Tag Archives: digital measurment

Measuring the Digital World

After several months in pre-order purgatory, my book, Measuring the Digital World is now available. If you’re even an occasional reader of this blog, I hope you’ll find the time to read it.

I know that’s no small ask. Reading a professional book is a big investment of time. So is reading Measuring the Digital World worth it?

Well, if you’re invested in digital optimization and analytics, I think it is – and here’s why. We work in a field that is still very immature. It’s grown up, as it were, underneath our feet. And while that kind of organic growth is always the most exciting, it’s also the most unruly. I’m betting that most of us who have spent a few years or more in digital analytics have never really had a chance to reflect on what we do and how we do it. Worse, most of those who are trying to learn the field, have to do so almost entirely by mentored trial-and-error. That’s hard. Having a framework for how and why things work makes the inevitable trial-and-error learning far more productive.

My goal in Measuring the Digital World wasn’t so much to create a how-to book as to define a discipline. I believe digital analytics is a unique field. A field defined by a few key problems that we must solve if we are to do it well. In the book, I wanted to lay out those problems and show how they can be tackled – irrespective of the tools you use or the type of digital property you care about.

At the very heart of digital analytics is a problem of description. Measurement is basic to understanding. We are born with and soon learn to speak and think in terms of measurement categories that apply to the physical world. Dimensionality, weight, speed, direction and color are some of the core measurement categories that we use over and over and over again in understanding the world we live in. These things don’t exist in the digital world.

What replaces them?

Our digital analytics tools provide the eyes and ears into the digital world. But I think we should be very skeptical of the measurement categories they suggest. Having lived through the period when those tools where designed and took their present shape, I’ve seen how flawed were the measurement conceptions that drove their form and function.

It’s not original, but it’s still true to say that our digital analytics tools mostly live at the wrong level and have the wrong set of measurement categories – that they are far too focused on web assets and far too little on web visitors.

But if this is a mere truism, it nevertheless lays the ground work for a real discipline. Because it suggests that the great challenge of digital is how to understand who people are and what they are doing using only their viewing behavior. We have to infer identity and intention from action. Probably 9 out of every 10 pages in Measuring the Digital World are concerned with how to do this.

The things that make it hard are precisely the things that define our discipline. First, to make the connection between action and both identity and intention, we have to find ways to generate meaning based on content consumption. This means understanding at a deep level what content is about – it also means making the implicit assumption that people self-select the things that interest them.

For the most part, that’s true.

But it’s also where things get tricky. Because digital properties don’t contain limitless possibilities and they impose a structure that tries to guide the user to specific actions. This creates a push-pull in every digital world. On the one hand, we’re using what people consume to understand their intention and, at the very same time, we’re constantly forcing their hand and trying to get them to do specific actions! Every digital property – no matter its purpose or design – embodies this push-pull. The result? A complex interplay between self-selection, intention and web design that makes understanding behavior in digital a constant struggle.

That’s the point – and the challenge – of digital analytics. We need to have techniques for moving from behavior to identity and intention. And we need to have techniques that control for the structure of digital properties and the presence or absence of content. These same challenges are played out on Websites, on mobile apps and, now, on omni-channel customer journeys.

This is all ground I’ve walked before, but Measuring the Digital World embodies an orderly and fairly comprehensive approach to describing these challenges and laying out the framework of our discipline. How it works. Why it’s hard. What challenges we still face. It’s all there.

So if you’re an experienced analyst and just want to reflect your intuitions and knowledge against a formal description of digital analytics and how it can be done, this book is for you. I’m pretty sure you’ll find at least a few new ideas and some new clarity around ideas you probably already have.

If you’re relatively new to the field and would like something that is intellectually a little more meaty than the “bag of tips-and-tricks” books that you’ve already read, then this book is for you. You’ll get a deep set of methods and techniques that can be applied to almost any digital property to drive better understanding and optimization. You’ll get a sense, maybe for the first time, of exactly what our discipline is – why it’s hard and why certain kinds of mistakes are ubiquitous and must be carefully guarded against.

And if you’re teaching a bunch of MBA or Business Students about digital analytics and want something that actually describes a discipline, this book is REALLY for you (well…for your students). Your students will get a true appreciation for a cutting edge analytics discipline, they’ll also get a sense of where the most interesting new problems in digital analytics are and what approaches might bear fruit. They’ll get a book that illuminates how the structure of a field – in this case digital – demands specific approaches, creates unique problems, and rewards certain types of analysis. That’s knowledge that cuts deeper than just understanding digital analytics – it goes right to the heart of what analytics is about and how it can work in any business discipline. Finally, I hope that the opportunity to tackle deep and interesting problems illuminated by the book’s framework, excites new analysts and inspires the next generation of digital analysts to go far beyond what we’ve been able to do.


Yes, even though I’m an inveterate reader, I know it’s no trivial thing to say “read this book”. After all, despite my copious consumption, I delve much less often into business or technical books. So many seem like fine ten-page articles stretched – I’m tempted to say distorted – into book form. You get their gist in the first five pages and the rest is just filler. That doesn’t make for a great investment of time.

And now that I’ve actually written a book, I can see why that happens. Who really has 250 pages worth of stuff to say? I’m not sure I do…actually I’m pretty sure there’s some filler tucked in there in a spot or two. But I think the ratio is pretty good.

With Measuring the Digital World I tried to do something very ambitious – define a discipline. To create the authoritative view of what digital analytics is, how it works, and why it’s different than any other field of analytics. Not to answer every question, lay out every technique or solve every problem. There are huge swaths of our field not even mentioned in the book. That doesn’t bother me. What we do is far too rich to describe in a single book or even a substantial collection. Digital is, as the title of the book suggests, a whole new world. My goal was not to explore every aspect of measuring that world, but only to show how that measurement, at its heart, must proceed. I’m surely not the right person to judge to what extent I succeeded. I hope you’ll do that.

Here’s the link to Measuring the Digital World on Amazon.

[By the way, if you’d like signed copy of Measuring the Digital World, just let me know. You can buy a copy online and I’ll send you a book-plate. I know it’s a little silly, but I confess to extreme fondness for the few signed books I possess!]

Continuous Improvement

Is it a Method or a Platitude?

What does it take to be good at digital? The ability to make good decisions, of course. If you run a pro football team and you make consistently good decisions about players and about coaches, and they, in turn, make consistently good decisions about preparation and plays, you’ll be successful. Most organizations aren’t setup to make good decisions in digital. They don’t have the right information to drive strategic decisions and they often lack the right processes to make good tactical decisions. I’ve highlighted four capabilities that must be knitted together to drive consistently good decisions in the digital realm: comprehensive customer journey mapping, analytics support at every level of the organization, aggressive controlled experimentation targeted to decision-support, and constant voice of customer research. For most organizations, none of these capabilities are well-baked and it’s rare that even a very good organization is excellent at more than two of these capabilities.

The Essentials for Digital Transformation
                          The Essentials for Digital Transformation

There’s a fifth spoke of this wheel, however, that isn’t so much a capability as an approach. That’s not so completely different from the others as it might seem. After all, almost every enterprise I see has a digital analytics department, a VoC capability, a customer journey map, and an A/B Testing team. In previous posts, I’ve highlighted how those capabilities are mis-used, mis-deployed or simply misunderstood. Which makes for a pretty big miss. So it’s very much true that a better approach underlies all of these capabilities. When I talk about continuous improvement, it’s not a capability at all. There’s no there, there. It’s just an approach. Yet it’s an approach that, taken seriously, can help weld these other four capabilities into a coherent whole.

The doctrine of continuous improvement is not new – in digital or elsewhere. It has a long and proven track record and it’s one of the few industry best practices with which I am in whole-hearted agreement. Too often, however, continuous improvement is treated as an empty platitude, not a method. It’s interpreted as a squishy injunction that we should always try to get better. Rah! Rah!


Taken this way, it’s as contentless as interpreting evolutionary theory as survival of the fittest. Those most likely to survive are…those most likely to survive. It is the mechanism of natural selection coupled with genetic variation and mutation that gives content to evolutionary doctrine. In other words, without a process for deciding what’s fittest and a method of transmitting that fitness across generations, evolutionary theory would be a contentless tautology. The idea of continuous improvement, too, needs a method to be interesting. Everybody wants to get better all the time. There has to be a real process to make it interesting.

There are such processes, of course. Techniques like Six Sigma famously elaborate a specific method to drive continuous improvement in manufacturing processes. Unfortunately, Six Sigma isn’t directly transferable to digital analytics. We lack the critical optimization variable (defects) against which these methods work. Nor does it work to simply substitute a variable like conversion rate for defects because we lack the controlled environment necessary to believe that every customer should convert.

If Six Sigma doesn’t translate directly into digital analytics, that doesn’t mean we can’t learn from it and cadge some good ideas, though. Here are the core ideas that drive continuous improvement in digital, many of which are rooted in formal continuous improvement methodologies:

  1. It’s much easier to measure a single, specific change than a huge number of simultaneous changes. A website or mobile app is a complex set of interconnecting pieces. If you change your home page, for example, you change the dynamics of every use-case on the site. This may benefit some users and disadvantage others; it may improve one page’s performance and harm another’s. When you change an entire website at once, it’s incredibly difficult to isolate which elements improved and which didn’t. Only the holistic performance of the system can be measured on a before and after basis – and even that can be challenging if new functionality has been introduced. The more discrete and isolated a change, the easier it is to measure its true impact on the system.
  2. Where changes are specific and local, micro-conversion analytics can generally be used to assess improvement. Where changes are numerous or the impact non-local, then a controlled environment is necessary to measure improvement. A true controlled environment in digital is generally impossible but can be effectively replicated via controlled experimentation (such as A/B testing or hold-outs).
  3. Continuous improvement can be driven on a segmented or site-wide basis. Improvements that are site-wide are typically focused on reducing friction. Segmentation improvements are focused on optimizing the conversation with specific populations. Both types of improvement cycles must be addressed in any comprehensive program.
  4. Digital performance is driven by two different systems (acquisition of traffic and content performance). Despite the fact that these two systems function independently, it’s impossible to measure performance of either without measuring their interdependencies. Content performance is ALWAYS relative to the mix of audience created by the acquisition systems. This dependency is even tighter in closed loop systems like Search Engine Optimization – where the content of the page heavily determines the nature of the traffic sent AND the performance of that traffic once sourced (though the two can function quite differently with the best SEO optimized page being a very poor content performer even though it’s sourcing its own traffic).
  5. Marketing performance is a function of four things: the type of audience sourced, the use-case of the audience sourced, the pre-qualification of the audience sourced and the target content to which the audience is sourced. Continuous improvement must target all four factors to be effective.
  6. Content performance is relative to function, audience and use-case. Some content changes will be directly negative or positive (friction causing or reducing), but most will shift the distribution of behaviors. Because most impacts are shifts in the distribution of use-cases or journeys, it’s essential that the relative value of alternative paths be understood when applying continuous improvement.

These are core ideas, not a formal process. In my next post, I’ll take a shot at translating them into a formal process for digital improvement. I’m not really confident how tightly I can describe that process, but I am confident that it will capture something rather different than any current approach to digital analytics.


With Thanksgiving upon us now is the time to think about the perfect stocking stuffer for the digital analyst you like best. Pre-order “Measuring the Digital World” now!