Tag Archives: DM1

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.

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.

An Easy Introduction to In-Store Measurement and Retail Analytics with DM1

My last post made the case that investing in store measurement and location analytics is a good move from a career perspective. The reward? Becoming a leader in a discipline that’s poised to grow dramatically. The risk? Ending up with a skill set that isn’t much in demand. For most people, though, risk/reward is only part of the equation. There are people who will expend the years and the effort to become a lawyer even without liking the law – simply on the basis of its economic return. I’m not a fan of that kind of thinking. To me, it undervalues human time and overvalues the impact of incremental prosperity. So my last and most important argument was simple: in-store measurement and location analytics is fun and interesting.

But there’s not a ton of ways you can figure out if in-store measurement is your cup of tea are there?

So I put together another video using our DM1 platform that’s designed to give folks a quick introduction to basic in-store measurement.

It’s a straightforward, short (3 minute) introduction to basic concepts in store-tracking with DM1 – using just the Store Layout tool.

The video walks through three core tasks for in-store measurement: understanding what customer’s do in-store, evaluating how well the store itself performed, and drilling into at least one aspect of performance drivers with a look at Associate interactions.

The first section walks through a series of basic metrics in store location analytics. Starting with where shoppers went, it shows increasingly sophisticated views that cover what drew shoppers into the store, how much time shoppers spend in different areas, and which parts of the store shoppers engaged with most often:

retail analytics: measuring store efficiency and conversion with DM1

The next section focuses on measures of store efficiency and conversion. It shows how you can track basic conversion metrics, analyze how proximity to the cash-wrap drives impulse conversion, and analyze unsuccessful visits in terms of exit and bounce points.

DM1 Layout Overview Video

Going from what to why is probably the hardest task in behavioral analytics. And in the 3rd section, I do a quick dive into a set of Associate metrics to show how they can help that journey along. Understanding where associates ARE relative to shoppers (this is where the geo-spatial element is critical), when and where Associates create lift, and whether your deployment of Associates is optimized for creating lift can be a powerful part of explaining shopper success.

retail analytics with dm1 - analyzing associate performance, STARs and lift with DM!

The whole video is super-quick (just 3 minutes in total) and unlike most of what I’ve done in the past, it doesn’t require audio. There’s a brief audio introduction (about 15 seconds) but for the rest, the screen annotations should give you a pretty good sense of what’s going on if you prefer to view videos in quiet mode.

I know you’re not going to learn in-store measurement in 3 minutes. And this is just a tiny fraction of the analytic capability in a product like DM1. It’s more of an amuse bouche – a little taste –  to see if you find something enjoyable and interesting.

I’m going to be working through a series of videos intended to serve that purpose (and also provide instructional content for new DM1 users). As part of that, I’m working on a broader overview right now that will show-off more of the tools available. Then I’m going to work on building a library of instructional vids for each part of DM1 – from configuring a store to creating and using metadata (like store events) to a deep-dive into funnel-analytics.

I’d love to hear what you think about this initial effort!

Check it out:

Store Testing & Continuous Improvement

Continuous improvement is what drives the digital world. Whether applied as a specific methodology or simply present as a fundamental part of the background against which we do business, the discipline of change and measure is a fundamental part of the digital environment. A key part of our mission at Digital Mortar is simply this: to take that discipline of continuous improvement via change and measurement and bring it to stores.

Every part of DM1 – from store visualizations to segmentation to funnel analytics – is there to help measure and illuminate the in-store customer journey. You can’t build an effective strategy or process for continuous improvement without having that basic measurement environment. It provides the context that let’s decision-makers talk intelligently about what’s working, what isn’t and what change might accomplish.

But as I pointed out in my last post, some analytic techniques are particularly useful for the role they play in shaping strategy and action. Funnel Analysis, I argued, is particularly good at focusing optimization efforts and making them easily measurable. Funnels help shape decisions about what to change. Equally important, they provide clear guidance about what to measure to judge the success of that change. After all, if you made a change to improve the funnel, you’re going to measure the impact of the change using that same funnel.

That’s a good thing.

One of the biggest mistakes in enterprise measurement (and – surprisingly – even in broader scientific contexts) is failing to commit to your measurement of success when you start an experiment. It turns out that you can nearly always find some measure that improved after an experiment. It just may not be the right measure. If folks are looking for a way to prove success, they’ll surely find it.

Since we expect our clients to use DM1 to drive store testing, we’ve tried to make it easy on both ends of the process. Tools like funnel analysis help analysts find and target areas for improvement. At the other end of the process, analysts need to be able to easily see whether changes actually generated improvement.

This isn’t just for experimentation. As an analyst, I find that one of the most common tasks I have do is compare numbers. By store. By page. By time-period. By customer segment. Comparison provides basic measurement of change and context on that change.

Which makes comparison the core capability necessary for analyzing store tests but also applicable to many analytics exercises.

Though comparison is a fundamental part of the analytic process, it’s surprising how often it’s poorly supported in bespoke analytics tools. It took many years for tools like Adobe’s Workspace to evolve – providing comprehensive comparison capabilities. Until quite recently in digital analytics, you had to export reports to Excel if you wanted to lay key digital analytic data points from different reports side-by-side.

DM1’s Comparison tool is simple. It’s not a completely flexible canvas for analysis. It just takes any analytic view DM1 provides and allows you to use it in a side-by-side comparison. Simple. But it turns out to be quite powerful in practice.

Suppose you’re running a test in Store A with Store B as a control. DM1’s comparison view lets you lay those two Stores side-by-side during the testing period and see exactly what’s different. In this view, I’ve compared two similar stores by area looking at which areas drove the most shopper conversions:

Retail Analytics and Store Testing: Store Comparison in DM1

You can use ANY DM1 visualization in the Comparison. The funnel, the Store Viz or traditional reports and charts. In this view, I’ve compared the Shopper Funnel around a single merchandising category at two different stores. Not only can I see which store is more effective, I can see exactly where in the funnel the performance differences occur:

Retail Analytics and Store Testing: Time Comparison

Don’t have a control store? If you’re only measuring the customer journeys in a single store or if your store is a concept store, you won’t have another store to use as a control. No problem, DM1’s comparison view lets you compare the same store across two different time periods. You can compare season over season or consecutive time periods. You don’t even have to evenly match time periods. Here I’ve compared the October Funnel to Pre-Holiday November:

Retail Analytics and Funnels: Store Testing

Store and Date/Time are the most common type of comparison. But DM1’s comparison tool lets you compare on Segments and Metrics as well. I often want to understand how a single segment is different than other groups of visitors. By setting up a segmentation visualization, I can quickly page through a set of comparison segments while holding my target group constant. In the first screen, I’ve compared shoppers interested in Backpacks with shoppers focused on Team Gear in terms of how effective interactions with Associates are. With one click, I can do the same comparison between Women’s Jacket shoppers and Team Gear:

Funnel Analytics and Store TestingStore Analytics Comparison: Store Testing Segments

The ability to do this kind of comparison in the context of the visualizations is unusual AND powerful. The Comparison tool isn’t the only part of DM1 that supports comparison and contextualization. The Dashboard capability is surprisingly flexible and allows the analyst to put all sorts of different views side by side. And, of course, standard reporting tools like Charts and Table provide significant ways to do comparisons. But particularly when you want to use bespoke visualizations like Funnels and DM1’s store visualizations, having the ability to lay them side by side and quickly adjust metrics and view parameters is extraordinarily useful.

If you want to create a process of continuous improvement in the store, having measurement is THE essential component. Measurement that can help you identify and drive potential store testing opportunities. And measurement that can make understanding the real-world impact of change in all its complexity.

DM1 does both.

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Retail Analytics: Store Visualization and DM1

Location analytics isn’t really about where the shopper was. After all, a stream of X,Y coordinates doesn’t tell us much about the shopper. The interesting fact is what was there – in the store – where the shopper was. To answer most questions about the shopper’s experience (what they were interested in, what they might have bought but didn’t, whether they had sales help or not, and what they passed but didn’t consider), we have to understand the store. In my last post, I explained why the most common method of mapping behavior to the store – heatmaps – doesn’t work very well. Today, I’m going to tackle how DM1 does it differently and (in my humble opinion) much better.

Here are the seven requirements I listed for Store Visualization and where and why heatmaps come up short:

Store Visualization: Heatmaps and retail analytics

Designing DM1’s store visualization, I started with the idea that its core function is to represent how an area of the store is performing. Not a point. An area. That’s an important distinction. Heatmaps function rather like a camera exposure. There’s an area down there somewhere of course – but it’s only at the tiny level of the pixel. That’s great for a photograph where the smaller the pixel the better, but analytically those points are too small to be useful. Besides, store measurement isn’t like taking a picture. The smaller the pixel the more accurate the photo. But our measurement capture systems aren’t accurate enough to pinpoint a specific location in the store. Instead, they generate a location with a circle of error that, depending on the system being used, can actually be quite large. It doesn’t make a lot of sense to pretend that measurement is happening at a pixel location when the circle of error on the measurement is 5 feet across!

This got me thinking along the lines of the grid system used in classic board games I played as a kid. If you ever played those games, you know what I’m talking about. The board was a map (of the D-Day beaches or Gettysburg or all of Europe) and overlaid on the map was a (usually hexagonal) grid system that looked like this:


Units occupied grid spaces and their movement was controlled by grid spaces. The grid became the key to the game – with the map providing the underlying visual metaphor. This grid overlay is obviously artificial. Today’s first person shooter games don’t need or use anything like it, but strategy games like Civ still do. Why? Because it’s a great way to quantize spatial information about things like how far a unit can move or shoot, the distance to the enemy, the direction of an attack, the density of units in a space and much, much more.

DM1 takes this grid concept and applies it to store visualization. Picture a store:

store journey analytics

Now lay a grid over it:

Visualizing Store Data

And you can take any place the shopper spends time and map it to a grid-coordinates:

Mapping customer data to the store

And here’s where it really gets powerful. Because not only can you now map every measurement ping to a quantifiable grid space, you can attach store meta-data to the grid space in a deterministic and highly maintainable way. If we have a database that describes GridPoint P14 as being part of Customer Service on a given day, then we know exactly what a shopper saw there. Even better, by mapping actual traffic and store meta-data to grid-points, we can reliably track and trend those metrics over time. No matter how the shape or even location of a store area changes, our trends and metrics will be accurate. So if grid-point P14 is changed from Customer Service to Laptop Displays, we can still trend Customer Service traffic accurately – before, after and across the change.

That’s how DM1 works.

Here’s a look at DM1 displaying a store at the Section level:

Retail Analytics: Store Visualization in DM1

In this case, the metric is visits and each section is color-coded to represent how much foot traffic the section got. These are fully quantified numbers. You can mouse over any area and get the exact counts and metrics for it. Not that you don’t need a separate planogram to match to the store. The understanding of what’s there is captured right along side the metric visualization. Now obviously, Section isn’t the grid level for the store. We often need to be much more fine-grained. In DM1, you can drill-down to the actual grid level to get a much more detailed view:

Retail Analytics: Store Detail in DM1

How detailed? As detailed as your collection system will support. We setup the grid in DM1 to match the appropriate resolution of your system. You’re not limited to drilling down, though. You can also drill up to levels above a Section. Here’s a DM1 view at the Department level:

Retail Analytics: Store Meta Data and Levels in DM1

In fact, with DM1, you have pretty much complete flexibility in how you describe the store. You can define ANY level of meta-data for each grid-point and then view it on the store. Here, for example, is where promotions were placed in the store:

Retail Analytics: Store Merchandising Data Overlay

DM1 also takes advantage of the Store Visualization to make it easy to compare stores – head to head or the same store over time. The Comparison views shows two stores viewed (in this example) at the Section Level and compared by Conversion Efficiency:

Retail Analytics: Store Comparison in DM1

It takes only a glance to instantly see which Sections perform better and which worse at each store. That’s a powerful viz!

In DM1, pretty much ANY metric can be mapped on the store at ANY meta-data level. You can see visits, lingers, linger rate, avg. time, attributed conversions, exits, bounces, Associate interactions, STARs ratio, Interaction Success Rate and so much more (almost fifty metrics) – mapped to any logical level of the store; from macro-levels like Department or Floor all the way down the smallest unit of measurement your collection system can support. Best of all, you define those levels. They aren’t fixed. They’re entirely custom to the way you want to map, measure and optimize your stores.

And because DM1 keeps an historical database of the layouts and meta-data over time, it provides simple, accurate and easily intelligible trending over time.

I love the store visualization capability in DM1 and I think it’s a huge advance compared to heat-maps. As an analyst, I can tell you there’s just no comparison in terms of how useful these visualizations are. They do so much more and do it so much better that it hardly seems worth comparing them to the old way of doing things. But here it is anyway:

DM1 Retail Analytics Store Visualization Advantages

DM1’s store visualization is one powerful analytic hammer. But as good as they are, this type of store visualization doesn’t solve every problem. In my next post, I’ll show how DM1 uses another powerful visual paradigm for mapping and understanding the in-store funnel!

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