Category Archives: Venture Capital

How to Lead a Strategic Board Discussion

Have you ever been to a board meeting where 60 minutes were allocated on the agenda for discussion of a strategic topic?  What happened in that session?

  • You probably started late because board meetings are hard to keep on time.
  • Some exec, maybe the CEO, probably presented a “few slides” to “tee up” the discussion.
  • “A few” turned out to be 23.
  • Two or three questions were asked by the one board member closest to the topic.  The others said nothing.
  • Time ran out because you needed to get to the administrative section, approving prior-meeting minutes and such.
  • Everyone politely said, “great job,” but left the meeting frustrated.

This happens a lot.  Execs who dysfunctionally view survival as the goal of a board meeting might be happy with this outcome.  Think:  “we survived another one; now, let’s get back to work.”

For those execs, however, who actually want to both tap into the board’s expertise and build board-level consensus on a strategic topic, this is a terrible outcome.  No expertise was tapped.  No consensus was built (except perhaps that the company doesn’t run good board meetings).  So what went wrong and what should we do about it?

What Goes Wrong in Strategic Board Discussions
Startup boards are a tough audience.  They are homogenous in some ways:  everyone is typically smart, outspoken, successful, and aggressive [1].  That means leading any discussion is cat-herding.

But, when it comes to strategic discussions, the board is heterogenous in three critical dimensions [2]:

  • Operating experience
  • Technology understanding
  • Financial knowledge

Startup boards are typically VC-dominated because, as a startup goes through the A, B, C, D series of funding rounds, it typically adds one VC board member per round [3].  Thus the typical, sub-$100M [4] startup board has 1-2 founders, one VC for each funding round [5], and one or possibly two independents.

Patagonia vests [6] aside, not all VCs are alike.  When it comes to operating experience, VCs generally fall into one of three different categories [7]:

  • Deep.  Former founders, who founded, grew, and eventually sold their companies, or highly successful 10+ year executives from brand-name companies.  In high school, members of the former group were in the programming club [8].  You’ll find these people working at early-stage VC firms.
  • Moderate.  People who worked for roughly 4 to 10 years, often in product but sometimes in sales or corpdev, at a larger tech company, often with an MBA sandwiched in the middle.  Often they studied CS or engineering undergrad.  In high school, they were in the entrepreneurship club.  You’ll find these people at a wide range of VC firms.
  • Light.  People who typically majored in economics or finance (sometimes CS), worked for 2 to 4 years in management consulting or at a tech firm, attended a top business school, joined a VC firm as an associate, and then worked (usually hard and against the odds) their way up to partner.  In high school, they were in the investing club.  You’ll find these people at later-stage VC firms.

Independent board members come in different flavors as well:

  • General managers.  Active or former CEOs of startups and/or business unit GMs at big companies.  These people typically have a good overview of the business and know the functional area they grew up in, these days typically sales or product.
  • Go-to-market executives.  Active or former sales or marketing leaders, i.e., CROs or CMOs.  These people understand go-to-market, but may be light on both technical understanding and financial knowledge.
  • Finance executives.  Active or former CFOs who lead the audit committee and who work the company’s CFO to ensure the company’s financial affairs are in order.  These people are typically light on technical understanding and go-to-market (GTM) knowledge (but they know that GTM is too expensive and they don’t like it).

Now, imagine having a deep conversation about {multi-cloud, serverless, re-architecture, UI/UX, positioning, pricing, branding, ABM, PLG, company strategy, category consolidation, international expansion, channels} with a group consisting of two product-oriented company founders, three VCs (one deep, one moderate, and one light in operating experience), and two independent directors (one former CEO with a sales background and the other a former CFO).

As the saying goes, “you can’t fix what you can’t see.”  Hopefully in this part of the post we’ve shined a bright light on the problem.  You want to discuss an inherently difficult issue (otherwise it wouldn’t have made the agenda).  You’re working with one heck of heterogeneous group. And, for the cherry on top, most of the group members are type-A personalities.  No wonder these sessions are hard to lead [9].

How To Lead a Strategic Board Discussion
Since this exercise is almost a Kobayashi Maru, sometimes the smartest strategy is change the rules.  Rather than teeing up an impossible discussion, instead propose to create a working group of those members who are most interested (and presumably expert) in the chosen topic.  Team those board members with the relevant executive staff, run a series of meetings that dive deep into the topic, and then report back into the larger group. Sometimes, as the WOPR computer concluded in War Games, the only wining move is not to play.

The benefits of these working groups are many:

  • You engage the board members and really tap into their expertise.
  • The smaller group size and more informal setting lead to more interesting and interactive discussions.
  • You create an opportunity for the executive staff to increase their visibility and build relationships with board members [10].

Personally, I’ve participated in numerous such working groups on various topics (e.g., pricing, metrics, GTM planning and modeling, sales process, positioning/branding, product strategy, and reluctantly, compensation) and find them invariably superior to jumping into a hard topic with a big heterogeneous group.

That said, once in a while you do need to lead such a discussion, so in that situation what should you do?  Do these five things:

  • Make a deck.  If you start the discussion from scratch without a tee-up, it will likely be a mess.  Use a deck to frame the topic and maintain control.  However, that deck is not a presentation.  It should be built specifically to lead a discussion.  Don’t just cut and paste slides from your internal meetings.
  • Baseline the audience.  Writing for the person in the room with the least expertise and familiarity with the topic, write 3-5 slides that describe the challenge you are facing and the decision you need to make.  Try to decompose the overall question to three sub-questions about which you will lead a discussion.  This will likely clarify your own thinking on the question greatly.  If it’s a one-hour session, this part, including explanatory Q&A, should take 10 minutes.
  • Ask three questions. The final three slides should each have one question in the title and blank body.  Stay on each one for 15 minutes.
  • Balance participation.  Remember your goal is to enable a discussion, not necessarily to make the final decision.  So lead a discussion.  It’s not a discussion if you and the alpha board member are the only people talking.  (That’s called watching two people talk.)  Keep track of who’s talking and do so naturally, i.e., without “going around the room” (which also isn’t a discussion, it’s a serial Q&A).
  • Summarize what you heard and either promise to get back to them with your final decision, propose splitting off a working group, or some other concrete action so that they know the next steps going forward.

Remember if you’re clear on the goal — to have a good discussion — and you build the deck and lead the group to stay focused on that goal, you might not arrive at an easy decision in 60 minutes, but you will indeed have delivered on what you promised — a good, board-level discussion about a complex issue.

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[1]  As is well known, they are also often homogenous in other, undesirable ways (e.g., race, gender) that I will acknowledge but not address as it’s not the purpose of this post.  For more on this topic, you can start here.

[2]  This is why pattern-matching across portfolio companies, executive staffing, and compensation are popular topics with boards.  They are safe topics, in the sense that everyone gets to participate in the discussion.  On the other extreme, it’s why product and major engineering decisions get so little time relative to their importance.  Go-to-market lies somewhere in the middle.

[3]  This is somewhat less true in today’s markets because (a) many VCs are more willing to invest without taking a board seat and (b) some, more indexing-oriented, later-stage VCs do not as a matter of practice want board seats because their business model is about deploying large amounts of capital across a broad range of companies.

[4]  Around $100M they may typically start reconfiguring in preparation for an upcoming IPO.

[5]  Where that number, using an Excel formula, is = code(uppercase(last-round-letter)) – 64.  You’re welcome.

[6]  A little satire from Fortune and/or my favorite scene from The Internship, which is about academic elitism in Silicon Valley in general and not VC in specific.

[7]  These buckets are definitionally stereotypes with all attached strengths and weaknesses.  While I was tempted to write “typically” and “often” before every sentence, I elected not to for word parsimony.  Place accept in the spirit given.

[8]  I add this colorful detail, which will invariably be wrong a lot, both for fun and to help paint the picture.  In each instance, I know at least one person, and usually more than one person, who fits this profile.  But no, I don’t always ask people what clubs they were in during high school.  To ensure contemporary naming (e.g., back when I was a member, it was called “computer club”), the club names come from the list at the high school that most of my children attended.

[9]  This why boards frequently talk about “safer” topics (in the sense that everyone can more easily participate) such as pattern matching across companies, executive staffing, and compensation — and a key reason why major engineering and product decisions get low airtime relative to their importance on many boards.

[10]  One of the smartest things e-staffers can do is to build relationships with their VC board members.  This isn’t always easy — everyone is pressed for time, sometimes it can make the CEO uncomfortable, and it’s not strictly necessary — but five years later when the VC is looking for a CXO for a hot portfolio company, whether you get the call or not may well be a function of that relationship or lack thereof.

Seed-Stage Positioning: Lock and Load

With angel and seed money flowing, and a great environment for company creation, I’ve been talking to a lot of seed-stage and pre-seed stage startups of late.  They often ask me about positioning.  Listening to myself talk, I realized that I didn’t really sound like me, and it made me wonder why.

What I Normally Sound Like on Positioning
When people ask me about positioning and messaging, I usually sound like this:

  • As Fausto Coppi said about cycling, positioning is suffering.  The marketer who suffers the most wins.
  • When challenging a marketer in a positioning meeting, the ideal response to every question is: “yes, we thought of that and ruled it out for these reasons.”  You should never be able to come up with something they haven’t thought about already.  Deeply.
  • Positioning is a labor of love.  You need to examine and re-examine all the messages and how they fit together over and over again [1].
  • Slapdash and positioning don’t belong on the same page, let alone the same paragraph or the same sentence.
  • Get Shit Done is the watchword in marketing, today.  There’s no room for perfectionism, except for one thing:  positioning.

In short, I feel about positioning the way David Ogilvy felt about copywriting.

What I Sound Like Talking to Seed-Stage Startups
Lately, in talking with seed-stage startups, however, I’ve heard myself sounding like this:

  • Perfect is the enemy of good.
  • Put in enough thought and build enough consensus that you can execute without wanting to change it every day — but no more than that.
  • Stop worrying about category creation, and worry about proving product-market fit [2].
  • Analysts name categories, not vendors.  Guiding them to the right name is a problem we’ll hopefully get to have in 2-4 years.
  • Just be clear.  The emphasis needs to be 100% on clarity:  make it clear, make it simple, and don’t let confusion interfere.

Think hard but don’t agonize.  Then lock and load.  Debate it, decide it, train the team on it, and then go execute.  Don’t entertain revisiting the positioning unless you get material new information.  Think:  “I’ll write everyone’s concerns down in a Google Doc that we can revisit in two to four quarters — right now we’re in execution mode.” [3]

Why The Difference?
The positioning challenge is fundamentally different between a seed-stage and a larger company.  Managing this difference can be particularly hard for a larger-company director of product marketing who’s just joined their first seed- or early-stage startup.

At a larger company, the product marketer typically works in an existing category and needs to clearly message what their offering does and how it is different from the competition. That often involves amplification of subtle differences in an effort first to position yourself as different and then as better.  You’re trying to differentiate in a market where, to most buyers, everyone sounds the same.  You’re in a why buy mine situation, in a hot and growing market, fighting for share, and in that situation you literally cannot spend enough time and energy getting the optimal answer to the question:  why buy mine?  Anything less than perfect isn’t good enough.

At a seed-stage company you’re trying to see if anyone wants to buy what you’ve built.  Your founder saw a problem and built a solution to it.  But few people, if any, have actually bought it.  You don’t know if they will.  The number one thing your next-round investors will be looking for is how many people didYou’re selling to technology enthusiasts who want to try it because they try everything or, better yet, visionaries who are more than able to map the potential benefits of the product to their business — provided, of course, they understand what the product is.  So your job is simple:  explain what the product is in the clearest simplest, shortest form possible [4].  Anything more than that is wasted effort, better spent on engaging with more people instead of further honing the message.

When seed-stage companies get confused about this, here’s what I think is happening:

  • Perfectionism is winning over pragmatism.  Believe me, I get the desire to want to make it perfect, but in reality you’re just navel gazing.
  • It’s a form of avoidance.  It’s scary that people might fully understand what you built and then say, “no thanks, I don’t need that.”  But that’s precisely what you need to find out.  Relish these conversations, even if reveal that you built the wrong light bulb.  If that’s true, you’ll find out eventually anyway.  Why not fail fast?
  • It’s a failure to understand marketing.  Founders sometimes think that marketing is supposed to dress up their practical but mundane idea so that people will buy it.  That’s wrong.  All that fancy dress-up just interferes with what you should be doing:  finding the right people to hear your idea and clearly telling them what it is. [5] [6]
  • Large-company people not adapting to required small-company practices.

In short, while at larger companies positioning is indeed suffering, at seed-stage companies, positioning is the quick search for simple clarity.

Get it.  Then lock and load.

# # #


[1] Certainly in your own mind, but also with other people and groups:  customers, prospects, sales, product, engineering … and via market research.

[2] Resisting the temptation to expound on category creation, let me say that the podcast episode linked with Stephanie McReynolds on category creation was, I believe, outstanding.  Listen to it for a great discussion on the topic.

[3] But we’re not going to spend hours every week — often just arguing with ourselves — wondering if we’re describing it optimally.

[4] One great way to do that is often via an origin story:  explaining why the founder built it.

[5] Companies often put more energy into what they want to say than who they want to say it to, and that’s a mistake.  Pitching an innovation idea to a conservative buyer might result in rejection, but not because the idea is bad but because the buyer is risk averse. In Geoffrey Moore terms, you want to find visionaries — people who understand technology and can envision how a given technology might solve a range of business problems.

[6] Don’t worry, if you’re talking to the right person (see prior endnote) with a good idea, they’ll tell you the business benefits.  Later in market evolution you’ll need to explain business benefits to people.  But early on, when you’re selling to visionaries, they’ll tell you.

Why Execution Matters

Some friends wonder why, as someone who considers himself a strategist, I care so much about execution.  Is it because I’m a perfectionist?  Well yes, but that’s only one reason (and perfectionism is a weakness [1] in business, not a strength).  Is it because I like metrics, measurement, and incremental improvement?  Yes, I do.  Is it because I like improving efficiency and minimizing cash burn?  Yes, even though that doesn’t matter nearly as much as it once did.

But are any of those answers the real, big reason why I care so much about execution?  No.  This quadrant is:

In business, while we all may have strong opinions about what’s going to work and what isn’t, if we have even a trace of humility we must admit that we don’t know.  Therefore, everything we do is an experiment.  When an idea succeeds, great — let’s scale it up.  But it’s also quite important to know, when an idea fails, why.

That’s why execution matters.  Because when an idea doesn’t clearly succeed you need to be able to determine whether you’re in Box 1 or Box 4 of the above quadrant [2].

Saying a new corporate initiative failed because of bad execution is like saying a lab experiment failed because the petri dishes were dirty.  It shouldn’t even be a possible cause of failure.  You should have controlled for that.  You should have hired professionals to run your experiment.

If you’re going to spend $5M of your investors’ money to try an idea, if it doesn’t work you should be able to explain why — and be damn sure that execution is not a probable reason.  The whole Silicon Valley model is about isolating and removing risk from the equation.  That’s why boards want startups to pay high salaries and lure experienced talent with lucrative stock options.  It’s not because VCs like high compensation packages and dilution.  It’s because they want to optimize the chance of something working and, when it doesn’t, they want to be able to say, “we spent $5M and proved that was a bad idea,” as opposed to, “we spent $5M and we’re not sure whether it didn’t work because it was a bad idea or it was just poorly executed.” [3]

If Sartre said, “hell is other people,” I’d say, “hell is not knowing why you failed.”

Let’s take European expansion as an example.  You have a nice US-based $30M SaaS business and you want to expand into Europe.  But you’re not fully committed, the board is split on the decision, you’re worried about the CAC impact of initially unproductive sales investments, and you’re not sure what to do.

You think, if you had the proper budget, you’d:

  • Open in both the UK and Germany to run the experiment in parallel
  • Hire only sellers with experience in your space, with the same profile as the ones you hire in the US.
  • Hire 3 in each country to make sure the outcome isn’t dependent on any one bad hire.
  • Hire 2 SDRs and 2 solutions consultants in each country to support the sellers (the same ratio you use in the US).
  • Hire a few other staff like in functions like customer success, support, and services to support the team.
  • Hire a VP of Europe to lead this — someone experienced but who still enjoyed being close to the action; they’d be expensive but they could scale the operation as it grew.
  • All in all, you’d hire maybe 20 people and spend $4M to get going.

But given the board situation, you decide to do it on the cheap:

  • You hire one seller in UK and one in Germany, who are both junior in their careers and have never worked in the space, but they’re cheap and seem aggressive.  Good sales DNA you convince yourself.
  • You tell them to work primarily through partners because you don’t think you can afford to go direct.
  • You have them share a UK-based solutions consultant who doesn’t speak German.
  • And that’s it.  You spend less than $1M, but at least you’re getting started.

Now, zoom forward to the end of the year.  It didn’t work.  The two sellers both quit and the solutions consultant is trying to support the few customers they sold.  It’s a mess.  And what have you learned?  That Europeans don’t want software in your category?  That a partner model doesn’t work in Europe?  That it’s hard to find good talent in Europe?

Nope.  You haven’t learned anything — except that bad execution leads to failure.  And you knew that already, didn’t you?

Maybe you think you learned not to hire junior sales reps, that reps need a proper level of supporting staff, and that customers like to get demos in their native language?  But you knew that already, too.  You haven’t learned a thing, but you’ve wasted $1M, damaged your brand, and most importantly, lost a year.

By the way, if you really wanted to know if you could hire junior sellers and make them successful — if that was really the question you were trying to answer — then shouldn’t you have held all the other variables constant and just tested that?  In the US, at perhaps smaller companies but in your same target market.  With your standard support ratios.  With experienced leadership.

Imagine if you ran the other play, the one with the proper budget.  If it worked, you’d be worried about scaling up, not still worried about how to open Europe.  If it didn’t work, then you’d have some really hard questions to answer.  Because we can be pretty sure it’s not the people or the support ratios or the sales model — or execution in general.  We’re probably in Box 1 — there’s likely something about the idea that’s wrong.  Maybe, to pick a trivial example, Europeans don’t want to buy your compliance software because it’s weak on supporting European regulations [4].

The Idea/Execution Quadrant
It’s no secret that I like quadrants.  I made this one because I wanted to separate business outcomes from idea quality and execution quality.  Box 2 and Box 3 were easy to label.  I frankly struggled with the labels of Box 1 and Box 4.  So let’s talk about them in more depth as I’ve lived in both [5].

Box 1:  Bad Idea, Good Execution.  For me, this was MarkLogic, an XML database system [6].  During my six years there we grew the company from $0 to $80M in revenues, so I have trouble labeling that box — as I initially did — “failure” [7].  While I also considered labeling it “partial failure,” in the end I chose “partial success.”  We brought great execution to a hostile environment [8] and had enough success to confuse people — by the numbers we resembled many destined-for-greatness companies, but those who looked beyond the numbers knew we were not [9].  As one very smart VC summarized it, “you’re still pushing” — a great definition, in fact, of partial success.  You’re driving growth and making numbers, but you have no tailwind from the market.  When you’re in Box 2, you’re HODL-ing to stay on top of the market — in Box 1, by comparison, you are fighting for every yard.  That’s why I labeled it “partial success.”

Because if you bring good execution to a bad idea [10], smart people can often figure it out, and you can drive some success.  But it won’t be easy and it won’t scale [11].

Box 4:  Good Idea, Bad Execution.  For me, this was Ingres, my first job out of college.  If I told you I joined a startup after school, stayed there 7 years, and it grew from $30M into a $240M division of a $400M company, you might be tempted to say “success.”  But you’d be wrong.  One clue is obvious:  division.  We grew, but not so much that we were able to stay an independent company.  The other is non-obvious.  During those same 7 years our archrival (a company called Relational Software) grew from $30M to $1B.  During that period Relational Software changed its name to match that of its product:  Oracle.

The idea was clearly good.  In terms of wealth generation, the RDBMS was the second best idea of the 20th century [12].  Did Ingres succeed?  While my quip is that I experienced great success and great failure simultaneously [13], there is no question.  No.  Ingres failed.  It got third place in the race of the century.   Second place (Sybase) was a set of steak knives.  Third place was for our acquiror, ASK, to get acquired by CA for less than 1x revenues.  Was our execution bad?  I think so, not only because of hindsight bias based on the result, but because I worked there.  While I go into considerable depth in this post, I think the simple answer is that Ingres never really understood either the stakes of the game it was playing or the power of increasing returns in software market leadership.

You could argue I should label Box 4 “failure,” but we did manage to grow the company, go public, and achieve other key milestones — so, optimists could argue that we were succeeding.  Semantically, would I argue the partial success is not success and thus failure?  Maybe.  Practically, do I want to label Box 4 “failure” and potential enable misguided optimists to argue that some success equals success and that they’re ergo in Box 2?  No.

That’s why Boxes 1 and 4 are so difficult.  You’re not sure if you’re succeeding or failing and you’re not sure why.  Which, in the end, is why execution matters.  So you succeed when your idea is good and so you can rule out execution as a factor when it’s not.

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[1]  As it took me way too long to realize, you need to focus on getting what matters right.  It’s one of several points discussed in this deck.

[2]  See the second part of the post for more discussion of the quadrant itself.

[3]  And now they need to decide if we want to spend another $5M to try it again, but this time with professionals.

[4]  And a professional team would likely figure that out and tell you.  The amateurs might offer 100 different excuses and/or forms of hope.

[5]  Happily, I’ve also done plenty of time in Box 2!

[6]  I am not speaking of MarkLogic in its contemporary form (about which I know fairly little), but as it was in the 2004-2010 period when I ran it.  Disclaimer:  I own some residual shares in MarkLogic.

[7]  Though a VC might easily do so.  The investor view on these things is pretty simple:  did I make a lot of money?  The operator view is different.  We grew a business, in an environment as hostile as a sea-floor vent, to $80M and solved plenty of hard problems for plenty of happy customers in the process.

[8]  Something like 15 of the 16 original XML database companies basically went out of business.  I’d call that hostile.

[9]  Specifically that NoSQL was emerging and that those systems (e.g., Hadoop, MongoDB) and their associated open source models, were going to become the mainstream way to solve the problems that our technology solved.

[10]  This begs the question of how to define the idea.  In MarkLogic’s case, what was the idea?  Building a search engine with database parts and using a distributed scale-out architecture were good ideas — very good, in fact.  But the overall business idea was that XQuery would do for MarkLogic what SQL did for Oracle.  That, because XQuery never took off (and was arguably a case of infantcide by the megavendors) was a bad idea, and the one that put us in Box 1.

[11]  Critical thinkers may be wondering if I’ve rationalized the company into Box 1 because (as a non-founder CEO) I was responsible for the execution and not the idea.  Let’s test that.  The team who took over the company was definitionally “better” in the minds of our top-tier VCs who brought them in and who are presumably skilled at evaluating such things.  Ergo, if you were in Box 4 and upgraded the team (and ergo execution), you should move to Box 2 and see improved results.  That didn’t happen.  Similarly, if you look at the idea compared to other companies pursuing the same idea (see endnote [8]) you can’t find any evidence that it was a good idea.  Had 1-2 competitors done materially better than we did in size, growth, or exit value that would suggest the idea was good and our execution bad, but that didn’t happen either.  Hence my belief that we were in Box 1.

[12]  First place was PC operating systems with Microsoft.

[13]  Because we did face the challenges of high growth.

Kellblog Predictions for 2022

Well it’s time for my annual predictions post, a series now in its eighth year.  Before diving in, let me remind readers that I do these predictions in the spirit of fun, they are not business or investment advice, and that all of my usual disclaimers and terms apply.  I’m starting to believe that the value of this series is more about the chosen topics than the predictions themselves because my formula for creating these posts is to select interesting topics that I want to ponder, research them, and figure out a prediction for each topic along the way.

Let’s start with a review of my 2021 predictions, keeping in mind one of my favorite quotes, often misattributed (including by me) to Yogi Berra:  “predictions are hard, especially about the future.”

Kellblog 2021 Predictions Review
On my own admittedly subjective and charitable self-scoring system, 2021 was a pretty good year for Kellblog predictions.

1.  Divisiveness decreases but unity remains elusive.  Hit.  This is totally subjective, but I’d say that divisiveness in the USA has decreased a bit and that unity has most certainly remained elusive.

2.  COVID-19 goes to brushfire mode.  Hit, until recently.  Well, it certainly felt like brushfire mode until December.  As I write, it’s still early in the omicron wave, so I’m going to remain optimistic that current predictions of omicron being more transmissible but less lethal will hold true.

3.  The new normal isn’t.  Hit.  I don’t think many people believe that we’re returning to pre-Covid norms when, and indeed if, we enter a post-Covid world.

4.  We start to value resilience, not just efficiency.  Hit.  I don’t frequently write about supply chain, but I made this prediction because for years I have wondered if, in our quest to wrest inefficiency from the supply chain, we were undervaluing resilience to Black Swan events from wars to infrastructure failures to natural disasters [1].  One person’s inefficiency is another person’s insurance.

5.  Work from home sticks.  Hit.  At this point perhaps for the wrong reasons (i.e., omicron), but where and how we work has already changed and many of those changes will become permanent.  McKinsey is producing some strong content on the future of work as is my friend Dan Turchin on his AI and the Future of Work podcast.

6.  Tech flight happens, but with a positive effect.  Hit.  A lot of Californians have moved to Texas, Arizona, and Nevada — but a lot have also moved to California (i.e., from the Bay Area to cheaper parts of the state).  Florida, despite the hype, nudges out Oregon for fifth place.  My point was that this is normal and healthy:  you can long Miami and Austin without shorting Palo Alto which, by the way, would have been a bad idea in 2020.

7.  Tech bubble relents.  Miss, until recently.  My world is probably best approximated by the WCLD ETF, which opened the year at $53, recently hit as high as $65, and (as I write) is at $51.  Taking the longer view, WCLD has nevertheless more than doubled over the past 5 years, so a lot of this depends on what you mean by “bubble” and “relent.”

Towards that end, revenue multiple is a better bubble indicator than share price, so let’s take a look at the latest from Jamin Ball at Clouded Judgement.

Are multiples down?  Yes, from a median high of nearly 20x to 12x, nearly 40%.  So I’d say yes on “relent.”  On “bubble,” well, we’re still at 12x compared to what I’d say is a normal (eyeballed) range of 6-10x — so we’re still running hot by historical standards.  [2]

8.  Net dollar retention becomes the top SaaS metric.  Hit, depending on what you mean by “top” [3], but my real point was the NDR would replace churn rates as a method for valuing the installed base and I think it has.  See my SaaStr 2020 talk or my GainSight Pulse 2021 talks for more.

9.  Data intelligence happens.  Partial hit.  I’d say it’s “happening” much more than “happened” because we’re still early days in a multi-year category transformation.  My friends at Alation continue to crush it driving their vision of data intelligence extending from the data catalog [4].

10.  Rebirth of EPM.  Hit.  While the second-generation EPM vendors [5] continue to prosper (i.e., Adaptive within Workday, Anaplan and Planful as independent companies) the industry is nevertheless being reborn underneath with new firms such Cube, Mosaic, OnPlan, and Pigment blazing the trail [6].  It’s exciting to watch.

Kellblog Predictions for 2022
Well, here we go with our predictions for 2022.

1. Covid goes from pandemic to endemic.  I’m not sure we ever had a realistic chance to keep the genie in the bottle, as they did in New Zealand, but at least our actions bought us time to create and deploy vaccines.  By the way, if you look at this chart, you might argue that New Zealand, in the end, failed to keep the genie in the bottle.  [7]

See the big bump?  Yes, it does seem that trying to bottle up Covid was destined to failure.  Or was it?  Look at the scale.  Then compare New Zealand to Louisiana, which has a similar population.

The New Zealand peak is 200, the Louisiana peak is 30x higher at 6,000.  If nothing else, and since this is something of BI-focused blog, Covid has taught us a lot about How Charts Lie.

But back to our prediction.  I think 2022 will be the year we stop thinking in pre-Covid and post-Covid terms, and accept that Covid-19 will become endemic.  Much as malaria brought us screened windows and cholera brought us clean water supplies, Covid will be with us for a long time and bring with it lasting (and hopefully in some cases, positive) changes to our day-to-day lives.

2.  Web3 hype peaks.  Is web3 going to change everything because, as Chris Dixon argues, the best entrepreneurs and developers have learned not to build atop centralized platforms?  Or, as Stephen Diehl so indelicately puts it, is web3 bullshit whereby, “the only problem to be solved by web3 is how to post-hoc rationalize its own existence?”  Or are Moxie Marlinspike’s first impressions right — e.g., the missing element in “crypto” is cryptography and that decentralizing the internals of underlying layers won’t prevent centralization at the more nimbly evolving layers above?

Is web3 a ploy to put crypto bros in charge where “the promise of decentralization is just a veneer — and blockchain is, in fact, the worst kind of vendor lock-in?”  Or, did the venerable Grady Booch get web3 right in his retweet below?

Maybe Tim O’Reilly, the person who coined the phrase web 2.0, has the best take [8], arguing simply that it’s too early to get excited about web3.

It sure does feel like 2005.  There are a bunch of new ideas in circulation.  Everyone is talking about them.  People are struggling to understand them and building frameworks to organize and explain them.  And sometimes it’s hard to tell what’s foundational to the new concept and what’s trying to hitchhike a ride on the back of it.  Based on this, I think we’re building towards a web3 hype peak that should happen in 2022 [9].

I’ve always believed that blockchain was invented to support a specific use-case (i.e., bitcoin) and, unsurprisingly, is good for that use-case but has otherwise largely been a technology in search of a business problem — particularly in the enterprise.  Imagine if you went to SIGMOD twenty years ago and predicted the database of the future would be:

You’d have been laughed out of the room.  Despite that, the reality is that database (i.e., blockchain technology) is quite useful for cryptocurrency applications.  The addition of smart contracts were a very a powerful extension that came with Ethereum.  Changing from proof-of-work to proof-of-stake may eliminate the crazy wasted compute and associated energy consumption [11].

But, as I’d say with any special-purpose database — from an OLAP server to an XML database to the Hadoop ecosystem:  it’s great at what it’s built for, but why should you use it for something else?  The default answer is you shouldn’t [12].

When it comes to the decentralization argument, enterprises are inherently centralized in power and rely on centralized systems run by a centralized IT department.  Moving enterprises to decentralized internal systems does nothing to change lock-in factors of their products (e.g., network effects that lock you into Facebook).  Nor necessarily does empowering distributed networks with decentralized technologies — see the above-linked proof-of-stake recentralization arguments.  And if blockchain means automatic freedom from intermediaries, why is Coinbase worth $50B again?

I think DAOs are an interesting concept (great primer here), but the blockchain linkage seems contrived [13] — I could make a Dunbar-number-sized group with organic governance rules and run it via in-person meetings, Zoom, Slack, or of course, Discord.  (Arguably, Richard Branson did, many times.)

I don’t know why anyone would pay $10M for a CryptoPunk or $300K for a Bored Ape, but I do understand collectibles:  an ape costs $300K in part for the same reason that a 1943 bronze Lincoln cent costs $1M — scarcity.  I just thought we were going to use the Internet to eliminate scarcity, not artificially create it.

Finally, I think the self-referentiality of this ecosystem is interesting.  If you want to buy a non-fungible token (NFT) of a Bored Ape, you’re going to need to pay in Ether because that’s the currency the price is listed in.  Which in turn increases demand for Ether.  Note interestingly that while you can use Ether to buy an NFT, you can’t use an NFT to buy Ether because NFTs are not fungible, as Alexis Gay says, “in the sense that you couldn’t funge them.”

3.  Disruptors get disrupted.  When I graduated from college, Oracle (founded 1977) was a ~$30M brash upstart challenging the entrenched leader, IBM, who no one ever got fired for selecting.  I watched Oracle aggressively grow to $1B in revenues, flail several times trying to organically expand into applications, give up on building applications and instead acquire them, inexplicably get into hardware with the acquisition of Sun, and eventually plateau at $40B, effectively having become IBM in the process.  As the saying goes, we become our parents.

Salesforce (founded 22 years later) is well into that cycle, going from brash disruptor to organic grower to M&A-driven grower, though they do a better job of preserving the entrepreneurial spirit if not growth (both were growing at ~25% at the $20B mark).

This is an ongoing pattern driven by Clayton Christensen’s cycles of disruptive innovation.  If you watch this cycle long enough, you can see the disruptors get disrupted — e.g. Siebel was disrupted by Salesforce who was disrupted by Zendesk who is being disrupted by Freshworks.  What drives these disruptive cycles:

  • Feature creep, which leads to market overshoot over time.
  • Management changes, as leadership teams drift from a spirit of value creation for customers to value extraction from them.
  • Specialization, as market leaders build breadth with integration of good-enough products, an opportunity is created for great, point solutions (which often later expand to challenge the core product).
  • Technology platform changes, which antiquate previous architectures, allow new solutions to be built more quickly, and enable entirely new classes of applications.

For several reasons, I believe in 2022 we are going to see many disruptors get disrupted.  Why?

  • Change to cloud-native.  First-generation cloud solved a deployment problem; second-generation solves a development problem as well.  When I build new apps, I can rely not just on my previously developed or open source modules, but on live, running services.  Upstarts can stand yet again on the shoulders of giants.
  • Flood of venture capital (VC).  VC is flowing at unprecedented rates driving record funding amounts at both the early company-creation stage (e.g., seed, angel) and the later growth stage as well.
  • High-growth.  The combination of Covid accelerating digital transformation and unprecedented VC financing has accelerated software company growth (aka, the Covid boost).  At the second order, I can’t help but wonder if accelerating the growth cycle hastens the aforementioned process that creates new disruption opportunities.  Software companies become their parents faster.
  • Product-led growth (PLG).  SaaS provided provided both a market disruption opportunity and a total available market (TAM) expansion in each market segment.  While I’ll cover PLG more below, I think it will have a similar effect, providing both a disruption opportunity in existing segments while simultaneously expanding their potential.

4. Venture capital continues to flow.  2018 was the first year since “the OB” (the original bubble) that we again reached 2000-era levels of VC financing.  2019 dipped a bit, but 2020 came back strong, and 2021 looks to be a blockbuster [14].

PitchBook data reveals that while total funding and mega-funding (where the round raises $100M+) are up, deal is count slightly down, meaning average deal sizes are up and consistent with my view that VC today is have or have-not market.  The haves can raise can raise a ton of money and on good terms.  But the have-nots — those who have yet to demonstrate a strong team, product-market fit, or a scalable growth model — cannot, and face a frustrating form of hunger in the land of plenty.

They keys to success in this environment are two:

  • Raise when the raising’s good.  If you can raise money, you (likely) should.  If you can’t, figure out why — dig beyond superficial, “nice” explanations into real reasons, and then go fix them.  Fast.
  • But trigger spending on business signals.  You undoubtedly raised your most recent financing on the back of an aggressive operating plan.  But don’t, don’t, don’t — for example — hire 10 sellers because they’re in the plan:  hire them because the CRO made the last 10 productive and wants to hire 10 more.

One of these years — maybe 2022, maybe thereafter — VC will be in tighter supply.  So raise money in large quantity when you can.  Fear not dilution — you’ll likely be raising at (what are, by historical standards) stratospheric valuations.  Most of all, while you shouldn’t follow my miserly great-aunt Jo’s expense strategy (whose dying words were “don’t spend”), you should spend if, only if, and when it makes good business sense to do so.

5.  The metaverse remains meta.  If you’ve not taken the 10 minutes yet, you should probably look at this Facebook/Meta, rebranding launch video, a well-produced but at times amazingly awkward metaverse concept video.

The metaverse vision has provoked a range of reactions from dystopian nightmare to dead-on-arrival to heated discussions of “reality privilege” and accusations about the new billionaire utopian boondoggle.

It’s also invited a fair bit of parody, my favorite being the Icelandic tourism board’s, Icelandverse.

Back to the metaverse, I find the vision more Oasis-style (Ready Player One) dystopia than utopia.  While I find the idea of reality privilege interesting intellectual banter, no, I don’t think the best solution to humankind’s problems is to hook everyone into an alternative, virtual reality.  Good sci fi?  Yes.  Good reality?  No. Not in the least.

  • Are virtual worlds fun for immersive gaming?  Yes.
  • Do you need virtual (or crypto) currencies in those worlds?  No, they’re just an add-on money-making opportunity like a Starbucks card [15].  You can buy an upgraded weapon in a game today via a regular credit card [16].
  • Do you need virtual museums in which to hang your NFTs?  They’re cool and I guess collectors do like to show off their collectibles, so maybe [17].  That said, CryptoPunks weigh in at a slim 576 pixels so I don’t think you’ll need fancy display capabilities for some NFTs at least.
  • Do you need virtual real-estate within your virtual world?  Second Life had a full economy with Linden dollars and real-estate, so the idea’s not new, but metaverse real-estate is setting records today.  If the key to real estate is location, location, location, that’s not really a constraint in the virtual world.  That said, a key theme of web3 seems to be manufactured scarcity (which generative NFT collections do well) and which ultimately comes down to a simple matter of trust [18].
  • Can augmented reality help business applications, like customer service?  Yes, I think AR has numerous practical enterprise use-cases and, if nothing else, all the VR technology will benefit more pragmatic use-cases in enterprise.

6.   PLG momentum builds.  While I generally have a negative reaction to hype, and I don’t like the either/or nature of the slogan below, I do think PLG is a good idea.

Let me separate PLG into what I see as two pieces:

  • PLG as business strategy, where the business is built around a model in which marketing and community relations drive end-users to try a product, hopefully like it, buy the ability to use it (or use it more fully), tell their colleagues (directly or virally, e.g., through a Calendly invite), and repeat the cycle.  While Slack, Zoom, and Dropbox are frequently-cited examples, a full list might include over 300 companies.  (You can read a great anatomy of them, here.)
  • PLG as as set of product requirements.  I think PLG brings three core, generic product requirements, none of which have frankly been common to previous generations of enterprise software:  build a product that (a) is quick to deliver end-user value, (b) is easy and even fun for the end-user to use, and (c) is built with the company’s revenue growth strategy in mind, e.g., in-built virality and carefully-selected functional and enterprise-level pay gates.

Many of the concepts behind PLG aren’t new.  Open source has always been about building a community of users who love the product, though historically composed of developers and not end-users.  Market-seeding isn’t new, though prior-generation seeders like Crystal Reports did so not through marketing- and community-driven downloads and trials, but channels of distribution [19].  Consumerization of enterprise software isn’t a new idea , but I’d argue that it’s only become real with the advent of PLG.  Velocity sales models aren’t new either, but they’re also a key part of PLG.

Some PLG ideas are new:

  • User-experience (UX) as job #1.  Only when UX became critical to business/sales strategy did it get serious commitment instead of lip service (in the enterprise at least).
  • Growth teams, subordinating functional silos to united teams of marketers, engineers, analysts, and designers working together to drive growth.
  • Digital experience tools, that go beyond useability testing labs to track what users actually do in the software with an eye towards making it better — such as Pendo, Heap, and Amplitude.

While I think it’s serious overstatement to say, “sales- and marketing-led growth is dead; long live product-led growth,” I think it’s equally dangerous to dismiss PLG along with quarter-zip sweaters as the latest VC fad.  PLG brings many good ideas that companies should consider and map to their own business models.  Despite the risk of PLG noise drowning out PLG signal, I believe companies will increasingly and intelligently apply PLG principles in 2022 — and if you’re not thinking about how to do that, you should be.

7.  Year of the privacy vault.  While I’m not an expert in this field, I am learning more, and I see a lot of exciting things happening in information security:

  • Innovations in digital identity from companies like Ory and Presidio Identity [20].
  • Innovations in cloud security and governance from companies like Cyral and Privacera [20].
  • Innovations in enterprise privacy from DataGrail [20].

The emerging and ever-changing nature of information security is a big part of what interests me, because it means that a lot of smart people with interesting ideas are attacking numerous problems from different angles.  While this leaves me in a near-perpetual state of confusion, I’ll repeat what I’ve often said about the metadata space:  anyone who isn’t confused doesn’t really understand the situation (Edward R. Murrow).  In metadata, I feel like I finally do understand the space.  In information security, well, I’m still working at it.

In the past ~25 years, there’s a particular feeling I’ve had only on rare occasion:

I’ve met a lot of great entrepreneurs and worked with a lot of great companies during those years, but only those three times did I have the immediate reaction:

  • This is obvious.  (Well, post facto obvious, once you understood it.)
  • This is huge; everyone needs this.
  • I need to be a part of this.

In Anshu’s case it admittedly took more than one drink for me to understand the idea, but what I liked about it, what made it seem so post facto obvious was this:

  • Enterprises, where possible, should get out of the business of handling sensitive information.  I know it’s not always possible, but if the data is non-core to operations, why not delegate storing it to someone else?  While hospitals need to store medical images, does TurboTax really need to store your social security number to file your taxes once per year?  It’s hard.  Let someone else do it.
  • You can replace sensitive data with tokens.  You don’t need to store someone’s credit score when you can store a token that maps to it and isolate the score to a separate database.  It’s classic indirection.  But it usually means you can’t then do anything with the data — unless you incorporate the ideas in the next two bullets.
  • You actually need an API more often than you need access.  Most of the time you don’t need direct access to sensitive data, you just need to do something with it.  You don’t need to know someone’s credit score; you need to know if you can make them a loan and at what interest rate.  That is, you can pass a token for credit score to a service that returns approval status and approved rate in a loan approval application.
  • You can encrypt data without losing the ability to work with itPolymorphic encryption lets you verify the last four digits or a social security number or return all phone numbers in the same area code without first decrypting the data.  This means you can get utility from encrypted data.  Not being a security person, this idea was entirely new and fairly mind-blowing to me [22].
  • Vaults are an existing design pattern.  Google, Apple, and Netflix have taken a low-trust, tokenized vault approach to handling sensitive information in their internal systems.

We will see if my spider sense was correct a third time.  While my sense is most developed in data and analytics, I love modularization, normalization, and specialization and this play is about all three.  To hear the Skyflow story directly from Anshu himself, watch the video here.

8.  MSDS is the new MBA.  For decades, and often contrary to prevailing fashion, I’ve counseled people to consider getting an MBA during their career journey for any of the following reasons:

  • The knowledge.  MBA coursework is generally useful in business, regardless of the caliber of school you attend.
  • The network.  At a top school, you will likely become part of a great network that will benefit you throughout your career.
  • The career-change opportunity.  The MBA offers a unique chance to switch roles or industries (e.g., from engineering into product, from consumer to enterprise).

Given the time and cost of MBAs, it’s popular these days to say that MBAs aren’t worth the trouble.  Autocomplete confirms these doubts.

While I frequently still recommend MBAs to those who seek my advice, I find myself increasingly asking them:  have you considered a master of science in data science?  Such programs can be done in as little as half the time and at half the cost of an MBA, have numerous online and hybrid options, are offered by many prestigious schools, provide superior analytical training, and offer similar career change opportunity.

While a top-tier MBA will still be de rigeur in investment banking, VC, and management consulting for the foreseeable future, I do believe that mid-career professionals will increasingly evaluate the MBA and the MSDS as alternative means to advance their careers — and that many will take the MSDS route.

9.  Get ready for social impact.  Millennials, and for that matter, many of the rest of us, increasingly demand purpose in our work.  If we’re going to spend 40, 50, or more hours per week working, then we’d like the company to provide both a paycheck and a sense of purpose.  In the workplace, according to a recent Gallup report, millennials want leadership to change its approach:

The sense of purpose, however, goes well beyond the workplace and includes the desire to address societal concerns related to sustainability, capitalism, human rights, and social justice.  While Boomers and Xers were content to Party Like It’s 1999, the next generation wants to focus on the future and solving the world’s largest problems.  Good.

This drives for whom and how they want to work, the products they buy, the brands they value, the vacations they take, the causes they support, the hobbies they pursue, the lifestyles they lead, and the money they invest.  In short, everything.

This era has brought us everything from local organic produce and forks over knives to the 1% Pledge, the B Corp, DEI, impact investing, ESG funds, stakeholder capitalism, carbon offsets, and data rights as human rights.

I think Europe is leading the US on many of these changes so, as per the famous William Gibson quote, I get a glimpse into the future through my work with Balderton Capital which has not only committed itself to a set of sustainable future goals (SFGs), but also recently announced their first annual progress report on them.

ESG momentum will build in 2022.

10.  The rise of causal inference.  For the past decade I’ve told people that data science was the new plastics — in the sense of the famous quote from The Graduate.

While I think that was spot-on, this year I have a new “one word” —  causal inference.  Why?

  • Most of the data science we do today is some sort of classification and regression.  We can group like entities, we can predict into which group a new one will fall.  We can build a mathematical model of an independent variable and make predictions about it based on dependent variables.  It’s cool stuff, but in the end, this is about correlation.  How things move together.
  • Yet, we all know that correlation does not imply causation.  We know that windmill rotation doesn’t make the wind blow [24].  We know that waking up dressed doesn’t cause headaches and that ice cream sales don’t cause drownings [25]. Yet, most businesspeople today forget that when they’re interpreting data.  We say that correlation does not imply causation and then we say stuff like, “all of the customers who churned last quarter filed more than five severity-one cases in the past year!”  [26]
  • The first-generation of data science has given us lots of data and some great modeling tools to interpret data.  The bad news is that we — not data scientists, but regular analysts and business people — are not very good at interpreting it.
  • Where possible, we need to figure not just where variables correlate but what actually causes what.  To do so normally requires an experiment (i.e., a RCT) but sometimes causal questions can be correctly answered using observational data.  The insight about how to do that, by the way, is not trivial — it won the 2021 Nobel Prize in Economics.
  • The big guys are doing it.  A decade ago the hyperscalers had data science teams and typical companies, even large ones, didn’t.  Today, the hyperscalers have causal inference teams and typical companies don’t.  To the extent you believe the big guys are leading indicators of the mainstream, you should believe that determining not just correlation, but causation, is coming soon to a business meeting near you [27].  You can get ready the easy way or the hard way.

If you made it this far, thank you!  Read the links — there’s gold in those hills.  Remember that I write this post in the spirit of fun and to force myself to research interesting topics.  Have a happy, healthy, and Rule of 40 positive 2022.

Peace out / Dave.

# # #


[1]  I did study seismology (i.e., geophysics) after all.  Earthquakes happen.

[2]  As mentioned in last year’s post there are plenty of possible reasons for this including the possibility that the companies are higher quality and/or growing faster — see last year’s post.

[3]  Some might argue growth is top — particularly if you define top as most correlated to revenue multiple.  Based on data as of this writing, the R^2 between EV/NTM-revenue multiple and NTM-revenue-growth is 0.52 vs. 0.24 for NDR.  Play around here for more.

[4]  Reminder that I am an angel investor in and sit on the board of Alation.

[5]  Who are the first-generation cloud EPM vendors

[6]  I am an investor in Planful and Cube, an advisor to OnPlan, and occasionally chat with Mosaic and Pigment, among others.  Hey, I like EPM.

[7]  Louisiana actually has about a 10% smaller population (4.6M) than New Zealand (5.1M)

[8]  Tim’s What is Web 2.0 post is well worth reading both for the history lesson and, more subtly, to beam you back to a time where something was emerging and what it looked like for people to try and understand and describe it.

[9]  Gartner has a blockchain hype cycle (that lists numerous web3 technologies) but not a web3 hype cycle.  Currently, NFTs are at the approximate peak of that cycle.

[10]  Technically, BASE as a database concept didn’t exist at the time.

[11]  Though not without its own problems.

[12]  A repeated pattern in database history — everyone wants to rule the world because it’s a big world to rule.  Most of the time, however — and relational databases are a notable exception — the new database is not a great general-purpose alternative.  The reductio argument here is there should be no general-purpose databases as every purpose is a special one.

[13]  See prior comment about hitchhiking.

[14]  Sources include Statista, PitchBook, CBInsights, and (in one case) my estimates.

[15]  In addition to providing Starbucks with consumer data, they have $1.6B in prepaid value today.  Remember a big part of how Warren Buffet got to be Warren Buffet:  float.

[16]  Yes, I understand that games can force you into their currency by providing rewards in game-units and that you can create a one-way transformation between cash and game-units (i.e., you can buy units with cash, but not cash with units).

[17]  Museums provide access as their core function but also offer security, preservation, and education (e.g., docents) surrounding their works.

[18]  Trust that the promoters will keep their promise about number and trait distribution of works and avoid the tendency to excessively extract value by minting more and/or derivative works (e.g., mutant apes) that potentially undermine the original collection and devalue traits.  Creating scarcity is easy.  Preserving it might well be hard.

[19]  In many cases because, well, the Internet didn’t exist yet.  Microsoft helped to put Crystal Reports on the map by distributing it with Visual Studio.

[20]   Disclaimers:  I’m an advisor to Presidio Identity.  Ory is a Balderton portfolio company.  I’m an advisor to and investor in Cyral.  I have done some consulting with Privacera. I am an investor in DataGrail.

[21]  Which quite happily I made.

[22]  Read up on fully homomorphic encryption which enables you to perform calculations on data without first decrypting it.  While fully homomorphic encryption is prohibitively computationally expensive, another key Skyflow insight was that many “numbers” aren’t fully treated as numbers in practice — e.g., you might verify the last 4 digits of an SSN but you’re never going to multiply two of them.

[23]  The SFGs linked come from Balderton Capital where I work part-time as an EIR.

[24]  Reverse causation.

[25]  The third-cause fallacy.  Going to bed drunk increases both waking up dressed and having a headache.  Warm weather increases both swimming rates (which increase drownings) and ice cream sales.

[26]  They also all had, e.g., brown-eyed CIOs, more than $500M in revenues, and parking lots with more than 200 spaces.

[27]  Irony alert, I’m making a correlation-based argument here!

How to Know if You Have the Right Executives on Your Leadership Team

Is your leadership team world-class?  Are some of your executives holding your company back?  Could you grow faster if you replaced your head of sales?

Does your board think you have the right leadership team?  Heck, does your leadership team think you have the right leadership team?  Do your rank-and-file employees?

When you stick with a VP who helped build the company but who seems to be past their sell-by date, are you demonstrating loyalty as a strength or conflict-aversion as a weakness?

These are some of the questions that keep founder/CEOs up at night.  While founders who’ve spent time at larger companies have some experience with SVPs and CXOs at different scale, for many founders this is entirely greenfield territory.  Think:  I’ve built this great $10M ARR company but I have never run (or been a C-level executive at) a $50M ARR company, ergo I really have no idea what the proverbial “next-level” team looks like.

Or, simply put, how do you know if you have the right people around executive staff table?  To determine the answer, do these 5 things:

  • Evaluate performance.  An obvious sign that someone is in over their head is a lack of performance, missing targets (e.g., new ARR), OKRs, or hiring goals — either in terms of number or quality (particularly when staffing their own leadership team).  Someone who’s not performing is definitionally already in over their head today; we don’t need to wonder about tomorrow.
  • Get 360 degree feedback.  From your team’s leadership coach (if you have one), from the e-staff peer group, from a formal 360 degree feedback program, from employee satisfaction surveys (e.g., CultureAmp), and from the board.  This informs you with a holistic view of how the executive is seen within the organization.
  • Do calibration meetings.  Always be calibrating — always seek out next-level or next-next-level executives and have a coffee with them.  The only way I know to develop your own sense of “seniority” is to meet lots of senior people.  Use your board and your network to get access.  Ask questions about current issues you’re facing and the road ahead.  You’ll build your network, have people you can rely on for future advice, and — who knows — maybe one day you’ll come back and hire some of them.  The next time one of your board members says, “your CXO isn’t world-class,” ask them for three introductions to people who are.
  • Listen to your gutDo you look forward to meeting with them?  Do they bring or take energy?  Are meetings more productive when they come or when don’t?  Do they suck the air out of the room?  Are they Eeyore or Pooh?  If you consistently don’t look forward to meeting with one of your direct reports, it’s an important tell of a major problem.  The e-staff is helping you build your company; you should be excited to meet with each and every one of them, every time.  If you’re not, you need to ask yourself why.
  • Ask.  Sit down and ask the executive how they’re doing, how they feel about the organization and the road ahead, if they’re still having fun and enjoying their job, and if they feel like they are up to (and up for) the challenges ahead.  Sometimes, they’ll share their concerns and you can build a program to support them.  Sometimes, they won’t be candid, effectively denying themselves help or redeployment.  Sometimes, as once happened to me, they’ll say they’ve been thinking about going into real estate with their brother.  You won’t find out if you don’t ask.

The most important part of this process is realizing that you have options and using them.  Unless you want to create an up-or-out culture of disposable people, you need to consider all your options for an executive who has run out of runway:

  • Redeployment.  Moving them to a different, senior post (e.g., taking your old VP of Marketing and having them go to London to help open Europe).
  • Layering up.  Restructuring so as to add an additional layer above the executive.  People generally don’t like this but will try and/or tolerate it if they understand why you’re making the change and believe that they can potentially learn something.  (Unvested in-the-money options don’t hurt, either.)
  • Benching.  Given them an important job but one below their capabilities until such time as you find something you really need them to do.  Think:  you’re still on the team but not playing this period while I figure out what to do with you.  Too few executives do this because it’s nominally expensive, but the cost of not doing it is a loss of talent and organizational knowledge.
  • Leave of absence.  In some cases, rather than carry the player on the bench, both the company and the executive could benefit from a leave of absence for a few months where, in a good scenario, the executive returns into a needed role, refreshed and ready for a new challenge.

The more fluid and flexible your culture — e.g., defining jobs more as tours of duty while building the company than as functional empire building — the more options you will have.  But it all starts with answering the question:  do I have the right executives around the table?

Do the 5 actions above to figure it out.