Tag Archives: Voice of Customer

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!]

Analytics with a Strategic Edge

The Role of Voice of Customer in Enterprise Analytics

The vast majority of analytics effort is expended on problems that are tactical in nature. That’s not necessarily wrong. Tactics gets a bad rap, sometimes, but the truth is that the vast majority of decisions we make in almost any context are tactical. The problem isn’t that too much analytics is weighted toward tactical issues, it’s really that strategic decisions don’t use analytics at all. The biggest, most important decisions in the digital enterprise nearly always lack a foundation in data or analysis.

I’ve always disliked the idea behind “HIPPOs” – with its Dilbertian assumption that executives are idiots. That isn’t (mostly) my experience at all. But analytics does suffer from what might be described as “virtue” syndrome – the idea that something (say taxes or abstinence) is good for everyone else but not necessarily for me. Just as creative folks tend to think that what they do can’t be driven by analytics, so too is there a perception that strategic decisions must inevitably be more imaginative and intuitive and less number-driven than many decisions further down in the enterprise.

This isn’t completely wrong though it probably short-sells those mid-level decisions. Building good creative takes…creativity. It can’t be churned out by machine. Ditto for strategic decisions. There is NEVER enough information to fully determine a complex strategic decision at the enterprise level.

This doesn’t mean that data isn’t useful or should not be a driver for strategic decisions (and for creative content too). Instinct only works when it’s deeply informed about reality. Nobody has instincts in the abstract. To make a good strategic decision, a decision-maker MUST have certain kinds of data to hand and without that data, there’s nothing on which intuition, knowledge and experience can operate.

What data does a digital decision-maker need for driving strategy?

Key audiences. Customer Journey. Drivers of decision. Competitive choices.

You need to know who your audiences are and what makes them distinct. You need (as described in the last post) to understand the different journeys those audiences take and what journeys they like to take. You need to understand why they make the choices they make – what drives them to choose one product or service or another. Things like demand elasticity, brand awareness, and drivers of choice at each journey stage are critical. And, of course, you need to understand when and why those choices might favor the competition.

None of this stuff will make a strategic decision for you. It won’t tell you how much to invest in digital. Whether or not to build a mobile app. Whether personalization will provide high returns.

But without fully understanding audience, journey, drivers of decision and competitive choices, how can ANY digital decision-maker possibly arrive at an informed strategy? They can’t. And, in fact, they don’t. Because for the vast majority of enteprises, none of this information is part-and-parcel of the information environment.

I’ve seen plenty of executive dashboards that are supposed to help people run their business. They don’t have any of this stuff. I’ve seen the “four personas” puffery that’s supposed to help decision-makers understand their audience. I’ve seen how limited is the exposure executives have to journey mapping and how little it is deployed on a day-to-day basis. Worst of all, I’ve seen how absolutely pathetic is the use of voice of customer (online and offline) to help decision-makers understand why customers make the choices they do.

Voice of customer as it exists today is almost exclusively concerned with measuring customer satisfaction. There’s nothing wrong with measuring NPS or satisfaction. But these measures tell you nothing that will help define a strategy. They are at best (and they are often deeply flawed here too) measures of scoreboard – whether or not you are succeeding in a strategy.

I’m sure that people will object that knowing whether or not a strategy is succeeding is important. It is. It’s even a core part of ongoing strategy development. However, when divorced from particular customer journeys, NPS is essentially meaningless and uninterpretable. And while it truly is critical to measure whether or not a strategy is succeeding, it’s even more important to have data to help shape that strategy in the first place.

Executives just don’t get that context from their analytics teams. At best, they get little pieces of it in dribs and drabs. It is never – as it ought to be – the constant ongoing lifeblood of decision-making.

I subtitled this post “The Role of Voice of Customer in Enterprise Analytics” because of all the different types of information that can help make strategic decisions better, VoC is by far the most important. A good VoC program collects information from every channel: online and offline surveys, call-center, site feedback, social media, etc. It provides a continuing, detailed and sliceable view of audience, journey distribution and (partly) success. It’s by far the best way to help decision-makers understand why customers are making the choices they are, whether those choices are evolving, and how those choices are playing out across the competitive set. In short, it answers the majority of the questions that ought to be on the minds of decision-makers crafting a digital strategy.

This is a very different sort of executive dashboard than we typically see. It’s a true customer insights dashboard. It’s also fundamentally different than almost ANY VoC dashboard we see at any level. The vast majority of VoC reporting doesn’t provide slice-and-dice by audience and use-case – a capability which is absolutely essential to useful VoC reporting. VoC reporting is almost never based on and tied into a journey model so that the customer insights data is immediately reflective of journey stage and actionable arena. And VoC reporting almost never includes a continuous focus on exploring customer decision-making and tying that into the performance of actual initiatives.

It isn’t just a matter of a dashboard. One of the most unique and powerful aspects of digital voice-of-customer is the flexibility it provides to rapidly, efficiently and at very little cost tackle new problems. VoC should be a core part of executive decision-making with a constant cadence of research, analysis, discussion and reporting driven by specific business questions. This open and continuing dialog where VoC is a tool for decision-making is critical to integrating analytics into decisioning. If senior folks aren’t asking for new VoC research on a constant basis, you aren’t doing it right. The single best indicator of a robust VoC program in digital is the speed with which it changes.

Sadly, what decision-makers mostly get right now (if they get anything at all) is a high-level, non-segmented view of audience demographics, an occasional glimpse into high-level decision-factors that is totally divorced from both segment and journey stage, and an overweening focus on a scoreboard metric like NPS.

It’s no wonder, given such thin gruel, that decision-makers aren’t using data for strategic decisions better. If our executives mostly aren’t Dilbertian, they aren’t miracle workers either. They can’t make wine out of information water. If we want analytics to support strategy – and I assume we all do – then building a completely different sort of VoC program is the single best place to start. It isn’t everything. There are other types of data (behavioral, benchmark, econometric, etc.) that can be hugely helpful in shaping digital strategies. But a good VoC program is a huge step forward – a step forward that, if well executed – has the power to immediately transform how the digital enterprise thinks and works.

 

This is probably my last post of the year – so see you in 2016! In the meantime, my book Measuring the Digital World is now available. Could be a great way to spend your holiday down time (ideally while your resting up from time on the slopes)! Have a great holiday…