Tag Archives: Digital Mortar

Creating a Measurement Language for the Store

Driving real value with analytics is much harder than people assume. Doing it well requires solving two separate, equally thorny problems. The first – fairly obvious problem – is being able to use data to deepen your understanding of important business questions. That’s what analytics is all about. The second problem is being able to use that understanding to drive business change. Affecting change is a political/operational problem that’s often every bit as difficult as doing the actual analysis. Most people have a hard time understanding what the data means and are reluctant to change without that understanding. So, giving analysts tools that help describe and contextualize the data in a way that’s easy to understand is a double-edged sword in the best of ways – it helps solves two problems. It helps the analyst use the data and it helps the analyst EXPLAIN the data to others more effectively. That’s why having a rich, powerful, UNDERSTANDABLE set of store metrics is critical to analytic success with in-store customer tracking.

Some kinds of data are very intuitive for most of us. We all understand basic demographic categories. We understand the difference between young and old. Between men and women. We live those data points on a daily basis. But behavioral data has always been more challenging. When I first started using web analytics data, the big challenge was how to make sense of a bunch of behaviors. What did it mean that someone viewed 7 pages or spent 4.5 minutes on a Website? Well, it turned out that it didn’t mean much at all. The interesting stuff in web analytics wasn’t how many pages a visitor had consumed – it was what those pages were about. It meant something to know that a visitor to a brokerage site clicked on a page about 529 accounts. It meant they had children. It meant they were interested in 529 accounts. And depending on what 529 information they chose to consume, it might indicate they were actively comparing plans or just doing early stage research. And the more content someone consumed, the more we knew about who they were and what they cared about.

Which was what we needed to optimize the experience. To personalize. To surface the right products. With the right messages. At the right time. Knowing more about the customer was the key to making analytics actionable and finding the right way to describe the behavior with data was the key to using analytics effectively.

So when it comes to in-store customer measurement, what kind of data is meaningful? What’s descriptive? What helps analysts understand? What helps drive action?

The answer, it turns out, isn’t all that different from what works in the digital realm. Just as the key to understanding a web visit turns out to be understanding the content a visitor selected and consumed, the key to understanding a store visit turns out to be understanding the store. You have to know what the shopper looked at. What was there when they stopped and lingered. What was along the corridor that they traversed but didn’t shop. You have to know the fitting room from the cash-wrap and an endcap from an aisle and you have to know what products were there. What’s more, you have to place the data in that context.

Here’s what the data from an in-store measurement collection system looks like in its raw form, frame by frame:

TimeXY
04:06.03560
06:50.0966
09:10.02374
11:02.01892
11:35.03398
13:15.02874
14:25.0781
16:16.04175
19:09.04962
21:03.04572
23:23.05583
23:58.05490
24:09.04086
25:05.01590
27:24.0779
27:45.04399
28:42.03797
29:25.04580
32:07.04775
33:05.01677
35:31.03765
36:08.03475
36:33.0973
39:16.03576
40:07.01397

That’s a visit to a store. A little challenging to make sense of, right?

It’s our job to translate that into a journey with the necessary context to make the data useful.

That starts by mapping the data onto the store:

store journey analytics

By overlaying the measurement frames, we can distinguish the path the user took through the store:

StoreFrame1

With simple analysis of the frames, we can figure out where and when a customer shifted from navigating the store to actually spending time. And that first place the shopper actually spends time, has special significance for understanding who they are.

In DM1, the first shopping point is marked as the DRAW. It’s where the shopper WENT FIRST in the store:storeFrame2

In this case, Customer Service was the Draw – indicating that this shopping visit is a return or in-store pickup. But the visit didn’t end there.

Following the journey, we can see what else the customer was exposed to and where else they actually spent time and shopped. In DM1, we capture each place the shopper spent time as a LINGER:

storeFrame3

Lingers tell us about opportunity and interest. These are the things the shopper cared about and might have purchased.

But not every linger is created equal. In some places, the shopper might spend significantly more time – indicating a higher level of engagement. In DM1, these locations are called out on the journey as CONSIDERS:

storeframe4

Having multiple levels of shopper engagement lets DM1 create a more detailed picture of the shopper and a better in-store funnel. Of course, one of the keys to understanding the in-store funnel is knowing when a shopper interacts with an Associate. That’s a huge sales driver (and a huge driver – positive or negative – to customer experience). In DM1, we track the places where a shopper talked with and Associate as INTERACTIONS. They’re a key part of the journey:

storeFrame5

Of course, you also want to know when/if a customer actually purchased. We track check-outs as CONVERSIONS – and have the ability to do that regardless of whether it’s a traditional cash-wrap or a distributed checkout environment:

storeFrame6

Since we have the whole journey, we can also track which areas a customer shopped prior to checkout and we’ve created two measures for that. One is the area shopped directly before checkout (which is called the CONVERSION DRIVER) and the other captures every area the customer lingered prior to checkout – called ATTRIBUTED CONVERSIONS.

StoreFrame8

To use measurement effectively, you have to be able to communicate what the numbers mean. For the in-store journey, there simply isn’t a standardized way of talking about what customers did. With DM1, we’ve not only captured that data, we’ve constructed a powerful, working language (much of it borrowed from the digital realm) that describes the entire in-store funnel.

From Visits (shopper entering store), to Lingers (spending time in an area), to Consideration (deeper engagement), to Investment (Fitting Rooms, etc.), to Interactions (Associate conversations) to Conversion (checkout) along with metrics to indicate the success of each stage along the way. We’ve even created the metric language for failure points. DM1 tracks where customers Lingered and then left the store without buying (Exits) and even visits where the shopper only lingered in one location before exiting (Bounces).

Having a rich set of metrics and a powerful language for describing the customer journey may seem like utter table-stakes to folks weaned on digital analytics. But it took years for digital analytics tools to offer a mature and standardized measurement language. In-store tracking hasn’t had anything remotely similar. Most existing solutions offer two basic metrics (Visits and Dwells). That’s not enough for good analytics and it’s not a rich enough vocabulary to even begin to describe the in-store journey.

DM1 goes a huge mile down the road to fixing that problem.

[BTW – if you want to see how DM1 Store Visualization actually works, check out these live videos of DM1 in Action]

Segmentation is the Key to Marketing Analytics

The equation in retail today is simple. Evolve or die. But if analytics is one of the core tools to drive successful  evolution, we have a problem. From an analytics perspective, we’re used to a certain view of the store. We know how many shoppers we get (door counting) and we know what we sold. We know how many Associates we had. We (may) know what they sold. This isn’t dog food. If you had to pick a very small set of metrics to work with to optimize the store, most of these would belong. But we’re missing a lot, too. We’re missing almost any analytic detail around the customer journey in the store. That’s a particularly acute lack (as I noted in my last post) in a world where we’re increasingly focused on delivering (and measuring) better store experiences. In a transaction-focused world, transactions are the key measures. In an experience world? Not so much. So journey measurement is a critical component of today’s store optimization. And there’s the problem. Because the in-store measurement systems we have available are tragically limited. DM1, our new platform, is designed to fix that problem.

People like to talk about analytics as if it just falls out of data. As if analysts can take any data set and any tool and somehow make a tasty concoction. It isn’t true. Analytics is hard work. A really great analyst can work wonders, but some data sets are too poor to use. Some tools lock away the data or munge it beyond recognition.  And remember, the most expensive part of analytics is the human component. Why arm those folks with tools that make their job slow and hard? Believe me, when it comes to getting value out of analytics, it’s hard enough with good tools and good data. You can kid yourself that it’s okay to get by with less. But at some point you’re just flushing your investment and your time away. In two previous posts, I called out a set of problems with the current generation of store customer measurement systems. Sure, every system has problems – no analytics tool is perfect. But some problems are much worse than others. And some problems cripple or severely limit our ability to use journey data to drive real improvement.

When it comes to store measurement tools, here are the killers: lack of segmentation, lack of store context, inappropriate analytics tools, inability to integrate Associate data and interactions, inability to integrate into the broader analytics ecosystem and an unwillingness to provide cleaned, event-level data that might let analysts get around these other issues.

Those are the problems we set out to solve when we built DM1.

Let’s start with Segmentation. Segmentation can sound like a fancy add-on. A nice to have. Important maybe, but not critical.

That isn’t right. Marketing analytics just is segmentation. There is no such thing as an average customer. And when it comes to customer journey’s, trying to average them makes them meaningless. One customer walks in the door, turns around and leaves. Another lingers for twenty minutes shopping intensively in two departments. Averaging the two? It means nothing.

Almost every analysis you’ll do, every question you’ll try to answer about store layout, store merchandising, promotion performance, or experience will require you to segment. To be able to look at the just the customers who DID THIS. Just the customers who experienced THAT.

Think about it. When you build a new experience, and want to know how it changed behavior you need to segment. When you change a greeting script or adjust a presentation and want to know if it improved store performance you need segmentation. When you change Associate interaction strategies and want to see how it’s impacting customer behavior you need segmentation. When you add a store event and want to see how it impacted key sections, you need segmentation. When you want to know what other stuff shoppers interested in a category cared about, you need segmentation. When you want to know how successful journeys differed from unsuccessful ones, you need segmentation. When you want to know what happens with people who do store pickup or returns, you need segmentation.

In other words, if you want to use customer journey tracking tools for tracking customer journeys, you need segmentation.

If your tool doesn’t provide segmentation and it doesn’t give the analyst access to the data outside it’s interface, you’re stuck. It doesn’t matter how brilliant you are. How clever. Or how skilled. You can’t manufacture segmentation.

Why don’t most tools deliver segmentation?

If it’s so important, why isn’t it there? Supporting segmentation is actually kind of hard. Most reporting systems work by aggregating the data. They add it up by various dimensions so that it can be collapsed into easily accessible chunks delivered up into reports. But when you add segmentation into the mix, you have to chunk every metric by every possible combination of segments. It’s messy and it often expands the data so much that reports take forever to run. That’s not good either.

We engineered DM1 differently. In DM1, all the data is stored in memory. What does that mean? You know how on your PC, when you save something to disk or first load it from the hard drive it takes a decent chunk of time? But once it’s loaded everything goes along just fine? That’s because memory is much faster than disk. So once your PowerPoint or spreadsheet is loaded into memory, things run much faster. With DM1, your entire data set is stored in-memory. Every record. Every journey. And because it’s in-memory, we can pass all your data for every query, really fast. But we didn’t stop there. When you run a query on DM1, that query is split up into lots of chunks (called threads) each of which process its own little range of data – usually a day or two. Then they combine all the answers together and deliver them back to you.

That means that not only does DM1 deliver reports almost instantaneously, it means we can run even pretty complex queries without pre-aggregating anything and without having to worry about the performance. Things like…segmentation.

Segmentation and DM1

In DM1, you can segment on quite a few different things. You can segment on where in the store the shopper spent time. You can segment on how much time they spent. You can segment on their total time in the store. You can segment on when they shopped (both by day of week and time of day). You can segment on whether they purchased or not. And even whether they interacted with an Associate.

If, for example, you want to understand potential cross-sells, you can apply a segment that selects only visitors who spent a significant amount of time shopping in a section or department. Actually, this undersells the capability because it’s in no way limited to any specific type of store area. You can segment on any store area down to the level of accuracy achieved by the collection architecture.

What’s more, DM1 keeps track of historical meta-data for every area of the store. Meaning that even if you changed, moved or re-sized an area of the store, DM1 still tracks and segments on it appropriately.

So if you want to see what else shoppers who looked at, for example, Jackets also considered, you can simply apply the segmentation. It will work correctly no matter how many times the area was re-defined. It will work even in store roll-ups with fundamentally different store types. And with the segment applied, you can view any DM1 visualization, chart or table. So you can look at where else Jacket Shoppers passed through, where they lingered, where they engaged more deeply, what else they were likely to buy, where they exited from, where they went first, where they spent the most time, etc. etc. You can even answer questions such as whether shoppers in Jackets were more or less likely to interact with Sales Associates in that section or another.

Want to see if Jacket shoppers are different on weekdays and weekends? If transactors are different from browsers? If having an Associate interaction significantly increases browse time? Well, DM1 let’s you stack segments. So you can choose any other filter type and apply it as well. I think the Day and Time part segmentation’s are particularly cool (and unusual). They let you seamlessly focus on morning shoppers or late afternoon, weekend shoppers or even just shoppers who come in over lunchtime. Sure, with door-counting you know your overall store volume. But with day and time-part segmentation you know volume, interest, consideration, and attribution for every measured area of the store and every type of customer for every hour and day of week.

DM1’s segmentation capability makes it easy to see whether merchandise is grouped appropriately. How different types of visitor journeys play out. Where promotional opportunities exist. And how and where the flow of traffic contradicts the overall store layout or associate plan. For identified shoppers, it also means you can create extraordinarily rich behavioral profiles that capture in near real-time what a shopper cares about right now.

It comes down to this. Without segmentation, analytics solutions are just baby toys. Segmentation is what makes them real marketing tools.

The Roadmap

DM1 certainly delivers far more segmentation than any other product in this space. But it’s still quite a bit short of what I’d like to deliver. I mean it when I say that segmentation is the heart and soul of marketing analytics. A segmentation capability can never be too robust.

Not only do we plan to add even more basic segmentation options to DM1, we’ve also roadmapped a full segmentation builder (of the sort that the more recent generation of digital analytics tools include). Our current segmentation interface is simple. Implied “ors” within a category and implied “ands” across segmentation types. That’s by far the most common type of segmentation analysts use. But it’s not the only kind that’s valuable. Being able to apply more advanced logic and groupings, customized thresholds, and time based concepts (visited before / after) are all valuable for certain types of analysis.

I’ve also roadmapped basic machine learning to create data-driven segmentations and a UI that provides a more persona-based approach to understanding visitor types and tracking them as cohorts.

The beauty of our underlying data structures is that none of this is architecturally a challenge. Creating a good UI for building segmentations is hard. But if you can count on high performance processing event level detail in your queries (and by high-performance I mean sub-second – check out my demos if you don’t believe me), you can support really robust segmentation without having to worry about the data engine or the basic performance of queries. That’s a luxury I plan to take full advantage of in delivering a product that segments. And segments. And segments again.

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.

In-Store Customer Analytics: Broken Inside and Out

In my last post, I described four huge deficiencies in the current generation of in-store tracking solutions. The inability to track full customer journeys, do real segmentation, or properly contextualize data to the store make life very hard on a retail analyst trying to do interesting work. And over-reliance on non-analytic heatmaps – a tool that looks nice but is analytically unrewarding – just makes everything worse.

Of course, you don’t need to use one of these solutions. You can build an analytics warehouse and use some combination of extraordinarily powerful general purpose tools like Tableau, Datameer, Watson, and R to solve your problems.

Or can you?

Here are three more problems endemic to the current generation of in-store tracking solutions that limit your ability to integrate them into a broader analytics program.

Too Much or Too Little Associate Data

In retail, the human factor is often a critical part of the customer journey. As such, it needs to be measured. In-store counting solutions have tended toward two bad extremes when it comes to Associate data. Really, really bad solutions have just tracked Associates as customers. That’s a disaster. In the online world, we worked to screen-out the IP addresses of employees from our actual web site counting even though it was a tiny fraction of the overall measurement total. In the store world, it’s not a tiny fraction – especially given the flaws of zone-counting solutions. We’ve seen cases where a small number of associates can look like hundreds of customers. So including associate data in the store customer counts is pretty much a guarantee that your data will be garbage. On the other hand, tracking associates just so you can throw their data away isn’t the right answer either. Those interactions are important – and they are important at the journey level. Solutions that throw this data away or aggregate it up to levels like hour or day counts are missing the point. Your solution needs to be able to identify which visits had interactions, which didn’t, and which were successful. If it can’t do that, it’s not going to solve any real-world problems.

Which brings me to…

Lack of Bespoke Analytics

One of the obvious truths about analytics in the modern world is that no bespoke analytics solution is going to deliver everything you need. Even mature, enterprise solutions like Adobe Analytics don’t deliver all of the visualization and analytics you need. What bespoke analytics tools should deliver is analytics uniquely contextualized to the business problem. This business contextualization is hard to get out of general purpose tools; so it’s the real life-blood of industry and application targeted solutions. If a solution doesn’t deliver this, it’s ripe for replacement by general purpose analytic platforms. But by going exclusively to general purpose solutions, the organization will lose the shorter time to value that targeted analytics can provide.

Unfortunately, the vast majority of in-store customer tracking tools seem to deliver the sort of generic reports and charts that you might expect from an offshore outfit doing $10/hour Tableau reports. The whole point of bespoke solutions is to deliver analytics contextualized to the problem. If they are just doing a bad job of replicating general purpose OLAP tools you have to ask why you wouldn’t just pipe the data into an analytic warehouse.

Which brings me to my final point…

Lack of a True Event Level Data Feed

No matter how good your bespoke analytics solution is, it won’t solve every problem. It isn’t going to visualize data better than Tableau. It won’t be as cognitive as Watson. Or as good a platform for integration as Datameer. And its analytics capabilities are not going to equal SAS or R. Part of being a good analytics solution in today’s world is recognizing that custom-fit solutions need to integrate into a broader data science world. For in-store customer journey tracking, this is especially important because the solution and the data collection mechanism are often bound together (much as they are in most digital analytics). So if you’re solution doesn’t open up the data, you CAN’T use that data in other tools.

That should be a deal killer. Any tool that doesn’t provide a true, event level data feed (not aggregated report-level data which is useless in most of those other solutions) to your analytics warehouse doesn’t deserve to be on an enterprise short-list of customer journey tracking tools.

Open integration and enterprise data ownership should be table stakes in today’s world.

Summing it Up

There’s a lot not to like about the current generation of in-store customer journey solutions. For the most part, they haven’t delivered the necessary capabilities to solve real-world problems in retail. They lack adequate journey tracking, real segmentation, proper store contextualization, bespoke analytics, and open data feeds. These are the tools essential to solving real-world problems. Not surprisingly, the widespread perception among those who’ve tried these solutions is that they simply don’t add much value.

For us at Digital Mortar, the challenge isn’t being better than these solutions. That’s not how we’re measuring ourselves, because being better isn’t enough. We have to be good enough to drive real-world improvement.

That’s much harder.

In my next post(s), I’ll show how we’ve engineered our new platform, DM1, to include these capabilities and how that, in turn, can help drive real-world improvement.

Four Fatal Flaws with In-Store Tracking

I didn’t start Digital Mortar because I was impressed with the quality of the reporting and analytics platforms in the in-store customer tracking space. I didn’t look at this industry and say to myself, “Wow – here’s a bunch of great platforms that are meeting the fundamental needs in the space at an enterprise level.” Building good analytics software is hard. And while I’ve seen great examples of SaaS analytics platforms in the digital space, solutions like Adobe and Google Analytics took many years to reach a mature and satisfying form. Ten years ago, GA was a toy and Adobe (Omniture SiteCatalyst at the time) managed to be both confusing and deeply under-powered analytically. In our previous life as consultants, we had the opportunity to use the current generation of in-store customer journey measurement tools. That hands-on experience convinced me that this data is invaluable. But it also revealed deep problems with the way in-store measurement is done.

When we started building a new SaaS in-store measurement solution here at Digital Mortar, these are the problems in the technology that we wanted to solve:

Lack of Journey Measurement

Most of today’s in-store measurement systems are setup as, in essence, fancy door counters. They start by having you draw zones in the store. Then they track how many people enter each zone and how long they spend there (dwell time).

This just sucks.

It’s like the early days of digital analytics when all of our tracking was focused on the page view. We kept counting pages and thinking it meant something. Till we finally realized that it’s customers we need to understand, not pages. With zone counting, you can’t answer the questions that matter. What did customers look at first? What else did customers look at when they shopped for something specific? Did customers interact with associates? Did those interactions drive sales? Did customer engagement in an area actually drive sales? Which parts of the store were most and least efficient? Does that efficiency vary by customer type?

If you’re not asking and answering questions about customers, you’re not doing serious measurement. Measurement that can’t track the customer journey across zones just doesn’t cut it. Which brings me to…

Lack of Segmentation

My book, Measuring the Digital World, is an extended argument for the central role of behavioral segmentation in doing customer analytics. Customer demographics and relationship variables are useful. But behavior – what customers care about right now – will nearly always be more important. If you’re trying to craft better omni-channel experiences, drive integrated marketing, or optimize associate interactions, you must focus on behavioral segmentation. The whole point of in-store customer tracking is to open up a new set of critically important customer behaviors for analysis and use. It’s all about segmentation.

Unfortunately, if you can’t track the customer journey (as per my point above), you can’t segment. It’s just that simple. When a customer is nothing more than a blip in the zone, you have no data for behavioral segmentation. Of course, even if you track the customer journey, segmentation may be deeply limited in analytic tools. You could map the improvement of Adobe or Google Analytics by charting their gradually improving segmentation capabilities. From limited filtering on pre-defined variables to more complex, query-based segmentation to the gradual incorporation of sophisticated segmentation capabilities into the analyst’s workbench.

You can have all the fancy charts and visualizations in the world, but without robust segmentation, customer analytics is crippled.

Lack of Store Context

When I introduce audiences to in-store customer tracking, I often use a slide like this:

In-store Customer Analytics

The key point is that the basic location data about the customer journey is only meaningful when its mapped to the actual store. If you don’t know WHAT’S THERE, you don’t have interesting data. The failure to incorporate “what’s there” into their reporting isn’t entirely the fault of in-store tracking software. Far too many retailers still rely on poor, paper-based planograms to track store setups. But “what’s there” needs to be a fundamental part of the collection and the reporting. If data isn’t stored, aggregated, trended and reported based on “what’s there”, it just won’t be usable. Which brings me to…

Use of Heatmaps

Heatmaps sure look cool. And, let’s face it, they are specifically designed to tackle the problem of “Store Context” I just talked about. Unfortunately, they don’t work. If you’ve ever tried to describe (or just figure out) how two heat-maps differ, you can understand the problem. Dialog like: “You can see there’s a little more yellow here and this area is a little less red after our test” isn’t going to cut it in a Board presentation. Because heat-maps are continuous, not discrete, you can’t trend them meaningfully. You can’t use them to document specific amounts of change. And you can’t use them to compare customer segments or changed journeys. In fact, as an analyst who’s tried first hand to use them, I can pretty much attest that you can’t actually use heat-maps for much of anything. They are the prettiest and most useless part of in-store customer measurement systems. If heat-maps are the tool you have to solve the problem of store context, you’re doomed.

These four problems cripple most in-store customer journey solutions. It’s incredibly difficult to do good retail analytics when you can’t measure journeys, segment customers, or map your data effectively onto the store. And the ubiquity of heat-maps just makes these problems worse.

But the problems with in-store tracking solutions don’t end here. In my next post, I’ll detail several more critical shortcomings in the way most in-store tracking solutions are designed. Shortcomings that ensure that not only can’t the analyst effectively solve real-world business problems with the tool, but that they can’t get AT THE DATA with any tools that might be able to do better!

Want to know more about how Digital Mortar can drive better store analytics? Drop me a line.

In-Store Customer Journey Tracking: Can You Really Do This?

When I describe my new company Digital Mortar to folks, the most common reaction I get is: “Can you really do this?”

Depending on their level of experience in the field, that question has one of two meetings. If they haven’t used existing in-store customer tracking solutions, the question generally means: is the technology practical and is it actually OK to use it (i.e. does it violate privacy policies)? If they have experience with existing in-store customer tracking solutions what they mean is: “does your stuff actually work as opposed to the garbage I’ve been using?”

I’m going to tackle the first question today (is the technology practical and legal) and leave the second for next time.

Is the Technology Practical?

Yes. As my post last week made clear, the various technologies for in-store customer tracking have challenges. Data quality is a real problem. There are issues with positional accuracy, visitorization, journey tracking, and even basic reliability. This is still cutting or even bleeding-edge technology. It’s like digital analytics circa 2005 not digital analytics 2017. But the technologies work. They can be deployed at scale and for a reasonable cost. The data they provide needs careful cleaning and processing. But so does almost any data set. If chosen appropriately and implemented well, the technologies provide data that is immediately valuable and can drive true continuous improvement in stores.

How Hard is it to Deploy In-Store Tracking?

Unfortunately, the in-store customer tracking technologies that don’t take at least some physical in-store installation (Wi-Fi Access Point based measurement and piggybacking off of existing security cameras) are also the least useful. Wi-Fi measurement is practical for arenas, airports, malls and other very large spaces with good Wi-Fi opt-in rates. For stores, it just doesn’t work well enough to support serious measurement. Security cameras can give you inaccurate, zone based counts and not much else.  Good in-store measurement will require you install either measurement focused cameras or passive sniffers. Of the two, sniffers are lot easier. You need a lot less of them. The placement is easier. The power and cabling requirements are lower. And they are quite a bit cheaper.

Either way, you should expect that it will take a few weeks to plan out the deployment for a new store layout. This will also involve coordination with your installation partner. Typically, the installation is done over one or two evenings. No special closing is required. With sniffers, the impact on the store environment is minimal. The devices are about the size of a deck of playing cards, can be painted to match the environment and any necessary wiring is usually hidden.

After a couple week shake down, you’ll have useable measurement and a plan you can roll out to other stores. Subsequent stores with the same or similar layout can be done as quickly as your installation partner will schedule them. And the post-install shake-down period is less.

So if you’re planning a Pilot project, here’s the timeline we use at Digital Mortar:

Month 1

  • Select Store Targets: We typically recommend 3 stores in a Pilot – one test and two control stores with similar layout and market.
  • Select Initial Store
  • Design Implementation for the Initial Store
  • Train Installation Partner
  • Do initial 1 store installation

Month 2

  • Test the initial installation and tune plan if necessary
  • Rollout to additional stores
  • Provide initial reporting
  • Targeted analysis to develop store testing plan

Month 3

  • Run initial test(s)
  • Analyze control vs. test
  • Assess findings and make optimization recommendations
  • Evaluate pilot program

This kind of Pilot timeline gets you live, production data early in Month 2 with initial store findings not long after. And it gets you real experience with the type of analysis, testing and continuous improvement cycle that make for effective business use.

Is it Ok to Use Location Analytics?

Yes. In-store tracking technology is already widely used. The majority of major retailers have tried it in various forms. There is an established community of interest focused on privacy and compliance in location analytics (the Future of Privacy Forum) that is supported by the major technology players (including giants like Cisco who do this routinely), major retailers, most of the vendors specific to the space, and plenty of heavy-hitters from a political standpoint. They’ve published guidelines (with input from the FTC) on how to do this. In many respects, the landscape is similar to digital. To do this right, you must have a documented and published privacy policy and you MUST adhere to your own privacy policy. If you offer an online opt-out, you must provide and honor an online opt-out. If you offer an in-store opt-out, you must provide it. To abide by the privacy standards, you must treat the visitor’s phone MAC address as PII information. You must not keep and match the visitor’s MAC address without opt-in and you should make sure that is hashed or transformed when stored.

And, of course, in the EU the tracking guidelines are significantly more restrictive.

In almost all respects, this is identical to the use of cookies in the digital world. And, as with the digital world, it’s not hard to see where the blurry lines are. Using in-store customer journey tracking to improve the store is non-controversial – the equivalent of using first-party cookies to analyze and improve a website. Using appropriately described opt-ins to track and market to identified customers is fine as long as the usage is appropriately disclosed. Selling customer information begins to touch on gray areas. And identifying and marketing to users without opt-in using any kind of device fingerprinting is very gray indeed.

Bottom line? In-store customer tracking and location analytics is ready for prime-time. The technologies work. They can be deployed reasonably and provide genuinely useful data. Deployment is non-trivial but is far from back-breaking. And the proper uses of the data are understood and widely accepted.

In my next post, I’ll take up the analytic problems that have crippled existing solutions and explain how we’ve solved them.

An Overview of In-Store Tracking Technology

How does it work? Can you really do this? Is it legal? Those are the questions that I get asked the most about in-store customer journey tracking. The same kind of questions, to be honest, I used to get fifteen years ago in digital analytics. And when you have to answer questions like these, you know it’s still pretty raw out there. Collection technologies are a core part of measurement – whether it’s tags in digital analytics or PCAP files for in-store customer tracking. Technology matters. And with in-store tracking, the data collection technologies aren’t half-baked, but they aren’t well-cooked either.

Here’s what you need to know:

Collection Technologies

There are four (!) common approaches to in-store customer tracking: camera, wifi, passive network and mobile apps. Each has distinct characteristics and at least some advantages and disadvantages. Camera is pretty easy to understand. The cameras used for in-store measurement are video. Each camera has on-board processors that identify people, “blob” them, and then track them across their field of view. This yields a stream of data that is positionally very accurate and can also identify basic demographics around each visitor. The anonymized data is then passed to a central server where systems like ours can use it.

Your existing WiFi system can also be used to track customer journey data. This works whether or not people login to your access points. Phones regularly ping out looking for a network and those pings – anonymized – can be triangulated to figure out the position. Put those pings together, and you have a journey. One of the best things about WiFi tracking is that almost everybody already has the necessary hardware in place. That means there’s no new installation; and most of the top-tier providers of internet access points make it super easy to route the data directly to your cloud-based system. Often at no additional cost.

Passive network sniffers are small WiFi-like devices designed explicitly for in-store measurement. They work on principles similar to WiFi but they solve some problems that WiFi doesn’t. They track multiple bands, not just passive WiFi pings, and they can deliver better positional accuracy because they can be deployed in very large numbers quite cost-effectively.

Lastly, you can use code inside a mobile application to track the customer journey. Mobile apps can deliver a steady stream of positional data and have the unique benefit of being able to tie that data to the customer’s digital in-app experience.

So what’s not to like?

Well, each technology has some significant issues.

Cameras are expensive, installation is a challenge, each camera only covers a small zone, and camera systems do a remarkably poor job stitching together the customer journey. So as typically delivered, camera systems cost a lot and deliver limited measurement.

WiFi isn’t very accurate positionally – meaning it can’t be used effectively for much beyond door-counting in smaller and mid-size retail spaces. Worse, changes in MAC randomization in the IoS world have essentially eliminated the ability of WiFi systems to passively track customers with Apple devices. That means you either depend on users to connect to your WiFi (which does yield stable measurement) or you only measure your Android customers. Two bad solutions don’t add up to a good one.

Passive network sniffers improve on WiFi in terms of positional accuracy and their ability to fingerprint devices. But they don’t solve those problems perfectly and, of course, they don’t give you the no-installation, no hardware cost convenience that WiFi did.

Measurement using mobile apps? That’s great, just like everything with mobile apps provided you can get customers to actually download the app. Depending on customer app downloads for measurement is inherently a limiting factor.

Bottom line? There are places and times for every technology and there are ways to combine the technologies to yield better results (we do that). But this isn’t measurement nirvana. No solution is perfect and you’ll find plenty of things to hate in any direction you choose.

To get more detail on the ins-and-outs of in-store customer journey tracking technology (and it’s complicated), ping me. I’ll send you a DM whitepaper that gives you everything you need to know to choose wisely!

I’ll tackle practicality and legality next time!