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:
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.
