Tag Archives: digital analytics

The Role of General Purpose BI & Data Viz Tools for In-Store Location Analytics and Shopper Measurement

One of the most important questions in analytics today is the role for bespoke measurement and analytics versus BI and data visualization tools. Bespoke measurement tools provide end-to-end measurement and analytics around a particular type of problem. Google Analytics, Adobe Analytics, our own DM1 platform are all examples of bespoke measurement solutions. Virtually every industry vertical has them. In health care, there are products like GSI Health and EQ Health that are focused on specific health-care problems. In hospitality, there are solutions like IDeaS and Kriya that focus on revenue management. At the same time, there are a range of powerful, general purpose tools like Tableau, Spotfire, Domo, and Qlik that can do a very broad range of dashboarding, reporting and analytic tasks (and do them very well indeed). It’s always fair game to ask when you’d use one or the other and whether or not a general purpose tool is all you need.


It’s a particularly important question when it comes to in-store location analytics.  Digital analytics tools  grew up in a market where data collection was largely closed and at a time when traditional BI and Data Viz tools had almost no ability to manage event-level data. So almost every enterprise adopted a digital analytics solution and then, as they found applications for more general-purpose tools, added them to the mix. With in-store tracking, many of the data collection platforms are open (thank god). So it’s possible to directly take data from them.


Particularly for sophisticated analytics teams that have been using tools like Tableau and Qlik for digital and consumer analytics, there is a sense that the combination of a general purpose data viz tool and a powerful statistical analysis tool like R is all they really need for almost any data set. And for the most part, the bespoke analytics solutions that have been available are shockingly limited – making the move to tools like Tableau an easy decision.


But our DM1 platform changes that equation. It doesn’t make it wrong. But I think it makes it only half-right. For any sophisticated analytics shop, using a general purpose data visualization tool and a powerful stats package is still de rigueur. For a variety of reasons, though, adding a bespoke analytics tool like DM1 also makes sense. Here’s why:


Why Users Level of Sophistication Matters

The main issue at stake is whether or not a problem set benefits from bespoke analytics (and, equally germane, whether bespoke tools actually deliver on that potential benefit). Most bespoke analytics tools deliver some combination of table reports and charting. In general, neither of these capabilities are delivered as well as general purpose tools do the job. Even very outstanding tools like Google Analytics don’t stack up to tools like Tableau when it comes to these basic data reporting and visualization tasks. On the other hand, bespoke tools sometimes make it easier to get that basic information – which is why they can be quite a bit better than general purpose tools for less sophisticated users. If you want simple reports that are pre-built and capture important business-specific metrics in ways that make sense right off the bat, then a bespoke tool will likely be better for you. For a reasonably sophisticated analytics team, though, that just doesn’t matter. They don’t need someone else to tell them what’s important. And they certainly don’t have a hard time building reports in tools like Tableau.


So if the only value-add from a bespoke tool is pre-built reports, it’s easy to make the decision. If you need that extra help figuring out what matters, go bespoke. If you don’t, go general purpose.


But that’s not always the only value in bespoke tools.



Why Some Problems Benefit from Bespoke

Every problem set has some unique aspects. But many, many data problems fit within a fairly straightforward set of techniques. Probably the most common are cube-based tabular reporting, time-trended data visualization, and geo-mapping. If your measurement problem is centered around either of the first two elements, then a general purpose tool is going to be hard to beat. They’ve optimized the heck out of this type of reporting and visualization. Geo-mapping is a little more complicated. General purpose tools do a very good job of basic and even moderately sophisticated geo-mapping problems. They are great for putting together basic geo-maps that show overlay data (things like displaying census or purchase data on top of DMAs or zip-codes). They can handle but work less well for tasks that involve more complicated geo-mapping functions like route or area-size optimization. For those kinds of tasks, you’d likely benefit from a dedicated geo-mapping solution.


When it comes to in-store tracking, there are 4 problems that I think derive considerable benefit from bespoke analytics. They are: data quality control, store layout visualization and associated digital planogram maintenance, path analysis, and funnel analysis. I’ll cover each to show what’s at stake and why a bespoke tool can add value.



Data Clean-up and Associate Identification

Raw data streams off store measurement feeds are messy! Well, that’s no surprise. Nearly all raw data feeds have significant clean-up challenges. I’m going to deal with electronic data here, but camera data has similar if slightly different challenges too. Data directly off an electronic feed typically has at least three significant challenges:


  • Bad Frame Data
  • Static Device Identification
  • Associate Device Identification


There are two types of bad frame data: cases where the location is flawed and cases where you get a single measurement. In the first case, you have to decide whether to fix the frame or throw it away. In the second, you have to decide whether a single frame measurement is correct or not. Neither decision is trivial.


Static device identification presents it’s own challenge. It seems like it ought to be trivial. If you get a bunch of pings from the same location you throw it away. Sadly, static devices are never quite static. Blockage and measurement tend to produce some movement in the specific X/Y coordinates reported – so a static device isn’t remotely still. This is a case where our grid system helps tremendously. And we’ve developed algorithms that help us pick out, label and discard static devices.


Associate identification is the most fraught problem. Even if you issue employee devices and provide a table to track them, you’ll almost certainly find that many Associates carry additional devices (yes, even if it’s against policy). If you don’t think that’s true, you’re just not paying attention to the data! You need algorithms to identify devices as Associates and tag that device signature appropriately.


Now all of these problems can be handled in traditional ETL tools. But they are a pain in the ass to get right. And they aren’t problems that you’ll want to try to solve in the data viz solution. So you’re looking at real IT jobs based around some fairly heavy duty ETL. It’s a lot of work. Work that you have to custom pay for. Work that can easily go wrong. Work that you have to stay on top of or risk having garbage data drive bad analysis. In short, it’s one of those problems it’s better to have a vendor tackle.



Store Layout Visualization

The underlying data stream when it comes to in-store tracking is very basic. Each data record contains a timestamp, a device id, and X,Y,Z coordinates. That’s about it. To make this data interesting, you need to map the X,Y,Z coordinates to the store. To do that involves creating (or using) a digital planogram. If you have that, it’s not terribly difficult to load that data into a data viz tool and use it as the basis for aggregation. But it’s not a very flexible or adaptable solution. If you want to break out data differently than in those digital planograms, you’ll have to edit the database by hand. You’ll have to create time-based queries that use the right digital layouts (this is no picnic and will kill the performance of most data viz tools), and you’ll have to build meta-data tables by hand. This is not the kind of stuff that data visualization tools are good at, and trying to use them this way is going to be much harder – especially for a team where a reasonable, shareable workflow is critical.


Contrast that to doing the same tasks in DM1.


Digital Mortars DM1 retail analytics and shopper tracking - digital planogram capabilityDM1 provides a full digital store planogram builder. It allows you create (or modify) digital planograms with a point and click interface. It tracks planograms historically and automatically uses the right one for any given date. It maintains all the meta-data around a digital planogram letting you easily map to multiple hierarchies or across multiple physical dimensions. And it allows you to seamlessly share everything you build.


Digital Mortars DM1 retail analytics and shopper tracking - store layout and heatmapping visualizationOnce you’ve got those digital planograms, DM1’s reporting is tightly integrated. It’s just seamless to display metrics across every level of metadata right on the digital planogram. What’s more, our grid model makes the translation of individual measurement points into defined areas seamless and repeatable at even fine-grained levels of the store. If you’re relying on pre-built planograms, that’s just not available. And keep in mind that the underlying data is event-based. So if you want to know how many people spent more than a certain amount of time at a particular area of the store, you’ll have to pre-aggregate a bunch of data to use it effectively in a tool like Tableau. Not so in DM1 where every query runs against the event data and the mapping to the digital planogram and subsequent calculation of time spent is done on the fly, in-memory. It’s profoundly more flexible and much, much faster.



Path Analysis

Pathing is one of those tasks that’s very challenging for traditional BI tools. Digital analytics tools often distinguished themselves by their ability to do comprehensive pathing: both in terms of performance (you have to run a lot of detailed data) and visualization (it’s no picnic to visualize the myriad paths that represent real visitor behavior). Adobe Analytics, for example, sports a terrific pathing tool that makes it easy to visualize paths, filter and prune them, and even segment across them. Still, as nice as digital pathing is, a lot of advanced BI teams have found that it’s less useful than you might think. Websites tend to have very high cardinality (lots of pages). That makes for very complex pathing – with tens of thousands or even hundreds of thousands of slightly variant paths adding up to important behaviors. Based on that experience, when we first built DM1, we left pathing on the drawing board. But it turns out that pathing is more limited in a physical space and, because of that, actually more interesting. So our latest DM1 release includes a robust pathing tool based on the types of tools we were used to in digital.

Digital Mortars DM1 retail analytics and shopper tracking - Full Path Analysis

With the path analysis, you start from any place in the store and you can see how people got there and where they went next. Even better, you can keep extending that view by drilling down into subsequent nodes. You can measure simple footpath, or you can look at paths in terms of engagement spots (DM1 has two different metrics that represent increasing levels of engagement) and you can path at any level of the store: section, department, display…whatever.

And, just like the digital analytics tools, you can segment the paths as well. We even show which paths had the highest conversion percentages.


Sure, you could work some SQL wizardry and get at something like this in a general purpose Viz tool. But A) it would be hard. B) it would slow. And C), it wouldn’t look as good or work nearly as well for data exploration.



Funnel Analysis

Digital Mortars DM1 funnel analytics for retail and shopper tracking

When I demo DM1, I always wrap-up by showing the funnel visualization. It shows off the platforms ability to do point to point to point analysis on a store and fill in key information along the way. Funnel analysis wraps up a bunch of stuff that’s hard in traditional BI. The visualization is non-standard. The metrics are challenging to calculate, the data is event-driven and can’t be aggregated into easy reporting structures, and effective usage requires the ability to map things like engagement time to any level of meta-data.

Digital Mortar's DM1 retail analytics shopper tracking funnel analytics

In the funnels here, you can see how we can effectively mix levels of engagement: how long people spent at a given meta-data defined area of the store, whether or not they had an interaction, whether they visited (for any amount of time) a totally different area of the store, and then what they purchased. The first funnel describes Section conversion efficiency. The second looks at the cross-over between Mens/Womens areas of the store.

And the third traces the path of shoppers who interacted with Digital Signage. No coding necessary and only minutes to setup.


That’s powerful!


As with path analysis, an analyst can replicate this kind of data with some very complicated SQL or programmatic logic. But it’s damn hard and likely non-performance. It’s also error-prone and difficult to replicate. And, of course, you lose the easy maintainability that DM1’s digital planograms and meta-data provide. What might take days working in low-level tools takes just a few minutes with the Funnel tool in DM1.



Finally, Don’t Forget to Consider the Basic Economics

It usually costs more to get more. But there are times and situations where that’s not necessarily the case. I know of large-scale retailers who purchase in-store tracking data feeds. And the data feed is all they care about since they’re focused on using BI and stats tools. Oddly, though, they often end up paying more than if they purchased DM1 and took our data feed. Odd, because it’s not unusual for that data feed to be sourced by the exact same collection technology but re-sold by a company that’s tacking on a huge markup for the privilege of giving you unprocessed raw data. So the data is identical. Except even that’s not quite right. Because we’ve done a lot of work to clean-up that same data source and when we process it and generate our data feed, the data is cleaner. We throw out bad data points, analyze static and associate devices and separate them, map associate interactions, and map the data to digital planograms. Essentially all for free. And because DM1 doesn’t charge extra for the feed, it’s often cheaper to get DM1 AND feed than just somebody else’s feed. I know. It makes no sense. But it’s true. So even if you bought DM1 and never opened the platform, you’d be saving money and have better data. It would be a shame not to use the software but…it’s really stupid to pay more for demonstrably less of the same thing.


Bottom Line

I have a huge amount of respect for the quality and power of today’s general purpose data visualization tools. You can do almost anything with those tools. And no good analytics team should live without them. But as I once observed to a friend of mine who used Excel for word processing, just because you can do anything in Excel doesn’t mean you should do everything in Excel! In store analytics, there are real reasons why a bespoke analytics package will add value to your analytics toolkit. Will any bespoke solution replace those data viz tools? Nope. Frankly, we don’t want to do that.


I know that DM1’s charting and tabular reporting are no match for what you can do easily in those tools. That’s why DM1 comes complete with a baked-in, no extra charge data feed of the cleaned event-level data and a corresponding visitor-level CRM feed. We want you to use those tools. But as deep analytics practitioners who are fairly expert in those tools, we know there’s some things they don’t make as easy as we’d like. That’s what DM1 is designed to do. It’s been built with a strong eye on what an enterprise analyst (and team) needs that wouldn’t be delivered by an off-the-shelf BI or data viz tool.


We think that’s the right approach for anyone designing a bespoke analytics or reporting package these days. Knowing that we don’t need to replace a tool like Tableau makes it easier for us to concentrate on delivering features and functionality that make a difference.

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.

Connecting Marketers to Machine Learning: A Traveler’s Guide Through Two Utterly Dissimilar Worlds

Artificial Intelligence for Marketing by Jim Sterne

There are people in the world who work with and understand AI and machine learning. And there are people in the world who work with and understand marketing. The intersection of those two groups is a vanishingly tiny population.

Until recently the fact of that nearly empty set didn’t much matter. But with the dramatic growth in machine learning penetration into key marketing activities, that’s changed. If you don’t understand enough about these technologies to use them effectively…well…chances are some of your competitors do.

AI for Marketing, Jim Sterne’s new book,  is targeted specifically toward widening that narrow intersection of two populations into something more like a broad union. It’s not an introduction to machine learning for the data scientist or technologist (though there’s certainly a use and a need for that). It’s not an introduction to marketing (though it does an absolutely admirable job introducing practical marketing concepts). It’s a primer on how to move between those two worlds.

Interestingly, in AI for Marketing, that isn’t a one way street. I probably would have written this book on the assumption that the core task was to get marketing folks to understand machine learning. But AI for Marketing makes the not unreasonable assumption that as challenged as marketing folks are when it comes to AI, machine learning folks are often every bit as ignorant when it comes to marketing. Of course, that first audience is much larger – there’s probably 1000 marketing folks for every machine learner. But if you are an enterprise wanting two teams to collaborate or a technology company wanting to fuse your machine learning smarts to marketing problems, it makes sense to treat this as a two-way street.

Here’s how the book lays out.

Chapter 1 just sets the table on AI and machine learning. It’s a big chapter and it’s a bit of grab bag, with everything from why you should be worried about AI to where you might look for data to feed it. It’s a sweeping introduction to an admittedly huge topic, but it doesn’t do a lot of real work in the broader organization of the book.

That real work starts in Chapter 2 with the introduction to machine learning. This chapter is essential for Marketers. It covers a range of analytic concepts: an excellent introduction into the basics of how to think about models (a surprisingly important and misunderstood topic), a host of common analytics problems (like high cardinality) and then introduces core techniques in machine learning. If you’ve ever sat through data scientists or technology vendors babbling on about support vector machines and random forests, and wondered if you’d been airlifted into an incredibly confusing episode of Game of Drones, this chapter will be a godsend. The explanations are given in the author’s trademark style: simple, straightforward and surprisingly enjoyable given the subject matter. You just won’t find a better more straightforward introduction to these methods for the interested but not enthralled businessperson.

In Chapter 3, Jim walks the other way down the street – introducing modern marketing to the data scientist. After a long career explaining analytics to business and marketing folks, Jim has absorbed an immense amount of marketing knowledge. He has this stuff down cold and he’s every bit as good (maybe even better) taking marketing concepts back to analysts as he is working in the other direction.  From a basic intro into the evolution of modern marketing to a survey of the key problems folks are always trying to solve (attribution, mix, lifetime value, and personalization), this chapter nails it. If you subscribe to the theory (and I do) that any book on Marketing could more appropriately have been delivered as a single chapter, then just think of this as the rare book on Marketing delivered at the right length.

If you accept the idea that bridging these two worlds needs movement in both directions, the structure to this point is obvious. Introduce one. Introduce the other. But then what?

Here’s where I think the structure of the book really sings. To me, the heart of the book is in Chapters 4, 5 and 6 (which I know sounds like an old Elvis Costello song). Each chapter tackles one part of the marketing funnel and shows how AI and machine learning can be used to solve problems.

Chapter 4 looks at up-funnel activities around market research, public relations, social awareness, and mass advertising. Chapter 5 walks through persuasion and selling including the in-store journey (yeah!), shopping assistants, UX, and remarketing. Chapter 6 covers (you should be able to guess) issues around retention and churn including customer service and returns. Chapter 7 is a kind of “one ring to rule them all”, covering the emergence of integrated, “intelligent” marketing platforms that do everything. Well….maybe. Call me skeptical on this front.

Anyway, these chapters are similar in tone and rich in content. You get the core issues explained, a discussion of how AI and machine learning can be used, and brief introductions into the vendors and people who are doing the work. For the marketer, that means you can find the problems that concern you, get a sense of where the state of machine learning stands vis-à-vis your actual problem set, and almost certainly pick-up a couple of ideas about who to talk to and what to think about next.

If you’re into this stuff at all, these four chapters will probably get you pretty excited about the possibilities. So think of Chapter 8 as a cautionary shot across the bow. From being too good for your own good to issues around privacy, hidden biases and, repeat after me, “correlation is not causation” this is Pandora’s little chapter of analytics and machine learning troubles.

So what’s left? Think about having a baby. The first part is exciting and fun. The next part is long and tedious. And labor – the last part – is incredibly painful. It’s pretty much the same when it comes to analytics. Operationalizing analytics is that last, painful step. It comes at the end of the process and nobody thinks it’s any fun. Like the introduction to marketing, the section on operationalizing AI bears all the hallmarks of long, deep familiarity with the issues and opportunities in enterprise adoption of analytics and technology. There’s tons of good, sound advice that can help you actually get some of this stuff done.

Jim wraps up with the seemingly obligatory look into the future. Now, I’m pretty confident that none of us have the faintest idea how the future of AI is going to unfold. And if I really had to choose, I guess I prefer my crystal ball to be in science fiction form where I don’t have to take anything but the plot too seriously. But there’s probably a clause in every publisher’s AI book contract that an author must speculate on the how wonderful/dangerous the future will be. Jim keeps it short, light, and highly speculative. Mission accomplished.


Summing Up

I think of AI for Marketing as a handy guidebook into two very different, neighboring lands. For most of us, the gap between the two is an un-navigable chasm. AI for Marketing takes you into each locale and introduces you to the things you really must know about them. It’s a fine introduction not just into AI and Machine Learning but into modern marketing practice as well. Best of all, it guides you across the narrow bridges that connect the two and makes it easier to navigate for yourself.  You couldn’t ask for a wiser, more entertaining guide to walk you around and over that bridge between two utterly dissimilar worlds that grow everyday more necessarily connected.


Full Disclosure: I know and like the author – Jim Sterne – of AI for Marketing. Indeed, with Jim the verbs know and like are largely synonymous. Nor will I pretend that this doesn’t impact my thoughts on the work. When you can almost hear someone’s voice as you read their words, it’s bound to impact your enjoyment and interpretation. So absolutely no claim to be unbiased here!


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:

The Myth of the Single KPI for Testing

Continuous Improvement through testing is a simple idea. That’s no surprise. The simplest, most obvious ideas are often the most powerful. And testing is a powerful idea. An idea that forms and shapes the way digital is done by the companies that do it best. And those same companies have changed the world we live in.

If testing and continuous improvement is a process, analytics is the driver of that process; and as any good driver knows, the more powerful the vehicle, the more careful you have to be as a driver. Testing analytics seems so easy. You run a test, you measure which worked better. You choose the winner.

It’s like reading the scoreboard at a football game. It doesn’t take a lot of brains to figure out who’s ahead.

Except it’s usually not that easy.

Sporting events just are decided by the score. Games have rules and a single goal. Life and business mostly don’t. What makes measuring tests surprising tricky is that you rarely have a single unequivocal measure of success.

Suppose you add a merchandising drive to a section of your store or on the product detail page of your website. You test. And you generate more sales of that product.



Let’s start with the obvious caveat. You may have generated more sales, but you gave up margin. Was it worth it? Usually, the majority of buyers with a discount would have bought without one. Still, that kind of cannibalization is fairly easy to baseline and measure.

Here’s a trickier problem. What else changed? Because when you add a merchandising drive to a product, you don’t just shift that product’s buying pattern. The customer who buys might have bought something else. Maybe something with a better margin.

To people who don’t run tests, this may come as a bit of surprise. Shouldn’t tests be designed to limit their impact so that the “winner” is clear? ‘

Part of a good experimental design is, indeed, creating a test that limits external impacts. But this isn’t the lab. Limiting the outside impact of a test isn’t easy and you can  never be sure you’ve actually succeeded in doing that unless you carefully measure.

Worse, the most important tests usually have the most macro-impact. Small creative tests can often be isolated to a single win-loss metric. Sadly, that metric usually doesn’t matter or doesn’t move.

If you need proof of that, check out this meta-study by Will Browne & Mike Jones (those names feel like generic test products, right?) that looked at the impact of different types of test. Their finding? UI changes of the color and call-to-action type had, essentially, zero impact. Sadly, that’s what most folks spend all their time testing. (http://www.qubit.com/sites/default/files/pdf/qubit_meta_analysis.pdf)

If your test actually changes shopper behavior, believe me, there will be macro impacts.

It’s usually straightforward to measure the direct results of a store test. It’s often much harder to determine the macro impact. But it’s something you MUST look at. The macro impact can be as or more important than the direct impact. What’s more, it often – I’ll say usually – runs in the opposite direction.

So if you fail to measure the macro impact of a store test and you focus only on the obvious outcome, you’ll often pick the wrong result or grossly overstate the impact. Either way, you’re not using your analytics to drive appropriately.

Of course, one of the very real challenges you’ll face is that many tools don’t measure the macro impact of tests at all. In the digital world, the vast majority of dedicated testing tools require you to focus on a single KPI and provide absolutely no measurement of macro impacts. They simply assume that the test was completely compartmentalized. That works okay for things like email testing, but it’s flat-out wrong when it comes to testing store or website changes.

If your experiment worked well enough to change a shopper’s behavior and got them to buy something, the chances are quite good that it changed more than just that behavior. You may have given up margin. You likely lost some sales elsewhere. You almost certainly changed what else in the store or the site the shopper engaged with. That stuff matters.

In the store world, most tools don’t measure enough to give you even the immediate win-loss results. To heck with the rest of the story. So it can tempting, when you first have real measurement, to focus on the obvious: which test won. Don’t.

In some of my recent posts, I’ve talked about the ways in which DM1 – our store testing and measurement platform – lets you track the full customer journey, segmentfunnel and compare. Those capabilities are key to doing test measurement right. They give you the ability to see the immediate impact of a test AND the ways in which a change affected macro customer behavior.

You can see an example of how this works (and how important that macro behavior is in store layout) in this DM1 video that focuses on the Comparison capabilities of the tool.


It’s the right way to use all that power a store testing program can provide.

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.

Click here to sign-up for a Demo of DM1.

Analyzing the In-Store Journey as a Funnel with DM1

Visualizing the customer journey in the context of the store is the foundation for analyzing in-store data. The metrics and the store context provide a framework for translating customer measurement data into something that is immediately understandable as a shopper’s journey. But visualizing information is just the first step in making it actionable. Understanding the data is, of course, essential. But you can understand data quite well and still have no idea what to do with it. In fact, that’s a problem we see all the time with analytics. And while it’s a problem that no technology solution can solve entirely (since there are always business and organizational issues to be tackled),  there are analytic and reporting techniques that can really help. We’ve built a number of them into DM1, starting with in-store funnel analytics.

The idea behind a conversion funnel is simple. The customer journey is chopped up into discrete steps based on increasing likelihood to purchase. If we analyze the journey by those discrete steps, we can work to optimize the flow from one step to the next. Improve the flow between any funnel step and the next, and the chance is excellent that you’ll improve the overall funnel conversion as well. Funnels give you a specific place to start. They let you figure out which parts of the overall customer journey are already working well and which aren’t. They let you focus on specific areas with the confidence that if you can improve performance you’ll make a significant difference. And they make it possible to easily measure success. All you have to measure is the number of people moving from one step to the next.

Funnels are THE paradigm for analytics and optimization in eCommerce. In fact, it was largely on their ability to help merchants understand and improve eCommerce funnels that digital analytics solutions first gained traction. And to this day, eCommerce testing and analytics practitioners almost always work by breaking down the customer journey into funnel steps and then working to optimize each step. While the measurement of funnels is itself interesting, I think the real value in funnel analysis is the process it supports. That ability to target specific aspects of the journey, figure out which ones are the most broken, and then test possible improvements is at the heart of so much of the continuous improvement that makes digital players successful.

One of our big goals with Digital Mortar is to bring the in-store funnel paradigm and the discipline of continuous improvement to the store. DM1 delivers on the technology and analytic part of that program.

With DM1, you can start a funnel at any place in the store and at any stage in the customer journey. But the most natural place to start is with a shopper entering the store. As you can see, DM1 lets you choose any area of the store you’ve defined and lets you pick from a range of engagement metrics.

Retail Analytics - In-Store Shopper Funnel DM1


Nearly 84 thousand shoppers entered the store in October. Since that’s where the measurement starts, this first step of the funnel doesn’t have any fallout. Everyone I measured, by definition, entered the store. It’s worth noting – and I get asked this a lot – that you CAN track Retail Analytics - In-Store Shopper Funnelpass-by traffic if you setup the measurement system appropriately. Doing so allows you to extend the funnel outside the store!

I could build a store-wide funnel, looking at conversion across the whole store. But it’s usually more interesting and actionable to focus a bit. So my funnel is going to focus on a specific section of the store – Team Gear.Retail Analytics - In-Store Shopper Funnel Linger and Consideration

Adding “Visits to Team Gear” to the funnel, I can see that around 15 thousand shoppers – about 18% of store visitors – visited Team Gear. It took the average visitor about 2 minutes before entry to reach Team Gear. Which makes sense because this area is pretty front of store

But one of the real complexities to in-store measurement is that since shoppers are navigating a physical environment they often pass-thru areas without being interested in them. That doesn’t happen much in digital.

I want to know how many people SHOPPED in Team Gear out of the folks who had the opportunity. And I caRetail Analytics - In-Store Shopper Funnel falloutn see that by selecting Lingers as my metric in the next funnel step. These last two steps illustrate a powerful metric in store measurement that’s simply never been available before. Stores have been able to measure conversion (checkouts/door entries) at the macro level, but at the area level this gets reduced to sales per square foot.

That isn’t reflective of the real opportunity a square foot provides. By measuring where shoppers actually WENT and where they SHOPPED, we have a real KPI of how well a section is performing given its opportunity.

Only about 1 in 7 shoppers who passed through Team Gear actually Shopped there. That’s a problem I’d probably want to tackle.

From here, I can add Fitting Room and CashWrap to the funnel. At every step along the way I can see how many shoppers I’m losing from the total opportunity. I can also see how much time is passing and how many stops the shopper made in-between.

In the end, I have a customer funnel for Team Gear that runs from Store Entry to Cash-Wrap that looks like this:

Retail Analytics - In-Store Shopper Funnel and Funnel Analytics

Any start place. Any level of engagement. Any steps in between. DM1 builds the funnels you need to support analytics and testing.

Pretty cool.

There’s no doubt in my mind that the picture of the shopper journey that DM1 provides drives better understanding. But as I said earlier, analytics isn’t improvement. It’s a way to drive improvement.

The funnel paradigm works less because of it’s analytics potential than because of the process it helps define. In-store funnels focus optimization efforts and make them easily measurable. Whether I tackle the step with the highest abandonment rate, try to build the initial opportunity, or attempt to remove distractions between key steps, funnel analysis helps guide my reasoning about what to test in the store and provides a fully baked way to measure whether store changes drove the desired behavior.