Tag Archives: Voice of Customer

Seeing into the Soul of the Shopper

The integration of behavioral research and Voice of Customer is remarkably fruitful. I saw that first hand in a project we recently completed with our partner Insight Safari for a top 10 retailer in the United States. For the project, Insight Safari’s research teams fanned out across the country and did shopper interviews at stores in markets like Pittsburgh, Dallas, and Los Angeles. This is deep qualitative research – designed to get to the emotional core of shopping decisions and see how attitudes and intent shape in-store behavior. It’s what Insight Safari does. But this time there was behavioral twist. Shoppers were given a mobile device with Digital Mortar’s tracking built-in. And at the end of the journey, the survey giver was able to tailor the survey based on a detailed map of exactly what the shopper had done.

In-Store VoC Shopper Journey Analytics with Digital Mortar

Integrating a behavioral view into an attitudinal project enriches the project immeasurably. But it’s not trivial to do and there’s a reason why Insight Safari is uniquely well-positioned to do this. To understand both the challenge and the opportunity, a little background on voice-of-customer and behavioral analytics is necessary.

 

VoC and Behavioral Analytics

Voice of Customer research usually tries to capture four key elements about a shopper. Who the shopper is (demographics), what the shopper did (behavior), why the shopper did it (drivers), and how the shopper felt about it (satisfaction). One of the things that makes opinion research an art is finding the right balance between each of these. And there’s always an opportunity cost – any time you spend in one category inevitably reduces the amount of time you spend in another. Beyond the opportunity cost, though, it’s particularly challenging to disentangle a shopper’s description of behavior and drivers. Ask a shopper why they came to the store and then ask them what they did, and the answer they give to the first is highly likely to influence the answer they give to the second. What’s more, the shopper is prioritizing the shopping session by their internal measures of what they did – they forget the distractions, the things they looked at but didn’t buy, and the places they went that turned out not to be important. But if you’re the store – understanding those failure points is what you’re really after!

Many of the shopping sessions that we tracked with Insight Safari lasted 30 minutes to an hour. Think about that. How likely are you to be able to say what you looked at and explain how you navigated a store over that amount of time?

Insight Safari sometimes hires a videographer to (literally) stalk the shoppers and record sample journeys in the store. But that strategy falls victim to its own kind of quantum uncertainty – the act of measurement alters the system dynamics too much. We’re all used to having a phone in our pocket – but unless you’re a Kardashian, having a videographer following you around just doesn’t feel natural.

It turns out that of the four primary types of data collection for VoC, understanding what the shopper did is actually the hardest to get right with self-reporting. There’s an amusing anecdote we like to tell from our days in digital analytics. One of our clients had a very negative satisfaction score for internal search sessions (super common for various reasons ranging from the type of sessions that use internal search to most internal search engines being really crappy). Interestingly, though, when we actually integrated their online survey data to their behavioral data, we found that almost a third of the visitors who complained most about search on the site hadn’t actually “searched”.  We were asking about internal search – typing in keywords in a little box – but they were thinking about “searching the site and not finding what they were looking for”.

What’s more, we found that for a significant percentage of visitors, their self-reported site visit reason just didn’t square with their actual behavior. A shopper might report that they were in the store to pick up clothes for the kids, but spend nearly all their time in the beauty aisle. It’s not that shoppers are lying about their behavior. Mostly they just aren’t objective or reflective about it. But getting through those layers of thoughtlessness is hard – sometimes flat out impossible. And getting even a remotely accurate approximation of the shopper’s behavior takes deep, detailed questioning that inevitably chews up a lot of time (opportunity cost) and leaves the analyst wondering how accurate the behavioral account really is.

So imagine if instead of having to interrogate the shopper about behavior – did you do this? What about this? Did you look at this on the way? Did you stop here? Did you go down this aisle? – you could just SEE their behavior. Every twist, every turn, every linger point!

Suddenly you’re not guessing about shopper behavior or the accuracy of self-reporting. You can focus the interview entirely on the why and the satisfaction of the experience. And you can use details of the behavior to call back elements of the journey to the shopper’s mind. What were you looking at when you stopped here? Why did you go back to the electronics department 3 times? What made you turn down this aisle?

It’s powerful.

But it’s not as easy as it looks, either. And in understanding why this is a little harder than it seems illuminates what makes Insight Safari particularly able to take advantage of the Digital Mortar behavioral data.

 

The Biggest Challenge Integrating Behavioral Data into the Survey Process

Voice of Customer data runs the gamut from highly quantitative (large sample sizes, standardized surveys) to fully anecdotal (guided focus groups). There’s interesting work to be done at any place along this spectrum and Insight Safari customizes the research approach to fit the business questions in play. But their specialty and primary focus is on going deep into shopper motivations and psyche – and that’s best done in the more personal, anecdotal end of the spectrum. At the same time, they like to have enough data to infer how common core shopper motivations are and how likely those are to play out in a given store. So Insight Safari usually works in the range of hundreds of surveys – not tens of thousands like we did in digital analytics and not 5-10 like a focus group company.

Most companies who take hundreds of surveys, rely on quite a bit of standardization in the survey design. Each shopper essentially takes the same survey with minor deviations for branching.

This sucks for a variety of reasons. Unless you know specifically what you’re looking for, it’s likely to miss the interesting parts of most shopper’s journeys. And if you’ve ever worked with this kind of data, you know that it’s almost certain to raise issues that leave you wishing you’d been able to ask one more question to really understand what the shopper was thinking! It can be frustrating!

But a rigid survey design also means that the behavioral data isn’t mapped into the questioning process. It can’t be – because you don’t the behaviors in advance. So while it’s possible to compare, for example, stated visit intent with actual shopping behavior, you aren’t using the data to drive the questions.

Insight Safari doesn’t work that way. Their survey givers aren’t part-times hired the day before to hang out in the store. They use research professionals – the equivalent of full-on focus group leaders – who are deeply knowledgeable about survey research. So their survey isn’t a rigid framework but a kind of loose framework that ensures they collect like kinds of data from each shopper but leaves the giver free to delve into interesting answers in great depth.

That turns out to be perfect for integrating behavioral data.

When shoppers finished their journey, the survey giver would enter the survey respondent id on their iPad and then get the detailed break-down of what the shopper did. Right then. While they were talking with the shopper.

And Insight Safari’s pros seamlessly integrated that into the flow of questions – even using the path visualization to directly explore decisions with the shopper. Most companies just don’t use survey givers skilled enough to do that. That’s no big knock. I’m not skilled enough to do that. Being able to drive intelligent field research takes an unusual combination of people skills, empathy, and objective analytic prowess. You have to be able think fast, be nice, and listen closely. It’s the equivalent of having no prep-time and still being able to do a great interview. Not easy.

There are ways to take the behavioral data and create survey integrations that are more mechanistic but still capture much of the uniqueness of the shopper journey. But there aren’t many companies who could take this time of in-store behavioral data and integrate it as deeply and seamlessly into their process as Insight Safari.

 

A Little About the Software

We customized our system pretty extensively for Insight Safari. We build a small mobile app (Android-based) that had a really, really simple user interface to it. The survey giver just had to press a button to start a survey and, when the phone was returned, press the stop button to end recording. The App pinged out every 2 seconds with the shoppers geo-location and included the survey id. We store that information in our real-time database. The shopper never has to do anything with the phone or app. They can carry it or it was attached to their cart.

The App also created a local store of the information in case there were connectivity problems (we had a few but not many). This allowed the App to send the survey data whenever connectivity was restored.

When the survey giver got the phone back and pressed Stop, the phone sends a message to the server and the session is closed. Once it’s closed it’s immediately surfaced in a custom report in the DM1 platform showing the most recent surveys completed.

The survey giver can search for any previous respondent, but almost never has to do that. They just click on the most recent survey closed and get the detailed behavioral report.

That report includes two elements: a tabular breakdown of the visit by time spent and graphical animation of the shopper visit laid on the digital planogram of the store. The tabular view is sorted by time spent and shows all the sections in the store the shopper visited, how much time they spent, and whether they returned to the section (went to it more than once). The animation is built on top of the store layout view (a core part of DM1) and replays the journey in fifteen seconds with time spent relative to replay time.

In-Store Shopper Measurement and VoC Store Surveys and Digital Mortar

This custom report view is what the survey giver uses to drive the survey.

But it’s not the only report available. Since all the data is collected, it can also be analyzed quantitatively in the core DM1 Workbench and it can even be segmented by survey response variables uploaded through the meta-data interface.

It’s a compelling combination – helping drive the survey itself, providing a rich quantification of the data afterward, and making it easy for Insight Safari to show how specific individual patterns translate into significant population segments.

 

And a Little bit About the Results

Obviously the findings are both totally proprietary and highly particularized to the client. This isn’t the sort of research that leads to some standardized best-practice recommendation. But there are some high-level aspects of the project that I found striking.

First, while there are some very straightforward shopping visits where the behavior is crisp and matches closely to intent, the number of those visits is dramatically lower than what we see when we look at Websites. Most visits are amazingly complex squiggly patterns that bear only a passing resemblance to any kind of coordinated exploration of the store.

Sure, there are visits where, for example, a race-track pattern is dominant. But in almost all those visits there are least a few strange loops, diversions, and short-cuts. Further, the degree to which shopper intent doesn’t capture the intricacy (or even the primary focus) of the visit is much more visible in store visits than in comparable Website visits. Stores just are better distractors than Websites – and the physical realities of navigating a space create many more opportunities for divergence.

Second, the ability to see how experiential elements like coffee bars impacted both the behavior and emotional impact of the shopper journey was fascinating. It’s really hard to measure how these elements are driving PoS, but when you hear how people talk about it and how much it changes their sense and description of the shopping experience, it really comes alive. Making shoppers want to come to the store is part and parcel of today’s retail mission. And hearing how a smile from a barista can transform a chore into a reprieve from the daily grind is just one of the ways that VoC can make behavioral data sing.

And lastly, these behavior patterns are often most telling for what shoppers didn’t do. In case after case, we could see shopper’s lop-off parts of the journey that seemed like the logical extensions of their basic path. Some of those turning points were highly individual and probably hard to action – but others showed up with a consistency that made it clear that for some journeys, the store layout just wasn’t optimal.

 

Get a Piece of the Action

I don’t think there’s a store in the world that wouldn’t benefit from this kind of thoughtful research. Intelligent Voice of Customer is always provocative and useful. And the integration of Digital Mortar’s behavioral journey mapping into the Insight Safari process lets them do what they do at a level that simply can’t be matched with any other technique. It truly is the best of both worlds.

To learn more, give either of us a shout!

Join me for what I hope will be a really challenging webinar (hosted by the 4A’s) on improving customer experience with analytics. Feb. 15th at 1pm EST.

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

Getting Started with Digital Transformation

For most of this year I’ve been writing an extended series on digital transformation in the enterprise. Along the way, I’ve described why organizations (particularly large ones) struggle with digital, the core capabilities necessary to do digital well, and ways in which organizations can build a better, more analytic culture. I’ve even put together a series of videos that describe how enterprises are currently driving digital and how they can do better.

I think both the current-state (what we do wrong) and the end-state (doing digital right) are compelling. In the next few posts, I’m going to wrap this series up with a discussion around how you get from here to there.

I don’t suppose anyone thinks the journey from here to there is trivial. Doing digital the way I’ve described it (see the Agile Organization) involves some pretty fundamental change: change to the way enterprises budget, change to the way they organize, and change to the way they do digital at almost every level. It also involves, and this is totally unsurprising, investments in people and technology and more than a dollop of patience. It would actually be much easier to build a good digital organization from scratch than to adapt the pieces that exist in the typical enterprise.

Change is harder than creation. It has more friction and more fail points. But change is the reality for most enterprise.

So where do you start and how do you go about building a great digital organization?

I’m going to answer that question here from an analytics perspective. That’s the easy part. Once I’ve worked through the steps in building analytics maturity and digital decisioning, I’ll tackle the organizational component, wherein I expect to hazard a series of guesses, speculation and unlikely theory to paper over the fact that almost no one has done this transformation successfully and every organization has fundamentally unique structures and people that make its dynamics deeply specific.

The foundation of any analytics program is, of course, data. One of the most satisfying developments in digital analytics in the past 3-5 years has been the dramatic improvement in the state of data collection. It used to be that EVERY engagement we undertook began with a plodding slog through data auditing and clean-up. These days, that’s more the exception than the rule. Still, there are plenty of exceptions. So the first step in just about any analytics effort is to make sure the data foundation is solid. There’s a second aspect to this that’s worth pointing out. For a lot of my clients, basic data collection is no longer much of an issue. But even where that’s true, there are often significant gaps in digital analytics data collection for personalization. So many Adobe designs are predicated on meeting reporting requirements that it’s not at all unusual for key personalization elements like filtering selections, image expansions, sorting behaviors and DHTML exposures to go largely untracked. That’s true on both the Web and Mobile sides. Part of auditing your data collection should be a careful look at whether your capturing all the personalization cues you could – and that’s often a critical foundational element for the steps to follow.

Right along with auditing your data collection comes building a comprehensive customer journey framework. I’ve added the word “framework” here not to be all “consulty” but to emphasize that a customer journey isn’t built once as a static map. That’s the old way – and it’s wrong in every respect (so be careful what you buy). It’s wrong because it’s not segmented. It’s wrong because it’s too high-level. And most of all it’s wrong because it’s too static. So while a customer journey framework is more a capability and a process than a “thing”, it’s also true that you have to start somewhere. Getting that initial segmented journey map in place provides the high-level strategic framework for your digital strategy and for your analytics and testing. It’s the key strategic piece welding your operational capabilities to your strategic vision.

My third foundational building block is (Chorus sings refrain) “2-Tiered segmentation”. I’ve written voluminously on digital segmentation and how it works, so I won’t add much more here. But if journey mapping is the piece linking your strategic vision to your operational capabilities, 2-tiered segmentation is the equivalent piece linking at the tactical level. At every touchpoint in a customer journey there is the need to understand who somebody is and where in their journey they are. That’s what 2-tiered segmentation provides.

Auditing your data, creating a journey mapping and tying that to a digital segmentation are truly foundational. They are all “you can’t get there from here without going through these” kind of activities. Almost every significant report, analysis and decision that you make will rely on these three activities.

That’s not really true for my next two foundational activities. I chose building an integrated voice of customer (VoC) capability as my fourth key building block. If you’ve read my book, you know that one of the main uses for a VoC program is to refine and tune your journey map and segmentation. So in one sense, this capability may be prior to either of those. But you can do enough VoC to support those two activities without really building a full VoC program. And what I have in mind here is a full program. What do I mean by a full program? I mean an enterprise feedback management system that makes it easy to deploy surveys at any point in the journey across any device. I mean a set of organizational processes that ideate, design, deploy, interpret and socialize VoC information constantly. I mean an enterprise-wide reporting capability that integrates different VoC sources, classifies them, tracks them, and provides drill-down (and that’s important because VoC data is virtually useless without cross-tabulation) access to them across the organization. I also mean a culture where one of the natural and immediate parts of making a decision is looking at what customer’s think and – if that isn’t available – launching a survey to figure it out. I put VoC as part of this foundational set because I think it’s one of the easiest ways to deliver real wins to the organization. I also like the idea of driving a combination of tactical (data, segmentation) and strategic (journey, VoC) initiatives in your early phases. As I’ve pointed out elsewhere, we analytics folks tend to over-focus on the tactical.

Finally, I’ve included building a campaign measurement framework into the initial set of foundational activities. This might not be the right choice for every organization, but if you spend a significant amount of money on marketing, it’s a critical element in evolving your maturity. Like data audits, a lot of my clients are already pretty good at this. For many folks, campaigns are already measured using a pretty rich and well-thought out framework and the pain point tends to be deeper – around attribution and mix. But I also see organizations jumping right to questions of attribution before they’ve really done the work necessary to pick the right KPIs to optimize against. That’s a prescription for disaster. If you don’t put in the intellectual sweat equity to understand how campaigns should be measured (and it’s often surprisingly complicated in real-world businesses where conversion rate is rarely the be-all-and-end-all of optimization), then your attribution modelling is doomed to fail.

So here’s the first five things to tackle in building out the analytics part of a digital transformation effort:

foundational Transformation Step 1Small

These five activities provide a rich foundation for analytics driven transformation along with some core strategic analytic capabilities. I’ll cover what comes after this in my next post.

Digital Transformation of the Enterprise (with a side of Big Data)

Since I finished Measuring the Digital World and got back to regular blogging, I’ve been writing an extended series on the challenges of digital in the enterprise. Like many analysts, I’m often frustrated by the way our clients approach decision-making. So often, they lack any real understanding of the customer journey, any effective segmentation scheme, any real method for either doing or incorporating analytics into their decisioning, anything more than a superficial understanding of their customers, and anything more than the empty façade of a testing program. Is it any surprise that they aren’t very good at digital? This would be frustrating but understandable if companies simply didn’t invest in these capabilities. They aren’t magic, and no large enterprise can do these things without making a significant investment. But, in fact, many companies have invested plenty with very disappointing results. That’s maddening. I want to change that – and this series is an extended meditation on what it takes to do better and how large enterprises might truly gain competitive advantage in digital.

I hope that reading these posts is useful to people, but I know, too, that it’s hard to get the time. Heaven knows I struggle to read the stuff I’d like to. So I took advantage of the slow time over the holidays to do something that’s been on my wish list for about 2 years now – take some of the presentations I do and turn them into full online webinars. I started with a whole series that captures the core elements of this series – the challenge of digital transformation.

There are two versions of this video series. The first is a set of fairly short (2-4 minute) stories that walk through how enterprise decision-making gets done, what’s wrong with the way we do it, and how we can do better. It’s a ten(!) part series and meant to be tackled in order. It’s not really all that long…like I said, most of the videos are just 2-4 minutes long. I’ve also packaged up the whole story (except Part 10) in single video that runs just a little over 20 minutes. It’s shorter than viewing all 10 of the others, but you need a decent chunk of uninterrupted time to get at it. If you’re really pressed and only want to get the key themes without the story, you can just view Parts 8-10.

Here’s the video page that has all of these laid out in order:

Digital Transformation Video Series

Check it out and let me know what you think! To me it seems like a faster, better, and more enjoyable way to get the story about digital transformation and I’m hoping it’s very shareable as well. If you’re struggling to get analytics traction in your organization, these videos might be an easy thing to share with your CMO and digital channel leads to help drive real change.

I have to say I enjoyed doing these a lot and they aren’t really hard to do. They aren’t quite professional quality, but I think they are very listenable and I’ll keep working to make them better. In fact, I enjoyed doing the digital transformation ones so much that I knocked out another this last week – Big Data Explained.

This is one of my favorite presentations of all time – it’s rich in content and intellectually interesting. Big data is a subject that is obscured by hype, self-interest, and just plain ignorance; everyone talks about it but no one has a clear, cogent explanation of what it is and why it’s important. This presentation deconstructs the everyday explanation about big data (the 4Vs) and shows why it misses the mark. But it isn’t designed to merely expose the hype, it actually builds out a clear, straightforward and important explanation of why big data is real, why it challenges common IT and analytics paradigms, and how to understand whether a problem is a big data problem…or not. I’ve written about this before, but you can’t beat a video with supporting visuals for this particular topic. It’s less than fifteen minutes and, like the digital transformation series, it’s intended for a wide audience. If you have decision-makers who don’t get big data or are skeptical of the hype, they’ll appreciate this straightforward, clear, and no-nonsense explication of what it is.

You can get it on my video page or direct on Youtube

This is also a significant topic toward the end of Measuring the Digital World where I try to lay out a forward looking plan for digital analytics as a discipline.

I’m planning to do a steady stream of these videos throughout the year so I’d love thoughts/feedback if you have suggestions!

Next week I hope to have an update on my EY Counseling Family’s work in the 538 Academy Awards challenge. We’ve built our initial Hollywood culture models – it’s pretty cool stuff and I’m excited to share the results. Our model may not be as effective as some of the other challengers (TBD), but I think it’s definitely more fun.

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!]