the whole organization being twisted out of shape [5].  The good news is that, as with chiropractors, one adjustment can pop the whole system back into alignment.

Now, before we move onto fixing this, there’s one more problem we haven’t discussed yet — and give yourself ten pats on the back if you figured out before I got here:

Who ever said the customer defined success as getting the software implemented?

Oh shit.  We were so tied up trying to deliver a $25K services package that costs $40K to deliver that we forgot about the customer.  What customer equates implementation with success?  None.  Zero.  Nobody.

“Hey, it’s all set up now, you can login, gotta go!” is not the credo of a success-oriented consultant.

But what do we call our consultants again?  Implementation consultants.

What do implementation consultants think they do?  Well, implementations.

When an implementation consultant reads their own business card, what does it tell them they their job is?  Implementations.

Are implementations what customers want?  No.

So why do we have implementation consultants again?  I have no idea.

What do customers what?  Overall they want success, but what’s a good proxy?  How about attaining their first business objective?  If you sell:

  • A recruiting app, running your first recruiting campaign
  • A financial planning app, it’s making your first plan
  • A demandgen marketing app, it’s running your first demandgen campaign
  • A customer service app, it’s your first day running the call center
  • A deflection app, it’s deflecting your first cases
  • A sales enablement app, it’s training your first reps
  • An IT support app, it’s handing your first tickets

So, what’s the fix here?  While not all of this will be possible or recommended in all situations, here’s the long list:

  • Re-frame services as in the success business, not the implementation business
  • Eliminate the job title implementation consultant in favor of consultant
  • Get services to make plans that end not with implementation, but with the achievement of an agreed-to first business objective.
  • Increase your services pricing, if needed, so they can both deliver success and break even.
  • Hire more experienced consultants who can better make customers successful and don’t be afraid to charge more for them.  (They’re worth it.)
  • Agree to an ARR price before negotiating the services price; refuse to trade one off against the other.
  • Involve your services team in the sale well before the contract is signed so they propose the right prix fixe package (e.g., small, medium, large) or create an appropriately-sized bespoke statement of work.
  • Modify your product so it is not at a competitive disadvantage on required implementation work.

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Notes
[1] With one-year contracts, a failed implementation that takes 6-9 months to fail typically results in churn, whereas with three-year contracts, you will often get another swing at the problem.

[2] These horrific unit economics result in an LTV/CAC of 1.0 and make the company totally uninvestable.  The CAC would be even higher if hard-ass investor added the services losses back into the CAC on the theory they were subsidizing sales.

[3] Product-led growth business models are great, but when companies that are not designed for them try to emulate pieces of the business model, they can get into trouble.  Implementation is an area that quickly goes awry when companies not built for PLG attempt bottom-up, try-and-buy, viral go-to market strategies.

[4] In which case, an obvious solution is to reduce the deployment workload requirements of the product.

[5] Put differently, the sales bone is connected to the services bone, and the services bone is connected to the customer success bone.

How Startup CEOs Should Think About the Coronavirus, Part II

[Updated 3/10 12:09]

This is part II in this series. Part I is here and covers the basics of management education, employee communications, and simple steps to help slow virus transmission while keeping the business moving forward.

In this part, we’ll provide:

  • A short list of links to what other companies are doing, largely when it comes to travel and in-office work policies.
  • A discussion of financial planning and scenario analysis to help you financially navigate these tricky waters.

I have broken out the list of useful information links and resources (that was formerly in this post) to a separate, part III of this series.

What Other Companies are Saying and Doing

Relatively few companies have made public statements about their response policies. Here are a few of the ones who have:

Financial Planning and Scenario Analysis: Extending the Runway

It’s also time to break out your driver-based financial model, and if you don’t have one, then it’s time to have your head of finance (or financial planning & analysis) build one.

Cash is oxygen for startups and if there are going to be some rough times before this threat clears, your job is to make absolutely sure you have the cash to get through it. Remember one of my favorite all-time startup quotes from Sequoia founder Don Valentine: “all companies go out of business for the same reason. They run out of money.”

In my opinion you should model three scenarios for three years, that look roughly like:

  • No impact. You execute your current 2020 operating plan. Then think about the odds of that happening. They’re probably pretty low unless you’re in a counter-cyclical business like videoconferencing (in which case you probably increase targets) or a semi-counter-cyclical one like analytics/BI (in which case maybe you hold them flat).
  • 20% bookings impact in 2020. You miss plan bookings targets by 20%. Decide if you should apply this 20% miss to new bookings (from new customers), expansion bookings (new sales to existing customers), renewal bookings — or all three. Or model a different percent miss on each of those targets as it makes sense for your business. The point here is to take a moderately severe scenario and then determine how much shorter this makes your cash runway. Then think about steps you can take to get that lost runway back, such as holding costs flat, reducing costs, raising debt, or — if you’re lucky and/or have strong insiders — raising equity.
  • 40% bookings impact in 2020. Do the same analysis as in the prior paragraph but with a truly major bookings miss. Again, decide whether and to what extent that miss hits new bookings, expansion bookings, and renewal bookings. Then go look at your cash runway. If you have debt make sure you have all covenant compliance tests built into your model that display green/red — you shouldn’t have to notice a broken covenant, it should light up in big letters (YES/NO) in a good model. Then, as in the prior step, think about how to get that lost runway back.

Once you have looked at and internalized these models, it’s time for you and your CFO to call your lead investors to discuss your findings. And then schedule a discussion of the scenario analysis at your next board meeting.

Please note that it’s not lost on me that accelerating out of the turn when things improve can be an excellent way to grab share in your market. But in order to so, you need to have lots of cash ready to spend in, say, 6-12 months when that happens. Coming out of the corner on fumes isn’t going to let you do that. And, as many once-prodigal, now-thrifty founders have told me: “the shitty thing is that once you’ve spent the money you can’t get it back.” Without dilution. With debt. Maybe without undesirable structure and terms.

Now is the time to think realistically about how much fuel you have in the tank, if you can get more, how long should it last, and how much you want in the tank 6-12 months out.

Ten Questions Founder CEOs Should Always Be Able to Answer About Their Startups

I’m working with more early-stage companies these days (e.g., pre-seed, seed, seed-plus [1]) and one of the things I’ve noticed is that many founders cannot clearly, succinctly, and confidently answer some basic questions about their businesses.  I decided to write this post to help entrepreneurs ensure they have their bases are covered when speaking to angel investors, seed firms, or venture capitalists.

Note that Silicon Valley is the land of strong convictions, weakly held so it’s better in most cases to be clear, confident, and wrong than it is to waffle, equivocate, and be right.  I often have to remind people of this — particularly founders recently out of PhD programs — because Sand Hill Road is about the dead opposite of graduate school when it comes to this philosophy [2].

Here are ten questions that early-stage founder/CEOs should be able to answer clearly, succinctly, and confidently — along with a few tips on how to best answer them.

1. Who is the target customer?  Be precise, ideally right down to a specific job title in an organization.  It’s great if the answer will broaden over time as the company grows and its strategy naturally expands, but up-front I’d name the people you are targeting today.  Wrong:  “The Office of the CIO in IT organizations in F5000 enterprises around the world.”  Right:  “VPs of financial planning and analysis in 250-1000 employee Services firms in North America.” 

I’m admittedly fanatical about this, but I want to know what it says on the target buyer’s business card [3] .  I can’t tell you the number of times that I’ve heard “we sell to the CIO,” only to be introduced to someone whose business card said “director of data warehousing.”  If you don’t know who you’re selling to, you’re going to have trouble targeting them.

2. What problem do you solve for them?  When you meet one of these people, what do you tell them?  Right:  “We sell a solution that prevents spear phishing.”  Wrong:  “We sell a way to improve security culture at your organization” [4]. The latter answer is wrong because while an improvement in security culture may be a by-product of using your solution, it is not the primary benefit

First-order benefit:  our solution stops spear phishing.  Second-order benefit:  that means you avoid data breaches and/or save millions in ransomware and other breach-related costs.  Third-order benefit:  that means you protect your company’s reputation and your valuable brand.  Fourth-order benefit:  using our solution ends up increasing security culture and awareness.  People generally go shopping for the first-order benefit — they may buy into higher-order benefits, they may say they like your company’s approach and/or vision — but budgets and shopping lists get made on the first-order.  Don’t be selling security culture when customers are buying anti-spear-phishing.

3. How do they solve that problem today?  The majority of startups solve a problem that is already being solved in some way today.  Be realistic about this. Unless you are solving a brand-new problem (e.g., orchestrating containers at the dawn of the container revolution), then somehow the problem is either being solved today (e.g., in Excel, a legacy app, a homegrown system) or the buyer has deliberately decided not to solve it, likely because they think it’s unsolvable (e.g., baldness cures [5]).

If they are already solving the problem in some way, your new solution more likely represents an optimization than a breakthrough.  And even breakthrough companies, such as VMware [6], solved very practical problems early on (e.g., providing multiple environments on a laptop without having to physically change hard drives). 

As another example: even if you’re using advanced machine learning technology to automate trouble ticket resolution and — technically speaking, customers aren’t doing that today — they certainly are handling trouble tickets and the alternative to automatic resolution is generally a combination of human work and case deflection.

4. Why is your solution superior to the status quoOnce you can clearly describe how customers solve the problem today, then you should be able to clearly answer why your solution is superior to the status quo.  Note that I’m not asking how your technology works or why it’s superior — I’m asking why it provides a better solution for the customer. Sticking with the trouble ticket example:  “our solution is superior to human resolution because it’s faster (often by days if not hours), cuts ticket resolution cost by 90%, and results in greatly superior end-user satisfaction ratings.”  That’s a benefits-driven explanation of why it’s superior. 

5. Why is your technology different from that offered by other suppliers? Marketers call this differentiation and it’s not really just about why your technology is different from alternatives, it’s about why it’s better. The important part here is not to deep dive into how the technology works. That’s not the question; the question is why is your technology is better than the alternatives. The most common incorrect answer to this question is a long speech about how the technology works. (See this post for tips on how to build a feature, function, benefit marketing message.)

Example 1: traditional databases were built for and work well at storing structured data, but they have little or no capability for handling unstructured data. Unlike traditional databases, our technology is built using a hybrid of database and search engine technology and thus provides excellent capabilities for storing, indexing, and rapidly querying both structured and unstructured data.

Example 2: many planning systems require you to throw out the tool that most people use for planning today — Excel. Unlike those systems, our product integrates and leverages Excel as part of the solution; we use Excel formula language, Excel formatting conventions, and provide an Excel add-in interface that preserves and leverages your existing Excel knowledge. We don’t throw the baby out with the bathwater.

6. How many target customers have you spoken to — and what was their reaction to your presentation?  First, you means you, the founder/CEO.  It doesn’t mean your salesperson or co-founder.  The answer to the first part of the question is best measured in scores; investors want to know that you are in the market, talking with customers, and listening to their feedback.  They assume that you can sell the technology [7], the strategic question for later is the transferability of that skill.  They also want to know how target customers react to your presentation and how many of them convert into trials or purchases. 

7. Who’s using your product and why did they select it? It’s not hard to sell government labs and commercial advanced research divisions one of pretty much anything. It’s also not hard, in brand new categories, to sell your software to people who probably shouldn’t have purchased it — i.e., people not knowing all their options in the nascent market picked the wrong one. And that’s not to mention the other customers you can get for the wrong reason — because a board member had a friend on the executive staff, because someone was a big donor, etc. Customers “buy” (and I use air quotes become sometimes these early “customers” didn’t pay anything at all) the wrong software all the time, particularly in the early days of a market.

So the question isn’t who downloaded or tried your product, the question is who’s using it — and when they selected it did they know all their options and still choose you? Put differently, the question is “who’s not an accidental customer” and why did that set of non-accidental customers pick you over the alternative? So don’t give a list of company brand names who may or may not be active users. Instead tell a few deep stories of active customers (who they could ask to call), why they picked the software, and how it’s benefiting them.

8.  What is the TAM for solving this problem?   There are a lot of great posts about how to build a total available market (TAM) analysis, so I won’t explain how to do it here. I will say you should have a model that calculates an answer and be able to explain the hopefully simple assumptions behind that model. While I’m sure in b-school every VC undoubtedly said that “getting 1% of a $10B market is a bad strategy,” when they got into the workplace something changed. They all love big TAMs [8]. Telling a VC you’re aiming for 50% of an $800M TAM will not get you very far. Your TAM better be in the billions if not the tens of them.

9.  Why are you and your team the best people to invest in? Most interesting ideas attract several startups so, odds are, you have fairly direct competitors pretty much from inception. And, particularly if you’re talking with a VC at a larger firm, they have probably researched every company in the nascent space and met most of them [9]. So the question here is: (of all the teams I’ve met in this space) why are you the folks who are going to win?

I’d expect most startups in your space have smart people with strong educations, with great backgrounds at the right companies. That’s become the table stakes. The real question is thus why is your team of smart, well educated, and appropriately experienced people better than the others [10]:

  • A lot of this is confidence: “of course, we’re the right folks, because we’re the ones who are going to win.” Some people feel like they’re doing a homework assignment while others feel like they’re building a winning company. Be the latter. We know the stakes, we know the second prize is a set of steak knives, and we are going to win or die trying. #swagger
  • Drivers vs. passengers. Big successful enterprise software companies have definitionally employed a lot of people. So if you’re doing a sales-related category it’s not hard to companies full of ex-Siebel and ex-Salesforce people. The real question thus becomes: what did your people do at those prior companies? Were they drivers (who drove what) or were they passengers just along for the ride. If they drove, emphasize the amazing things they did, not just the brand names of where they worked.
  • Completeness. Some startups have relatively complete teams while others have only a CEO and CTO and a few functional directors. The best answer is a fairly complete team that’s worked together before. That takes a lot of hiring and on-boarding risk off the table. Think: give us money and we can start executing right away.
  • Prior exactly-relevant experience. Saying Mary was VP of ProductX Sales carrying a $500M number at BigCo is quite different from saying Mary just scaled sales at her last startup from $10M to $100M and is ready to do the exact same thing here. The smaller the gap between what people just did and what you’re asking them to do, the better.
  • Finally, and this is somewhat tongue in cheek, remember my concentric circles of fundraising from this post. How VCs see founders and entrepreneurs:

10.  If I give you money what are going to do with it? The quantitative part of this answer should already be in the three-year financial model you’ve built so don’t be afraid to reference that to remind people that your plan and financial model are aligned [11]. But then drill down and give the detail on where the money is planned to be spent. For extra credit, talk about milestone- or ARR-based spend triggers instead of dates. For example, say once we have 3 sales reps hitting their numbers we will go out and hire two more. The financial plan has that happening in July, but if July comes and we haven’t passed that milestone we won’t pull the trigger. Ditto for most hiring across the company. And ditto for marketing: e.g., we’ve got a big increase in programs budget in the second half of next year but we won’t release that money until we’re sure we’ve correctly identified the right marketing programs in which to invest.

It’s also very important that demonstrate knowledge of a key truth of VC-backed startups: each round is about teeing-up the next one. So the key goal of the Series A round should be to put the company in a position to successfully raise a Series B. And so on. Discuss the milestones you’re aiming to achieve that should support that tee-up process. And don’t forget the SaaStr napkin for getting a rough idea of what typical rounds look like by series.

Bonus: origin story. If I were to add one question it would be: tell me how you came to found your company? Or, using the more modern vernacular: tell me about your origin story? If yours is good and your founders are personable and videogenic, then I’d even make it into a short video, like the founders of Hashicorp did. You’re going to get asked this question a lot, so why not work on building the optimal answer and then videoing it.

# # #

Notes

[1] My, how things have changed.  The net result is that the new choke-point is series A (prediction 9).  Seed and angel money seems pretty easy to raise; A-rounds seem pretty hard — if you’ve already raised and spent $2M in seed capital then you should have something to show for it. 

[2] Most of the graduate student types I meet tend to be quite circumspect in their replies.  “Well, it could be this, but we don’t really know so it could be that.  Here are some arguments in favor of this and some against.”  In business, it’s better to be seen as decisive and take a clear stand.  As long as you are also perceived as open-minded and responsive to data, you can always change your mind later.  But you don’t want to be seen as fence-sitter, endlessly equivocating, and waiting for more data before making a decision.

[3] Or the more modern equivalent: an email footer or LinkedIn profile.

[4] Unless a company is shopping for training to improve security culture.  In which case, it’s a first-order benefit.

[5] Reminder that I have moral authority to talk about this :-). This type of problem is often called “latent pain” in sales, because it’s a pain the buyer is unaware they have because they don’t believe there is a solution. Ergo, they just get used to it. Thus, the first job of sales and marketing is to awaken the buyer to this latent pain.

[6] Yes, I know that virtual machines predate VMware considerably, particularly IBM’s VM/CMS operating system, so it wasn’t the creation of the virtual machine that I’d call a breakthrough, but using it to virtualize Microsoft and later Linux servers.

[7] If you can’t, it’s hard to assume that someone else will be able to.  Perhaps you’re not a natural-born seller, but if you were passionate enough about your idea to quit your job and found a company that should generally compensate.  Authenticity works.

[8] Most probably on the logic that they don’t want 1% of a $5B market, they want 40%. That is, they want both: big share and big TAM. And, if you mess up, there’s probably a safer landing net in the $5B market than the $500M one. Quoting the VC adage: great markets make great companies.

[9] This is the big difference between angels and funds. Angels typically meet one team with one idea, evaluate both and make a decision. Early-stage funds meet a company then research every company in the space and then pick a winner.

[10] I’m doing this in the abstract; it’s much easier in the concrete if you make a table and line up some key attributes of your team members vs. those of the competition. You use that table to come up with the arguments, but you don’t ever use that table externally with investors and others.

[11] I’m surprised how many folks dive into answer this question completely ignoring the fact that you’ve likely already put a three-year financial model in front of them that provides the high-level allocation of spend already. While it doesn’t seem to slow down some entrepreneurs, I think it far better to be a founder who refers to his plan a bit too much than a founder acts as if the financial plan doesn’t even exist.

Kellblog’s 10 Predictions for 2020

As I’ve been doing every year since 2014, I thought I’d take some time to write some predictions for 2020, but not without first doing a review of my predictions for 2019.  Lest you take any of these too seriously, I suggest you look at my batting average and disclaimers.

Kellblog 2019 Predictions Review

1.  Fred Wilson is right, Trump will not be president at the end of 2019.  PARTIAL.  He did get impeached after all, but that’s a long way from removed or resigned. 

2.  The Democratic Party will continue to bungle the playing of its relatively simple hand.  HIT.  This is obviously subjective and while I think they got some things right (e.g., delaying impeachment), they got others quite wrong (e.g., Mueller Report messaging), and continue to play more left than center which I believe is a mistake.

3.  2019 will be a rough year for the financial markets.  MISS.  The Dow was up 22% and the NASDAQ was up 35%.  Financially, maybe the only thing that didn’t work in 2019 were over-hyped IPOs.  Note to self:  avoid quantitative predictions if you don’t want to risk ending up very wrong.  I am a big believer in regression to the mean, but nailing timing is the critical (and virtually impossible) part.  Nevertheless, I do use tables like these to try and eyeball situations where it seems a correction is needed.  Take your own crack at it.

4.  VC tightens.  MISS.  Instead of tightening, VC financing hit a new record.  The interesting question here is whether mean reversion is relevant.  I’d argue it’s not – the markets have changed structurally such that companies are staying private far longer and thus living off venture capital (and/or growth-stage private equity) in ways not previously seen.  Mark Suster did a great presentation on this, Is VC Still a Thing, where he explains these and other changes in VC.  A must read.

5. Social media companies get regulated.  PARTIAL.  While “history may tell us the social media regulation is inevitable,” it didn’t happen in 2019.  However, the movement continued to gather steam with many Democratic presidential candidates calling for reform and, more notably, none other than Facebook investor Roger McNamee launching his attack on social media via his book Zucked: Waking Up To The Facebook Catastrophe.  As McNamee says, “it’s an issue of ‘right vs. wrong,’ not ‘right vs. left.’”

 

6. Ethics make a comeback.  HIT.  Ethics have certainly been more discussed than ever and related to the two reasons I cited:  the current administration and artificial intelligence.  The former forces ethics into the spotlight on a daily basis; the later provokes a slew of interesting questions, from questions of accidental bias to the trolley car problem.  Business schools continue to increase emphasis on ethics.  Mark Benioff has led a personal crusade calling for what he calls a new capitalism.

7.  Blockchain, as an enterprise technology, fades away.  HIT.  While I hate to my find myself on the other side of Ray Wang, I’m personally not seeing much traction for blockchain in the enterprise.  Maybe I’m running with the wrong crowd.  I have always felt that blockchain was designed for one purpose (to support cybercurrency), hijacked to another, and ergo became a vendor-led technology in search of a business problem.  McKinsey has a written a sort of pre-obituary, Blockchain’s Occam Problem, which was McKinsey Quarterly’s second most-read article of the year.  The 2019 Blockchain Opportunity Summit’s theme was Is Blockchain Dead?  No. Industry Experts Join Together to Share How We Might Not be Using it Right which also seems to support my argument. 

8.  Oracle enters decline phase and is increasingly seen as a legacy vendor.  HIT.  Again, this is highly subjective and some people probably concluded it years ago.  My favorite support point comes from a recent financial analyst note:  “we believe Oracle can sustain ~2% constant currency revenue growth, but we are dubious that Oracle can improve revenue growth rates.”  That pretty much says it all.

9.  ServiceNow and/or Splunk get acquired.  MISS.  While they’re both great businesses and attractive targets, they are both so expensive only a few could make the move – and no one did.  Today, Splunk is worth $24B and ServiceNow a whopping $55B.

10.  Workday succeeds with its Adaptive Insights agenda.  HIT.  Changing general ledgers is a heart transplant while changing planning systems is a knee replacement.  By acquiring Adaptive, Workday gave itself another option – and a far easier entry point – to get into corporate finance departments.  While most everyone I knew scratched their head at the enterprise-focused Workday acquiring a more SMB-focused Adaptive, Workday has done a good job simultaneously leaving Adaptive alone-enough to not disturb its core business while working to get the technology more enterprise-ready for its customers.  Whether that continues I don’t know, but for the first 18 months at least, they haven’t blown it.  This remains high visibility to Workday as evidenced by the Adaptive former CEO (and now Workday EVP of Planning) Tom Bogan’s continued attendance on Workday’s quarterly earnings calls.

With the dubious distinction of having charitably self-scored a 6.0 on my 2019 predictions, let’s fearlessly roll out some new predictions for 2020.

Kellblog 2020 Predictions

1.  Ongoing social unrest. The increasingly likely trial in the Senate will be highly contentious, only to be followed by an election that will be highly contentious as well.  Beyond that, one can’t help but wonder if a defeated Trump would even concede, which could lead to a Constitutional Crisis of the next level. Add to all that the possibility of a war with Iran.  Frankly, I am amazed that the Washington, DC continuous distraction machine hasn’t yet materially damaged the economy.  Like many in Silicon Valley, I’d like Washington to quietly go do its job and let the rest of us get back to doing ours.  The reality TV show in Washington is getting old and, happily, I think many folks are starting to lose interest and want to change the channel.

2.  A desire for re-unification.  I remain fundamentally optimistic that your average American – Republican, Democrat, or the completely under-discussed 38% who are Independents — wants to feel part of a unified, not a divided, America.  While politicians often try to leverage the most divisive issues to turn people into single-issue voters, the reality is that far more things unite us as Americans than divide us.  Per this recent Economist/YouGov wide-ranging poll, your average American looks a lot more balanced and reasonable than our political party leaders.  I believe the country is tired of division, wants unification, and will therefore elect someone who will be seen as able to bring people together.  We are stronger together.

3.  Climate change becomes the new moonshot.  NASA’s space missions didn’t just get us to the moon; they produced over 2,000 spin-off technologies that improve our lives every day – from emergency “space” blankets to scratch-resistant lenses to Teflon-coated fabrics.  Instead of seeing climate change as a hopeless threat, I believe in 2020 we will start to reframe it as the great opportunity it presents.  When we mobilize our best and brightest against a problem, we will not only solve it, but we will create scores to hundreds of spin-off technologies that will benefit our everyday lives in the process.  See this article for information on 10 startups fighting climate change, this infographic for an overview of the kinds of technologies that could alleviate it, or this article for a less sanguine view on the commitment required and extent to which we actually can de-carbonize the air. Or check out this startup which makes “trees” that consume the pollution of 275 regular trees.

4.  The strategic chief data officer (CDO).  I’m not a huge believer in throwing an “O” at every problem that comes along, but the CDO role is steadily becoming mainstream – in 2012 just 12% of F1000 companies reported having a CDO; in 2018 that’s up to 68%.  While some of that growth was driven by defensive motivations (e.g., compliance), increasingly I believe that organizations will define the CDO more strategically, more broadly, and holistically as someone who focuses on data, its cleanliness, where to find it, where it came from, its compliance with regulations as to its usage, its value, and how to leverage it for operational and strategic advantage.   These issues are thorny, technical, and often detail-oriented and the CIO is simply too busy with broader concerns (e.g., digital transformation, security, disruption).  Ergo, we need a new generation of chief data officers who want to play both offense and defense, focused not just tactically on compliance and documentation, but strategically on analytics and the creation of business value for the enterprise. This is not a role for the meek; only half of CDOs succeed and their average tenure is 2.4 years.  A recent Gartner CDO study suggests that those who are successful take a more strategic orientation, invest in a more hands-on model of supporting data and analytics, and measure the business value of their work.

5.  The ongoing rise of DevOps.   Just as agile broke down barriers between product management and development so has DevOps broken down walls between development and operations.  The cloud has driven DevOps to become one of the hottest areas of software in recent years with big public company successes (e.g., Atlassian, Splunk), major M&A (e.g., Microsoft acquiring GitHub), and private high-flyers (e.g., HashiCorp, Puppet, CloudBees).  A plethora of tools, from configuration management to testing to automation to integration to deployment to multi-cloud to performance monitoring are required to do DevOps well.  All this should make for a $24B DevOps TAM by 2023 per a recent Cowen & Company report.  Ironically though, each step forward in deployment is often a step backward in developer experience, why is one reason why I decided to work with Kelda in 2019.

6. Database proliferation slows.  While 2014 Turning Award winner Mike Stonebraker was right over a decade ago when he argued in favor of database specialization (One Size Fits All:  An Idea Whose Time Has Come and Gone), I think we may now too much of a good thing.   DB Engines now lists 350 different database systems of 14 different types (e.g., relational, graph, time series, key-value). Crunchbase lists 274 database (and database-related) startups.  I believe the database market is headed for consolidation.  One of the first big indicators of a resurgence in database sanity was the failure of the (Hadoop-based) data lake, which happened in 2018-2019 and was the closest thing I’ve seen to déjà vu in my professional career – it was as if we learned nothing from the Field of Dreams enterprise data warehouse of the 1990s (“build it and they will come”).  Moreover, after a decade of developer-led database selection, developers and now re-realizing what database people knew along – that a lot of the early NoSQL movement was akin to throwing out the ACID transaction baby with the tabular schema bathwater.

7.  A new, data-layer approach to data loss prevention (DLP).  I always thought DLP was a great idea, especially the P for prevention.  After all, who wants tools that can help with forensics after a breach if you could prevent one from happening at all — or at least limit one in progress?  But DLP doesn’t seem to work:  why is it that data breaches always seem to be measured not in rows, but in millions of rows?  For example, Equifax was 143M and Marriott was 500M.  DLP has many known limitations.  It’s perimeter-oriented in a hybrid cloud world of dissolving perimeters and it’s generally offline, scanning file systems and database logs to find “misplaced data.”  Wouldn’t a better approach be to have real-time security monitored and enforced at the data layer, just the same way as it works at the network and application layer?  Then you could use machine learning to understand normal behavior, detect anomalous behavior, and either report it — or stop it — in real time.  I think we’ll see such approaches come to market in 2020, especially as cloud services like Snowflake, RDS, and BigQuery become increasingly critical components of the data layer.

8. AI/ML continue to see success in highly focused applications.  I remain skeptical of vendors with broad claims around “enterprise AI” and remain highly supportive of vendors applying AI/ML to specific problems (e.g., Moveworks and Astound who both provide AI/ML-based trouble-ticket resolution).  In the end, AI and ML are features, not apps, and while both technologies can be used to build smart applications, they are not applications unto themselves.  In terms of specificity, the No Free Lunch Theorem reminds us that any two optimization techniques perform equivalently when averaged across all possible problems – meaning that no one modeling technique can solve everything and thus that AI/ML is going to be about lots of companies applying different techniques to different problems.   Think of AI/ML more as a toolbox than a platform.  There will not be one big winner in enterprise AI as there was in enterprise applications or databases.  Instead, there will be lots of winners each tackling specific problems.  The more interesting battles will those between systems of intelligence (e.g., Moveworks) and systems of record (e.g., ServiceNow) with the systems-of-intelligence vendors running Trojan Horse strategies against systems-of-record vendors (first complementing but eventually replacing them) while the system-of-record vendors try to either build or acquire systems of intelligence alongside their current offerings. 

9.  Series A rounds remain hard.  I think many founders are surprised by the difficulty of raising A rounds these days.  Here’s the problem in a nutshell:

  • Seed capital is readily available via pre-seed and seed-stage investments from angel investors, traditional early-stage VCs, and increasingly, seed funds.  Simply put, it’s not that hard to raise seed money.
  • Companies are staying in the seed stage longer (a median of 1.6 years), increasingly extending seed rounds, and ergo raising more money during seed stage (e.g., $2M to $4M).
  • Such that, companies are now expected to really have achieved something in order to raise a Series A.  After all, if you have been working for 2 years and spent $3M you better have an MVP product, a handful of early customers, and some ARR to show for it – not just a slide deck talking about a great opportunity.

Moreover, you should be making progress roughly in line with what you said at the outset and, if you took seed capital from a traditional VC, then they better be prepared to lead your round otherwise you will face signaling risk that could imperil your Series A.

Simply put, Series A is the new chokepoint.  Or, as Suster likes to say, the Series A and B funnel hasn’t really changed – we’ve just inserted a new seed funnel atop it that is 3 times larger than it used to be.

10.  Autonomy’s former CEO gets extradited.  Silicon Valley is generally not a place of long memories, but I saw the unusual news last month that the US government is trying to extradite Autonomy founder and former CEO Mike Lynch from the UK to face charges.  You might recall that HP, in the brief era under Leo Apotheker, acquired enterprise search vendor Autonomy in August, 2011 for a whopping $11B only to write off about $8.8B under subsequent CEO Meg Whitman a little more than a year later in November, 2012.  Computerworld provides a timeline of the saga here, including a subsequent PR war, US Department of Justice probe, UK Serious Fraud Office investigation (later dropped), shareholder lawsuits, proposed settlements, more lawsuits including Lynch’s suing HP for $150M for reputation damages, and HP’s spinning-off the Autonomy assets.  Subsequent to Computerworld’s timeline, this past May Autonomy’s former CFO was sentenced to five years in prison.  This past March, the US added criminal charges of securities fraud, wire fraud, and conspiracy against Lynch.  Lynch continues to deny all wrongdoing, blames the failed acquisition on HP, and even maintains a website to present his point of view on the issues.  I don’t have any special legal knowledge or specific knowledge of this case, but I do believe that if the US government is still fighting this case, still adding charges, and now seeking extradition, that they aren’t going to give up lightly, so my hunch is that Lynch does come to the US and face these charges. 

More broadly, regardless of how this particular case works out, in a place so prone to excess, where so much money can be made so quickly, frauds will periodically happen and it’s probably the most under-reported class of story in Silicon Valley.  Even this potentially huge headline case – the proposed extradition of a British billionaire tech mogul —  never seems to make page one news.  Hey, let’s talk about something positive like Loft’s $175M Series C instead.

To finish this up, I’ll add a bonus prediction:  Dave doesn’t get a traditional job in 2020.  While I continue to look at VC-backed startup and/or PE-backed CEO opportunities, I am quite enjoying my work doing a mix of boards, advisory relationships, and consulting gigs.  While I remain interested in looking at great CEO opportunities, I am also interested in adding a few more boards to my roster, working on stimulating consulting projects, and a few more advisory relationships as well.

I wish everyone a happy, healthy, and above-plan 2020.