How to Drive Digital Transformation when You’re Not a Digital Expert : Addressing the Reverse Hierarchy of Understanding

In my last post I described some of the biggest challenges to a traditional enterprise trying to drive digital transformation. This isn’t just the usual “this stuff is hard” blather – there are real hurdles for the traditional large enterprise trying to do digital well. The pace of change and frictionless competition drive organizations used to winning through “weight of metal” not agility, crazy. The need for customer-centricity penalizes organizations setup in careful siloes. And these very real hurdles are exacerbated by the way digital often creates poor decision-making in otherwise skilled organizations because of what I termed the reverse hierarchy of understanding.

The reverse hierarchy of understanding is a pretty simple concept. Organizations work best when the most senior folks know the most about the business. When, in other words, knowledge and seniority track. For the most part (and despite a penchant for folks lower down in the organization to always think otherwise), I think they do track rather well in most companies. That, at least, has been my fairly consistent experience.

There are, of course, many pockets of specialized knowledge in a large company where knowledge and seniority don’t track. The CFO may not be able to drive TM1. The CTO probably doesn’t know Swift. That’s not a problem. However, when something is both strategic and core to the business, it’s critical that knowledge and seniority track appropriately. If they don’t, then it’s hard for the enterprise to make good decisions. The people who are usually empowered to make decisions aren’t as qualified as they typically are, and the folks who have the specific knowledge probably don’t have either the strategic skills or business understanding to fill-in. And, of course, they probably don’t have the power either.

Digital can create exactly this inversion in the appropriate hierarchy of decision-making in the traditional enterprise, and it does so at many levels in the organization. Digital has become strategic and core far more rapidly than most large organizations can adapt, creating reverse hierarchies of understanding that can cripple efforts to do digital better.

So if you want to transform a traditional business and you know your organization has a reverse hierarchy of understanding (or maybe just a complete lack of understanding at every level), what do you do?

There’s not one answer of course. No magic key to unlocking the secret to digital transformation. And I’ve written plenty of stuff previously on ways to do digital better – all of which still applies. But here are some strategies that I think might help – strategies geared toward tackling the specific problem created by reverse hierarchies of understanding.



I’m sensitive to the many draw-backs to incubating digital inside a larger organization. If incubation succeeds, then it creates long-term integration challenges. It potentially retards the growth of digital expertise in the main business and it may even cannibalize what digital knowledge there is in the organization. These are all real negatives. Despite that, I’ve seen incubation work fairly effectively as a strategy. Incubation creates a protected pocket in the organization that can be staffed and setup in a way that creates the desired knowledge hierarchy through most levels.  Would I always recommend incubation? Absolutely not. In many organizations, years of at least partial learning and transfusions of outside talent have created enough digital savvy so that incubation is unnecessary and probably undesirable. If digital knowledge in your organization is still nascent and particularly if you have layers of management still skeptical or negative to digital, then incubation is a strategy to consider.



And speaking of talent transfusions, the role of appropriate hiring in effectively transforming the organization can hardly be overstated. The best, simplest and most impactful way to address the reverse hierarchy of understanding is to…fix the problem. And the easiest way to fix the problem is by hiring folks with deep digital understanding at multiple levels of the organization. In some cases, of course, this means hiring someone to run digital. If you’re a traditional enterprise looking to hire a chief digital officer, the natural place to look is to organization’s that are great in digital – especially the companies that dominate the Web and that we all, rightly, admire. I tell my clients that’s a mistake. It’s not that those folks aren’t really good at digital; they are. What they aren’t good at is digital transformation. If you’ve grown up managing digital platforms and marketing for a digital pure-play, chances are you’re going to be massively frustrated trying to change a traditional enterprise. To drive transformation, you have to be a great coach. That isn’t at all the same as being a great player. In fact, not only isn’t it the same, it’s negatively correlated. The best coaches are almost NEVER the best players.

Getting the right person to lead digital isn’t the place where most organizations go wrong though. If you’re committed to digital transformation, you need to look for digital savvy in every hiring decision that is at all related to your digital enterprise. You need digital savvy in HR, in accounting, analytics, in customer, in supply chain, in branding and corporate communication. Etc. Etc. This is the long game, but it’s ultimately the most important game you’ll play in digital transformation – especially when you’re trying to drive transformation outside of massive disruption. In my last post, I mentioned FDR’s many efforts to prepare the U.S. for WWII before there was any political consensus for war. Every leader is constrained by the realities on the ground. Great leaders find ways to at least lay the essential groundwork for transformation BEFORE – not after – disaster strikes. You need to make sure that digital savvy becomes a basic qualifier for a wide range of positions in your organization.



Dare I say that analytics has the potential to play a decisive role in solving the reverse hierarchy of understanding? Well, at the very least, it can be a powerful tool. In a normal hierarchy of understanding, seniority comes pre-loaded with better intuitions. Intuitions born of both experience and selection. And those intuitions, naturally, drive to better decisions. It’s darn hard to replace those intuitions, but analytics is a great leveler. A good analyst may not be quite the decision-maker that an experienced expert is – but at the very least a good analyst equipped with relevant data will come much closer to that level of competent decisioning than would otherwise be possible.

Thankfully, this works both ways. Where senior decision-makers can’t rely on their experience and knowledge, they, too, benefit from analytics to close the gap. An executive willing to look at analytics and learn may not be quite in the league of an experienced digital expert, but they can come surprisingly close.

This works all up and down the organization.

So how do you get your team using analytics? I addressed this in depth in a series of posts on building analytic culture. Read this and this. It’s good stuff. But here’s a simple management technique that can help drive your whole team to start using analytics. Every time there’s an argument over something, instead of voicing an opinion, ask for the numbers. If your team is debating whether to deliver Feature X or Feature Y in digital, ask questions like “What do our customers say is more important?” or “Which do high-value customers say they’ll use more?”

Ask questions about what gets used more. About whether people like an experience. About whether people who do something are actually more likely to convert. If you keep asking questions, eventually people are going to start getting used to thinking this way and will start asking (and answering) the questions themselves.

Way back in the early days of Semphonic, I often had junior programmers ask me how to do some coding task. At the time, I was still a pretty solid programmer with years of experience writing commercial software in C++. But since I wasn’t actively programming and my memory tends to be a bit short-term, I almost never just knew the answer. Instead, I’d ask Google. Almost always, I could find some code that solved the problem with only a few minutes’ search. Usually, we’d do this together staring at my screen. Eventually, they got the message and bypassed me by looking for code directly on Google.

That’s a win.

Nowadays, programmers do this automatically. But back in the aughts, I had to teach programmers that the easiest way to solve most coding problems is to find examples on Google. In ten years, looking at digital analytics and voice of customer will be second-nature throughout your organization.  But for right now, if you can make your team do the analytics work to answer the types of questions I’ve outlined above, you’ll have dramatically raised the level of digital sophistication in your organization. This isn’t as foreign to most good enterprise leaders as I used to think. Sure, folks at the top of most companies are used to offering their opinions. But they’re also pretty experienced at having to make decisions in areas where they aren’t that expert and they know that asking questions is a powerful tool for pushing people to demonstrate (or arrive at) understanding. The key is knowing the right questions to ask. In digital, that usually means asking customer-focused questions like the one’s I enumerated above.



I’m probably too deeply involved in the sausage-making to give good advice on how organizations should use consulting to drive transformation. But here’s a few pointers that I think are worth bearing in mind. Consulting is a tempting way to solve a reverse hierarchy of understanding. You can bring in hired guns to build a digital strategy or drive specific digital initiatives. And if you’re lucky or choose wisely, there’s no reason why consultants can’t provide real benefits – helping speed up digital initiatives and supplement your organizational expertise. I genuinely believe we do this on a pretty consistent basis. Nevertheless, consultants don’t fix the problems created by a reverse hierarchy of understanding; they are, at best, a band aid. Not only is it too expensive to pay consultants to make your decisions on a continuing basis, it just doesn’t work very well. There are so many reasons why it doesn’t work well that I can attempt only a very partial enumeration: outside of a specific project, your consultant’s KPIs are almost never well aligned with your KPIs (we’re measured by how much stuff we sell), it’s difficult to integrate consultants into a chain of command and often damaging if you try too hard to do so, consultants can become a crutch for weaker managers, and consultants rarely understand your business well enough to make detailed tactical decisions.

Don’t get me wrong. Building talent internally takes time and there aren’t many traditional enterprises where I wouldn’t honestly recommend the thoughtful use of consulting services to help drive digital transformation. Just don’t lose sight of the fact that most of the work is always going to be yours.


That last sentence probably rings true across every kind of problem! And while digital transformation is legitimately hard and some of the challenges digital presents ARE different, it’s good to keep in mind that in many respects it is just another problem.

I’ve never believed in one “right” organization, and when it comes to digital transformation there are strong arguments both for and against incubation. I think a decision around incubation ultimately comes down to whether digital needs protection or just expertise. If the former, incubation is probably necessary. If the latter, it may not be. Similarly, we’re all used to the idea that if we need new expertise in an organization we probably have to hire it. But digital introduces two twists. First, the best candidate to lead a digital transformation isn’t necessarily the best digital candidate. Second, real digital transformation doesn’t just come from having a leader or a digital organization. You should bake digital qualifications into hiring at almost every level of your organization. It’s the long game, but it will make a huge difference. And when it comes to leveling the playing field when faced with a reverse hierarchy of knowledge, remember that analytics is your friend. Teaching the organization to use analytics doesn’t require you to be an analytics wizard. It mostly demands that you ask the right questions. Over and over. Finally, and this really is no different in digital transformation than anywhere else, consulting is kind of like a cold medicine – it fixes symptoms but it doesn’t cure the disease. That doesn’t mean I don’t want my bottle of Nyquil handy when I have a cold! It just means I know I won’t wake up all better. The mere fact of a reverse hierarchy of understanding can make over-reliance on consulting a temptation. When you’re used to knowing better than everyone, it’s kind of scary when you don’t. Make sure your digital strategy includes thought about the way to use and not abuse your consulting partners (and no, don’t expect that to come from even the best consultants).

Keep these four lessons in mind, and you’re at least half-way to a real strategy for transformation.

Join me for what I hope will be a really challenging webinar (hosted by the 4A’s) on improving customer experience with analytics.

What You Will Learn

  • How behavioral segmentation creates a framework for continuous improvement
  • How you can most effectively use VoC to enhance a segmentation framework
  • How you can get around the common limitations of in-line VoC, such as sample bias and survey fatigue
  • What changes in the organization are required to really operationalize this type of process

Digital Transformation and the Reverse Hierarchy of Understanding

Why is it so hard for the traditional enterprise to do digital well? That’s the question that lurks at the heart of every digital transformation discussion. After all, there’s plenty of evidence that digital can be done well. No one looks at the myriad FinTech, social, and ecommerce companies that are born digital and says “Why can’t they do digital well?” When digital is in your DNA it seems perfectly manageable. Of course, mastering any complex and competitive field is going to be a challenge. But for companies born into digital, doing it well is just the age-old challenge of doing ANY business well. For most traditional enterprises, however, digital has been consistently hard.

So what is it that makes digital a particular challenge for the traditional enterprise?

That was the topic of my last conversational session at the Digital Analytics Hub this past week in Monterey (and if you didn’t go…well, sucks for you…great conference). And with a group that included analytics leaders in the traditional enterprise across almost every major industry and a couple of new tech and digital pure plays, we had the right people in the room to answer the question. What follows is, for the most part, a distillation of a discussion that was deep, probing, consistently engaging, and – believe it or not – pretty darn enlightening. Everything, in short, that a conversation is supposed to be but, like digital transformation itself, rarely succeeds in being.

There are some factors that make digital a peculiar challenge for everyone – from startup to omni-channel giant. These aren’t necessarily peculiar to the large traditional enterprise.

Digital changes fast. The speed of change in digital greatly exceeds that in most other fields. It’s not that digital is entirely unique here. Digital isn’t the only discipline where, as one participant put it, organizations have to operate in chaos. But digital is at the upper-end of the curve when it comes to pace of change and that constant chaos means that organizations will have to work hard not just to get good at digital, but to stay good at digital.

The speed of change in digital is a contributing factor to and a consequence of the frictionless nature of digital competition and the resulting tendency toward natural monopoly. I recently wrote a detailed explanation of this phenomenon, beginning with the surprising tendency of digital verticals to tend toward monopoly. Why is it that many online verticals are dominated by a single company – even in places like retail that have traditionally resisted monopolization in the physical world? The answer seems to be that in a world with little or no friction, even small advantages can become decisive. The physical world, on the other hand, provides enough inherent friction that gas stations on opposite sides of the street can charge differently for an identical product and still survive.

This absence of friction means that every single digital property is competing against a set of competitors that is at least national in scope and sometimes global. Local markets and the protection they provide for a business to start, learn and grow are much harder to find and protect in the digital world.

That’s a big problem for businesses trying to learn to do digital well.

However, it’s not quite true that it’s an equal problem for every kind of company. In that article on digital monopoly, I argued for the importance of segmentation in combating the tendency toward frictionless monopoly. If you can find a small group of customers that you can serve better by customizing your digital efforts to their particular needs and interests, you may be able to carve out that protected niche that makes it possible to learn and grow.

Big enterprise – by its very (big) nature – loses that opportunity. Most big brands have to try an appeal to broad audience segments in digital. That means they often lack the opportunity to evolve organically in the digital world.

Still, the challenges posed by a frictionless, high-chaos environment are almost as daunting to a digital startup as they are to a traditional enterprise. The third big challenge – the demand in digital for customer centricity – is a little bit different.

Digital environments put a huge premium on the ability to understand who a customer is and provide them a personalized experience across multiple touches. It’s personalization that drives competitive advantage in digital and the deeper and wider you can extend that personalization, the better. Almost every traditional enterprise is setup to silo each aspect of the customer journey. Call-Center owns one silo. Store another. Digital a third. That just doesn’t work very well.

Omni-channel enterprises not only have a harder challenge (more types of touches to handle and integrate), they are almost always setup in a fashion that makes it difficult to provide a consistent customer experience.

Customer-centricity, frictionless competition and rapidity of change are the high-level, big picture challenges that make digital hard for everyone and, in some respects, particularly hard for the large, traditional enterprise.

These top-level challenges result, inevitably, in a set of more tactical problems many of which are specific to the large traditional enterprise that wasn’t created specifically to address them. Looming large among these is the need to develop cross-functional teams (engineers, creative, analytics, etc.) that work together to drive continuous improvement over time. Rapidity of change, frictionless competition and the need for cross-silo customer-centricity make it impossible to compete using a traditional project mentality with large, one-time waterfall developments. That methodology simply doesn’t work.

Large, traditional enterprise is also plagued by IT and Marketing conflicts and Brand departments that are extremely resistant to change and unwilling to submit to measurement discipline. This is all pretty familiar territory and material that I’ve explored before.

Adapting to an environment where IT and Marketing HAVE to work together is hard. A world where traditional budgeting doesn’t work requires fundamental change in organizational process. A system where continuous improvement is essential and where you can’t silo customer data, customer experience or customer thinking is simply foreign to most large enterprises.

This stuff is hard because big organizations are hard to change. To get the change you want, a burning platform may be essential. And, in fact, in our group the teams that had most successfully navigated large enterprise transformation came from places that had been massively disrupted.

No good leader wants to accept that. If you lead a large enterprise, you don’t want to have to wait till your company’s very existence is threatened to drive digital transformation. That sucks.

So the real trick is finding ways to drive change BEFORE massive disruption makes it a question of survival.

And here, a principle I’ve been thinking about and discussed for the first time at the DA Hub enjoyed considerable interest. I call it the reverse hierarchy of understanding.

Organizations work best when an organization’s management hierarchy generally matches to its knowledge hierarchy. And believe it or not, my general experience is that that’s actually the case most of the time. We’re all used to specialized pieces of knowledge and specific expertise existing exclusively deep down in the organization. A financial planner may have deep knowledge of TM1 that the CFO lacks. But I’ve met a fair number of CFO’s and a fair number of financial planners and I can tell you there is usually a world (or perhaps two decades) of difference in their understanding of the business and its financial imperatives.

When that hierarchy doesn’t hold, it’s hard for a business to function effectively. When privates know more than sergeants, and sergeants know more than lieutenants and lieutenants know more than generals, the results aren’t pretty. Tactics and strategy get confused. The rank and file lose faith in their leaders. Leaders – and this may be even worse – tend to lose faith in themselves.

The thing about digital is that it does sometimes create a true reverse hierarchy of understanding in the large traditional enterprise. This doesn’t matter very much when digital is peripheral to the organization. Reverse hierarchies exist in all sorts of peripheral areas of the business and they don’t spell doom. But if digital become core to the organization, allowing a reverse hierarchy to persist is disastrous.

And here’s where digital transformation is incredibly tricky for the large traditional enterprise. You can’t invert the organization. Not only is it impossible, it’s stupid. Large traditional organizations can’t simply abandon what they are – which means that they have to figure out how to work with two separate knowledge hierarchies while they transform.

So the trick with digital transformation is building a digital knowledge hierarchy and finding ways to incorporate it in the existing management hierarchy of the business. It’s also where great leadership makes an enormous difference. Because most companies wait too long to begin that process – ultimately relying on a burning platform to drive the essential change. But while it’s hard to effect complete transformation without the pressure of massive disruption, it’s eminently possible to prepare for transformation by nurturing a digital knowledge hierarchy.

Think of it like FDR building out the U.S. military prior to WWII. He couldn’t fight the war, but he could prepare for it. We tend to define great leaders by what they do in crisis. But effecting change in crisis is relatively easy. The really great leaders have the vision to prepare for change before the onset of crisis.

So how can leadership address a reverse hierarchy of understanding in digital – especially since they are part of the problem? That’s the topic for my next post.


[A final thanks to all the great participants in my Digital Analytics Hub Conference session on this topic. You guys were brilliant and I hope this post does at least small justice to the conversation!]

Burning Down the House

Nowhere is the challenge of getting people to understand how to use data better illustrated than the methodology wars being fought in the discipline of Psychology. If you haven’t heard of the methodology wars be assured that the battlefields – studies in psychological research – are being fought over like blocks of Stalingrad; and like that famous battle, not much is left standing in the aftermath.

I’m not sure exactly how the methodology wars started. Somehow, somewhere, someone decided to actually re-test a “classic” study in psychology. A study that’s been accepted into the core of the discipline – that established somebody’s reputation, made somebody a career. Only it didn’t replicate. They re-did the experiment as carefully as they could and it didn’t show the same result. Didn’t, usually, show any result at all. Increase the sample size to fix the problem and the signal becomes even clearer. Alas, the signal always seems to be that there is no signal.

Pretty soon people started calling into question nearly every Psych study done over the last fifty years and testing them. And many – and I mean many – have failed.

Slate’s article (and it’s really good – giving a great overview of the issue – so give it a read) recounts the latest block to burn down in psychology’s methodology wars. The research in question centered around the idea that our facial states feedback into our emotions. If we smile (even inadvertently) we will feel happier.

It’s an interesting idea – intuitively plausible – and apparently widely supported by a huge variety of studies in the field. It’s an idea which strikes me as perfectly reasonable and in which I have zero vested interested one way or another.

But when it was submitted to rigorous simultaneous validation in a number of different labs, it failed. Completely.

The original test involved 32 participants and a change in average subjective scoring between the two groups of 4.4 to 5.5. That means each group had sixteen participants. The improved second test had 92 total participants and showed a scoring difference of 4.3 to 51.

That’s a pretty small sample.

Especially for something that became received wisdom. A classic.

So how would people react if it turned out to be wrong?

Well, the Slate article answers that question pretty definitively. Because when the multi-lab tests came back, here’s what happened. Seventeen different labs replicated the experiment with nearly 2000 subjects. In half the participating labs, participants who smiled recorded a slightly higher average on the resulting happiness test (but much lower than in the original experiment). In the other half, it went the other way.

Net, net, there was no correlation at all. Zero.

Okay, so far you have just another sad story of a small sample size failure.

That’s not what really attracted my attention. Nope. What really made me laugh in utter disbelief was the comment of the “scientist” who had done the original research. Here it is, and I quote in full lest you think I’m about to exaggerate:

“Fritz Strack has no regrets about the RRR, but then again, he doesn’t take its findings all that seriously. “I don’t see what we’ve learned,” he said.

Two years ago, while the replication of his work was underway, Strack wrote a takedown of the skeptics’ project with the social psychologist Wolfgang Stroebe. Their piece, called “The Alleged Crisis and the Illusion of Exact Replication,” argued that efforts like the RRR reflect an “epistemological misunderstanding,” since it’s impossible to make a perfect copy of an old experiment. People change, times change, and cultures change, they said. No social psychologist ever steps in the same river twice. Even if a study could be reproduced, they added, a negative result wouldn’t be that interesting, because it wouldn’t explain why the replication didn’t work.

So when Strack looks at the recent data he sees not a total failure but a set of mixed results. Nine labs found the pen-in-mouth effect going in the right direction. Eight labs found the opposite. Instead of averaging these together to get a zero effect, why not try to figure out how the two groups might have differed? Maybe there’s a reason why half the labs could not elicit the effect.

[Bolding is mine]

So here’s a “scientist” who, despite presumably being familiar with the extensive literature on statistics and the methodology wars, somehow believes that because half the labs reported a number slightly above average the key thing to look at is why the other half didn’t. Apparently, the only thing that would satisfy him is if all the labs reported an exactly opposite result. Which, presumably, would result in a new classic paper that frowning makes you happier!

Ring, Ring!

Clue Phone.

It’s random variation calling for you, Dr. Strack!

Cause here’s the thing…you’d expect about half the labs to show a positive result when there is no correlation. If some labs didn’t report a positive result then the correlation would pretty much have to be negative, right?

This isn’t, as he appears to believe, half-corroboration. It’s the way every null result ever found actually looks out here in the real world. I’d advise him to try flipping a coin 100 times, repeatedly, and see how often it comes out 50 heads and 50 tails. He might be surprised to learn that about the half the time this test will yield more heads flips than tail flips. This does not mean that heads is more likely than tails and it does not suggest that researchers should focus on why some trials yielded more heads and other trials yielded more tails.


Okay, I get it. You published a study. You made a career out of it. It’s embarrassing that it turns out to be wrong. But it’s hard to know in this case which is the worse response – intellectual dishonesty or sheer stupidity. Frankly, I think the latter. Because I don’t care how dishonest you are, some explanations should be too embarrassing to try on for size. And the idea that the right interpretation of these results would be to look for why some labs had slightly different results than others clearly belongs in that category.

I find the defense based on the difficulties of true replication more respectable. And yet, what are we to make of an experiment so delicate that it can’t be replicated AT ALL even with the most careful controls? How important can any inference we make from such an experiment plausibly be? By definition, it could only fit the most narrow range of cases imaginable. And the idea that replication of an experiment doesn’t matter seems…you know…a tad unscientific.

From my perspective, it isn’t the original study that illustrates the extraordinary problem we have getting people to use data well. Yes, over-reliance on small sample sizes is all too common and all too easy. That’s unfortunate, not shameful. But the deeper problem is that even when data is used well, a lethal combination of self-interest and a near total lack of understanding of basic statistics make it all too possible for people to ignore the data whenever they wish.

As Simon and Garfunkel plaintively observed, “A man hears what he wants to hear, and disregards the rest”.

If it wasn’t so sad, it would be funny.

Dammit. It is funny.

For it’s easy to see that in this version of the psych methodology wars, the defenders have their own unique version of a foxhole – with posterior high in the air and head firmly planted in the sand.

[Getting close to the Digital Analytics Hub. If you love talking analytics, check it out. Would be great to see you there!]

Frictionless Competition and the Surprising Monopolization of the Internet

In the last few months I’ve been spending quite a bit of time thinking about the challenges in physical retail – stores. I’m going to be talking much more about that in the months to come, but thinking about the challenges in physical retail and whether and to what extent digital techniques might help, I’ve also had to think about why digital retail has evolved the way it has.

There’s no doubt that digital has disrupted and hurt traditional retail. But it’s a mistake to attribute that solely to advantages inherent in digital. After all, if it was just a matter of digital being superior to B&M, then Borders should have been fine moving online. That didn’t work out so well.

In fact, one of the most interesting aspects of our digital world is how a perfect leveling of the playing field has produced such a strong tendency to natural monopoly. This isn’t just about retail. In most of the key areas of internet – from retail to video streaming to music to search to ride summoning, we’ve seen an extraordinary tendency toward massive consolidation around a single leader.

It’s not exactly what most of us expected. By eliminating most barriers to entry, creating frictionless geographies, and creating technology environments that scale seamlessly to almost any size, the digital world has removed many of the traditional bastions of monopoly. Old-world monopolies used to spring from cases where scale precluded competition. If, for example, you owned the pipes that carried gas to homes or the wires that carried electricity, it was incredibly hard for anyone else to compete.

In today’s world, that kind of ownership has mostly vanished. You could argue that if you own search you own the pipes to the Web. But the analogy doesn’t hold. It doesn’t hold because anybody can create a competing search system at any time and every single internet user can have instant access to it. It doesn’t hold because there are multiple ways to pipe through the internet besides search. And it doesn’t hold because there really are no physical barriers to building or deploying that alternative search system.

So it wouldn’t be unreasonable to expect the digital world to have morphed into a wild west of tiny artisanal companies with meteoric rises, equally sudden collapses, and constant, ubiquitous competition. Mostly, though, that’s not the way it looks at all. It looks as if monopoly, despite the absence of physical barriers, is actually a more powerful tendency in the digital world than the physical world.

It’s not that hard to understand why things have gone this way. Natural monopolies around things like electricity delivery occurred because of the immense friction involved in setting up the delivery system. Economies of scale were absolutely decisive in such situations. But most traditional markets are resilient to natural monopoly because of fundamental facts of the physical world that worked AGAINST too much scale. In the physical world, it makes perfect sense to have gas stations on the opposite side of a street. And it’s quite likely that two such stations can not only co-exist but thrive despite their close proximity. After all, it’s a pain to cross the street when you want to get gas. I may prefer Whole Foods to Safeway or vice versa. But I often go the grocery store that’s closest to me regardless of brand. And when I lived in San Francisco I bought most of my Diet Coke and impulse snacks at the corner store up my block. No, it wasn’t nice and it wasn’t cheap. But it sure was close. I may like Sol Food in San Rafael better than Los Moles, but so do a lot of other people – and I hate standing in line.

The natural friction that the physical world carries in terms of geographic convenience and capacity help ensure that countless niches for delivery exist. Like my old corner store, in the physical world, you can o be worse at everything except location and still thrive.

That doesn’t happen in the digital world.

It turns out – and I guess this should be no surprise – that in a frictionless world, any small advantage can be decisive. A grocery has to be a LOT better than its competitors to get me to drive an extra 10 minutes. But online, the best grocery is always just a few milliseconds away.

It doesn’t have to be a lot better. In fact, the difference can be incredibly tiny. Absent friction, the size of the advantage is no longer that meaningful. The digital world can make even tiny advantages decisive.

So why doesn’t every aspect of the digital world turn into a monopoly?

The answer lies in segmentation. A very small advantage may be decisive in the digital world. But it’s hard to have an advantage to EVERYONE.

In areas like news and entertainment, for example, it’s impossible to produce content that is better for everyone. Age, education, interest, background, geography and countless other factors create an infinity of micro-fractures. Not only is the content itself differentiated, but it’s creation is almost equally fractured. A.O. Scott could no more produce a version of Real Housewives than Andy Cohen could write a NY Times film review.

Content creation turns out to be friction-full in a way that was somewhat obscured by the old limitations in distribution. In fact, it appears that the market for segmented content and the ability of content to create barriers to consolidation is almost limitless. That’s why there’s almost nothing so important to becoming a good digital company than content creation. It’s the best way there is to guard your marketspace.

All this suggests that there are two paths to success in the digital world. One path involves scale and the other segmentation. They aren’t mutually exclusive and the companies that do both well are formidable indeed.


It’s only a little more than a month till the Digital Analytics Hub in Monterey and a chance to talk all things digital – both practical and philosophical. After all, there is no monopoly on great conversation. Looking forward to talking deep analytics, natural monopolies, digital transformation and digital advantage!

A Guided Tour through Digital Analytics (Circa 2016)

I’ve been planning my schedule for the DA Hub in late September and while I find it frustrating (so much interesting stuff!), it’s also enlightening about where digital analytics is right now and where it’s headed. Every conference is a kind of mirror to its industry, of course, but that reflection is often distorted by the needs of the conference – to focus on the cutting-edge, to sell sponsorships, to encourage product adoption, etc.  With DA Hub, the Conference agenda is set by the enterprise practitioners who are leading groups – and it’s what they want to talk about. That makes the conference agenda unusually broad and, it seems to me, uniquely reflective of the state of our industry (at least at the big enterprise level).

So here’s a guided tour of my DA Hub – including what I thought was most interesting, what I choose, and why. At the end I hope that, like Indiana Jones picking the Holy Grail from a murderers row of drinking vessels, I chose wisely.

Session 1 features conversations on Video Tracking, Data Lakes, the Lifecycle of an Analyst, Building Analytics Community, Sexy Dashboards (surely an oxymoron), Innovation, the Agile Enterprise and Personalization. Fortunately, while I’d love to join both Twitch’s June Dershewitz to talk about Data Lakes and Data Swamps or Intuit’s Dylan Lewis for When Harry (Personalization) met Sally (Experimentation), I didn’t have to agonize at all, since I’m scheduled to lead a conversation on Machine Learning in Digital Analtyics. Still, it’s an incredible set of choices and represents just how much breadth there is to digital analytics practice these days.

Session 2 doesn’t make things easier. With topics ranging across Women in Analytics, Personalization, Data Science, IoT, Data Governance, Digital Product Management, Campaign Measurement, Rolling Your Own Technology, and Voice of Customer…Dang. Women in Analytics gets knocked off my list. I’ll eliminate Campaign Measurement even though I’d love to chat with Chip Strieff from Adidas about campaign optimization. I did Tom Bett’s (Financial Times) conversation on rolling your own technology in Europe this year – so I guess I can sacrifice that. Normally I’d cross the data governance session off my list. But not only am I managing some aspects of a data governance process for a client right now, I’ve known Verizon’s Rene Villa for a long time and had some truly fantastic conversations with him. So I’m tempted. On the other hand, retail personalization is of huge interest to me. So talking over personalization with Gautam Madiman from Lowe’s would be a real treat. And did I mention that I’ve become very, very interested in certain forms of IoT tracking? Getting a chance to talk with Vivint’s Brandon Bunker around that would be pretty cool. And, of course, I’ve spent years trying to do more with VoC and hearing Abercrombie & Fitch’s story with Sasha Verbitsky would be sweet. Provisionally, I’m picking IoT. I just don’t get a chance to talk IoT very much and I can’t pass up the opportunity. But personalization might drag me back in.

In the next session I have to choose between Dashboarding (the wretched state of as opposed to the sexiness of), Data Mining Methods, Martech, Next Generation Analytics, Analytics Coaching, Measuring Content Success, Leveraging Tag Management and Using Marketing Couds for Personalization. The choice is a little easier because I did Kyle Keller’s (Vox) conversation on Dashboarding two years ago in Europe. And while that session was probably the most contentious DA Hub group I’ve ever been in (and yes, it was my fault but it was also pretty productive and interesting), I can probably move on. I’m not that involved with tag management these days – a sign that it must be mature – so that’s off my list too. I’m very intrigued by Akhil Anumolu’s (Delta Airlines) session on Can Developers be Marketers? The Emerging Role of MarTech. As a washed-up developer, I still find myself believing that developers are extraordinarily useful people and vastly under-utilized in today’s enterprise. I’m also tempted by my friend David McBride’s session on Next Generation Analytics. Not only because David is one of the most enjoyable people that I’ve ever met to talk with, but because driving analytics forward is, really, my job. But I’m probably going to go with David William’s session on Marketing Clouds. David is brilliant and ASOS is truly cutting edge (they are a giant in the UK and global in reach but not as well known here), and this also happens to be an area where I’m personally involved in steering some client projects. David’s topical focus on single-vendor stacks to deliver personalization is incredibly timely for me.

Next up we have Millennials in the Analytics Workforce, Streaming Video Metrics, Breaking the Analytics Glass Ceiling, Experimentation on Steroids, Data Journalism, Distributed Social Media Platforms, Customer Experience Management, Ethics in Analytics(!), and Customer Segmentation. There are several choices in here that I’d be pretty thrilled with: Dylan’s session on Experimentation, Chip’s session on CEM and, of course, Shari Cleary’s (Viacom) session on Segmentation. After all, segmentation is, like, my favorite thing in the world. But I’m probably going to go with Lynn Lanphier’s (Best Buy) session on Data Journalism. I have more to learn in that space, and it’s an area of analytics I’ve never felt that my practice has delivered on as well as we should.

In the last session, I could choose from more on Customer Experience Management, Driving Analytics to the C-Suite, Optimizing Analytics Career-Oaths, Creating High-Impact Analytics Programs, Building Analytics Teams, Delivering Digital Products, Calculating Analytics Impact, and Moving from Report Monkey to Analytics Advisor. But I don’t get to choose. Because this is where my second session (on driving Enterprise Digital Transformation) resides. I wrote about doing this session in the EU early this summer – it was one of the best conversations around analytics I’ve had the pleasure of being part of. I’m just hoping this session can capture some of that magic. If I didn’t have hosting duties, I think I might gravitate toward Theresa Locklear’s (NFL) conversation on Return on Analytics. When we help our clients create new analytics and digital transformation strategies, we have to help them justify what always amount to significant new expenditures. So much of analytics is exploratory and foundational, however, that we don’t always have great answers about the real return. I’d love to be able to share thoughts on how to think (and talk) about analytics ROI in a more compelling fashion.

All great stuff.

We work in such a fascinating field with so many components to it. We can specialize in data science and analytics method, take care of the fundamental challenges around building data foundations, drive customer communications and personalization, help the enterprise understand and measure it’s performance, optimize relentlessly in and across channels, or try to put all these pieces together and manage the teams and people that come with that. I love that at a Conference like the Hub I get a chance to share knowledge with (very) like-minded folks and participate in conversations where I know I’m truly expert (like segmentation or analytics transformation), areas where I’d like to do better (like Data Journalism), and areas where we’re all pushing the outside of the envelope (IoT and Machine Learning) together. Seems like a wonderful trade-off all the way around.

See you there!
See you there!


Seven Pillars of Statistical Wisdom

I don’t review a lot of business books on my blog…mostly because I don’t like a lot of business books. A ridiculous percentage of business books seem to me either to be one-trick ponies (a good idea that could be expressed fully in a magazine article expanded to book length) or thinly veiled self-help books (self help books with ties as described in this spot-on Slate article). I HATE self-help books. Grit, Courage, Indecisiveness. It’s all the same to me.

On the other hand, The Seven Pillars of Statistical Wisdom isn’t really a business book. It’s a short (200 small pages), crisp, philosophical exploration of what makes statistics interesting. Written by a Univ. of Chicago Professor and published by Harvard University Press, it’s the best quasi-business book I’ve read in a long time.

I say quasi-business book because I’m not really sure who the intended audience is. It’s not super technical (thank god you can read it and know very little math), but it sometimes veers into explanations that assume a fairly deep understanding of statistics. Deeper, at least, than I have though I am most certainly not a formally trained statistician.

What Seven Pillars does extraordinarily well is examine a small core set of statistical ideas, explicate their history, and show why they are important, fundamental, and, in some cases, still controversial. In doing this, Seven Pillars provides a profound introduction into how to think statistically – not do statistics. Instead of focusing on how specific methods work, on definitions of statistical methods, or on specific issues in modern statistics (like big data), Seven Pillars tries to define what makes statistics an important way to think.

To give you a sense of this, here are the seven pillars:

Aggregation: Probably the core concept at the heart of all statistical thinking is the idea that you can sometimes GAIN insight while losing data. Stigler delves into basic concepts like the mean, shows how they evolved over the centuries (and it did take centuries) and explains why this fundamental insight is so important. It’s a brilliant discussion.

Information: If we gain information by losing data, how do we know how much information we’ve gained? Or how much data we need? With this pillar, Stigler lays out why more is sometimes less and how the value of observations usually declines sharply. Another terrific discussion around a fundamental insight that comes from statistics but is constantly under siege from folk common-sense.

Likelihood: In this section, Stigler tackles how the concepts around confidence levels and estimation of likelihood evolved over time. This section contains an amusing and historically interesting discussion on arguments for and against the likelihood of miracles!

Intercomparison: Stigler’s fourth pillar is the idea that we can use interior measurements of the data (there’s an excellent discussion of the historical derivation of Standard Deviation for example) to understand it. This section includes a superb discussion of the pitfalls of purely internal comparison and the tendency of humans to find patterns and of data to exhibit patterns that are not meaningful.

Regression: The idea of regression to the mean is fundamental to statistical thinking. It’s an amazingly powerful but consistently non-intuitive concept. Stigler uses a genetics example (and a really cool Quincunx visualization) to help explain the concept. This is one of the best discussions in a very fine book. On the other hand, the last part of this section which covers multivariate and Bayesian developments is less wonderful. If you don’t already understand these concepts, I’m not sure Stigler’s discussion is going to help.

Design: The next pillar is all about experimental design – surely a concept that is fundamental not just to statistics but to our everyday practical application of it. I found the discussion of randomization in this section particularly interesting and potentially noteworthy and thought-provoking.

Residual: Pillar seven is, appropriately enough, about what’s left over. Stigler is concerned here to show how examining the unexplained part of the analysis leads to a great deal of productive thinking in science and elsewhere. The idea of nested models is introduced and this section somehow transitions into a discussion of data visualization with illustrations from Florence Nightingale (apparently a mean hand with a chart). I’m not sure this transition made perfect sense in the context of the chapter, but the discussion is fascinating, enjoyable and pointed enough to generate some real insight.

Stigler concludes with some thoughts around whether and where an eighth pillar might arise. There’s some interesting stuff here that’s highly appropriate to anyone in digital trying to extend analytics into high-dimensional, machine-learning spaces. The discussion is (too) brief but I think intentionally so.


Seven Pillars isn’t quite a great book, and I mean that as high-praise. I don’t read many books that I could plausibly describe as almost great. The quality of the explanations is extremely high. But it does a better job explicating the intellectual basis behind simpler statistical concepts than more complicated ones and there are places where I think it’s insufficiently forceful in illuminating the underlying ways of thinking not just the statistical methods. Perhaps that’s inevitable, but greatness isn’t easy!

I do think the book occasionally suffers from a certain ambiguity around its audience. Is it intended as a means to get deep practitioners thinking about more fundamental concepts? I don’t think so – too many of the explanations are historical and basic.

Is it intended for a lay audience? Please.

I think it fits two audiences very well, but perhaps neither perfectly.

First, there are folks like me who use statistics and statistical thinking on an everyday basis but are not formally trained. I’m assuming that’s also a pretty broad swath of my readers. I know I found it both useful and enlightening, with only a few spots where the discussion became obscure and overtly professional.

The second audience is students and potential students of statistics who need something that pulls them away from the trenches (here’s how you do a regression) and gets them to think about what their discipline actually does. For that audience, I think the book is consistently brilliant.

If there’s a better short introduction into the intellectual basis and foundation of statistical thinking, I don’t know it. And for those who confuse statistical thinking with the ability to calculate a standard deviation or run a regression, Seven Pillars is a heady antidote