Category Archives: Strategy

Book Review of Good Strategy, Bad Strategy by Richard Rumelt

Good Strategy, Bad Strategy by UCLA Anderson professor Richard Rumelt is by far my favorite book on strategy.  In this post I’ll explain why I love this book, provide an overview of Rumelt’s core concepts, and offer a few thoughts on (and dare I say an enhancement to) his strategy framework.

Why I Love This Book
I love this book for two reasons.  First, he skillfully eviscerates all of the garbage that far too often passes for strategy in corporate America.  It’s borderline therapeutic to watch him tear down case after case of junk that is pitched by executives and consultants as strategy.  His four telltales:

  • Fluff.  Corporate doublespeak that,“uses ‘Sunday’ words and apparently esoteric concepts to create the illusion of high-level thinking.”
  • Failure to face the challenge“Bad strategy fails to recognize of define the challenge.  If you can’t define the challenge, you cannot evaluate a strategy.”
  • Mistaking goals for strategy.  Here at the center of the OKR universe, it’s common to find companies with lists of “statements of desire” rather than “plans for overcoming obstacles.” [1]
  • Bad strategic objectives“Strategic objectives are ‘bad’ when they fail to address critical issues or when they are impracticable.”

His dismemberment of bad strategy is so surgical and so deft that it alone is worth the price of the book.

The second thing I love about this book is focus.  As my high school Latin teacher, Mr. Maddaloni, always reminded us:  focus is singular [2].  Most companies — often due to the group consensus process used to create strategy — fail at rising to the challenge of picking and end up with multiple, strategic foci instead of a single, strategic focus [3].

This can reflect avoidance of a dead moose issue threatening the company or simply lead to a laundry list of incoherent and unattainable goals.  Either way, Rumelt’s approach sidesteps this problem by forcing the company to focus on a single issue.

The Core Concepts of Good Strategy, Bad Strategy
Per Rumelt, “good strategy is coherent action backed up by an argument, an effective mixture of thought and action with a basic underlying structure called the kernel.”

Excerpt:

The kernel of a strategy contains three elements:

A diagnosis that defines or explains the nature of the challenge. A good diagnosis simplifies the often overwhelming complexity of reality by identifying certain aspects of the situation as critical.

A guiding policy for dealing with the challenge. This is an overall approach chosen to cope with or overcome the obstacles identified in the diagnosis.

A set of coherent actions that are designed to carry out the guiding policy. These are steps that are coordinated with one another to work together in accomplishing the guiding policy.

This is brilliant in its simplicity and in its recognition that a huge part of strategy is an accurate and insightful simplification of the situation:  determining which elements are essential and boiling it down to a short, simple narrative as to “what’s going on”  and ergo what to do about it.

I use a trick to indirectly make this point when I’m in a strategy meeting.  At some point the discussion inevitably fades into, “argh, this is so complicated, there are so, so many things to consider” and room is lost to a sense of hopelessness.  I’ll then ask one of the participants, “can you tell me the story of the last company you worked at?”

You’ll usually hear something like this in response:

  • “We pushed too far up market without the product to support it.”
  • “We got caught in a squeeze between a high-end enterprise vendor and low-end velocity disrupter.”
  • “We got out-marketed by a company with more capital and a more aggressive team.”

I’ll then say, “why do you suppose it’s so easy for us to tell short, simple stories about our prior employers but nearly impossible to make one about us?  What do you think we’ll say in four years about this company?”  It’s the same idea as Rumelt’s — to force simplification of the story to its core narrative and to focus on one thing in the diagnosis.  We do it naturally when looking at the past.  In the present, we resist it like the plague.

I believe that 80% of strategy is the diagnosis — and sometimes the diagnosis simply can’t get made through a group process, but instead has to be decided by the CEO [4] [5].  The other half, to paraphrase Yogi Berra, is the guiding policy and coherent actions.

Thoughts on the Framework
While I love the fact that Rumelt forces executives to diagnose the single most important challenge facing the company — and avoid creating lists of many such challenges — doing so is quite difficult for both good and bad reasons.

The good reason is that it forces “table stakes” conversations, well, “off the table.”  If it’s a discussion about something that everyone in the industry must do (e.g., build quality product, train and scale sales), then it’s almost definitionally not the single most important challenge facing that company.  That’s good, because while those table stakes operations are undoubtedly hard work, they are not strategic.  Operating executives too often confuse the two.

The bad reason it’s difficult is that you might get it wrong.  And in this framework, where everything is tied to a diagnosis about the company’s single-most important challenge, if you get the diagnosis wrong, the whole strategy collapses along with it.

The hardest part I’ve found is balancing immediate vs. longer-term challenges.  For example, say it’s 2003 and you’re at CRM leader Siebel Systems.

  • Your most immediate challenge is likely your direct competition, PeopleSoft or Oracle who are much larger than you and providing broad suites.
  • Your biggest strategic challenge is your indirect competitor Salesforce.com, who is disrupting the business model with software as a service.

Perhaps one of my friends who worked at Siebel at the time can weigh in with an informed comment, but my guess is that Siebel (who was doing $1.4B in annual revenue) minimized Salesforce (who reported doing a mere $65M in its S-1) and, to the extent they would have used a framework like this, would have picked the wrong challenge and gotten the wrong strategy as a result.

Another potential criticism of this framework is that it tends to orient you to competitive threats in a Silicon Valley that would much rather talk about vision (and making the world a better place) than competition.  In my experience, there are few vendors who have the luxury of being totally vision-driven, those who claim otherwise are often practicing revisionism [6], and there’s nothing in the framework, per se, that says the central challenge has to be competition-related.  It could be about building the product, creating distribution channels, or landing your first ten customers.  The framework doesn’t dictate the nature of the challenge, it simply demands that you pick one.

My last thought on the framework is that it appears to be missing an element [7].  In order to make a guiding policy from a diagnosis it helps to have a set of beliefs (or assumptions) as the bridge in between, because these beliefs are neither an explicit part of the guiding policy nor necessarily documented in the diagnosis.

So my slightly revised format of the template is:

  • Diagnosis:  the single most important challenge faced by the company (whether immediate or strategic)
  • Beliefs:  a short list of key assumptions that bridge from the diagnosis to the guiding policy.
  • Guiding policy:  the overall approach to dealing with the challenge
  • Coherent actions:  a set of actions designed to carry out the guiding policy

Or, in English form, given the diagnosis and this set of beliefs, we have chosen this guiding policy which is to be carried out through this set of coherent actions.

Closing Thoughts
I’d say that while I love this book it might have been better titled Bad Strategy, Good Strategy because it’s stronger at tearing apart the garbage that masquerades as strategy than at helping you build good strategy yourself [8].  That said, if you can learn by example and through emulation of the many good strategy examples Rumelt provides, it should be enough to help you and your company not only avoid falling for garbage instead of strategy, but building a good strategy yourself.

I’ll end with the best news of all:  I wrote Rumelt to ask him a few questions and he told me that he’s working on a new book that should address some of my issues.  I can’t wait to read it.

# # #

Notes
[1] OKRs are great and I love OKRs.  But OKRs are for establishing clarity about goals, their unambiguous measurement, and (typically by omission) their priority.  OKRs should be implied by a strategy, but the existence of OKRs (particularly an overly long or incoherent set) does not imply the existence of strategy.

[2] The plural, of course, being foci.

[3] A common case of this is simply failing to make a strategy at all, instead saying (as I’ve actually heard at strategy meetings), “well we’re going to need two financial goals, two sales goals, two product goals, a marketing goal, a customer goal, an alliances goal, and a people goal, so there you go, that’s 10, so let’s just sit down and start making them.  I know the people goal (“attract, develop, and retain the best talent”) and customer goal (“delight our customers”) already, so there’s only 8 more to go.”

[4] I’m slightly twisting Rumelt’s example of a Condorcet Paradox which was really about strategy formulation, not diagnosis, but to the extent that people often gun jump in offering a diagnosis that leads to their desired strategy it still holds.  Adapting his example, the Services person wants a diagnosis that leads to Solutions, the design head wants a diagnosis that leads to Chips, and the systems person wants a diagnosis that leads to Boxes.  The paradox actually occurs not there, but in how each ranks the relative strategies.

[5] If everyone on the team can agree to it, I’d argue it’s almost definitionally a bad strategy.  In a good strategy choices are made, some areas are resources, others are starved, and some are discontinued.  The Chips person voting for Solutions would be, as the saying goes, like the turkeys voting for Thanksgiving.

[6] In conference talks and podcasts it’s far cooler to talk about being vision-driven than talking about competitive strategies; thus I have found the best companies talk little about the competition externally, but are fiercely competitive internally.

[7] Hat tip to my friend Raj Gossain for figuring this out.

[8] By this I mean that while the book provides examples of good strategy, and a simple framework for expressing it, I find the framework missing an important element (beliefs) and the book doesn’t even attempt to outline a process whereby an executive team can work together to devise a good strategy.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Thanks Mark.  It’s a great list.

Upcoming PMM Hive Interview, Marketing Strategy in Hot vs. Cold Markets

Just a quick post to plug my upcoming appearance on a podcast / live interview hosted by PMM Hive, a product marketing community that was recently launched by my old friend and colleague Crispin Read, and that’s already loaded with some superb product marketing content.  Check it out.

I’m excited about this session both because it’s one of my favorite topics and because Crispin is one of my favorite marketers.  The topic is critical because too many marketers (and CEOs) hit rewind/play on their last successful experience without considering their situation and the marketing strategy that should support it.  Crispin’s great because he’s world-class at messaging and positioning, sharp as a tack, enjoys what we’ll call “spirited debate,” and has a dry English sense of humor that keeps things not only interesting, but fun.

The session is on July 8, 2020 at 9:00 California time.  You can register here if interested.  Playback should be available after the event if you’re interested and can’t make it.

Hope to see you there!

Congratulations, You’ve Created a Category. Now What?

(Revised 06/27/20)

I was talking to an old friend the other day who’s marketing chief at a successful infrastructure startup.  “Congratulations,” I said, “I know it was a long slog, but after about a decade of groundwork it looks like things have really kicked in.  I hear your company’s name all the time, I’m told business is doing great, and Gartner literally can’t stop talking about your technology and category.”

“Yes, we’ve successfully created a category,” he said, “But I have one question.  Now what?”

It reminded me, just for a minute, of the ending of The Candidate.

While it’s definitely a high-class problem, it’s certainly a great question and one you don’t hear very often.  These days a lot of very clever people are out dispensing advice on how to create a category — including some wise folks who first dissuade you from doing so — but nobody’s saying much about what to do once you’ve created one.  That’s the topic of this post. category2

Bad Fates That Can Befall Category Creators
Let’s start with the inverse.  Once you’ve created a category, what bad things can happen to it?

  • It can be superannuated.  Technology advances such that it’s not needed any more.  Think:  buggy whips or record cleaners [1].
  • You can lose it to someone else.  Lotus lost spreadsheets to Microsoft.  IBM lost databases to Oracle [2].  Through a more oblique attack, Siebel lost SFA to Salesforce.  Great categories attract new entrants, often big ones.
  • It can be enveloped, either as a feature by a product or as a sub-product by a suite.  Spellcheckers were enveloped as features by word processing products, which were in turn enveloped by office suites.  See the death of WordPerfect [3].

Given that we don’t want any of these things to happen to your category, what should we do about it?  I’ll answer that after a quick aside on my views on categories.

My Principles of Categories
Here are my principles of enterprise software categories:

  • Companies don’t name categories, analysts do.  Companies might influence analysts in naming a new category, but in the end analysts name categories, not vendors [6].
  • Categories sometimes converge, but not always.  Before the SaaS era, enterprise software categories almost always converged because IT was all-powerful and saw its role as entropy minimization [7].  SaaS empowered line of business buyers to end-run IT because they could simply buy an app without much IT support or approval [8].  This is turn led to category proliferation and serious “riches in the niches” where specific, detailed apps like account reconciliation have born multi-billion-dollar companies.
  • Category convergence is about buyers.  Analysts like predicting category convergence so much they get it wrong sometimes.  For example, while the analyst prediction that BI and Planning apps would converge [9] served as the face that launched 1000 ships for vendor consolidation [10], the reality was that BI was purchased by the VP of Analytics while Planning was purchased by the VP of FP&A.  You could put Brio and Hyperion under one roof via acquisition, but real consolidation never happened [11] [12].  Beware analyst-driven shotgun weddings between categories sold to different buyers.  They won’t result in lasting marriages.
  • In category definition, the buyer is inseparable from the category.  Each category is a two-sided coin that defines the buyer on one side and the software category on the other [13].  For example, when categories converge it’s either because the buyer stayed the same and decided to purchase more broadly or the buyer changed and what they wanted to buy changed along with it.  But if there is no buyer, there is no category.

What’s a Category Creator To Do?  Lead!
Having contemplated the bad things that can happen to your category and reviewed some basic principles of categories, there is one primary answer to the question:  lead.

You need to lead in three ways:

  • Grow like a weed.  Now is the time to invest in driving growth.  Nothing attracts competition like fallow land in a new category.  You created a category, you’re presumably the market share leader in the category, and now your job is to make sure you stay that way.  Now is the time to raise lots of VC and spend up to $1.70 to purchase each new dollar of ARR [13A].
  • Market your category leadership.  Tech buyers love to buy from leaders because buying from leaders is safe.  Reinforce your position as the category leader until you’re tired of hearing it.  Then do it again.  Never get bored with your own marketing.
  • Lead the evolution of your category by talking about your vision and your plan to realize it.  This makes you a safe choice because customers know you’re not resting on your laurels.  It also forces your would-be competitors to shoot at a moving target.

The vision for category evolution typically takes one of three forms:

  • Double down.  Make your thing the best thing in the market.  Stay incredibly close to your customers.  Understand and cater to their precise needs.  Your strategy is thus category defense via customer intimacy.  You simply know the buyer better.  Large companies can’t put their best people on everything, so this works when your best people are better than their average ones, they don’t put a massive investment in the space (instead preferring a good-enough solution), and the buyer cares enough to want to buy the best and can continue to do so [14].
  • Build out (i.e., lateral expansion)Move into adjacent categories, ideally sold to your existing buyer, giving yourself economies of scale in go-to-market and your buyer the ability to buy multiple products on one platform [15].  GainSight’s move into product analytics is one example.  Another is Salesforce’s systematic move across buyers, from VP of Sales to VP of Service to VP of Marketing.  This strategy works when you can afford to build or acquire into the adjacent category and, if the category involves a different buyer, that you can afford to invest in the major transition from being a single-buyer to a multi-buyer firm [16].
  • Build up (i.e., vertical expansion) [17]. Build up from your platform to create one or more applications atop it.  An ancient example would be Oracle expanding from databases into applications [18] which was first attempted via in-house development.  Anaplan is a contemporary example.  They first launched a multidimensional planning platform, had trouble selling the raw engine in finance (a more saturated market with more mature competition), shifted to build sales planning applications atop their platform, and successfully used sales planning as their beachhead market.  Once that vertical (i.e., upward) move from platform to application was successful, they then bridged (now laterally) into finance and later into supply chain applications.

What If You Can’t Afford to Lead?
But say you can’t afford any of those strategies.  Suppose you’re not a particularly well-funded company and your market is being attacked on all sides, by startups and megavendors alike.  What if staving off those attacks is not a viable strategy.  Then what?

If you’re at risk losing leadership in your category, then your strategy needs to be segment.  Pick a segment of the market you created and lead it.  That segment could be on several dimensions.

  • Size, by focusing on SMB, mid-market, or enterprise customers only — this works when requirements (or business model) vary significantly with size.
  • Vertical, by focusing on one or two vertical industries — ideally those with idiosyncratic requirements that can serve as entry barriers to horizontal players.
  • Use-case, by focusing on a specific use-case of a platform that supports multiple use-cases.  For example, what if Ingres, instead of focusing on appdev tools after placing 4th round I of the RDBMS market, instead had focused on data warehousing, a distinct use-case and one to which the technology was well-suited?

Conclusion
If you’re reading this because you’ve created a category, congratulations.  You’ve done an incredibly difficult thing.  Hopefully, this post helps you think about your most important question going forward:  now what?

# #  #

Notes

[1] I struggle to find software examples of this because the far more common fate is envelopment, typically into a feature — e.g., spellchecker.  I suspect it happens more in hardware as the underlying components get smarter, they eliminate the need for higher-level controllers and caches.

[2] Despite both inventing the relational database and being the leader in the prior-generation database market with IMS.

[3] The precise cause of death is still debated and a final lawsuit concluded less than a decade ago.

[4] Software industry evolution led to the SaaS model, which then put huge importance on renewals which in turn led to the creation of the VP of Customer Success role which created both the demand for and buyer of Customer Success software.

[5] And either way, a great company.  (I know both the founder and the CEO, so see my disclaimer.  I can say I’ve also been a customer and a happy one.)

[6] I credit Arnold Silverman with pointing this out to me so clearly.

[7] To reduce the degree of disorder in a company’s software stack, IT had a strong tendency to prefer one-stop-shop value propositions over best-of-breed.  Ergo, vendors incented by economies of scale in go-to-market, were naturally aligned with buyers who wanted to buy more from fewer vendors.  Both forces pushed towards developing suites, either in-house or through acquisition.

[8] As I did in the early 2000s when I was CMO of a $1B company and the CIO said I needed to wait 4 years for lead management in Europe during our CRM deployment.  “That’s funny,” I thought, “we have leads today and if I wait 4 years for lead management, I can assure you of only two things:  I won’t be CMO anymore and the CIO will be the only person coming to my going-away party.”  That’s when I bought Salesforce.

[9] That was the initial use of the category name enterprise performance management (EPM), which later evolved before eventually, and only of late, being retired.  A key point here is that while these categories organ-rejected each other, that took place literally over the course of decades.  Thus, paradoxically, you likely would have been “dead right” as a BI vendor if you rejected the inclusion of financial planning in 2003 .

[10] Cognos acquiring Adaytum, Business Objects acquiring SRC and Cartesys, and Hyperion acquiring Brio, among others.

[11] Meaning you could ask someone who worked in the organization “which side” they worked on, and they would answer without hesitation.  You can’t sell financial planning systems without significant domain expertise that the BI side lacked, and that was more about DNA than training.  (For example, most EPM sales consultants had years of experience working in corporate finance departments before changing careers.)  It was more conglomeration than consolidation.

[12] Amazingly, this pattern repeated itself within EPM in the past decade.  EPM  was redefined as the convergence of financial planning with financial consolidation, both within the finance department, but again sold to different buyers.  Planning is sold to the VP of FP&A, Consolidation to the Corporate Controller.  While both report to the CFO, they are two different roles, typically staffed with two very different people.  Again, the shotgun wedding ended in divorce.

[13] Each category has one primary buyer.  A given buyer may buy in several different categories.  As a marketer, the former statement is 10x more important than the latter.

[13A] See my post on the CAC ratio.  Data source, the KeyBanc 2019 SaaS survey, shows median of $1.14 with mid 50-percentile range of $0.77 to $1.71.

[14] The tension here is between letting, e.g., the VP FP&A purchase their own best-of-breed Planning product versus a good-enough Planning module subsumed into a broader ERP suite decided upon by the CFO.  This is a real example because Planning exists on both sides today; there remain several successful SaaS planning vendors selling best-of-breed outside the context of a financial suite while most ERP vendors bundle good-enough Planning into their suite.

[15] When accomplished via M&A, the single-platform benefits are typically limited to pre-defined integration but can hopefully over time — sometimes a long time (think Oracle Fusion) — become realized.

[16] Typically this means creating product-line general managers along with specialized overlay sales and sales consultants, product management, product marketing, and consulting teams.  It also means the more difficult task of going to market with products at differing levels of maturity, something very hard to master in my experience.  Finally, in apps at least, the more you are multi-buyer, the more IT needs to get involved, and the firm must master not only the art of the sale to the various business buyers, but to IT as well.  Salesforce has done this masterfully.

[17] Vertical in the sense of up, i.e., atop your platform; not vertical in the sense of focusing on vertical markets.

[18] Which, for ancient software historians, was the failed strategy that Oracle gave a mighty try before giving up and acquiring PeopleSoft in 2005, the first in a long series of applications acquisitions.

A Missive to Marketing: Impose Simplicity

Markets are complex. Customers are complex. Products are complex, sometimes very. Heck, the world is complex. What’s a marketer to do?

Great marketing is about making things simple. We do that by imposing simplicity on a complex world. We might be attacked for so doing — people might accuse us of over-simplification. And we don’t want that either because we need to stay credible. Paraphrasing Einstein, we want to make things as simple as possible, but no simpler.

Consider product marketing. Enterprise software products are enormously complex, built by scores (or hundreds) of developers across quarters and years. They have deep functionality and subtle differences.

But a product marketer, operating in a TLDR world, can never say:

The difference between our product and their product is actually quite subtle and ultimately is about 100 little things; there’s really no one big thing that separates them.

No, no, no. The successful product marketer finds the most important subtle differences, groups them, and amplifies them. Here are our three silver bullet features. Here’s our white paper on The Five Things You Should Look For in a Schmumble.

In so doing, black-and-white is infinitely superior to gray. While sometimes unavoidable, speaking gray (i.e., “our schmumble is better than their schmumble”) is infinitely inferior to speaking black-and-white (e.g., “we have a schmumble; they don’t.”)

The successful product marketer takes a complex, gray world and transforms it into a simple, black-and-white one. If you don’t have row-level locking, you’re screwed. If you don’t have semi-additive measures, you’re screwed. If you don’t financial consolidation, you’re screwed.  If you don’t have hyperblocks, you’re screwed. 

The great marketer imposes simplicity on the product.

Consider corporate marketing, where the goal is simple. Take a complex competitive landscape and position the company as the leader. Not a leader. The leader. “A leader” is complex because it means there are multiple different companies, each of a different size, and each with its own angle on what constitutes the best product. That means customers need to understand all the competitors and their relative strengths and weaknesses. That’s a lot of work.

“The leader” is simple. Define the space in as simple terms as possible — carving it up to make yourself the leader — and then declare yourself the leader. It’s not always possible to do this — at one point, I called out Brio for effectively claiming they were the leading business intelligence vendor — on Great America Parkway in Santa Clara, California.

But if you can do it credibly, then back it up with awards, customer wins, customer counts, and financing rounds. It’s safe to buy from the leader.

The great marketer imposes simplicity on the market.

Consider customer targeting. The world is complex and gray when it comes to targeting. An ideal customer profile (ICP), typically the result of a regression used to identify the best target companies, isn’t black and white. It might output a score that varies from to 0.0 to 1.0. That’s gray. You need to make that black-and-white so sales can use it — e.g., by using it to identify named accounts for sales and account-based marketing (ABM), by using the score to create tiers that follow different processes in the high funnel, or by looking at the model to derive simple rules to say when some opportunities look better than others (e.g., we double our win rate when the customer is using Spark).

The great marketer imposes simplicity on targeting.

Consider messaging. The database reveals that the key contacts at our top 50 customers have over 80 different titles. If we stopped there, we’d end up wasting money buying overly broad lists and with an overly generic message. The great marketer interviews a broad set of customers and discovers there effectively two canonical personas in that set. Precise titles and hierarchical levels aside, there are two different animals: data analysts and data architects. And a VP of data architecture thinks a lot more like a director of data architecture than a VP of data analysis.

They look at things differently. The have different missions within the organization. They have different career backgrounds. They will respond different to sales and marketing messaging. If you want architects to come to your webinar, talk about data transformation initiatives and enterprise architecture. If you want data analytics people to come to your webinar talk about better decisions made more quickly on higher-quality data.

If you want to sell a data architect convince them your system is built for scalablity. If you want to sell a data analyst, convince them they’ll be more productive and make better analyses.

The great marketer imposes simplicity on messaging.

The hardest part of all this is believing with conviction that you have to do it. You’ll be accused of being inaccurate. Others will say you’re over-simplifying. You’ll be told, “well, it’s really not that simple” over and over again. You yourself will start to wonder.

Don’t forget that simplicity isn’t easy. Just as it takes a tough man to make a tender chicken, it takes a tough marketer to make a simple message. It’s your job.  A key skill in marketing is the ability to impose simplicity on a complex world.

Your career will depend on it.

Joining the Profisee Board of Directors

We’re announcing today that I’m joining the board of directors of Profisee, a leader in master data management (MDM).  I’m doing so for several reasons, mostly reflecting my belief that successful technology companies are about three things:  the people, the space, and the product.

I like the people at both an investor and management level.  I’m old friends with a partner at ParkerGale, the private equity (PE) firm backing Profisee, and I quite like the people at ParkerGale, the culture they’ve created, their approach to working with companies, and of course the lead partner on Profisee, Kristina Heinze.

The management team, led by veteran CEO and SAP alumnus Len Finkle, is stocked with domain experts from larger companies including SAP, Oracle, Hyperion, and Informatica.  What’s more, Gartner VP and analyst Bill O’Kane recently joined the company.  Bill covered the space at Gartner for over 8 years and has personally led MDM initiatives at companies including MetLife, CA Technologies, Merrill Lynch, and Morgan Stanley.  It’s hard to read Bill’s decision to join the team as anything but a big endorsement of the company, its leadership, and its strategy.

These people are the experts.  And instead of working at a company where MDM is an element of an element of a suite that no one really cares about anymore, they are working at a focused market leader that worries about MDM — and only MDM – all day, every day.  Such focus is powerful.

I like the MDM space for several reasons:

  • It’s a little obscure. Many people can’t remember if MDM stands for metadata management or master data management (it’s the latter).  It’s under-penetrated; relatively few companies who can benefit from MDM use it.  Historically the market has been driven by “reluctant spend” to comply with regulatory requirements.  Megavendors don’t seem to care much about MDM anymore, with IBM losing market share and Oracle effectively exiting the market.  It’s the perfect place for a focused specialist to build a team of people who are passionate about the space and build a market-leading company.
  • It’s substantial. It’s a $1B market today growing at 5%.  You can build a nice company stealing share if you need to, but I think there’s an even bigger opportunity.
  • It’s teed up to grow. On the operational side, I think that single source of truth, digital transformation, and compliance initiatives will drive the market.  On the analytical side, if there’s one thing 20+ years in and around business intelligence (BI) has taught me, it’s GIGO (garbage in, garbage out).  If you think the GIGO rule was important in traditional BI, I’d argue it’s about ten times more important in an artificial intelligence and machine learning (AI/ML) world.  Garbage data in, garbage model and garbage predictions out.  Data quality is the Achilles’ heel of modern analytics.

I like Profisee’s product because:

  • It’s delivering well for today’s customers.
  • It has the breadth to cover a wide swath of MDM domains and use-cases.
  • It provides a scalable platform with a broad range of MDM-related functionality, as opposed to a patchwork solution set built through acquisition.
  • It’s easy to use and makes solving complex problems simple.
  • It’s designed for rapid implementation, so it’s less costly to implement and faster to get in production which is great for both committed MDM users and — particularly important in an under-penetrated market – those wanting to give MDM a try.

I look forward to working with Len, Kristina, and the team to help take Profisee to the next level, and beyond.

Now, before signing off, let me comment on how I see Profisee relative to my existing board seat at Alation.  Alation defined the catalog space, has an impressive list of enterprise customers, raised a $50M round earlier this year, and has generally been killing it.  If you don’t know the data space well you might see these companies as competitive; in reality, they are complementary and I think it’s synergistic for me to work with both.

  • Data catalogs help you locate data and understand the overall data set. For example, with a data catalog you can find all of the systems and data sets where you have customer data across operational applications (e.g., CRM, ERP, FP&A) and analytical systems (e.g., data warehouses, data lakes).
  • MDM helps you rationalize the data across your operational and analytical systems.  At its core, MDM solves the problem of IBM being entered in your company’s CRM system as “Intl Business Machines,” in your ERP system as “International Business Machines,” and in your planning system as “IBM Corp,” to give a simple example.  Among other approaches, MDM introduces the concept of a golden record which provides a single source of truth of how, in this example, the customer should be named.

In short, data catalogs help you find the right data and MDM ensures the data is clean when you find it.  You pretty obviously need both.

Career Decisions: What To Look For In a Software Startup

So, you’re thinking of taking a job at a startup, but are nervous about the risk, perhaps having trouble telling one from another, and unsure about knowing what’s really important in startup success.  In this post, I’ll share what I consider to be a great checklist for CXO and VP-level positions, which we’ll try to adapt a bit to be useful for all positions.

1. Great core/founding team.  Startups are about people.  We live in a founder-friendly VC era.  Thus, there is a good chance one or all of the founding team will be around, and in influential positions, for a long time.  If you’re CXO/VP-level, make sure you spend time with this team during your interviews [1] and make sure you think they are “good people” who you trust and who you’d want to work with for a long time [2].  You might well be doing so.

2. Strong investors.  In venture capital (VC) land, you should view investors as long-term partners in value creation.  Their investments give them contractual rights (e.g., board seats) and you can assume they will be around for a long time [3].  Companies need two things from their investors:  advice (e.g., the wisdom acquired from having built a dozen companies before) and money.  While good advice is always important, money is absolutely critical in today’s startup environment where a hot category can quickly evolve into a financial arms race to see which company can “buy” the most customers the fastest [4].

While I won’t do a tiering of Silicon Valley VCs here, you want to see investors with both deep pockets who can fund the company through thick (e.g., an arms race) and thin (e.g., inside rounds) and strong reputations such that other VCs are willing and eager to invest behind them in future rounds [5].

3. Newer company/technology.  I’ll give you the hint now that this is basically a list of key factors ranked by difficulty-to-change in decreasing order.  So the third hardest-to-change key factor is technology.  If you’re considering going to work at a twelve-year-old startup [6], understand that it’s very likely built on twelve-year-old technology premised on a twelve-plus-year-old architecture.  While the sales-and-marketing types will emphasize “its proven-ness” you will want to know how much technical debt there is associated with this old architecture.

Great startups are lead by strong technologists who ensure that technical debt is continuously addressed and retired via, e.g., trust releases.  Bad startups are feature addicts who pile feature upon feature atop a deteriorating architecture, creating an Augean Stables of technical debt. But even in good startups, routine debt-retirement doesn’t prevent the need for periodic re-architecture.  The best way to avoid an architectural mess of either type is to go to a newer startup, led by strong technologists, where the product is most probably built atop a modern architecture and where they definitionally cannot have accumulated a mass of technical debt [7].  

4. Clean cap table.  I once took a job at a company where a VC friend of mine said, “they have a good business, but a bad cap table.”  Since I didn’t entirely understand what he meant at the time, I took the job anyway — but, wow, do I wish I’d spent more time trying to understand the phrase “bad cap table.”

A capitalization table (aka “cap table”) is simply a list of investors, the type and amount of shares they hold, shares held by founders, shares allocated to the stock option pool, warrants held by suppliers and/or debtholders, and along with information about any debt the company has acquired.  So, strictly speaking, how could this table be inherently good or bad?   It just “is.”  Nope.  There are good cap tables and bad cap tables and here’s a partial list of things that can make a cap table bad in the eyes of a future investor.

  • Upstream investors who they don’t know and/or don’t want to work with.  That is, who holds the shares matters.
  • Ownership division that gives either the founders or employees too few shares.  Most VCs have the right to retain their percentage ownership going forward so if the company is already 60% owned by VC1 after the Series A and 20% owned by VC2 after the Series B, the new investor may believe that there simply isn’t enough to go around.  Strong VCs truly believe in founder and employee ownership and if there isn’t enough of it, they may walk from a deal.
  • ARR not commensurate with total funding.  Say a company has consumed $50M in capital but has only $5M in ARR to show for it.  Barring cases with exceptional product development entry barriers, that’s not a great ratio and most likely the result of a pivot, where the company started out on hypothesis A and then moved to hypothesis B.  From the new investor’s perspective, the company spent (and wasted) $30M on hypothesis A before switching to hypothesis B and thus has invested only $20M in its current business.  While some new investors might invest anyway, others would want some sort of recapitalization to reflect the business reality before investing.
  • Parasites, such as departed founders or incubators.  Founders A and B, aren’t going to be that excited over the long term for making money for founder C while she is off doing a new startup.  And why would a future VC want to make money for founder C, when she has already left the company [8]?  These are problems that need to be addressed from the viewpoint of a new investor.
  • Network effects among investors.  If VC1 owns 40% and VC2 owns 20% and VC2 works almost exclusively with VC1, then you can assume VC1 has control of the company.   This may not be a deal killer, but it may make a new investor wary.
  • Undesirable structure.  While VCs almost always buy preferred shares (as opposed to the common shares typically held by founders and employees), the specific preferences can vary.  New VC investors typically don’t like structure that gives preferred shares unusual preferences over the common because they worry it can demotivate employees and founders.  Such structure includes participating preferencesmultiple liquidation preferences, and redemption rights.

And that’s only a partial list.  At the CXO level, I think you have the right to ask about the cap table, but it’s much harder for job titles below that.  So I understand that you won’t always be able to access this information, but here’s what you can do:  (1) look at Crunchbase for financial history to try and identify some of these problems yourself, and (2) try to find a VC friend and get his/her opinion on the company.  VCs, particularly those at the bigger firms, are remarkably well informed and look at lots of deals, so they can usually give you an inkling about potential problems.

5. Strong market opportunity.  I’ve always done best when the need for the product was obvious.  Best example:  Business Objects in the 1990s — data warehouses were being built and it was obvious that there were no good tools to access them.  Business Objects eventually sold for nearly $7B.  Best counter-example:  MarkLogic in the 2000s — several years after Gartner wrote a note called XML DBMS:  The Market That Never Was.  That nearly twenty-year-old company is still not liquid, though through exceptional execution it has built a nice business despite strong headwinds; but there was nothing either obvious or easy about it.  In my other direct experience, the markets for Ingres (RDBMS), Salesforce.com, and Host Analytics (cloud EPM) were obvious.  The market for Versant (ODBMS) was not.

Another test you can apply to the market is the Market Attractiveness Matrix, which positions the type of buyer vs. the need for the product.  Selling SFA to sales, e.g., would be in the most attractive category while selling soft productivity improvement tools to HR would be in the least.

mam

Finally, I also like markets where the pricing is tied to something that inherently goes up each year (e.g., number of salespeople, size of stored documents, potentially usage) as opposed to things that don’t (e.g., number of HR or FP&A people, which increases — but in a more logarithmic fashion).

6. Strongest competitor in the market.  The problem with obvious market opportunities, of course, is that they attract multiple competitors.  Thus, if you are going to enter a competitive market you want to ensure the company you’re joining is the leader in either the overall market or a specific segment of it.  Given the increasing returns of market leadership, it is quite difficult to take away first place from a leader without they themselves faltering.  Given that hope is not a strategy [9], unless a runner-up has a credible and clear plan to be first in something [10], you should avoid working at runner-up vendors.  See the note below for thoughts on how this relates to Blue Ocean Strategy [11].

7.  Known problems that you know how to fix.  I’ve worked at epic companies (e.g., Business Objects, Salesforce) and I’ve worked at strugglers that nearly clipped the tree-tops on cash (e.g., Versant) and I can assure you that all companies have problems.  That’s not the question.  The questions are (1) do they get what really matters right (see previous criteria) and (2) are the things that they get wrong both relatively easy to fix and do you know how to fix them?

Any CXO- or VP-level executive has a set of strengths that they bring to their domain and the question is less “how good are you” than “does the company need what you bring?”  For example, you wouldn’t want a sales-and-marketing CEO — no matter how good — running a company that needs a product turnaround.  The key here is to realistically match what the company (or functional department) needs relative to what you can bring.  If you’re not a CXO- or VP-level executive, you can still apply the same test — does the company’s overall and functional leadership bring what the company needs for its next level of evolution?

8. Cultural compatibility.  Sometimes you will find a great organization that meets all these criteria but, for some reason, you feel that you don’t fit in.  If that happens, I’d not pursue the opportunity because you are likely to both be miserable on a daily basis and not succeed.  Culture runs deep in both people and in companies and when it’s a not a fit, it’s very hard to fake it.  My favorite, well-documented example of this was Dan Lyons at HubSpot, detailed in his book Disrupted.  HubSpot is a great company and I’m pretty sure Dan is a great guy, but wow there was a poor fit [12].  My advice here is to go with your gut and if something feels off even when everything else is on, you should listen to it.

I know it’s very hard to find companies that meet all of these criteria, but if and when you find one, I’d jump in with both feet.  In other cases, you may need to make trade-offs, but make sure you understand them so you can go in eyes wide open.

# # #

Notes

[1] A lack of desire to spend time with you during the recruiting process should be seen as a yellow/red flag — either about you as a candidate or their perceived importance of the position.

[2] In this context, “a long time” means 5-10 years.  The mean age of high-growth SaaS IPO companies in last year’s IPO class was 14 years.

[3] While “shareholder rotations” are possible (e.g., where firm B buys out firms A’s position) they are pretty rare and typically only happen in older companies (e.g., 10 years+).

[4] In my opinion, VC a few decades back looked more like, “let’s each back 5 companies with $20M and let the best operators win” whereas today it looks more like, “let’s capitalize early and heavily on the increasing-returns effects of market leadership and stuff (i.e., foie-gras) our startups with money, knowing that the market winners will likely be those who have raised the most cash.”  Note that while there is debate about whether this strategy yields the best returns (see foie gras link), there is less debate about whether this generates large companies in the market-leader pack.

[5] As one later stage investor told me:  “we prefer to work with syndicates with whom we’ve worked before” suggesting larger firms with more deals are preferable upstream and “if it ends up not working out, we’d much rather be in the deal with a highly respected firm like Sequoia, Accel, Lightspeed, or A16Z than a firm no one has ever heard of.”  Your upstream investors have a big impact on who is willing to invest downstream.

[6] Quip:  what do you call a twelve-year-old startup?  Answer:  a small business.  (Unless, of course, it’s high-growth and within striking distance of an IPO.)

[7] I would argue, generally, that newer startups tend to be built with newer business model assumptions as well.  So picking a newer company tends to ensure both modern architecture and contemporary thought on the business model.  For example, it’s hard to find a five-year-old enterprise software company built on an on-premises, perpetual license business model.

[8] Or, if an incubator makes itself a virtual cofounder in terms of common stock holdings in return for its incubation services.

[9] i.e., hoping the other company screws up.

[10] Either a segment or a segment that they believe will grow larger than today’s overall market.

[11] I feel obliged to mention that not all non-obvious market opportunities are bad.  As a big fan of Blue Ocean Strategy, I’d argue that the best market opportunities are semi-obvious — i.e., obvious enough that once you dig deeper and understand the story that they are attractive, but not so obvious that they attract a dozen ocean-reddening competitors.  Of recent enterprise software companies, I’d say Anaplan is the best example of Blue Ocean Strategy via its (emergent) strategy to take classic financial planning technology (hypercubes) and focus on sales planning in its early years.

[12] Note that I am not saying HubSpot is a perfect company and we can argue at great length (or more likely, quite briefly) about the strengths and weaknesses in typical Silicon Valley cultures.  And that’s all interesting academic debate about how things should be.  What I am saying is that when it comes to you, personally, for a job, why make yourself miserable by joining an organization where you know up-front that you don’t fit in?