A Deeper Dive in How To Use Digital Mortar’s DM1

Over the last year, we’ve released a string of videos showing DM1 in action. These are marketing videos, meant to show off the capabilities of the platform and give people sense of how it can be used. Last week, though, we pushed a set of product How-To videos out to our YouTube channel. These videos are designed to walk new users through aspects of the product and are also designed to support users of our Sandbox. For quite awhile we’ve had a cloud-based Sandbox that partners can use to learn the product. In the next month or so, we’re going to take that Sandbox to the next level and make it available on the Google Cloud as part of a test drive. That means ANYONE will be able to roll their own DM1 instance for 24 hours – complete with store data from our test areas.

The videos are designed to help users go into the Sandbox and experiment with the product productively.

There are four videos in the initial set and here’s a quick look at each:

Dashboards: When I demo the product, I don’t actually spend much time showing the DM1 Dashboard. Sometimes I don’t show it at all since I tend to focus on the more interesting analytic stuff. But the Dashboard is the first thing you see when you open the product – and it’s also the (built-in) reporting view that most non-analysts are going to take advantage of. The Dashboard How-to walks through the (very simple) process of creating Panels (reports) and Alerts in the Dashboard and shows each type of viz and alert. Alerts, in particular, are interesting. Using Alerts, you can choose to always show a KPI, or have it pop only when a metric exceeds some change or threshold. From my marketing videos, you probably wouldn’t even realize DM1 has this capability, but it’s actually pretty cool.


Workbench: This is a quick tour of the entire Analytics Workbench. Most of this is stuff you do see in my other videos since this is where I tend to spend time. But the How-To video walks through the Left-Navigation options in the Workbench more carefully than I usually do in Marketing Videos and also shows Viz types like the DayMap that I often give short shrift.


Store Configuration: Digital Planograms are at the heart of DM1 and they underlie ALL the reporting in the Analytics Workbench (and are flat out the Viz in the Layout view). We’ve built a very robust point-and-click Configuration tool for building those Planograms. It’s a huge part of the product and a major differentiator. There’s nothing else like it out there. But because it’s more plumbing than countertop, I usually don’t show it at all in marketing videos. The How To vid shows how you can open, edit and save an existing digital planogram and how easy it is to create a new one.


Metadata: The store configurator maps the store and allows you to assign any part of the store to….well that’s where metadata comes in. DM1’s Admin interface includes a meta-data builder where you describe the Sections, Departments, Displays, Functions, Team Areas, etc. that matter to you. Meta-data is what makes basic locational data come alive. And DM1’s very robust capability let’s you define unlimited hierarchies, unlimited levels per hierarchy, and unlimited categories per level. What’s the word of the day around metadata? Unlimited. It’s pretty powerful but it’s really pretty easy to do as well and the How To vid gives you a nice little taste. And holy frig – I forgot to mention that not everyone on my team thought I should say “holy frig” in this video – but I left it in anyway.


It’s really capabilities like the Metadata builder and the Store Configurator that make DM1 true enterprise analytics. They provide the foundational elements that let you manage complex store setups and generate consistently interesting analytic reporting. Even if you’re not a user yet, check em out. If nothing else, you’ll be ready for a Test-Drive!

A Year in Store Analytics

It’s been a little more than a year now for me in store analytics and with the time right after Christmas and the chance to see the industry’s latest at NRF 2018, it seems like a good time to reflect on what I’ve learned and where I think things are headed.

Let’s start with the big broad view…

The Current State of Stores

Given the retail apocalypse meme, it’s obvious that 2017 was a very tough year. But the sheer number of store closings masked other statistics – including fairly robust in-store spending growth – that tell a different story. There’s no doubt that stores saddled with a lot of bad real-estate and muddied brands got pounded in 2017. I’ve written before that one of the unique economic aspects of online from a marketplace standpoint is the absence of friction. That lack of friction makes it possible for one player (you know who) to dominate in a way that could never have happened in physical retail. At the same time, digital has greatly reduced overall retail friction. And that reduction means that shoppers are not inclined to shop at bad stores just to achieve geographic convenience. So the unsatisfying end of the store market is getting absolutely crushed – and frankly – nothing is going to save it. Digital has created a world that is very unforgiving to bad experience.

On the other hand, if you can exceed that threshold, it seems pretty clear that there is a legitimate and very significant role for physical stores. And then the key question becomes, can you use analytics to make stores an asset.

So let’s talk about…

The Current State of In-Store Customer Analytics

It’s pretty rough out there. A lot of companies have experimented with in-store shopper measurement using a variety of technologies. Mostly, those efforts haven’t been successful and I think there are two reasons for that. First, this type of store analytics is new and most of the stores trying it don’t have dedicated analytics teams who can use the data. IT led projects are great for getting the infrastructure in the store, but without dedicated analytics the business value isn’t going to materialize. I saw that same pattern for years in web analytics before the digital analytics function was standardized and (nearly always) located on the business side. Second, the products most stores are using just suck. I really do feel for any analyst trying to use the deeply flawed, highly aggregated data that gets produced and presented by most of the “solutions” out there. They don’t give analysts enough access to the data to be able to clean it, and they don’t to a very good job cleaning it themselves. And even when the data is acceptable, the depth of reporting and analytics isn’t.

So when I talk to company’s that have invested in existing non Digital Mortar store analytics solutions, what I mostly hear is a litany of complaints and failure. We tried it, but it was too expensive. We didn’t see the value. It didn’t work very well.

I get it. The bottom line is that for analytics to be useful, the data has to be reasonably accurate, the analytics platform has to provide reasonable access to the data and you must have resources who can use it. Oh – and you have to be willing to make changes and actually use the data.

There’s a lot of maturing to do across all of these dimensions. It’s really just this simple. If you are serious about analytics, you have to invest in it. Dollars and organizational capital. Dollars to put the right technology in place and get the people to run it. Organizational capital to push people into actually using data to drive decisions and aggressively test.

Which brings me to….

What to invest in

Our DM1 platform obviously. But that’s just one part of bigger set of analytics decisions. I wrote pretty deeply before the holidays on the various data collection technologies in play. Based on what I saw at NRF, not that much has changed. I did see some improvement in the camera side of the house. Time of Flight cameras are  interesting and there are at least a couple of camera systems now that are beginning to do the all-important work of shopper stitching across zones. For small footprint stores there are some viable options in the camera world worth considering. I even saw a couple of face recognition systems that might make point-to-point implementations for analytics practical. Those systems are mostly focused on security though – and integration with analytics is going to be work.

I haven’t written much about mobile measurement, but geo-location within mobile apps is – to quote the Lenox mortgage guy – the biggest no-brainer in the history of earth. It’s not a complete sample. It’s not even a good sample. But it’s ridiculously easy to drop code into your mobile app to geo-locate within the store. And we can take that tracking data and run it into DM1 – giving you detailed, powerful analytics on one of the most important shopper segments you have. It costs very little. There’s no store side infrastructure or physical implementation – and the data is accurate, omni-joinable and super powerful. Small segment nirvana.

The overall data collection technology decision isn’t simple or straightforward for anyone. We’ve actually been working with Capgemini to integrate multiple technologies into their Innovation Center so that we can run workshops to help companies get a hands-on feel for each and – I hope – help folks make the right decision for their stores.

People is the biggest thing. People is the most expensive thing. People is the most important thing. It doesn’t matter how much analytic technology you bring to the table – people are the key to making it work. The vast majority of stores just don’t have store-side teams that understand behavioral data. You can try to create that or you can expand the brief of your digital or omni-channel teams and re-christen them behavioral analytics teams. I like option number two. Why not take advantage of the analytics smarts you actually have? The data, as I’ve said many times before, is eerily similar. We’ve been working hard to beef up partnerships and our own professional services to help too. But while you can use consultants to get a serious analytic effort off the ground, over time you need to own it. And that means deciding where it lives in your organization and how it fits in.

Which I know sounds a lot like…

Everything old is new again

I make no bones about the fact that I dived into store measurement because I thought the lessons of digital analytics mostly applied. In the year sense, I’ve found that to be truer than I knew and maybe even truer than I’d like. Many of the challenges I see in store analytics are the ones we spent more than decade in digital analytics gradually solving. Bad data quality and insufficient attention to making it right. IT organizations focused on collection not use. A focus on site/store measurements instead of shopper measurement.

Some of the problems are common to any analytic effort of any sort. An over-willingness to invest in technology not people (yeah – I know – I’m a technology vendor now I shouldn’t be saying this!). A lack of willingness to change operational patterns to be driven by analytics and measurement and a corresponding challenge actually using analytics. Far too many people willing to talk the talk but unable or unwilling to walk the walk necessary to do analytics and to use it. These are hard problems and it’s only select companies that will ever solve them.

Through it all I see no reason to change the core beliefs that drove me to start Digital Mortar. Shopper analytics is critical to doing retail well. In a time of disruption and innovation, it can drive massive competitive advantage if an organization is willing to embrace it seriously. But that’s not easy. It takes organizational commitment, some guts, good tools and real smarts.

Digital Mortar can provide a genuinely good tool. We can help with the smarts. Guts and commitment? That’s up to you!

The State of Store Tracking Technology

The perfect store tracking data collection would be costless, lossless, highly-accurate, would require no effort to deploy, would track every customer journey with high-precision, would differentiate associates and shoppers and provide shopper demographics along with easy opt-out and a minimal creep factor.

We’re not in a perfect world.

In my last post, I summarized in-store data collection systems across the dimensions that I think matter when it comes to choosing a technology: population coverage, positional accuracy, journey tracking, demographics, privacy, associate data collection and separation, ease of implementation and cost. At the top of this post, I summarized how each technology fared by dimension.

In-store tracking technologies rated

As you can see, no technology wins every category, so you have to think about what matters most for your business and measurement needs.

Here’s our thinking about when to use each technology for store tracking:

Camera: Video systems provide accurate tracking for the entire population along with shopper demographics. On the con-side, they are hard to deploy, very expensive, provide sub-standard journey measurement and no opt-out mechanism. From our perspective, camera makes the most sense in very small foot-print stores or integrated into a broader store measurement system where camera is being used exclusively for total counting and demographics.

WiFi: If only WiFi tracking worked better what a wonderful world it would be. It’s nearly costless and there’s almost no effort to deploy. It can differentiate shoppers and Associates and it provides an opt-out mechanism. Unfortunately, it doesn’t provide the accuracy necessary to useful measurement in most retail situations. If you’re an airport or an arena or a resort, you should seriously consider WiFi tracking. But for most stores, the problems are too severe to work around. With store WiFi, you lose tracking on your iPhone shoppers and you get less coverage on all devices. Worse, the location accuracy isn’t good enough to place shoppers in a reasonable store location. It’s easy to fool yourself about this. It’s free. It’s easy. What could go wrong? But keep two things in mind. First, bad data is worse than no data. Making decisions on bad data is a surefire way to screw up. Second, most of the cost of analytics is people not technology. When you give your people bad tools and bad data, they spend most of their time trying to compensate. It just isn’t worth it.

Passive Sniffer (iViu): There’s a lot to like with this system and that’s why they are – by far – our most common go to solution in traditional store settings. iViu devices provide full journey measurement with good enough accuracy. They cover most of the population and what they miss doesn’t feel significantly biased. The devices are inexpensive and easy to install, so full-fleet measurement is possible and PoC’s can be done very inexpensively. They do a great job letting us differentiate and measure Associates and they provide a reasonable opt-out mechanism for shoppers. Even if this technology doesn’t win in most categories, it provides “good-enough” performance in almost every category.

Combining Solutions

This isn’t necessarily an all or nothing proposition. You can integrate these technologies in ways that (sorta) give you the best of both worlds. We often recommend camera-on-entry, for example, even when we’re deploying an iViu solution. Why? Well, camera-on-entry is cheap enough to deploy, it provides demographics, and it provides a pretty accurate total count. We can use that total to understand how much of the population we’re missing with electronic detection and, if the situation warrants it, we can true-up the numbers based on the measured difference.

In addition, we see real value in camera-based display tracking. Without a very fine-grained RFID mesh, electronic systems simply can’t do display interaction tracking. Where that’s critical, camera is the right point solution. In fact, that’s part of what we demoed at the Capgemini Applied Innovation Exchange last week. We used iViu devices for the overall journey measurement and Intel cameras for display interaction measurement.

Similarly, in large public spaces we sometimes recommend a mix of WiFi and iViu or camera. WiFi provides the in-place full journey measurement that would be too expensive to get at any other way. But by deploying camera at choke-points or iViu in places where we need more accurate positional data, we can significantly improve overall collection and measurement without incurring unreasonable costs.

Summing Up

In a very real sense, we have no dog in this hunt. Or perhaps it’s more accurate to say we back every dog in this hunt We don’t make hardware. We don’t make more money on one system than another. We just want the easiest, best path to getting the data we need to drive advanced analytics. Both camera systems and WiFi have the potential to be better store tracking solutions with improvements in accuracy and cost. We follow technology developments closely and we’re always hoping for better, cheaper, faster solutions. And there are times right now when using existing WiFi or deploying cameras is the right way to go. But in most retail situations, we think the iViu solution is the right choice.

And the fact that their data flows seamlessly into DM1 in both batch and – with Version 2 – real-time modes? From your perspective, that should be a big plus.

Open data systems are a huge advantage when it comes to planning out your data collection strategy. And finding the right measurement software to drive your analytics is – when you get right down to it – the decision that really matters.

And the good news? That’s the easiest decision you’ll ever have to make. Because there’s really nothing else out there that’s even remotely competitive to DM1.

Data Collection for Store Location Analytics: Picking a Technology Winner

Data collection technology is at the heart of in-store customer location analytics. In my past two posts, I’ve described some of the cool analytics and measurement that our second release of DM1 brings to the enterprise. And in a way, this is the only stuff that matters. It’s what you use to solve problems. But you can’t solve those problems and DM1 can’t give you the measurement you need, without a workable data collection technology: a technology that’s reliable, accurate, and cost-effective to deploy. In digital analytics, tagging was that technology. And while tagging can seem mysterious, a basic tag is really nothing more than 20 lines of javascript code that any competent programmer could write in a day. For in-store location analytics, it’s a lot more challenging. It’s so challenging, in fact, that we’ve struggled to find data collection technologies that meet our needs. We’ve engineered the DM1 platform to be hardware and collection neutral. We take data from a variety of sources and we’ll engineer the best possible measurement from that source. But we do have a favorite – and it’s a solution that’s become our go to suggestion for MOST clients. It’s called iViu.

The iViu technology uses passive network sniffers. These little devices track smart-phones (and potentially other electronics like Smart Watches). They triangulate on the signal the device sends out to position the phone. And because they can identify the phone, they are able to track the full shopper journey from just outside the store to cash-wrap or exit. Like almost all in-store tracking devices, they can’t identify who somebody is, though they can track the same device over time. So the iViu data does let us track same-store usage (at least outside of the EU where, to be fully compliant with EU guidelines we throw away device signatures at the end of each day) and even the same shopper at different locations under your real estate portfolio. But it doesn’t tell us who the shopper is or give us a natural join key to household or digital data.

In laying out the basics, I’ve glossed over all the complexity involved – and there’s a lot. In store location analytics, data collection technologies compete along several critical dimensions: coverage, accuracy, journey measurement, demographics, privacy, associate tracking, ease of implementation and cost. Each technology and each vendor has its own unique strengths. I’m going to cover each of these factors, explain where iViu fits in, and summarize why we usually end up choosing their technology. The three main location analytics technology contenders are Camera, Passive Network Sniffers (iViu) and off the shelf WiFi access points.

Population Coverage: Ideally, a counting system will measure every shopper who comes in the store. If a counting system doesn’t collect everyone, it’s important to understand the breadth of its coverage and whether it introduces any deep bias into the measurement. In terms of population coverage, you can’t beat camera systems. They are the best technology around for getting 100% coverage. They rarely miss anyone and if anything, their pitfall is that they can be prone to overcounting.  All electronic mechanisms are limited to tracking shoppers with smart-phones. In the U.S. (and most of the world), that isn’t much of a problem. It does mean you likely won’t be counting smaller kids. Of more significance, however, is that the phone must be an emitter – with either its Bluetooth or WiFi turned on. Best estimates are that about 15% of people don’t enable those signals. So an iViu device will typically get signals from about 80-85% of the population. And we think that’s a relatively unbiased group – probably a more accurate sample than what we get in the digital world using cookies. One big advantage to the iViu system versus other electronic systems is that iViu does a much better job tracking iPhones or other devices that employ MAC randomization. Why are iPhones an issue? Beginning with iOS8, Apple started randomizing the MAC address of the device when it pings out to the world. WiFi access points and most electronic detectors use the MAC address as the device identifier. So every time an iOS device pings a typical collector, it will look like a different device. In practice, this means that WiFi based coverage will lose all iOS devices that aren’t connected to your network. That’s a pretty huge problem. In addition, iViu devices are dedicated to measurement and they do a much better job of listening than standard WiFi access points. In side by side tests, iViu devices pick up more shoppers, more consistently.

So for Population Coverage, we see it this way:

Camera: Best

iViu: Good

WiFi: Poor

Accuracy: There are lots of different ways to think about accuracy and many different use-cases and data quality problems. I’m going to focus here on basic positional accuracy – the ability to locate the shopper at particular place in the store. Positional accuracy isn’t vital for applications like door-counting – but our DM1 platform absolutely depends on it. We map location to the store and report and segment based on shopper interest. If the mapped location is wrong, our analytics are wrong. With camera systems, each camera covers a specific area of the store and it’s relatively easy to map the location of the person to the specific part of the area the camera covers. For electronic tracking, it’s more complicated. Most electronic systems work by using either (or both) the relative signal strength detected and a triangulation of the signal across devices. But while the methods used are similar, the end result is quite different depending on the implementation and the vendor. Using the iViu devices, we can usually get an accuracy of location down to about 1.5 meters. That’s almost always good enough for the type of measurement we do with DM1. With off-the-shelf WiFi systems, the accuracy is more like 10 meters (and that’s often best case). We can live with that level of accuracy if we’re doing measurement on a mall, stadium, airport or a resort. But for a store, it just isn’t good enough to work with.

So for accuracy we see it this way:

Camera: Good

iViu: Good

WiFi: Poor

Journey Measurement: All the interesting questions in shopper and store optimization involve the journey – the ability to track the shopper visit across the store. It’s fundamental to DM1 and big part of what our analytics bring to the table. In theory, all the data collection technologies should be able to track the journey. In practice, however, we’ve found that electronic systems do this vastly better than the current crop of camera systems. Electronic systems have the fairly easy task of distinguishing one phone from another. Camera systems have to track people. And while there have been dramatic improvements in facial recognition, those improvements are challenged by real-world measurement situations and often haven’t found their way into workable/available technology. A typical camera only covers a 20×20 foot area. So even a modest mall store will require a goodly number of cameras to track its full footprint. As shoppers move from zone-to-zone, the system has to be able to determine that it’s the same person. Camera systems suck at this. Suppose a camera system gets a zone crossing right 90% of the time. That sounds pretty good, until you realize that you have about a 50% chance of following a shopper across 100 feet of your store. It’s because of this limitation that so many camera-based systems are, essentially, zone counters. They count the shoppers in an area and their linger time. They don’t count journeys. It’s not a software problem. It’s a collection problem.

Camera: Poor

iViu: Good

WiFi: Good

Demographics: We’re behavioral analysts, but while we tend to believe that real behavior trumps demographics, it doesn’t mean we think demographics don’t matter. Age and gender are nearly always interesting analytic variables and it’s a distinct advantage to be able to collect them. The scorecard here is simple. Camera does a pretty good job of this. No electronic system does this at all.

Camera: Good

iViu: No

WiFi: No

Privacy: There’s an undeniable creep factor involved with in-store tracking and it can be a legitimate barrier to measurement. All of the technologies involved here do essentially the same thing and all of them do it anonymously. Some of our clients have preferred video to electronic measurement on privacy grounds. I frankly don’t understand that thinking, but privacy is an area where the arguments turn more on perception than reality. Both technologies are providing the same basic measurement and the only significant difference is that electronic measurement provides an opt-out mechanism and video doesn’t. For electronic measurement, there’s a national, online opt-out registry (and, of course, you can always turn off phone WiFi too). There is no equivalent system to opt-out of video measurement and if there was, I imagine it would involve sending in your face – which kind of sucks. I do think video benefits from the fact that most stores have already deployed it for security purposes (though the camera’s you use are usually different), but it’s hard to understand why it’s better to measure people one way than another when the implications for them are identical. I’m going to call this one a wash.

Camera: Ok

iViu: Ok

WiFi: Ok

Associate Tracking: Coming from the digital world, our focus when we started Digital Mortar was all about shoppers. But we quickly realized that tracking associates was critical. First, because associates are a huge part of the customer experience. You can’t really measure shopper journeys unless you can measure when and if they talked to an associate. But there’s also real value in understanding whether you had enough staff on the floor. If they were in the right places. If the type of associate, their training or experience or tactics, made a substantial performance difference. With electronic tracking, it’s pretty easy to measure associates (they just have to carry a device). In most cases, you don’t even have to register that device. DM1 automatically detects devices with employee behaviors and classifies them appropriately. We do that to minimize compliance issues. With camera, it’s a different story. Camera systems either conflate employees with shoppers (which is a disaster) or use supplementary electronic means to remove them from the data. Not only does this introduce complexity into the system, it makes it much harder to track and measure interactions. We’ve also seen minor compliance issues (a few associates forgetting to pick up their tags occasionally) have significant negative implications on measurement quality. It’s worth mentioning here that if you only want to measure associates, there are other technologies worth considering that require code on a mobile device but which will provide VERY accurate and detailed associate tracking.

Camera: Poor

iViu: Good

WiFi: Fair

Ease of Implementation: Let’s face it, having to put hardware into stores is a hassle. One of the big benefits to WiFi based measurement is that it can take advantage of existing access points that were put there to provision WiFi to customers or to support store functions. Every other form of measurement takes new hardware and store installation. But there is a pretty big difference in the level of effort required. It takes a lot of cameras to cover a store. The cameras have to be in the ceiling and they have to precisely placed. It’s real work to get right and it’s often expensive, involves some degree of retrofitting and is time consuming. An iViu device will cover something like 10x the area of a camera – so you need a lot less of them. It’s easier to install. It doesn’t have to precisely placed and it doesn’t have to ceiling mounted. We’ve put iViu devices under tables, on top of or behind displays, on pillars and even in drawers. They do require power (plug or PoE), but they’re a snap to setup – it’s pretty much plug and play – and we’ve found that we can install in most locations without huge difficulty.

Camera: Poor

iViu: Fair

WiFi: Good

Cost: You know how athletes who sign huge new contracts always say “It wasn’t about the money” and you’re thinking – “Of course it was about the money”? Money matters. Realistically, a store can only afford to spend so much on measurement. Worse, the more you sink into the hardware, the less you can spend on the stuff that actually makes a difference – the analytics software and the people to drive it. At Digital Mortar, we’re believers in comprehensive measurement. We want to measure every store – not one store out of a hundred. And if you’re trying to measure Associates, optimize locally, or do real-time interactions, measuring a single store just doesn’t cut it. So having a measurement technology that’s cheap enough to go fleet-wide? We think that’s priceless. From that perspective, you can’t beat WiFi since it’s usually already in place and even if you have to provision it, the cost is reasonable and there are extra benefits. But, as noted, there’s pretty limited analytics you can do with a 10 meter margin of error. For a system that provides robust measurement, we like the fact that iViu devices are very cost-effective. The hardware for most stores costs less than $5k (that’s to buy the devices not an annual cost). Even very, very large stores will cost less than $20K per store. Camera systems are often far more costly – to the point that they are generally impractical for very large stores and make deploying to a large number of stores impossible.

Camera: Poor

iViu: Good

WiFi: Very Good

As you can see, there isn’t one solution that’s perfect in every respect. And in the real world, we often find reasons to deploy each technology. In Part 2 of this post, I’ll summarize the findings, explain why – given it’s overall profile – iViu makes sense for most retail stores, and also talk a little bit about the ways that you can blend technologies to get the best of each world.

A Peek Into the Future of Store Analytics

We just did our first non-incremental release of the DM1 store analytics platform since we brought it to market. It brings new analytics views to the Workbench, a host of UI and analytic tweaks, new cloud options and, best of all, real-time and full-store playback functionality to the product. Real-time creates a bevy of opportunities to operationalize measurement in both operations and marketing. So DM1 can drive more value, faster. What’s next? At the end of my last post, I described some of the juicier features slated for upcoming release: a real-time, dedicated Store Managers console, full pathing and even some machine learning applications. But I want to step back from a feature list and talk a bit about where we see the DM1 platform headed and how we try to balance and prioritize new functionality as we shape the product. It’s hard to do because we love all the new features.

From a personal perspective, no part of building Digital Mortar is more interesting or more intellectually challenging than building DM1. On the one hand, building SaaS systems in the cloud today is incredibly gratifying. You can build powerful, beautiful stuff so much faster and easier than back in the ‘80s when I first started programming or even in the late ‘90s when Semphonic took an abortive shot at building a web analytics tool. But an embarrassment of riches is still an embarrassment. Throwing stuff at a wall doesn’t make for a coherent product road-map. So when we think about new feature prioritization for DM1, we start with our core product vision.

DM1 is designed to be the measurement backbone of the store. We see the store as a learning machine with the core methodologies we brought from digital: continuous improvement through test & measure driven by analytics based on behavioral segmentation (what people actually do) and the ability to break-down shopper journeys into discrete, analyzable steps.

That core vision shaped our initial DM1 release (what Valley folks love to call the MVP – an acronym that is surely designed to suggest the sporting world’s Most Valuable Player award but actually stands for Minimally Viable Product). When DM1 went live, just about every piece of it was specifically targeted to this core vision. It provided direct access to a bunch of journey metrics that described how the store performed, it included basic shopper segmentation to analyze cross-sell patterns and do simple day-time parting, and it included a pretty robust funnel tool for breaking down shopper journeys into individual (step) components.

Let’s call this basic shopper-journey, store measurement system DM1’s core. It’s the engine that drives and integrates every other aspect of what the product might eventually do. Coming out of the digital analytics world, we tend to map a lot of our thinking into that model. The DM1 core is the equivalent of Adobe Analytics in the broader Adobe Marketing Suite. It’s the analytic and measurement engine.

Right now, most of our focus will continue to be on building that core engine.

Of the significant features we have slated for short term development, here are the ones that contribute directly to the core function of the program:

New and More Comprehensive Associate Reporting: Track individual and team performance on the floor with optional integration to VoE, employee meta-data, and VoC from in-store visits. DM1 already includes a lot of generalized Associate analytics, but this report will distill that into a set of reports that are much easier to digest, understand and act on.

D3 Integration: DM1’s current charting capabilities are pretty basic. We use an off-the-self package and we provide straightforward bar and line charting. Probably the best part of the charting is how seamlessly DM1 picks the best chart types, intelligently maps to separate axes, and lets you easily combined “like indices” in a single chart. But we’re far from pushing the envelope on what we can do visually and by using D3 for our charting package, we’ll be able to considerably expand the range of our visualizations and support even deeper on-chart customization.

Full Pathing: We’ve been tinkering since day 1 with ways to bring full pathing to store analytics. On the one hands, it’s not really all that hard. The amount of data is much less than we’re used to in digital. Our engine passes the data exhaustively with every query, so full pathing isn’t going to strain us from a performance perspective. But stores don’t have discrete waypoint like pages on a Website which makes each shopper’s path potentially a snowflake. We’ve tried various strategies to meaningfully aggregate paths within the store and I think we’ll be able to produce something that’s genuinely interesting and useful in the next few months. This will supplement the funnel analytics and provide richer and more varied analysis of how shoppers flow through the store.

Segmentation Builder: DM1’s current segmentation capabilities are limited to basic filtering on a set of pre-defined types. It does provide a pretty nice ability to segment on uploaded meta-data, but you can’t build more complex segments using Boolean logic or Regex. Not only do I think that’s important for a lot of analytic purposes, it’s also something we can support fairly easily.

Machine Learning for Segmentation: On that same theme, I’m a believer in data-driven segmentation. Data-driven segmentation uses more data, is richer, more reflective of reality, and usually more interesting than rule-based segmentations even if produced in a fairly rich builder. Both GCP and Azure offer pretty amazing ML capabilities that will allow us to build out a good data-driven segmentation capability for DM1. I think the harder part is doing the UI justice.

Store Groups: DM1 handles lots of stores, but right now, the store is the ultimate unit of analysis. We don’t support regions or fleet-wide aggregations. There are a lot of analytic and reporting problems that would be solved or made much easier with Store Groups. It’s a capability we’ve considered since Day 1 and sooner rather later I except it to be in the product.

Fully Integrated Dashboard: V2 didn’t do much to evolve the dashboard capability of the product, but we have a pretty clear direction in mind. In the next release, I expect the Dashboard to be capable of containing ANY Workbench view. That’s a simple elegant way to let analysts customize the dashboard to their taste and produce exactly what they need for the business. I remember a computer scientist from the original deep-blue chess program saying something to the effect that “Exhaustive search means never having to say your sorry”. No matter how much capability we build out in the dashboard, analysts are always going to want something from the Workbench if it does more. So I think it just makes sense to unify them and let the Dashboard do EVERYTHING the Workbench does.

Not everything we have in mind is about the core though. In the next few months, we plan to release a Store Manager Console based on the new real-time capabilities. The Store Manager Console is a whole new companion capability for DM1 targeted to a fundamentally different type of user. DM1 core is for the corporate analyst. It’s a big, powerful enterprise measurement tool. It’s definitely more than most Store Managers could handle.

But while the centralized model works really well in digital analytics (since Websites are wholly centralized), it’s less than ideal in the store world. There are a lot of decisions that need to happen locally. DM1’s Store Manager console will continuously monitor the store. It will keep track of shopper patterns, monitor queue times, alert if shoppers aren’t getting the help they need, and make it easy for Store Managers to allocate staff most effectively and message them when plans need to change.

It’s a way to bring machine smarts and continuous attention to the Store Managers iPad. Most of the capabilities we’re baking into the Store Manager Console (SMC) were actually delivered in V2. The real-time store tracking, simulator and Webhooks for messaging are the core capabilities we needed to deliver the SMC and were always a part of that larger vision.

As I hope our rate of progress has already made clear, we’re ambitious. Software design usually embodies deep trade-offs between functionality and ease-of-use or performance. Those trade-offs are challenging but not inevitable. We’ve seen how digital analytics tools like Google Analytics and data viz tools like Tableau have sometimes been able to step outside existing paradigms to deliver more functionality side-by-side with better usability. Most of what we’ve done so far in DM1 is borrow creatively from two decades worth of increasing maturity in digital analytics. Still, tools like our Funnel Viz and – particularly – our Store Layout Viz have tackled location/store specific problems and genuinely advanced the state-of-the-art. As we tackle pathing and machine learning, I hope to do quite a bit more of that and find ways to bring more advanced analytics to the table even while making DM1 easier to use.

Bringing Real-time Analytics to the Store

When we released the first full production version of DM1 in May, it was a transformational leap in customer location analytics. Now, six months later, we’re releasing the first major upgrade to DM1 – and it’s a doozy. We’re adding powerful new approaches to improving the store and driving actionable results while putting the competition even further in the rear-view mirror (they’re back there somewhere). Some of the coolest new features include:

Real-time View:  See and track what’s happening in the store as it happens

Real-time Messaging: Integrate with Associate and Shopper Mobile devices based on what’s happening right now

Playback: See what happened in the store using a time-lapse simulation of actual shopper behavior

Week-Time: A cool new visualization that shows a Day-Time part analysis for ANY metric or segment

Flexible Cloud Hosting: Azure or Google Cloud, your choice.

And that’s just the big stuff. There have been a host of small tweaks, fixes, performance enhancements and visual improvements in the intervening months.

That initial version of DM1 had a pretty remarkable feature set for a V1 product in a brand new market. It delivered a revolutionary store visualization tool that mapped its powerful metrics into the store at any level of abstraction – from Display to Section to Department to Floor to Store. And the metric set if provided shattered existing door-counting paradigms by delivering real journey metrics. What shoppers did first. Where they spent the most time. Every place they shopped. What got the most consideration. What converted. What didn’t. And DM1 V1 integrated a rich set of Associate tracking metrics into the basic product as well. Associate presence, intra-day STARs, interaction rates and times, and associated lift. DM1 also delivered powerful grid-based reporting and charting, a really cool funnel analytics tool and a powerful side-by-side comparison tool.

With the new features, we’ve focused on adding a set of capabilities that extend DM1 deeper into the store and make it easier to integrate into broader store marketing and operations efforts. The biggest part of that is, of course, real-time.

We’ve always collected store data in real-time – and all of our collection technologies support near real-time data about the shopper. But in the V1 release, everything we did was batch processing and next-day analytics. That isn’t just because batch processing is easier (though that did matter to us). It was also because many years of experience in digital analytics convinced us that the applications for real-time analytics were fairly rare. There was a time when real-time reporting was a huge part of the digital analytics sales process. But in the end, the dominant tools in digital analytics deliver intra-day but far from real-time analytics. So we figured if a very mature market like digital analytics could do without real-time, we didn’t need it for our V1 release.

But the store is different beast than a Website, and we’re finding that having a real-time view of what’s happening makes it possible to add value at the Store Manager level AND support both CRM and queue management applications that help improve the customer experience.

So real-time became a central piece of our V2 release. And once we built out the core capability, we took advantage of it in multiple ways.

The Realtime view show’s what’s happening right now in the store. Associates and Shoppers are shown (using different symbols) and you can quite literally track exactly where they are and where they are going. We even color-code Shoppers to make it easy to identify how long they’ve been in the store.

Check it out:

realtime, location analytics, store analytics, store traffic, DM1, Digital Mortar

Pretty cool. The Playback capability provides the same view but processes historical data. Since it’s historical, it can collapse time periods into a time-lapse view. So you can see an hour or a day in five minutes.

Being able to see real-time data is cool and highly useful for folks on the ground, but I won’t claim the analytic implications are enormous. What is enormous, though, is the capability to tie DM1’s tracking and measurement into real-time messaging systems. We’ve built a straightforward Webhook messaging interface that lets you get three kinds of data out of DM1: current metric data (for uses like queue length), real-time shopper data, and real-time Associate data.

The real-time shopper data can be based off any customer key we have. And it provides a CRM view that includes Entry Time, Total-Time in Store, Presence and Lingers by Section, Current Location, Time since Interaction, and Associate ID of last interaction. You can use this data to generate real-time messages via your mobile application or in-store beacons to the shopper.

As cool as this shopper interface is, I’ve long been a believer that messaging Associates is even more fundamental and important. With shoppers, opt-in is always a challenge. And barring a truly compelling application, I think it’s tough to get enough opt-ins for messaging to make it impactful. But Associates are increasingly being armed with digital tools that allow them to do more (like distributed PoS) and serve the customer better. Being able to optimize Associate interactions with real-time data and positioning is a huge leap forward in operational sophistication.

In addition to real-time, we’ve made a boatload of analytic enhancements too. And one of my favorite new views is the WeekTime report. We’d already build a custom report around staffing that laid out STARs (Shopper to Associate Ratio) by day of week and time of day (down to the hour). But that report wasn’t a thing of beauty and, in any case, it was specific to the STARs metric.

Because Day-time parting is so fundamental to the store, we wanted a generalized analytics tool that would do the job. The WeekTime tool lays out any metric by day-of-week and time-of-day.

Here are some examples of the WeekTime tool. The first view shows when the store had the most shoppers. The second view shows when shoppers spent the most time in the store.

Shopper Traffic by Day-TimeAvg. Shop Time by Day-Time
Day-Time Traffic View for DM1 retail analyticsDM1 Avg. Time by Shopper for Store Analytics

And like all DM1 workbench tools, the Weektime tools is driven by our big data event-level engine so it supports integrated segmentation across any time-period. It’s a really nice analytic addition to the Workbench and the Dashboard view. It makes it much easier to quickly visualize and understand how day-time parts are driving performance on any measurement.

One of the biggest changes we made in V2 isn’t functional but environmental. We built DM1 on Azure and it’s been a great platform. But we’ve seen that our clients are going in all sorts of directions in an incredibly competitive cloud marketplace. And if the rest of your infrastructure is on Google Cloud (GCP), then it just makes sense that DM1 live there too. So in V2, we offer the choice of cloud provider. We’ve ported the entire platform into GCP and – as a bonus gift to ourselves – made the deployment process a lot more automated and easier for us. Microsoft Azure or Google GCP – it’s now your choice. And it’s just part of making sure DM1 is the most technologically sophisticated AND seamless platform around.

V2 is another big step for us. But we’re just getting started. I keep an ever-growing Trello board of new features and some of the most exciting stuff to come includes a full real-time Store Manager tool, a much more comprehensive and beautiful Associate performance report, a store-change report that automatically shows you the impact of every store change in a period of time, the integration of D3 into our charting capability, a full pathing tool, a robust segmentation builder, and even an initial foray into machine learning.

Can hardly wait!