Tag Archives: retail analytics

What is in-store customer journey data for?

In my last post, I described what in-store customer data is. But the really important question is this – what do you do with it? Not surprisingly, in-store customer movement data serves quite a range of needs that I’ll categorize broadly as store layout optimization, promotion planning and optimization, staff optimization, digital experience integration, omni-channel experience optimization, and customer experience optimization. I’ll talk about each in more detail, but you can think about it this way. Half of the utility of in-store customer journey measurement is focused on you – your store, your promotions and your staff. When you can measure the in-store customer journey better, you can optimize your marketing and operations more effectively. It’s that simple. The other half of the equation is about the customer. Mapping customer segments, finding gaps in the experience, figuring out how omni-channel journeys work. This kind of data may have immediate tactical implications but it’s real function is strategic. When you understand the customer experience better you can design better stores, better marketing campaigns, and better omni-channel strategies.

I’m going to cover each area in a short post, starting with the most basic and straightforward (store layout) and moving up into the increasingly strategic uses.


Store Layout and Merchandising Optimization

While bricks&mortar hasn’t had the kind of measurement and continuous improvement systems that drive digital, it has had a long, arduous and fruitful journey to maturity. Store analysts and manager know a lot. And while in-store customer journey measurement can fill in some pretty important gaps, you can do a lot of good store optimization based on a combination of well-understood best practices, basic door-counting, and PoS information. At a high-level, retailers understand how product placement drives sales, what the value of an end-cap/feature is, and how shelf placement matters. With PoS data, they also understand which products tend to be purchased together. So what’s missing? Quite a bit, actually, and some of it is pretty valuable. With customer journey data you can do true funnel analysis – not just at the store level (PoS/Door Counting) but at a detailed level within the store. You’ll see the opportunity each store area had, what customer segments made up that opportunity, and how well the section of the store is engaging customers and converting on the opportunity. Funnel analysis forever changed the way people optimized websites. It can do the same for the store. When you make a change, you can see how patterns of movement, shopping and segmentation all shift. You can isolate specific segments of customer (first time, regular, committed shopper, browser) and see how their product associations and navigation patterns differ. If this sounds like continuous improvement through testing…well, that’s exactly what it is.

Questions you can Answer

  • How well is each area and section of the store performing?
  • How do different customer segments use the store differently?
  • How effective are displays in engaging customers?
  • How did store layout changes impact opportunity and engagement?
  • Are there underutilized areas of the store?
  • Are store experiences capturing engagement and changing shopping patterns?
  • Are there unusual local patterns of engagement at a particular store?

Next up? Optimizing promotions and in-store marketing campaigns.


Optimizing Omni-Channel with Analytics from the In-Store Customer Journey

I’m going to be co-hosting a webinar with my friend John Morrell at Datameer on Omni-Channel Analytics and using In-Store Customer Journey Data. It should be pretty cool stuff – and, of course, it’s free!

You can register here!

What is In-Store Customer Journey Data?

Analytics professionals love data and technology. So it’s easy for us (and I use “us” because I completely self-identify in both the category of analytics professional and someone who loves data and technology) to get excited about new data sources and new measurement systems – sometimes without thinking too carefully about what they are for or whether they are really useful. When I first got interested in the technologies to track in-store customer journeys, I’ll admit that its newness was a big part of its appeal. But while newness can get you through a “first date”, it can’t – by its very nature – sustain a relationship. In the last few months, as I’ve worked on designing and building our initial product, I’ve had to put a lot of thought into how in-store measurement technology can be used, what will drive real value, and what’s just “for show”. In my last post, I described using the “PoS Test” (asking whether, for any given business question, in-store customer journey data worked better or differently than PoS data) to help choose the reports and analysis that fit this new technology. But I can see that in that post I put the cart somewhat before the horse, since I didn’t really describe in-store customer journey data and it’s likely applications. I’m going to rectify that now.

To measure the in-store customer journey you track customers as they move through your physical environment. The underlying data is really a set of way-points. Each point defines a moment in time when the customer was at a specific location. This is the core journey measurement data.

By aggregating those points and then mapping them to the actual store layout, you have data about how many people entered your store, where they went, and how long they spent near or around any store section. This mapping to the store is the point where concerns about accuracy crop up. After all, the waypoints themselves don’t have any meaning. It’s only when they are overlaid on top of the store that they become interesting. The more precisely you an place the customer with respect to the store, the more you can do with the data.

By tracking key waypoints along the journey (such as dressing rooms or registers), the basic journey data can be used to help build an in-store conversion funnel. Add Point-of-Sale data (and you’d be crazy not to) and you have the full conversion funnel at a product level and all the experience that went with it. For those coming from a digital world, this may feel like the complete journey. It has everything we measure in the digital world and can support all of the same analytic techniques – from funnel analysis to functional and real-estate optimization to behavioral segmentation. But in physical retail, there’s an additional, critical component: measuring staff interactions. It’s hard to overstate the importance of human interactions in physical retail; so if you want to really map the in-store customer journey you have to add in associate interactions. For any given customer journey, you’ll want to know whether, when, how long and with whom a customer interacted.

For most stores, this combination of journey waypoint data, store mapping, PoS data, and staff interaction data is the whole of customer journey data and it’s powerful. At Digital Mortar, though, we’re trying to build a comprehensive measurement backbone for the store that includes detailed digital experiences in store (mobile, digital signage, and specialized in-store experiences) AND a set of variables that encompass the background environment for a customer visit.

In-store digital experiences are a key part of a modern retail customer journey and if you can’t integrate them into your omni-channel picture of a customer you don’t have key ingredients of the experience. I also happen to believe that custom digital experiences will be a crucial differentiator in the evolution of retail experience.

What about the background environment – what does that mean? There’s a lot more environment in physical retail than there is in digital. Weather, for example, is a critical part of the background environment – impacting store traffic but also dramatically changing in-store journeys and purchase patterns. Other important environment variables include store promotions (local and national), advertising campaigns, mall traffic and promotions, road traffic, events, what digital signage was showing and even what music was playing during a customer visit. The more environment data you have, the better chance you have of understanding individual customer journeys and figuring out what shapes them in meaningful ways.


Summing Up

The in-store customer journey data begins with the waypoint data. That’s the core data that describes the actual customer experience in the store. To be useful, that data has to be mapped accurately to the store layout and the merchandise. You have to know what’s THERE! Integrating PoS data provides the key success metrics you need to understand what parts of the experience worked and to build full in-store funnels. Associate interactions data adds the human part of the experience and opens the door to meaningful staffing optimization. And the picture is completed by adding in digital interaction data and as much background data as you can get – particularly key facts about weather and promotions. Taken together, this data provides remarkable insight into the in-store funnel and customer experience. And to prove it, my next post will tackle the actual uses of this data and the business questions it can (and should) answer!

Why do we need to track customers when we know what they buy?

Digital Mortar is committed to bringing a whole new generation of measurement and analytics to the in-store customer journey. What I mean by that “new generation” is that our approach embodies more complete and far more accurate data collection. I mean that it provides far more interesting and directive reports. And I mean that our analytics will make a store (or other physical space) work better. But how does that happen and why do we need to track customers inside the store when we know what they buy? After all, it’s not as if traditional stores are unmeasured. Stores have, at minimum, PoS data and store merchandising and operations data. In other words, we know what we had to sell, we know how many people we used to sell it, and we know how much (and what and what profit) we actually sold.

That stuff is vital and deeply explanatory.

It constitutes the data necessary to optimize assortment, manage (to some extent) staffing needs, allocate staff to areas, and understand which categories are pulling their weight. It can even, with market basket analysis, help us understand which products are associated in customer’s shopping behaviors and can form the basis for layout optimization.

We come from a digital analytics background – analyzing customer experience on eCommerce sites we often had a similar situation. The back-office systems told us which products were purchased, which were bought together, which categories were most successful. You didn’t need a digital analytics solution to tell you any of that. So if you bought, implemented and tried to use a digital analytics solution and those were your questions…well, you were going to be disappointed. Not because a digital analytics solution couldn’t provide answers, it just couldn’t provide better answer than you already had.

It’s the same with in-store tracking systems; which is why when we’re building our system, evaluating reports or doing analysis for clients at Digital Mortar, I find myself using the PoS test. The PoS test is just this pretty simple question: does using the customer in-store journey to answer the question provide better, more useful information than simply knowing what customers bought?

When the answer yes, we build it. But sometimes the answer is no – and we just leave well enough alone.

Let me give you some examples from real-life to show why the PoS test can help clarify what In-Store tracking is for. Here’s three different reports based on understanding the in-store customer journey:

#1: There are regular in-store events hosted by each location. With in-store tracking, we can measure the browsing impact of these events and see if they encourage people to shop products.

#2: There are sometimes significant category performance differences between locations. With in-store tracking, we can measure whether the performance differences are driven by layout, by traffic type, by weather or by area shop per preferences.

#3: Matching staffing levels to store traffic can be tricky. Are there times when a store is understaffed leaving sales, literally, on the table? With in-store tracking we can measure associate / customer rations, interactions and performance and we can identify whether and how often lowered interaction rates lost sales.

I think all three of these reports are potentially interesting – they’re perfectly reasonable to ask for and to produce.

With #1, however, I have to wonder how much value in-store tracking will add beyond PoS data. I can just as easily correlate PoS data to event times to see if events drive additional sales. What I don’t know is whether event attendees browse but don’t buy. If I do this analysis with in-store tracking data, the first question I’ll get is “But did they buy anything?” If, on the other hand, I do the analysis with PoS data, I’m much less likely to hear “But did they browse the store?” So while in-store tracking adds a little bit of information to the problem, it’s probably not the best or the easiest way to understand the impact of store events. We chose not to include this type of report in our base report set, even though we do let people integrate and view this type of data.

Question #2 is quite different. The question starts with sales data. We see differences in category sales by store. So more PoS data isn’t going to help. When you want to know why sales are different (by day, by store, by region, etc.), then you’ll need other types of data. Obviously, you’ll need square footage to understand efficiency, but the type of store layout data you can bring to bear is probably even more critical than measures of efficiency. With in-store tracking you can see how often a category functions as a draw (where customers go first), how it gets traffic from associated areas, how much opportunity it had, and how well it actually performed. Along with weather and associate interaction data, you have almost every factor you’re likely to need to really understand the drivers of performance. We made sure this kind of analytics is easy in our tool. Not just by integrating PoS data, but by making sure that it’s possible to understand and compare how store layouts shape category browsing and buying.

Question #3 is somewhere in between. By matching staffing data to PoS data, I can see if there are times when I look understaffed.  But I’m missing significant pieces of information if I try to optimize staff using only PoS data. Door-counting data can take this one step further and help me understand when interaction opportunities were highest (and most underserved). With full in-store journey tracking, I can refine my answers to individual categories / departments and make sure I’m evaluating real opportunities not, for example, mall pass throughs. So in-store journey tracking deepens and sharpens the answer to Staffing Gaps well beyond what can be achieved with only PoS data or even PoS and door-counting data. Once again, we chose to include staff optimization reports (actually a whole bunch of them) in the base product. Even though you can do interesting analysis with just PoS data, there’s too much missing to make decision-makers informed and confident enough to make changes. And making changes is what it’s all about.


We all know the old saying about everything looking like a nail when your only tool is a hammer. But the truth is that we often fixate on a particular tool even when many others are near to hand. You can answer all sorts of questions with in-store journey tracking data, but some of those questions can be answered as well or better using your existing PoS or door-counting data. This sort of analytics duplication isn’t unique to in-store tracking. It’s ubiquitous in data analytics in general. Before you start buying systems, using reports or delving into a tool, it’s almost always worth asking if it’s the right/easiest/best data for the job. It just so happens that with in-store tracking data, asking how and whether it extends PoS data is almost always a good place to start.

In creating the DM tool, we’ve tried to do a lot of that work for you. And by applying the PoS test, we think we’ve created a report set that helps guide you to the best uses of in-store tracking data. The uses that take full advantage of what makes this data unique and that don’t waste your time with stuff you already (should) know.


The Road Goes Ever On

“It’s a dangerous business,” says Bilbo Baggins, “going out your front door. You step onto the road, and if you don’t keep your feet, there’s no knowing where you might be swept off to.” Eighteen years ago I stepped outside my comfortable door and was swept out into a digital world that I – like everyone else – knew very little about. There were dragons in that wilderness, as there always are. Some we slew and some we ran away from. Some are out there still.

But though a road may go on and on, a person sometimes finds another path. In the last few months I’ve felt rather like young Mr. Baggins, visited by dwarfs and a wizard, and confronted with a map of a great unexplored wasteland, a forbiddingly guarded, lonely mountain, and a vast treasure.

It’s not so easy to give up adventuring.

Eighteen years ago when I first started thinking seriously about digital analytics, we were the poor step-child of analytics. Web analytics (as we called it back then) was pathetic. To call it analytics was a misnomer – the right word being some polyglot mash-up of hubris, false-advertising and ignorance. Perhaps “faligris” was the word we needed but didn’t have. We looked with envy at the sophisticated analytics done for mass media, retail, and direct mail.


My how times have changed.

I’m not going to go all Pollyanna on you. There’s still a lot not to like about the way we do digital analytics. But here’s the thing – I’m not sure there’s a field that does it better. Without really realizing it, digital has spawned a discipline of continuous improvement that includes a fairly sophisticated view of dashboarding and reporting, interesting segmentation, a decent set of techniques for specific analysis problems, and – probably most impressive – a real commitment to experimentation. Sure, most companies get a lot of this wrong. My extended discussion of the perils and problems of digital transformation isn’t (really!) just grumpiness. But the companies that do it well are truly outstanding. And even in the flawed general practice there is much to like.

The best of digital analytics these days has nothing to be ashamed of and much to be proud of.

That’s why, on the map of the digital analytics world, there are more gardens than wasteland, more cultivated field than dangerous mountain. Digital Analytics is well past the “trough of despair” in the hype-cycle – delivering tremendous value on a consistent basis. That’s a great place to be, but I’m looking for a little more adventure.

A little backstory may be in order here. Not too long ago, one of my larger clients asked us to take a look at a solution they’d tried to measure their customer’s IN STORE journeys. Their vendor wired up a sample store with a network of cameras to detect and measure in-store customer paths. It looked a lot like digital analytics. Or should I say it looked a lot like web analytics? Because in almost every respect, it reminded me of the measurement capture, reporting and analysis we did back in 1997. The data capture was expensive and broke frequently. The data was captured at the wrong level of granularity and there was no detail feed available. The reporting was right there with Webtrends 1.0. The analysis – literally – became a standing joke with our client.

It was pathetic.

Well, even from this mess, there was interesting information to be salvaged. You COULD do better reporting even on the sadly broken data being collected. But it got me thinking. Because this was a “leader” in the field.

So naturally, I checked out the rest of the field. What I found were the same type of engineering heavy, analysis tone-deaf companies that I remembered back in the old days of the web before people like Omniture and Google figured out how to do this kind of thing right. I found technology solutions desperately looking for actual business problems. I found expensive implementations that still managed to miss the really important data. I found engineers not analysts.

I found opportunity.

Because this data and these systems are very much like digital analytics. The lessons we’ve learned there about collection, KPIs, reporting, segmentation, analysis and testing all feel fresh, important and maybe even revolutionary. And this time, there’s a chance to provide an end-to-end solution that combines technology with the kind of reporting and analytics I’ve always dreamed about. There’s a chance to be the Omniture AND Semphonic of a really cool space.

I just couldn’t resist.

So I’m going to be leaving EY and, for that matter, digital analytics. I’ll miss both keenly. These past years in digital analytics have been the best and most rewarding of my professional life. I’m proud of the work I’ve done. Proud of the work we’ve done together. Proud of the discipline we’ve created. But I want to take that work and build something new from the ground up.

I’m going to build a startup dedicated to bringing the best of digital discipline and measurement to the physical world. Helping stores, malls, stadiums, banks, hospitals and who knows what else understand how to use customer behavior to actually optimize customer experience.

I want to make the best experiences in the real world every bit as seamless, personalized, and optimized as the best experiences in the digital world already are.

It’s a dangerous world out there in physical retail. They’re struggling and they don’t really know how to get better. If there really was a map, I’m pretty sure it would have a big X with “there be dragons” printed right above.

I can’t wait.

Welcome to Digital Mortar!



Competitive Advantage and Digital Transformation – Optimizing Retail and eCommerce

In my last posts before the DA Hub, I described the first two parts of an analytics driven digital transformation. The first part covered the foundational activities that help an organization understand digital and think and decide about it intelligently. Things like customer journey, 2-tiered segmentation, a comprehensive VoC system and a unified campaign measurement framework form the core of a great digital organization. Done well, they will transform the way your organization thinks about digital. But, of course, thinking isn’t enough. You don’t build culture by talking but by doing. In the beginning was the deed. That’s why my second post dealt with a whole set of techniques for making analytics a constant part of the organization’s processes. Experimentation driven by a comprehensive analytics-driven testing plan, attribution and mix modelling, analytic reporting, re-survey, and a regular cadence of analytics driven briefings make continuous improvement a reality. If you take this seriously and execute fully on these first two phases, you will be good at digital. That’s a promise.

But as powerful, transformative and important as these first two phases are, they still represent only a fraction of what you can achieve with analytics driven-transformation. The third phase of analytics driven transformation targets areas where analytics changes the way a business operates, prices its products, communicates with and supports its customers.

The third phase of digital transformation is unique. In some ways, it’s easier than the first two phases. It involves much less organization and cultural transformation. If you done those first two phases, you’re already there when it comes to having an analytics culture. On the other hand, in this third phase the analytics projects themselves are often MUCH more complex. This is where we tackle big hard problems. Problems that require big data, advanced statistical analysis, and serious imagination. Well, that’s the fun stuff. Seriously, if you’ve gotten through the first two phases of an analytics transformation successfully, doing the projects in Phase Three is like a taking a victory lap.

There isn’t one single blueprint for the third phase of an analytics driven transformation. The work that gets done in the first two phases is surprisingly similar almost regardless of the industry or specific business. I suppose it’s like laying the foundation for a building. No matter what the building looks like, the concrete block at the bottom is going to look pretty much the same. At this third level, however, we’re above the foundation and what you do will depend mightily on your specific business.

I know that it depends on your business is not much of an answer. As a consultant, it’s not unusual to get caught up in conversations like this:

“So how much would it cost?”

“Well, that depends.”

“What kind of things does it depend on?”

“Well, it depends on how deeply you want to go into it, who you want to have do it, and how you want to get it done.”

All of this is true, of course, but none of it is helpful. I usually try to short-circuit these conversations by presenting a couple of real world alternatives.

I think this is more helpful (though it’s also more dangerous). Similarly, when I present the third phase of an analytics driven transformation I try to make it specific to the business in question. And the more I know about the business, the more pointed, interesting, and – I hope – convincing that third phase is going to look. But if I haven’t spent much time a business, I still customize that third phase by industry – picking out high-level analytics projects that are broadly applicable to everyone in the sector.

That’s what I’m going to try to do here, with the added benefit of picking a couple different industries and showing how the differences play out in this third phase. Do keep in mind, though, that the description of this third phase – unlike that of the first two – is meant to be suggestive only. No real-world third phase (certainly no optimal one) is likely to mirror what I lay out here. It might not even be very close. What’s more, unlike the first phase (at least) which is close-ended (when you’ve done the projects I suggest you’re done with that phase), phase three is open-ended. You never stop doing analytics projects at this level. And that’s a good thing.

For the first example, I decided to start with a classic retail e-commerce view of the world. It’s a sector where we all have, at the very least, a consumer’s understanding of how it works. There are many, many possible projects to choose from, but here are five I often present as a typical starting point.

The first is an analytically driven personalization program. With journey-mapping, 2-tiered segmentation and a robust experimentation program, an enterprise should be a in a good position to drive personalization. Most personalization programs bootstrap themselves by starting with fairly straightforward segmentations (already done) and rule-based personalization decisions targeted to “easy” problems like email offers and returning visitors to the Website. That’s fine. The very best way to build a personalization program is organically – build it by doing it with increasing sophistication in more and more channels and at more and more touchpoints.

Merchandising optimization is another very big opportunity. So much of the merchandising optimization I see is focused on product detail pages. That’s fine as far as it goes, but it misses the much larger opportunity to optimize merchandising on search and aisle pages via analytics. Traditional merchandising folks have been slow to understand how critical moving merchandising upstream is to effective digital performance. This turns out to be analytically both very challenging and very rich.

Assortment optimization (and I might be just as likely to pick pricing or demand signals here) has long been a domain of traditional retail analytics. As such, I have to admit I didn’t think much about it until the last few years. But I’ve come to believe that digital analytics can yield powerful preference information that is typically missing in this analysis. To do effective assortment optimization, you need to understand customer’s potential replacement options. In the offline world, this usually involves making simple guesses based on high-level product sales about which products will be substituted. Using online view data, we can do much, much better. This is a case where digital analytics doesn’t so much replace an existing technique as deepen and enrich it with data heretofore undreamed of. Assortment optimization with digital data gives you highly segmented, localized data about product substitution preferences. It’s a lot better.

I’ve become a strong advocated for a fundamental re-think of loyalty programs based on the idea that surprise-based loyalty with no formal earning system is the future of rewards programs. The advantages of surprise-based loyalty are considerable when stacked up against traditional loyalty programs. You can target rewards where you think they will create lift. You can take advantage of inventory problems or opportunities. You don’t incur ANY financial obligations. You create no customer resentment or class issues. You can scale them and localize them to work with a specially trained staff. And, of course, the biggest bonus of all – you actually create far more impact per dollar spent. Surprise-based loyalty is, inherently, analytic. You can’t really do it any other way. Where it’s an option, it’s always one of the biggest changes you can make in the way your business works.

Finally, I’ve picked digital/store integration as my fifth project for analytics-led transformation. There are a number of different ways to take this. The drives between store and site are complex, important and fruitful. Optimizing those drives should be one of the analytics priorities for any omni-channel retail. And that optimization is a combination of testing and analytics. In this case, however, I’ve chosen to focus on measuring and optimizing digital in-store experiences. You’re surely familiar with endless-aisle retail; where digital is integrated into the in-store experience. The vast majority of these physical-digital experiences have been quite ineffective. Almost always, they’ve been executed from a retail perspective. By which I mean that they’ve been built once, dropped into the store, and left to fail. That’s just not doing it right. In-store experiences are getting more digital. Digital signage is growing rapidly. Physical-digital experiences are increasingly common. But if you want actual competitive advantage out of these experiences, you’d better tackle them from a digital test-and-learn/analytics perspective. Anything less is a prescription for failure.

Digital Transformation Phase III Retail

So here’s my first round of Phase Three projects for an analytics driven transformation in retail. Each is big, complex and hard. They are also important. These are the projects that will truly transform your digital business. They are rubber-meets-the-road stuff that drive competitive advantage. It would be a mistake to try and execute on projects like this without first creating a strong analytics foundation in the organization. You’re chances of misfiring on doing or operationalizing the analytics are simply too great without that foundation. But if you don’t move past the first two phases into analytics like this, you’re missing the big stuff. You can churn out lots of incremental improvement in digital without ever touching projects like these. Those incremental improvements aren’t nothing. They may be valuable enough to justify your time and money. But if that’s all you ever do, you’ll likely find yourself wondering if it was all really worth it. Do any of these projects successfully, and you’ll never ask that question again.

Next week I’ll show a different (non-retail) set of projects and break-down what the differences tell us about how to make analytics a strategic asset.

[Just a reminder that if you’re interested in the U.S. version of the Digital Analytics Hub you can register here!]