Tag Archives: location analytics

Bringing Real-time Analytics to the Store

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

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

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

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

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

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

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

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

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

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

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

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

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

Check it out:

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

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

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

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

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

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

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

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

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

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

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

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

Can hardly wait!

Building a Career Around In-Store Measurement and Location Analytics

Over the nearly two decades I spent in digital analytics, I did a lot of selling. More than I ever wanted to. But during that time, I saw the process of selling digital analytics transformed. When I started, way back in the ‘90s, selling web analytics was evangelical. I had to convince potential clients that the Web mattered. Then I had to convince them that analytics mattered to the Web. If I got that far, I just to convince them that I was the right person to buy analytics from. But since there were only about five other people in the world doing it, that last part wasn’t so hard!

Over time, that changed. By 2005, most companies didn’t need to be convinced that the Web mattered. The role of analytics? That was still a hard sell. But by around 2012, selling digital analytics was no longer evangelical. Everyone accepted that analytics was a necessary part of digital. The only question, really, was how they would provision it.

I didn’t miss the evangelical sell. It’s a hard path. Most people are inherently conservative. Doing new stuff is risky. Most organizations are pretty poor at rewarding risk-taking. It’s great to suggest that analytics is powerful. That it will do the organization good. But for someone to take a risk on a new technology and process, there needs to be real upside. Think of it this way – just as VC’s expect out-sized returns when they invest hard-cash in risky startups, so do decision-makers who are willing to go outside the well-trodden path.

Well, with Digital Mortar, I’m right back in the evangelical world. I have to sell people on the value of in-store customer measurement and analytics – and often I have to do it within environments that are significantly disrupted and challenged. So here’s the question I ask myself – what’s in it for an influencer or a decision-maker?

I think that’s a surprisingly important question and one that doesn’t often get asked (or answered).

If you’re thinking about in-store measurement and analytics, here’s the personal questions I’d be asking if I were you (and my best guess at answers):

1. Is there a future career in this stuff?

There was a time when understanding how to create digital analytics tags was a really critical skill. That day has passed. Tagging is now a commodity skill often handled by offshore teams. In technology and analytics, in-demand skills come and go. And it’s critically important to keep building new skills. But which skills? Because there’s always lots of possible choices and most of them won’t end up being very important.

It’s pretty obvious that I believe location analytics has a big future or else I wouldn’t have started Digital Mortar. Here’s why I think this stuff matters.

I saw how compelling analytics became in the digital world. With increasing competition and interaction between digital and physical experiences, it’s just implausible to believe that we’ll continue in a world where online experiences are deeply quantified and physical experiences are a complete mystery. Every digital trend around customer centricity, experimentation and analytics is in-play in the physical world too – and all of them drive to the need for location analytics.

The thing is, measurement creates its own demand. Because once people understand that you can measure something, they WANT to know.

It will take a while. Change always does. But I have no doubt that in a few years, measurement of the physical customer journey will be well on its way to being the kind of table-stakes must-have that digital analytics is in the web world. That means new roles, new department, new jobs and new opportunities. Which brings me to…

2. Is there a real benefit to being an early adopter?

The people who got into digital analytics early carved out pretty admirable careers. Sure, they were a smart group, but in a new analytics domain, there is a real premium to early adoption. When that field starts to get traction, who gets to speak at Conferences? Who gets to write the books? It’s the early adopters. And if you’re the one speaking at conferences or writing the books, you get real opportunities to build a unique career. Being an early adopter of a technology that pans out is a huge win for your personal brand. It almost guarantees a set of terrific career options: leading a consultancy, having a cush job as an evangelist at a place like Google, getting recruited by a technology unicorn, or managing a large group at a premium company. All good stuff.

And by the way, it’s worth pointing out that this type of measurement isn’t limited to retail. You think resorts, arenas, and complex public spaces don’t need to understand the customer experience in their spaces? Location analytics won’t be in every industry. But it will be every WHERE.

But none of that stuff will happen unless you have some success.

3. Can I be successful right now?

How much success you need is easily exaggerated. Early adopters (and this is a good thing) are like fisherman. We mostly know how to tell a good story. But getting real success IS important. And fortunately, location analytics systems are good enough to do interesting measurement. The capture technologies have plenty of issues, but they work. And a platform like DM1 lets you do A LOT with the data. Best of all, if you’re already experienced with digital analytics, you know a bunch of what’s important about dealing with this data. That makes early adoption a little less frightening and a lot more likely to be successful.

There are real use-cases for this technology. Use-cases that have been hidden by the generally awful analytics capabilities of previously existing systems. This kind of measurement can identify and help solve line and queue management problems, answer questions around store and location design, resolve issues around staffing and associate optimization and feed better forecasting and allocation models, and drive powerful enhancements to customer CRM and personalization efforts.

4. How risky is it?

Middling. This stuff is still pretty new. But it’s starting to mature rapidly. The technology is getting better, the analytics software just got MUCH better, and the needs just keep growing. As with most analytics – the hard part is really organizational. Getting budget, getting authority, driving change – those are always the hardest tasks no matter how challenging things are on the data collection and analytics front. But no one’s ever seen this kind of data before. So the bar is incredibly low. When people have spent years living with hunch, intuition and door-counting as their sole metrics, you don’t have to provide world-beating analytics to look like a star.

5. Is it interesting (because no one wants to spend their life doing boring stuff)?

Yep. This stuff is deeply fascinating. Customer experience has long been one of the most interesting areas in analytics. People are great to study because their behavior is always complex. That makes the analytics a challenge. And because its people and behavior and the real-world, the problem set keeps morphing and changing. You’re not stuck analyzing the same thing for the next ten years.

Even better, identifying problematic customer behaviors is the table-set for actual business change. Once you’ve found a problem, you have to find a way to fix it. So the analytics drives directly into thinking about the business. I like that a lot. It means there’s a purpose to the measurement and the opportunity to brainstorm and design solutions not just analyze problems. If you enjoy doing digital analytics (or have always thought you might), this is an even richer and more complex set of analytic problems.

Yeah. It’s fun.

Which brings me to the bottom line. Risk is risk. A lot of businesses fail. A lot of technologies don’t take off. But I’m pretty confident that in-store journey measurement and location analytics will become a significant discipline in the next few years. If I’m right, there will be real dividends to being an early adopter. Both for the companies that do it and the people who drive it. And along the way there’s some fascinating analytics to be had and a whole bunch of really interesting stuff to learn. That doesn’t seem like such a bad deal.

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]

Evolve or Die: Analytics and Retail

In my last three posts, I assessed the basic technologies (wifi, camera, etc.) for in-store customer measurement and took a good hard look at the state of the analytics platforms using that measurement. My conclusion? The technologies are challenging but, deployed properly, can work at scale for a reasonable cost. The analytics platforms, on the other hand, have huge gaping holes that seriously limit the ability of analysts to use that data. Our DM1 platform is designed to solve most (I hope all) of those problems. But it’s not worth convincing anyone that DM1 is a better solution unless people get why this whole class of solution is so important.

Over about the same amount of time as those posts, I’ve seen multiple stories on the crisis in mall real-estate, the massive disruption driven in physical retail when eCommerce cross sales thresholds as a percentage of total purchases, and the historical and historically depressing pace of store closings in 2017.

It’s bad out there. No…that doesn’t really capture things. For lots of folks, this is potentially an extinction level event. It’s a simple Darwinian equation:

Evolve or die.

And people get that. The pace of innovation and change in retail has never been as high. Is it high enough? Probably not. But retailers and mall operators are exploring a huge number of paths to find competitive advantage. At a high-level, those paths are obvious and easily understood.

Omni-Channel is Key: You can’t out-compete in pure digital with “he who must not be named”…so your stores have to be a competitive advantage not an anchor. How does that happen? Integration of the digital experience – from desktop to mobile – with the store. Delivering convenience, experience, and personalization in ways that can’t be done in the purely digital realm.

Experience is Everything: If people have to WANT to go to stores (in a line I’ve borrowed from Lee Peterson that I absolutely love), delivering an experience is the bottom line necessary to success. What that experience should be is, obviously, much less clear and much more unique to each business. Is it in-store digital experiences like Oak Labs’ delivers – something that combines a highly-customized digital shopping experience integrated right into the store operation? Is it bringing more and better human elements to the table with personalized clienteling? Is it a fundamentally different mix of retail and experience providers sharing a common environment? It’s all of these and more, of course.

The Store as a Complex Ecosystem: A lot of factors drive the in-store experience. The way the store is laid out. The merchandising. The product itself. Presentations. In-store promotions. Associate placement, density, training and role. The digital environment. Music. Weather. It’s complicated. So changing one factor is never going to be a solution.  Retail professionals have both informed and instinctive knowledge of many of these factors. They have years of anecdotal evidence and real data from one-off studies and point-of-sale. What they don’t have is any way to consistently and comprehensively measure the increasingly complex interactions in the ecosystem. And, of course, the more things change, the less we all know. But part of what’s involved in winning in retail is getting better at what makes the store a store. Better inventory management. Better presentation. Better associates and better clienteling strategies. Part of winning in a massively disrupted environment is just being really good at what you do.

The Store in an Integrated Environment: Physical synergies exist in a way that online synergies don’t. In the friction free world of the internet, there’s precious little reason to embed one web site inside another. But in the physical world, it can be a godsend to have a coffee bar inside the store while my daughters shop! Taking advantage of those synergies may mean blending different levels of retail (craft shows, farmers markets) with traditional retail, integrating experiences (climbing walls, VR movies) or taking advantage of otherwise unusable real-estate to create traffic draws (museums, shared return centers).

In one sense, all of these things are obvious. But none of them are a strategy. They’re just words that point in a general direction to real decisions that people have to make around changes that turn out to be really hard and complex. That’s where analytics comes in and that’s why customer journey measurement is critically important right now.

Because nobody knows A) The right ways to actually solve these problems and  B) How well the things they’re trying to do are actually working.

Think about it. In the past, Point of Sale data was the ultimate “scoreboard” metric in retail and traffic was the equivalent for malls. It’s all that really mattered and it was enough to make most optimization decisions. Now, look at the strategies I just enumerated: omni-channel, delivering experience, optimizing the ecosystem and integrating broader environments…

Point-of-Sale and traffic measure any of that?

Not really. And certainly, they don’t measure it well enough to drive optimization and tuning.

So if you’re feverishly building new stores, designing new store experiences, buying into cutting edge digital integrations, or betting the farm on new uses for your real-estate, wouldn’t it be nice to have a way to tell if what you’re trying is actually working? And a way to make it work better since getting these innovative, complex things right the first time isn’t going to happen?

This is the bottom line: these days in retail, nobody needs to invest in customer measurement. After all, there’s a perfectly good alternative that just takes a little bit longer.

It’s called natural selection. And the answers it gives are depressingly final.