Category Archives: Strategy

Traditional B2B Sales is Dead, Long Live the UCE?

In the land of disruption, there’s always something dying and something lining up to replace it, so we’re pretty used to hearing things like “on-premise is dead, long live SaaS.”  Sometimes, they’re right.  Despite the 2008-era views of our resident luddite below, SaaS really did kill on-premises.

Sometimes they’re wrong.  Despite years of hearing, “the data warehouse is dead, long live the data lake,” the data warehouse is doing just fine, thanks.  Snowflake can tell you 60 billion reasons why.

Sometimes, they’re both right and wrong.  Data lakes are doing pretty well, too.  Not everything is zero sum.

You don’t hear this just about technologies, but business models, too.

When the Internet eliminated sellers’ monopoly power over information, I heard, “traditional B2B sales is dead, long live facilitating buying processes.”  This was right and wrong.  B2B sales wasn’t dead, it just changed.  When buyers can get more information themselves and advance further without needing sellers, reframing sales as facilitating buying is a good idea.

When product-led growth (PLG) became the rage, you started to hear it again:  “traditional B2B sales is dead, long live PLG.”  While companies like Atlassian really did dispense with traditional B2B sales, other companies — like Zendesk, Slack, and Twilio — showed the power of blending the two models.  Heck, even Atlassian eventually blended them.

I think of PLG as embracing the continuation of a trend already started by the Internet.  In phase one, buyers no longer needed sellers to get basic product information.  (It’s almost hard to believe, but back in the day, if you wanted even a white paper let alone a demo, you had to talk to a seller.)  In phase two, buyers no longer needed sellers to get hands-on product trial.  It’s the same transformation, just applied to the next two phases down the funnel.

While some companies consider trials customers (and ergo need to count them in churn), I think most enterprise startups should consider trials leads, and the ones who do the right things with the product become leads worthy of passing to sales.  Because they’re qualified by product usage and not marketing actions, they’re called PQLs instead of MQLs.  (Ask my friends at Correlated, or any of the new PLG CRMs, to learn more.)

The other day an old friend of mine, now a highfalutin GM at a big-name software company, forwarded me this article, Traditional B2B Sales and Marketing are Becoming Obsolete.  So, anticipating the content, I donned my “it’s PLG and enterprise, not PLG or enterprise,” gloves and got ready to fight.

But I was surprised.  Instead of saying, “B2B sales is dead, long live PLG,” the article threw me a curveball:

“B2B sales is dead, long live the unified commercial engine (UCE).”

Huh.  The what?

Who wrote this, I think?  Ah, it’s some guy from Gartner.  Before I can add, “and they should stick to IT prognostication,” I see that “some guy” is Brent Adamson, coauthor of The Challenger Sale, one of my top five favorite sales books.

Darn.  Now I have to read this eight-page article and figure out what I think.  The rest of this post is the result.

The Article:  Summary and Analysis
The article argues that it’s no longer enough to try and integrate (in the sense of align) sales and marketing, we should instead unify them.  That’s because buyers have more access to information (including hands-on trials), buyers have access to that information via multiple channels (e.g., vendor websites, review sites), and buyers don’t want to interact with salespeople (which is not exactly new, though he argues that younger people want to interact with sellers even less than older ones).

Sales is thus fighting for relevancy in the buying process and seeking to regain customer access.  The linear model is dead, long live the unified model.  In short:

Helping today’s B2B buyers buy isn’t a sales challenge, nearly so much as an information challenge (or, alternatively, an information opportunity).

He begins with motherhood and apple pie:

The companies that best provide customers the information they most urgently seek, specifically through the channels they most clearly prefer, are in a far better position to drive commercial success in today’s rapidly evolving digital commercial landscape.

He moves into rhetoric to amp things up:

While once a relatively accurate proxy for the underlying buying behavior it was meant to approximate, the serial commercial engine is hopelessly out of date — and dangerously out of sync — with how today’s B2B buyers buy.

With a requisite Gartner dash of profundity:

Today’s buyers are not only channel agnostic in terms of behavior, they’re digitally dominant in terms of preference.

I think that means people like to research shit online before buying it.  Got it.  Stipulated.

I always say that any good sales pitch is 80% tee-up and 20% knockdown.  Now, on the receiving end of such a pitch, I need to advise some caution in that approach.  At some point people want to hear your solution; I’m on page 6 of what’s barely 8 pages and still waiting.  It’s always easier to agree on the problem than the solution (e.g., child poverty, wealth inequality, climate change).  It’s why the 80/20 formula works — you get people agreeing with you, sounding smart, heads nodding, and then you shift to a credible solution that drives your agenda.

But you can’t wait too long to shift to the solution (so I should probably revise my rule to 60/40).  And you should introduce the solution from first principles, not via a case study (which you can always present later, as proof). And if you’re going to introduce the solution via a case study anyway, it shouldn’t be a 1300-person company based in Calgary that I’ve never heard of.

Yet, here I am, about to learn how SMART Technologies found the answer to this pervasive problem by “rebuilding it from the ground up.”  But first, I need to learn about SMART Technologies.  I am now at page 6.75 of an 8.25 page article and still not heard the solution.

The answer:  completely dismantle traditional sales, marketing, success, and service altogether and reconfigure them into a unified commercial engine (UCE).

I’m now thinking:

  • Can you partially dismantle something?
  • Can you completely dismantle something without it being altogether?
  • Where did success and service sneak into things?  While I’d certainly, almost definitionally, want to put all customer-facing teams into a unified engine, how is it that success and service are totally omitted from the argument’s tee-up?

You create a UCE by:

Careful mapping of customers’ buying journeys across a range of predictable “jobs to be done” as part of a typical educational technology purchase.

Never one to miss a gratuitous Clayton Christensen reference, I have to observe that while I am big believer in his work and the jobs-to-be-done framework, I think this is something of a misapplication.  Christensen’s point was about innovation — if you think of products as hired instead of bought, and hired to do specific jobs, then you will anchor yourself in the customer’s point of view when contemplating new products and features.  Think:  not how can we make this milkshake tastier, but how can we make this milkshake more effective when it’s hired as a one-handed commuter breakfast.  What we’re talking about at SMART is simply mapping customer journeys.

When you do that careful mapping, this happens (or, at least, this is what happened at SMART):

Through that initiative the team identified five common buying jobs (Learn, Buy, Order/Install, Adopt, Support) and established an internal team specifically deployed to support each one, reassigning nearly every member of legacy marketing, sales, service, and success staff as a result. In all, over 250 team members received new job designations as part of the process.

You can’t do a re-org these days without creating a center of excellence, so SMART created three:

SMART created three centers of excellence, where they consolidated otherwise duplicative efforts across traditional functional boundaries, one for data and analytics, and one for customer insights and positioning, and one for creative and digital experience.

Those, by the way, sound like a good idea.  I like centralized, specialized support teams, particularly in areas where we’re trying to present one face to the customer.

And then, the re-organization:

Finally, the team then deployed their staff in geographically aligned “pods,” where each pod contains members supporting each of the respective five buying jobs. So, the pod for the southeast United States, for example, is made up of combination of individuals tasked with supporting the entire range of customer jobs from Learn to Support across all relevant digital and in-person channels (including third-party distribution).

In short, run your regions in the USA more like you run countries in Europe.

It’s neither a bad idea nor some insanely different approach.  It does create the need, however, for sophisticated regional leaders who are capable of aligning on both dimensions of the matrix.  Concretely:  is the French country marketing manager part of the French team or the marketing team?  Answer:  it’s a trick question.  The answer is both and they need to learn which way to look, when, as they face managerial decisions — e.g., look to the CMO for questions on messaging and positioning, look to the French country manager when prioritizing campaigns and investments.

I’m going to ignore the end of the article where the VPs of sales and marketing proudly introduce themselves “the former heads” of their respective departments, because they both seem to still work at the company and do something, though the article doesn’t say what their new titles and jobs are.  I’ll assume, hype and semantics aside, that they’ve implemented some sort of functional vs. pod matrix.  As one does with countries in Europe.

Before wrapping up, let me challenge some of the more detailed points in the tee-up.

  • Yes, the machine is by default linear.  But that’s just the first pass.
  • Contacts that don’t make it MQL or SQL get put into nurture and nurture is not linear.  Nurture is a popcorn machine where we dump kernels in, expose them to heat, and over time and in a pretty random order, the kernels pop into recycled MQLs.  I’ve run companies where half of all MQLs are recycled.
  • There’s the question of whether we should nurture people or accounts, as we would in account-based marketing.  Nurturing accounts is definitely not linear, it’s like having one popcorn machine per account.
  • There is no 11th Commandment where God said that nurture shalt be digital only.  While a lot of nurture is automated digital, marketers should remember that a nurture track, broadly defined, can also involve live events (e.g., C-level dinners), dimensional marketing (e.g., mailing a coffee-table book or a Moleskine), and live interactions  (e.g., SDR or AE outreach).  Nurture doesn’t definitionally mean a sequence of emails, nor should it.
  • The part of the linear handoff I detest is when sales “waives off” marketing once an opportunity is in play.  This happens less frequently than it used to, but it reveals a deep lack of trust that should be fixed by destroying walls, not erecting them.

I’ll conclude by saying I think the article misses the most important point in organizational design.  When it comes down the game on the field, who calls the plays and the audibles?  Sure, we have a playbook, and we all know the play we’re supposed to be running.  But things have changed.  There’s a new participant in the meeting.  They mentioned a new competitor who we didn’t know was in the deal.

With a group of talented people, they’ll usually be several different and vocal opinions expressed on how to proceed.  The AE may want to reschedule the meeting.  The SC wants to proceed with the demo.  The consultant thinks we shouldn’t be in the deal in the first place.  The sales manager thinks we can win it because the champion has our back.  What do we do?  Who calls the plays and the audibles that modify them?

In my mind, the person with the quota wins.  As the old joke goes about breakfast:  the chicken is participating, but the pig is committed.  In a world where accountability for results legitimizes decision-making authority, it’s not enough to have a pod/commune and say we can all work it out.  Sometimes we can’t.  And often we can’t fast enough.

Whether you call that person the regional pod leader or the country manager, the role needs to exist and they need to take lead on deal strategy. Everything else is a supporting resource.  Which is why I think marketing alignment with sales is enough.  Yes, we need to collaborate and yes we need smart managers to work in the functional/regional matrix.  Yes, in the case of marketing, we need field marketing to ensure ground-level local alignment.

But do we need to reorganize everything into regional pods?  No.  We just need to work together and be aware that buyers have more information, options, and control than ever before.

# # #

End note
The irony is I ran a company in a pod structure, MarkLogic, where we had vertical pods (aka, business units) where we didn’t have sellers, SCs, and consultants.  We had Federal sellers, Federal SCs, and Federal consultants — all working for a VP/GM of Federal.  Ditto for information & media.  But we did it not in the name of “traditional B2B sales is dead because buyers have more information,” but in the name of a vertical go-to-market strategy where we wanted specialization and alignment.  Pods can work.  It’s all about strategy first and organizational design to support strategy.

Appearance on the Precursive Podcast: The Role of Services in Today’s SaaS Market

A few weeks back, I sat down with Jonathan Corrie, cofounder and CEO of Precursive — a Salesforce-native professional services (PS) delivery cloud that provides PS automation, task, and resource management — to discuss one of my favorite topics, the role of professional services in today’s SaaS businesses.

Jonathan released the 48-minute podcast today, available on both Apple and Spotify.

Topics we discussed included:

  • The Hippocratic oath and executive compensation plans (do no harm).
  • How to frame the sales / services working relationship (i.e., no chucking deals over the fence).
  • Why to put an andon cord in place to stop zero-odds-of-success deals early in the sales process.
  • How to package services, including the risks of tshirt-sized QuickStart packages.
  • How to market methodology instead of packages to convince customers of what matters:  success.
  • The myth of services cannibalization of ARR.  (This drives me crazy.)
  • The alternatives test:  would a customer pay someone else to be successful with your software?
  • Selling mistake-avoidance to IT vs. selling success to line-of-business executives.
  • How and why to bridge “air gaps” between functions (e.g., sales, customer success, services).
  • How to position the sales to CSM “handoff” as à la prochaine and not adieu.
  • The perils of checklist-driven onboarding approaches.
  • The beauty of defining organizational roles with self-introductions (e.g., “my name is Dave and my job is to get your renewal”).
  • The three types of CSMs — the best friend, the seller, and the consultant — and how to blend them and build career paths within the organization.
  • Top professional services metrics.  Caring about (versus maximizing) services margin via compensation plan gates.
  • The loose coupling between NPS and renewal.

Thanks again to Jonathan for having me, and the episode is available here.

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.

# # #

Notes

[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.

How Much Should You Bet on Educating the Market?

Using the Marketing Fundamental Tension Quadrant to Map Your Demandgen and Communications Strategy

Years ago I wrote a post on what I call the fundamental tension in marketing:  the gap between what we want to say and what our audience wants to hear.

For example, let’s say we’re a supply chain software company.  Our founders are super excited about our AI/ML-based algorithms for demand prediction.  Our audience, on the other hand, barely understands AI/ML [1] and wants to hear about reducing the cost of carrying inventory and matching marketing programs to inventory levels [2].

How then should we market our supply chain software?  Let’s use the following quadrant to help.

Let’s map AI/ML as a marketing message onto this framework.  Do we care about it?  Yes, a lot.  Does our audience?  No.  We’re in Box 4:  we care and they don’t, so we conclude that must therefore educate (as we might dangerously consider them) the unwashed in order to make them care about AI/ML.  We can write a white paper entitled, The Importance of AI/ML in Supply Chain Systems.  We can run a webinar with the same title.  By the way, should we expect a lot of people to attend that webinar?  No.  Why?  Because no one cares.

Market education is hard.  That’s not to say you shouldn’t do it, but realize that you are trying, in a world of competing priorities, to add one to the list and move it up to the top.  It can be done:  digital transformation is widely viewed as business priorities today.  But that took an enormous amount of work from almost the entire software industry.  Your one startup isn’t going to change the VP of Supply Chain’s priorities overnight.

Every good demandgen leader knows it’s far easier to start with things the audience already cares about and then bridge to things your company wants to talk about.  Using the movie theatre metaphor of the prior post, you put “Reduce Inventory Costs” on the marquee and you feature “AI/ML” in a lead role in the movie.

How do you determine those priorities?  I’ll scream it:  MARKET RESEARCH.  You find existing and/or run proprietary market studies targeting your business buyers, asking about their priorities.  Then you create marketing campaigns that bridge from buyer priorities to your messages.  If you’re lucky, you’re in Box 2 and everything aligns without the bridge.  But most software marketers should spend the majority of their time in Box 1, bridging between what’s important to the audience and what’s important to the company.

If you fail to build the bridge in Box 1 you’ll have a webinar full of people of who won’t buy anything.  If you put all your investment into Box 4 you’ll run a lot of empty webinars.

The number one mistake startup marketers make is that they try educate the market on too many things.  You need to care about AI/ML.  And reporting.  And, oh by the way, analytics.  And CuteName.  And features 5, 6, and 7.  And, no, no we’re not feature-driven marketing because we remember to mention benefits somewhere.  We are evangelists.  We are storytellers!

But you’re telling stories that people don’t want to hear.

My rule is simple:  every startup should have one — and only one — Box 4 message and supporting campaigns.  Sticking with our example:

  • We should have a superb white paper on the importance of AI/ML in supply chain systems.
  • We should make claims in our PR boilerplate and About Us page related to our pioneering AI/ML in supply chain systems.
  • We should run a strong analyst relations (AR) program to get thought leaders on board with the importance of AI/ML in supply chain.
  • We should commit to this message for, by marketing standards, an extraordinarily long time; it’s literally a decade-long commitment.  So choose it wisely.

To blast through 30 years of personal industry history:  for Oracle it was row-level locking; for BusinessObjects, the semantic layer; for Endeca, the MDEX engine; for MongoDB, NoSQL [3]; for Salesforce, SaaS (branded as No Software); for Anaplan, the hypercube; for GainSight, customer success; and for Alation, the data catalog [4].

To net out the art of enterprise software marketing, it’s:

  • Stay out of Box 3
  • If you’re lucky, you’re in your Box 2 [5].  Talk about what you want to say because it’s what they want to hear.
  • Spend most of your time in Box 1, bridging from what they want to hear to what you want to say.  This keeps butts in seats at programs and primes them towards your selling agenda.
  • Make one and only one bet in Box 4, use AR to help evangelize it, and produce a small number of very high quality deliverables to tell the story.

# # #

Notes

[1] Much as I barely understand a MacPherson strut, despite having been subjected to hearing about it by years of feature-driven automotive marketing.

[2] In other words, “sell what’s on the truck.”  An old example, but likely still true:  the shirt color worn by the model in a catalog typically gets 5x the orders of any other color; so why not do color selection driven by inventory levels instead of graphic design preferences?

[3] Or, as I always preferred, MyNoSQL, simultaneously implying both cheap and easy (MySQL) and document-oriented (NoSQL).  By the way, this claim is somewhat less clear to me than the proceeding two.

[4]  The more the company is the sole pioneer of a category, the more the evangelization is about the category itself.  The more the company emerges as the leader in a competitive market, the more the evangelization is about the special sauce.  For example, I can’t even name a GainSight competitor so their message was almost purely category evangelical.  Alation, by comparison, was close to but not quite a sole pioneer so I wrestled with saying “machine-learning data catalog” (which embeds the special sauce), but settled on data catalog because they were, in my estimation, the lead category pioneer.  See my FAQ for disclaimers as I have relationships past or present with many of the companies mentioned.

[5]  Any space-pioneering application is probably in Box 2.  Any technology platform is almost always in Box 3 or 4.  Any competitive emerging space probably places you in Box 1 — i.e., needing to do a lot of bridging from more generic buyer needs to your special sauce for meeting them.

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.

# # #

Notes

[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.