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.

Retail Analytics: Store Visualization and DM1

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

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

Store Visualization: Heatmaps and retail analytics

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

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

BoardGame

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

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

store journey analytics

Now lay a grid over it:

Visualizing Store Data

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

Mapping customer data to the store

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

That’s how DM1 works.

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

Retail Analytics: Store Visualization in DM1

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

Retail Analytics: Store Detail in DM1

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

Retail Analytics: Store Meta Data and Levels in DM1

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

Retail Analytics: Store Merchandising Data Overlay

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

Retail Analytics: Store Comparison in DM1

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

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

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

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

DM1 Retail Analytics Store Visualization Advantages

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

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

Retail Analytics and Store Visualization

Making sense of behavioral data is always a challenge. Suppose I tell you that a shopper visited your store, spent fourteen minutes, lingered twice and had a single Associate interaction. That’s a lot of data, but in most respects, it’s deeply uninformative. What did the shopper care about? What were they interested in? What did they pass by?  What worked? What didn’t? You can’t even begin to formulate answers to those questions based on the data I described. I know, because I spent years trying and largely failing to do interesting analytics with metrics like these in the digital world. What’s missing from these metrics is the context. If you don’t know what was in the store at the place where the shopper lingered, you can’t attach meaning to the action. For in-store analytics, the basic context that gives meaning to the data is the store; specifically, what was there in the store at the place the visitor lingered.

Context transforms the data in Table 1 to the data in Table 2:

In-store measurement data

We know a lot more (and lot more interesting stuff) about Shopper A and about store performance when the metrics are contextualized to the store. With the second table, we can likely guess that the shopper is a woman. That she’s interested in Jackets and Backpacks. That she entered the store shopping for Jackets. That the sales interaction wasn’t successful and likely concerned jackets.

This idea of contextualizing behavior to understand the customer better is incredibly simple and seemingly obvious. But it’s hugely powerful and when you’re suffering from a deluge of aggregated consumption metrics that lack this context, it can be surprisingly difficult to figure out what’s missing.

So when you see a report like this:

DayHourShoppers CountedTotal TimeAvg. Time
5/14/201710125 26,750214
11152 32,072211
12191 34,571181
1185 34,040184
2187 31,229167
3215 41,065191
4152 30,400200
587 17,574202
692 12,972141
5/15/201710133 27,797209
11145 30,015207
12212 44,732211
1210 41,370197
2242 46,222191
3206 40,170195
4187 34,969187
5161 27,209169
6163 25,265155
5/16/201710118 23,718201
11145 29,725205
12186 38,130205
1211 45,154214
2244 50,508207
3259 54,649211
4206 38,110185
5200 37,800189
6169 28,899171

It can sure seem like the data ought to be useful. And there are some things you can do with this kind of data. Just don’t try to use it to answer any questions about customers, their journey, or store performance.

Now we’re not the first people and Digital Mortar isn’t the first company to recognize that store context is vital to understanding the data created by in-store measurement. That’s why if you’re looking at in-store measurement platforms, you’ll almost certainly see some version of the store heatmap:

store analytics heatmap

(from BusinessInsider.com)

This type of store heatmap is certainly an attempt to contextualize behavior in terms of the store. And there’s no denying that heatmaps look cool. But if you’ve ever tried to use a heatmap like this for analytics, you’ve probably been massively frustrated.

The first problem with this type of heatmap is simple. It doesn’t really make it all that easy to know what’s in the store. Sure, if I’ve memorized a store layout, I might be able to map those colors onto actual store sections and products. But chances are, I’m going to have constantly flip back and forth between the heat-map and a planogram to make sure I know what I’m looking at.

The second big problem with heatmaps is that they don’t really provide a means of analysis. Look at these two heatmaps:

Visualizing Store Data

Can you tell that the yellow band in the upper left corner grew in size? That the red smear in the middle was a little less pink and little more red?

Neither could I.

And even if you could pick out the differences, how could possibly communicate the nature and extent of the changes to anyone else?

When you do analysis, you need to find important differences in the data and you need to be able to communicate them. Heatmaps like this suck at both tasks.

And let’s say you made a change in the store. After all, that’s the reason you’re doing store measurement, right? What happens with the planogram? Which view do you see? And how do you compare the before vs. after and quantify the changes?

When we set out to build DM1, the single biggest problem on our minds was how to visualize store behavioral data effectively. We wanted a tool that fixed the problems with heatmaps and that could make in-store analytics come alive.

From our perspective, the store visualization capability in DM1 had to:

  1. Show the data in the context of the store
  2. Make it easy to understand what was there without having to resort to a paper planogram or external source
  3. Handle changes to the store seamlessly
  4. Be able to visualize the store at different levels – from departments down to tables – to support different kinds of analysis
  5. Provide quantifiable analytics and measurements so that an analyst could look at complex flows and be able to say EXACTLY what changed and by how much
  6. Support a variety of different metrics
  7. Provide a means to trend metrics over time no matter how often the store layout changed

In other words, we wanted to make store visualization useful.

In my next post, I’ll show you what we built in DM1 and how it fulfills all of these requirements.