Tag Archives: digital analytics

The Road Goes Ever On

“It’s a dangerous business,” says Bilbo Baggins, “going out your front door. You step onto the road, and if you don’t keep your feet, there’s no knowing where you might be swept off to.” Eighteen years ago I stepped outside my comfortable door and was swept out into a digital world that I – like everyone else – knew very little about. There were dragons in that wilderness, as there always are. Some we slew and some we ran away from. Some are out there still.

But though a road may go on and on, a person sometimes finds another path. In the last few months I’ve felt rather like young Mr. Baggins, visited by dwarfs and a wizard, and confronted with a map of a great unexplored wasteland, a forbiddingly guarded, lonely mountain, and a vast treasure.

It’s not so easy to give up adventuring.

Eighteen years ago when I first started thinking seriously about digital analytics, we were the poor step-child of analytics. Web analytics (as we called it back then) was pathetic. To call it analytics was a misnomer – the right word being some polyglot mash-up of hubris, false-advertising and ignorance. Perhaps “faligris” was the word we needed but didn’t have. We looked with envy at the sophisticated analytics done for mass media, retail, and direct mail.


My how times have changed.

I’m not going to go all Pollyanna on you. There’s still a lot not to like about the way we do digital analytics. But here’s the thing – I’m not sure there’s a field that does it better. Without really realizing it, digital has spawned a discipline of continuous improvement that includes a fairly sophisticated view of dashboarding and reporting, interesting segmentation, a decent set of techniques for specific analysis problems, and – probably most impressive – a real commitment to experimentation. Sure, most companies get a lot of this wrong. My extended discussion of the perils and problems of digital transformation isn’t (really!) just grumpiness. But the companies that do it well are truly outstanding. And even in the flawed general practice there is much to like.

The best of digital analytics these days has nothing to be ashamed of and much to be proud of.

That’s why, on the map of the digital analytics world, there are more gardens than wasteland, more cultivated field than dangerous mountain. Digital Analytics is well past the “trough of despair” in the hype-cycle – delivering tremendous value on a consistent basis. That’s a great place to be, but I’m looking for a little more adventure.

A little backstory may be in order here. Not too long ago, one of my larger clients asked us to take a look at a solution they’d tried to measure their customer’s IN STORE journeys. Their vendor wired up a sample store with a network of cameras to detect and measure in-store customer paths. It looked a lot like digital analytics. Or should I say it looked a lot like web analytics? Because in almost every respect, it reminded me of the measurement capture, reporting and analysis we did back in 1997. The data capture was expensive and broke frequently. The data was captured at the wrong level of granularity and there was no detail feed available. The reporting was right there with Webtrends 1.0. The analysis – literally – became a standing joke with our client.

It was pathetic.

Well, even from this mess, there was interesting information to be salvaged. You COULD do better reporting even on the sadly broken data being collected. But it got me thinking. Because this was a “leader” in the field.

So naturally, I checked out the rest of the field. What I found were the same type of engineering heavy, analysis tone-deaf companies that I remembered back in the old days of the web before people like Omniture and Google figured out how to do this kind of thing right. I found technology solutions desperately looking for actual business problems. I found expensive implementations that still managed to miss the really important data. I found engineers not analysts.

I found opportunity.

Because this data and these systems are very much like digital analytics. The lessons we’ve learned there about collection, KPIs, reporting, segmentation, analysis and testing all feel fresh, important and maybe even revolutionary. And this time, there’s a chance to provide an end-to-end solution that combines technology with the kind of reporting and analytics I’ve always dreamed about. There’s a chance to be the Omniture AND Semphonic of a really cool space.

I just couldn’t resist.

So I’m going to be leaving EY and, for that matter, digital analytics. I’ll miss both keenly. These past years in digital analytics have been the best and most rewarding of my professional life. I’m proud of the work I’ve done. Proud of the work we’ve done together. Proud of the discipline we’ve created. But I want to take that work and build something new from the ground up.

I’m going to build a startup dedicated to bringing the best of digital discipline and measurement to the physical world. Helping stores, malls, stadiums, banks, hospitals and who knows what else understand how to use customer behavior to actually optimize customer experience.

I want to make the best experiences in the real world every bit as seamless, personalized, and optimized as the best experiences in the digital world already are.

It’s a dangerous world out there in physical retail. They’re struggling and they don’t really know how to get better. If there really was a map, I’m pretty sure it would have a big X with “there be dragons” printed right above.

I can’t wait.

Welcome to Digital Mortar!



Digital Transformation Dialogs

I’m going to wrap up this extended series on digital transformation with a back-and-forth dialog with an old friend of mine. I’ve known Scott K. Wilder since the early days of Web Analytics. He’s been an industry leader helping companies build communities, adapt to an increasingly social world, and drive digital transformation. In some of this current work, Scott has been working with companies to adopt collaborative working suites for their customers, partners and employees – which I think is a huge part of internal digital transformation. So I thought a conversation on the pitfalls and challenges might be interesting and useful.

GA: We all see these hype-cycle trends and right now there’s a lot of interest in digital transformation at the enterprise level. I think that’s driven by the fact that most large enterprises have tried pretty seriously for a while now to get better at digital and are frustrated with the results. Do you agree?

SW: Good question.

When you read white papers about the latest trends in the enterprise space, most of them highlight the importance of each company being digital transformed. This usually means leaving a legacy approach or operation and instead leveraging a new approach or business model that embraces technology.

Unfortunately, most companies fail when they undertake this endeavor. Sometimes they fail because just pay lip service to this initiative, never do anything beyond placing the goal of ‘going digital’ on a powerpoint slide they give a company All-Hands (I have witnessed this first hand). And sometimes, they just test out bunch of different programs without thinking through desired outcomes. (They throw a lot of virtual stuff against the internet wall hoping that something sticks).

Undergoing a Digital Transformation means many things to many people. It can imply focusing more on the customer. Or it can mean enabling employees collaborate better together. At the end of the day, however, a company needs to first focus on one simple end state. One change in behavior! Rather than trying to boil the whole ocean at once and try to do implement massive digital transformation across an organization, it’s better to start with a  simple project, try to leverage technology to accomplish a desired outcome, learn from the experience and then share the success with other parts of the organization

Start first with a relatively simple goal. And if you really want to change an organization, see if you can get employees volunteer to be your soldiers in arms and then closely work with them to define what digital success looks like. It could be as something getting employees to digitalize their interaction with each other more  or leveraging technology to improve a VOC process. Whatever it is. Start with one project.

Here’s one approach. Once the goal is to define, then ask for volunteers to work on figuring out how to achieve the desired outcome. No digital program or initiative is going to be successful without employee buy – in and involvement, so it behooves CEOs to find a bunch of enthusiastic volunteers to figure out the ‘how’ (If you remember you calculus Y = (x)x Senior managers can decide on the Y, and then let their team figure out the X or inputs.

Digital Transformations often fail because:

  • Executives often decide their company goals and then impose their approach on the employees. Digital Transformation initiatives also fail because CEOs want to change whole culture overnight. Unfortunately, however, they often forget Rome was not built in day. Even though a true Digital Transformation is often a journey, it is also important to start simple. Very simple!
  • There’s no buy in at the mid-level ranks in the company
  • There’s no True North or desired goal
  • There’s too much attention on the technology and not the cultural impact.

I have read articles that tell you true cultural change can only happen if you eliminate political infighting, distribute your decision making, etc. While all of that is important, it will require gutting your organization, laying off a lot of people and hand-picking new hires if you want to change things quickly.

To truly change a culture, however start simple. Pick a goal. Ask for employees to volunteer to work on it (take other work off their plate so they don’t have to work after house). Ask them to to involve leveraging digital technologies. Give the team room to succeed or fail.  Most importantly, be their guide along the way.

Once this small team completes their project, celebrate their success in front of others in the company. Have them highlight how they leveraged technology.

Once this group is successful, anoint each team member to be a digital transformation ambassador and have them then move into other groups of the organization and share their learnings, experiences, etc.

GA: I’m a big believer in the idea that to change culture you have to change behavior – that means doing things not talking about them. I like the idea of a targeted approach – huge organizational changes are obviously incredibly risky. That being said, I feel like most of what you’ve talked about could be applied to any kind of transformation project – digital or otherwise. I’m not disagreeing with that, but I’m curious if you agree that digital presents some unique challenges to the large enterprise. And if you do agree, what are those challenges and do they change/drive any aspects of a transformation strategy?

SW: There are definitely challenges in driving any type of transformative change in an enterprise environment. Here’s a list of challenges preventing a smooth adoption of digital technologies or hindering the ability to digitally transform an organization

As they say. It’s hard to teach an old dog new tricks. Companies get stuck in their old ways of doing things. For example, even though companies are testing the waters with Slack and Hipchat, two great collaborative platforms, few have made any progress in being weaned (a bit) off of email. For example, we all complain about email but refuse to reduce how often we use it). Part of the problem is the result is that those individuals, who are tasked with driving change in the organization actually tend to be the biggest resisters to change. The IT department, who I will pick on here, usually are decision makers and keepers of the digital platform budgets do not want to try something new. (Marketing is slowly getting more say here, but most marketing leads don’t understand new technologies). So IT and even Marketing wait as long as possible to make a decision about adopting newer collaborative technologies, such as Slack or Hipchat. And while they are doing an elaborate evaluation process, today’s tech savvy staff often just jumps in and starts using the latest and greatest technologies. They don’t ask for permission first. This was the case at Marketo with Slack. First, a small group of employees starting using it and soon others jumped in. There was resistance at the highest parts of the company. Eventually, IT, however had no choice and how to follow the wisdom of the crowd. Survey Monkey also started out this way. There are other challenges as well. Solution: Companies need do a better job at knowing understanding what tools their teams want to use and why they want to use them. If the troops are using Google Docs, for example, management needs to embrace this and not try and force their way (in this case, the Microsoft Office 365 way) down the throats of their employees. If there are security concerns, figure out a solution.

GA: I’ll just note that in many ways this reflects my discussion of a Reverse Hierarchy of Understanding in organizations

…What else?

SW:   Data and Privacy Issues: Companies, rightly so, are always concerned about data leakage, data security and privacy issues. Enterprises, especially the public ones and the ones in important transaction industries like Finance or Health Care, have to be sensitive to how data is shared within an organization. Solution: If an organization wants to adopt a newer technology, management needs to do more research in how other companies adopt newer technology while protecting their company secrets. Few companies develop breakthrough technologies and systems that they are the first to try something new. Probably someone has already created a similar service or implemented a similar technology. They have probably already dealt with similar issues. I am not saying just copy what they did but rather learn from their mistakes. Or what they did well.

An older workforce: A third challenge is that many enterprises attract an older workforce and/or are not sure how to integrate millennials into their organization. As I pointed out in my book, Millennial Leaders, it’s important to embrace a younger workforce and place these individuals on teams where they can help advise key decision makers. Younger employees are more likely to adopt new approaches, new technologies and new ways of doing things. Solution: Bring millennials into digital related conversations sooner than later. While decision making can still be top down, it’s important to give these younger folks a voice.

GA: Okay – I know you have more thoughts on this but I’m going to stop right there because I know you’re an expert on this Millennial stuff and I want to delve into it a bit. But that’s probably a discussion for Post #2…

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.

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

Organizing the Digital Enterprise

At the Digital Analytics Hub in Europe I facilitated a conversation around enterprise digital transformation. We covered a lot of interesting ground, but organizing digital in the enterprise was the most challenging part of that discussion.

It’s a topic you can easily find yourself going around in circles with as people trot out opinions that sound right but sail past each other. That’s especially true since different organizations start (and want to finish) in very different places.

To get around that, I framed the problem in “state-of-nature” terms. If you were starting a digital organization from scratch in an enterprise, how would you organize and staff it?

But before we could answer that question, we had to consider something even more basic.

Should a “digital” organization be separate?

There’s a pretty strong sense these days that walling off digital from the rest of the organization gets things wrong from the outset. Digital should be embedded right into the DNA of the core organization. In a mature organization, there was a pretty broad consensus that digital isn’t a separate function. On the other hand, what if you’re not mature? Can you embed digital directly and grow it right if it’s inside the huge, complex structures that pervade an existing large enterprise? Even strong proponents of the “digital needs to be organic in the organization” point of view seemed to concede that incubation as a separate organization is often necessary to getting digital done and setup right. Of course, taking the incubation strategy is going to leave you with an organizational debt that at some point will have to be paid. The more successful you are and the larger and faster digital grows, the harder it’s going to be to re-integrate digital back into the organization.

I see both sides of this argument (and I’m sure there are more than two sides to be had). I’m just not a big believer in hard-and-fast right answers when it comes to organizational design.

If you have a strong digitally-experienced leader on your executive team and you have solid relationships between marketing and IT, maybe you try to transform digitally within your existing structures. If you’re not that lucky (and that is pretty lucky), maybe incubation with a strategy for integration is the right answer.

Having gotten to the point where most people conceded that incubation might sometimes be necessary, we returned to the “state-of-nature” question and discussed building out an incubated organization. Most people set product teams at the heart of that organization and agreed that these product teams should be organized very much along the lines that I described in my videos on enterprise transformation: agile-based product teams that include IT, creative and analytics people (behavioral, customer and attitudinal) all working together. In this model, there’s no pass-off from design to implementation to measurement to testing. The same teams that built a project optimize it – and there’s analytics at every step of the process.

I believe this is an incredibly powerful model for getting digital products right – and it’s a model that resonated across a pretty wide swath of different organizations – from giant retailers to very modest start-ups.

But it’s far from a complete answer to creating a digital organization.

Suppose you have these great integrated teams for each digital product, how do you handle all the ancillary functions that the large enterprise has developed? Things like finance and HR, for example. Do they need to be re-created inside a digital organization?

My reaction – and it was common – was that such functions probably don’t need to be re-created inside digital. Including these functions in digital doesn’t seem fundamental to getting digital right.

This point-of-view, however, was immediately challenged when it came to HR. The difficulties of digital hiring are well known – and it isn’t just finding resources. Traditional HR approaches to finding people, vetting candidates, compensation, and promotion bands are all problematic in digital. And if you get the people element wrong, everything else is doomed.

So once again, if you’ve got HR folks willing to work with and adapt to the needs of your digital leader, maybe you can leave existing structures intact and keep HR centralized. But HR is the wrong place to wimp out and leave your digital team without the power to execute the way they need to.

Bottom line? If my digital leader really wanted to own their own HR, I’d say yes.

Other functions? I don’t really know. Is it fundamental to digital execution? Does it need to be done differently in digital? Wherever the answer is yes, then it’s going to be a debate about whether it should live inside an incubated digital organization or be an outside service to it.

There’s another challenge that cuts even closer to the bone and lies at the heart of the challenge to the large enterprise. If you have a single digital product (like a pure-play startup might), you don’t have to worry about the relationship between and across teams and functions. But in a larger enterprise – even when it’s incubated – digital is going to require multiple product teams.

How do management lines work across those teams? Are the IT folks across product teams in the Digital IT organization and are they “managed” by Digital IT? Or are they managed by their Product Owner? In one sense, the answer seems obvious. On a day-to-day basis they are managed by their Product Owner. But who owns their career? What’s a career path like? How do Digital IT folks (or analysts) across product teams communicate? Who makes centralized decisions about key technology infrastructure? Who owns the customer?

Every one of these is a deep, important question with real ramifications for how the organization works and how you take a single product model and scale it into something that preserves the magic of the integrated team but adapts to the reality of the large, multi-function enterprise.

It was here, not surprisingly, that one of the participants in our DA Hub conversation trotted out the “dotted line”. Now it happened to be a consultant from a fellow big-4 and I (too glibly, I’m afraid) responded that “dotted lines are what consultants draw when they don’t have a good answer to a problem”.

I both regret and endorse this answer. I regret it because it was far too glib a response to what is, in one sense, probably the right answer. I endorse it because I think it’s true. God knows I’ve drawn these dotted lines before. When we draw a dotted line we essentially leave it up to the organization to organically figure out how it should work in day-to-day practice. That’s not necessarily a bad thing. It’s probably the right answer in a lot of cases. But we shouldn’t kid ourselves that just because it might be the right answer that makes it a good answer. It’s not. It’s a “we’re not the right people at the right time to answer this question” kind of answer. Knowing enough to know you’re not the right people at the right time is a good thing, but it would be a mistake to confuse that with actually having a good answer to the question.

So here’s my best attempt at a non-dotted line organization that integrates Product Teams into a broader structure. It seems clear to me that you need some centralized capabilities within each function. For Digital IT, as an example, these centralized teams provide shared services including enterprise technology selection, key standards and data governance. In analytics, the centralized team will be responsible for the overall customer journey mapping, analytics technology selection and standardization, a centralized analytics warehouse, and standards around implementation and reporting.

How big and inclusive does the centralized team need to be? Thinking there’s one right answer to this question is a kind of disease akin to thinking there’s some right answer to questions like “how large should government be?” There isn’t. I tend to be in the “as small as practical” school when it comes to centralization – both politically and in the enterprise. The best IT, the best creative, the best analytics is done when it’s closest to the business – that means out there in those Product teams. That also means making sure you don’t incent your best people out of the product teams into centralized roles so that they can “advance” and make more money.

It used to drive me crazy to see good teachers promoted to administrative roles in schools. You can’t blame the teachers. When you’ve got a family to feed, a house to buy, a nice to car to own, you’re not going to stay a teacher when your only path to more money and prestige is becoming an assistant principal. But you don’t see the Cleveland Cavaliers promoting Lebron from player to coach. It’s a terrible mistake to confuse rank with value.

I’m a big believer in WIDE salary bands. One great developer is worth an army of offshore programmers and is likely worth more than the person managing them. Don’t force your best people to Peter Principle themselves into jobs they hate or suck at.

So instead of creating progressions from Product teams to central teams, I’d favor aggressive rotational policies. By rotating people into and out of those central teams, you ensure that central teams stay attuned to the needs of the Product teams where work is actually getting done. You also remove the career-path issues that often drive top talent to gravitate toward centralization.

Communications and collaboration is another tricky problem. Collaboration is one of the most under-invested capabilities in the organization and my Product team structure is going to make it harder to do well. For areas like analytics, though, it’s critical. Analysts need to communicate practices and learnings across – not just within – product teams. So I’d favor having at least one role (and maybe more) per area in the central team whose sole function is driving cross-team communication and sharing. This is one of those band-aids you slap on an organizational structure because it doesn’t do something important well. Every organizational structure will have at least a few of these.

In an ideal world, that collaboration function would probably always have at least two resource slots – and one of those slots would be rotated across different teams.

My final structure features highly integrated product teams that blend resources across every function needed to deliver a great digital experience. Those teams don’t dissolve and they don’t pass off products. They own not just the creation of a product, but its ongoing improvement. Almost needless to say, analytics (customer, behavioral and attitudinal) is embedded in that team right from the get-go and drives continuous improvement.

Those teams are supported by centralized groups organized by function (IT, Design, Analytics) that handle key support, integration and standardization tasks. These centralized teams are kept as small as is practical. Rotational policies are enforced so that people experience both centralized and product roles. Salary bands are kept very wide and the organization tries hard not to incent people out of roles at which they excel. Included in the centralized teams are roles designed to foster collaboration and communication between functional areas embedded in the product teams.

Finally, support functions like HR and Finance are mostly kept external. However, where compelling reasons exist to incubate them with digital, they are embedded in the central structure.

I won’t pretend this is the one right answer to digital organizational structure. Not only does it leave countless questions unanswered, but I’m sure it has many problems that make it fatally flawed in at least some organizations.

There are no final answers when it comes to organizational design. Every decision is a trade-off and every decision needs to be placed in the context of your organization history, culture and your specific people. That’s why you can’t get the right answer out of a book or a blog.

But if you’re building an incubated digital organization, I think there’s more right than wrong here. I’ve tried to keep the cop-outs and dotted lines to a minimum and focused on designing a structure that really will enable digital excellence. I don’t say deliver it, because that’s always up to the people. But if your Product Managers can’t deliver good digital experiences with this organization, at least you know it’s their fault.