Tag Archives: digital analytics

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.

Success!

Success?

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.

https://www.youtube.com/watch?v=lbpaeSmaE74&t=13s

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.

Creating a Measurement Language for the Store

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

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

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

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

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

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

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

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

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

That starts by mapping the data onto the store:

store journey analytics

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

StoreFrame1

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

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

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

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

storeFrame3

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

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

storeframe4

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

storeFrame5

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

storeFrame6

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

StoreFrame8

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

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

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

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

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

Segmentation is the Key to Marketing Analytics

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

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

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

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

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

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

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

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

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

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

Why don’t most tools deliver segmentation?

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

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

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

Segmentation and DM1

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

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

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

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

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

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

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

The Roadmap

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

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

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

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