Tag Archives: retail measurement

Evolve or Die: Analytics and Retail

In my last three posts, I assessed the basic technologies (wifi, camera, etc.) for in-store customer measurement and took a good hard look at the state of the analytics platforms using that measurement. My conclusion? The technologies are challenging but, deployed properly, can work at scale for a reasonable cost. The analytics platforms, on the other hand, have huge gaping holes that seriously limit the ability of analysts to use that data. Our DM1 platform is designed to solve most (I hope all) of those problems. But it’s not worth convincing anyone that DM1 is a better solution unless people get why this whole class of solution is so important.

Over about the same amount of time as those posts, I’ve seen multiple stories on the crisis in mall real-estate, the massive disruption driven in physical retail when eCommerce cross sales thresholds as a percentage of total purchases, and the historical and historically depressing pace of store closings in 2017.

It’s bad out there. No…that doesn’t really capture things. For lots of folks, this is potentially an extinction level event. It’s a simple Darwinian equation:

Evolve or die.

And people get that. The pace of innovation and change in retail has never been as high. Is it high enough? Probably not. But retailers and mall operators are exploring a huge number of paths to find competitive advantage. At a high-level, those paths are obvious and easily understood.

Omni-Channel is Key: You can’t out-compete in pure digital with “he who must not be named”…so your stores have to be a competitive advantage not an anchor. How does that happen? Integration of the digital experience – from desktop to mobile – with the store. Delivering convenience, experience, and personalization in ways that can’t be done in the purely digital realm.

Experience is Everything: If people have to WANT to go to stores (in a line I’ve borrowed from Lee Peterson that I absolutely love), delivering an experience is the bottom line necessary to success. What that experience should be is, obviously, much less clear and much more unique to each business. Is it in-store digital experiences like Oak Labs’ delivers – something that combines a highly-customized digital shopping experience integrated right into the store operation? Is it bringing more and better human elements to the table with personalized clienteling? Is it a fundamentally different mix of retail and experience providers sharing a common environment? It’s all of these and more, of course.

The Store as a Complex Ecosystem: A lot of factors drive the in-store experience. The way the store is laid out. The merchandising. The product itself. Presentations. In-store promotions. Associate placement, density, training and role. The digital environment. Music. Weather. It’s complicated. So changing one factor is never going to be a solution.  Retail professionals have both informed and instinctive knowledge of many of these factors. They have years of anecdotal evidence and real data from one-off studies and point-of-sale. What they don’t have is any way to consistently and comprehensively measure the increasingly complex interactions in the ecosystem. And, of course, the more things change, the less we all know. But part of what’s involved in winning in retail is getting better at what makes the store a store. Better inventory management. Better presentation. Better associates and better clienteling strategies. Part of winning in a massively disrupted environment is just being really good at what you do.

The Store in an Integrated Environment: Physical synergies exist in a way that online synergies don’t. In the friction free world of the internet, there’s precious little reason to embed one web site inside another. But in the physical world, it can be a godsend to have a coffee bar inside the store while my daughters shop! Taking advantage of those synergies may mean blending different levels of retail (craft shows, farmers markets) with traditional retail, integrating experiences (climbing walls, VR movies) or taking advantage of otherwise unusable real-estate to create traffic draws (museums, shared return centers).

In one sense, all of these things are obvious. But none of them are a strategy. They’re just words that point in a general direction to real decisions that people have to make around changes that turn out to be really hard and complex. That’s where analytics comes in and that’s why customer journey measurement is critically important right now.

Because nobody knows A) The right ways to actually solve these problems and  B) How well the things they’re trying to do are actually working.

Think about it. In the past, Point of Sale data was the ultimate “scoreboard” metric in retail and traffic was the equivalent for malls. It’s all that really mattered and it was enough to make most optimization decisions. Now, look at the strategies I just enumerated: omni-channel, delivering experience, optimizing the ecosystem and integrating broader environments…

Point-of-Sale and traffic measure any of that?

Not really. And certainly, they don’t measure it well enough to drive optimization and tuning.

So if you’re feverishly building new stores, designing new store experiences, buying into cutting edge digital integrations, or betting the farm on new uses for your real-estate, wouldn’t it be nice to have a way to tell if what you’re trying is actually working? And a way to make it work better since getting these innovative, complex things right the first time isn’t going to happen?

This is the bottom line: these days in retail, nobody needs to invest in customer measurement. After all, there’s a perfectly good alternative that just takes a little bit longer.

It’s called natural selection. And the answers it gives are depressingly final.

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.