Tag Archives: customer journey analytics

Omni-channel Analytics and In-store Customer Tracking

While digital experiences are just beginning to penetrate the physical store, the customer’s integration of digital and physical shopping behaviors is already robust. If you have bricks & mortar, you have to figure out how to use that fact to your advantage in delivering experience. That’s what omni-channel is all about. There have been a number of omni-channel retail initiatives in the past couple of years that were undeniably successful. Online to in-store pickup, flexible return, and store localized supply chains have become key ingredients to omni-channel success. But there’s a long way to go before those experiences are mature and optimized.

Not surprisingly, retailers have discovered (sometimes to their chagrin), that omni-channel initiatives have a real downside when it comes to store operations. If you’re staff is spending more time processing online returns, what happens to customer service and sales?

It’s all too easy to steal from Peter to pay Paul. You may be delivering great service to one customer while you’re simultaneously ignoring another. And the two facts may be deeply related. Unless you can measure what’s actually happening in store, you’ll consistently miss these types of interactions.

With in-store tracking technology, you can explore how those omni-channel initiatives are actually impacting store operations AND customer experience. You can track what customers do after a return or before a pickup. You can track the over-time behavior of omni-channel customers to understand the impact on loyalty. You can measure whether sales interactions increase, decrease and are changed by omni-channel duties. And there are at least a couple strategies for beginning to join the in-store customer experience to the digital world. That join is hard, but it allows you do better analysis of almost every aspect of your business. Even better, it opens up a world of new marketing opportunities.

If there’s any area of online display advertising that works, it’s re-marketing. With the store to digital join, you have the opportunity to do digital re-marketing based on in-store behavior. That’s taking show-rooming to a new (and better) level!

If you’re looking for a deep-dive into the single hottest area in modern retail and in-store customer analytics, check out this video introduction I put together. It provides a crisp, easy introduction to the ins-and-outs of omni-channel analytics with in-store customer data including the all-important digital to store join.

Questions you can Answer

  • How much do omni-channel initiatives impact store operations and sales interactions?
  • Are omni-channel tasks being handled by the right staff?
  • Are omni-channel customers significantly different in their store behaviors?
  • What are the best cross-sell and personalization opportunities around omni-channel visits?
  • How much can a digitally sourced visit be steered to traditional shopping without damaging the experience?
  • How omni-channel initiatives change the way the store layout functions and are their opportunities to advantage some kinds of promotions or products as a result?

Getting the Digital In-Store Experience Right

In creating Digital Mortar, we’ve made a huge leap from measuring and optimizing digital experiences to trying to do the same for store experiences. But we haven’t left digital entirely behind. It’s a huge part of Omni-Channel (of course), and you can hear more about that in this distilled down video. But it’s also, increasingly, a part of the store experience. And when digital experiences have made the leap from the web to the store, measurement has generally been left behind. That’s unacceptable.

Optimizing In-Store Digital Experiences

Digital is pervasive in our lives now and the in-store experience is increasingly digital. Integrated mobile apps, geo-fenced couponing, endless aisle digital supplementation, digital signage and integrated digital/product experiences have all gotten real interest and investment as retailers try to figure out the best ways to bring digital experience into the store. A lot – and I mean a LOT – of these experiences have failed. That doesn’t mean they’re bad ideas and it doesn’t mean they can’t succeed. I’ll say it again. Doing new stuff is hard.

But part of the reason the failure rate with these digital experiences is high is methodological. A friend of mine running retail analytics for his company describe it this way: “We spend a ton of money building something. Then we roll out these experiences, nobody measures them, nobody improves them, nobody figures out how they change operations or how operations needs to change. They just sit there. And after a while, they’re just gathering dust.”

I see plenty of in-store digital experiences that are gathering dust. Lumped in deserted areas of the store or with blue-screens of death. And I see plenty of digital signage that might as well be paper signage given how often it’s changed and customized.

If you’re rolling out a digital experience, you need to make sure that good digital measurement is incorporated. That might be a traditional measurement system like Adobe, but there are situations where tools like AppDynamics might be a better bet. Either way, it’s essential to measure the intra-tool experience.

If you’re investing in an in-store digital experience without baking in digital measurement, shame on you. And that measurement shouldn’t be the equivalent of so much App measurement (how often it’s opened). For hands-on digital experiences in the store, measuring success demands thoughtful and quite detailed data collection and analysis.

But what about the experience around the tool? How many customers might have engaged with it but didn’t. Of the customers who did, did it change the way they interacted with the store? Was it a draw or was it an impulse experience? Did using the experience increase total time in the store or did it cannibalize traditional browsing? Did users interact more or less with Staff? And was that a good thing?

If you can’t answer these types of questions when you deploy ANY type of digital experience in store, you can’t optimize the experience and you can’t really tell whether it’s working. That’s a prescription for failure.

Of course not all these types of digital experience use similar measurement techniques, Digital signage, for example, is a little bit different than hands-on digital experiences. Since digital signage has no interactions, traditional measurement techniques are worthless. You can’t put Adobe tags in digital signage (well, you can, it just isn’t useful in the traditional way). But you can still integrate the signage data into broader in-store customer journey tracking – in fact that’s really the only way to create a meaningful measurement context around the signage data and the only way to build a program of continuous improvement. Digital signage is sadly underutilized, under-localized, and under-optimized. All because it can’t really be measured.

Going digital in the store is part of the future retail has to embrace. Getting it right? That’s a job for analytics and in-store customer journey tracking.

Questions you can Answer

  • Are experiences used by a significant percentage of customers?
  • Are there some shopper types more or less likely to engage with a digital experience?
  • Are digital experiences additive to store engagement?
  • Do digital experiences increase or decrease staff interaction with customers?
  • Do digital experiences increase consideration time in a section?
  • Do digital experiences increase or decrease subsequent product consideration?
  • Are digital experiences a draw or an impulse?

The Strategic Uses of In-Store Customer Journey Measurement

Store layout, promotion and staff optimization are the immediate and obvious ways to use the core data from customer journey analytics. Together, they comprise the “you” part of the equation – optimizing your operational and marketing strategies. But the uses of in-store tracking don’t end there. There’s tremendous strategic value in being to understand customer journeys – a lesson we’ve learned over and over again in digital. When it comes to omni-channel, store and experience design, and the integration of new technologies to the store, you simply can’t do the job right without in-store journey measurement.

I cover the fundamentals of why the in-store journey matters and how to build in-store customer journey data in this new post on Digital Mortar.

 

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.