Tag Archives: Digital Mortar

Mobile Apps, Geo-Location and Shopper Analytics

The hardest part about doing enterprise shopper journey measurement and analytics is data collection. Putting new hardware in the store is no joke – and yet it’s often necessary to get the measurement you want. Still, often isn’t the same as always. Last week I talked about how you can get surprisingly powerful store measurement by taking data from your existing store WiFi and flowing it into our DM1 platform. Store Wifi gives you broad population coverage (no, shoppers don’t have to connect) but it isn’t very accurate positionally. On the other end of the measurement spectrum is geo-locating your mobile app users. It’s another way – and a good one – to get fascinating measurement about how shoppers navigate your store.

 

Geo-locating your mobile app users is easy and quite inexpensive. It can be done with no additional hardware in the store. It’s very accurate and, by feeding the data to DM1, you can get powerful and detailed analytics on what your mobile app users are doing in-store. When you add geo-location to your Mobile App (it just takes a few lines of code), it sends you a stream of positional data that tells you exactly where a shopper was throughout their in-store journey. Our DM1 platform ingests that stream, aggregates it, and provides you the store analytics to understand paths, funnels, usage, interactions, and much more.

That’s why, when I speak on geo-location analytics, I steal the line from Lenox Financial and describe mobile app geo-location as the biggest no brainer in the history of earth.

 

There’s only one real drawback to shopper measurement via mobile app and it’s the obvious one – it’s limited to the population of your mobile app users. For most retailers, that’s a small and totally non-random segment of their population.

 

Before I discuss the implications of that, here’s what you need to know about getting this kind of app-tracking to work and integrating it with Digital Mortar’s platform.

 

We’re all mobile phone users and we all know that our phones position us. Most of us could barely navigate our home city without Google or Waze or Apple Maps. I remember being in Venice and wondering how ANYONE ever got around there before GPS. It’s like the old D&D game – a maze of twisty passages, all alike. I imagine people just got lost a lot and that was probably part of the fun.

 

We also know that the built-in outdoor GPS positioning on the phone is pretty accurate but not super-precise. When you use it for walking you can often see just how dislocated that little blue-dot is from where your actually standing. And it can take some real mental work to figure out exactly where you are and when to turn if – as in places like Venice – you’re not navigating long straight blocks.

 

Indoor wayfinding has its own set of challenges. Indoor spaces by their very nature are more tightly packed so there’s a higher premium on positional accuracy. But indoor spaces are also more challenging from a measurement standpoint because signals are routinely blocked, distorted or mirrored. And, of course, indoor space are often importantly three dimensional. Outdoor mapping doesn’t have to worry about floors – but in buildings, knowing what floor you’re on is fundamental.

 

Fortunately, your typical smart phone these days has a whole grab bag of sensors that can be used for better indoor wayfinding. Good indoor wayfinding systems take advantage of the whole array of phone sensors – starting with GPS positioning but adding WiFi, BlueTooth signals, radio signals, magnetic fields, the inertial sensor platform and even barometric pressure.

 

This works pretty well since most environments these days are signal rich. It’s also very easy to improve the performance of indoor way-finding if you find that there are inside areas where positional accuracy isn’t great. In most cases, dropping a beacon or two will solve the problem.

 

Typically, indoor wayfinding systems work as code libraries. You put their code into your mobile app and make a few simple function calls. From a developer perspective, this type of integration is simple and straightforward. What’s more, unlike say digital analytics tagging where you need to tie measurement messaging tightly to the functionality, the geo-location libraries (at least when used for measurement) function almost as a stand-alone element of your App. So it’s trivial for developers to integrate the code – and it requires minimal design cycles. Compared to adding good digital analytics tagging to your App, it’s a breeze.

 

With a 3rd Party library in your App, there’s only two other things you need to do. The first is to fingerprint your location – this is essentially a calibration and mapping step where you translate the signals into site location. It’s not hard, but if you really want a turnkey setup, Digital Mortar can do this for you – it takes less than a day and involves no disruption of the site. It doesn’t even have to be done after hours.

 

The last step is to provision a feed from the 3rd Party Cloud instance (or your own cloud instance if you’re using a non-turnkey library that just sources the data to your servers) to our DM1 platform. Most providers provide a good, event-level feed as part of their core service. So all you have to do is turn it on. It’s not that much harder in the DIY world.

 

Keep in mind that most geo-location service providers are thinking about messaging, indoor way-finding and other interactive uses for their service – not analytics. So the analytics you’ll get out of the box is mostly non-existent or even less compelling that what you’d get from a WiFi vendor (and, as I mentioned last week, that aint great).

 

That’s what DM1 is for. Because there is no better source of data for our platform. The beauty of fully-configured mobile app services is that the positional accuracy is terrific. The event stream can be generated at a pre-determined frequency – so we’re not dependent on the somewhat random ping rates that come with other forms of electronic tracking. That means we can capture a full, accurate, and very detailed customer journey.

 

Even better, the nature of mobile apps is that they can provide a true omni-channel join. So you can take DM1’s CRM-based feed and integrate with your customer digital behavior to create a full journey customer database. Our CRM feed includes the customer id you pass us (usually a hashed identifier), basic visit information (visit time, length, and flags for purchase and interaction), and the time spent in each area of the store. Adding that to your customer record is powerful. And yes, it’s just for your mobile app users. But often, those are your very best customers.

 

Plus, there are important applications where the biases inherent in a mobile app sample aren’t particularly damaging. If, for example, you want to know how long customers are queuing at cash-wrap it’s perfectly possible to use mobile app data. When they are standing in line, they are there for the same amount of time as everyone else. And how mobile app users shop the store and take advantage of omni-channel experiences is, let’s just say, quite interesting and valuable.

That being said, it’s like any other case where you’re working with a non-random sample. You can’t assume that all your shoppers behave the way your mobile population does – and if you try to make those kinds of extrapolations, you’re going to get it wrong.

 

That’s why, though a mobile app feed might be the primary customer source you feed into DM1, it’s more likely that you’ll combine a mobile app feed with a full customer feed from iViu, WiFi or camera.

In-store shopper measurement technology compared reviews

In DM1, we keep each feed as a separate segment. With a little bit of a code tweak to your mobile app, we can also integrate your mobile app data directly with the iViu feed so there’s no double counting. But most times, you’ll work with them as separate populations.

 

Either way, you get the full power of DM1’s analytics on the mobile app shopper data. Pathing, funnels, store layout, segmentation, etc. etc.:

Digital Mortars DM1 - Shopper measurement and geo-location analytics. Path Analytics, Funnel Analysis

Finally, this is also one of the best ways to collect and integrate Associate tracking. DM1 provides full Associate measurement functionality allowing you to understand when and where you’re under or over staffed in the store. Adding geo-location to your associate devices is just as easy as it is on the shopper side – and this is something you can do even if you’re not heavily invested in customer-facing mobile apps.

 

 

So if you’re suitably excited, the next question ought to be – where do you get this and how much does it cost?

 

There are tons of options for adding geo-location measurement to your app. The easiest and most fully-baked come from providers like IndoorAtlas and Radar. Hey, even my old digital analytics friends at Adobe and Google do this. The most full-service systems include the code libraries, platforms for fingerprinting, and robust cloud feeds. They make going from App setup to DM1 analytics a walk in the park. There are plenty of DIY alternatives as well – many open-sourced and free.

 

The full-service platform vendors typically charge you per location based on broad square footage ranges. It’s quite inexpensive – though the out-of-the-box pricing models tend to work better for single, very large locations than for large numbers of mid-sized stores. Most of these companies seem to engage in enterprise pricing – meaning that the price you pay is largely a function of whatever you can negotiate. And if you’d prefer, we can provide developer support integrating an open-source solution into your App. It probably won’t be quite as robust, but if your primary goal is measurement it will more than get the job done.

 

From the standpoint of integrating with DM1, it’s pretty much out of the box. If we don’t support the feed already, we’ll create the integration as part of getting you setup – no charge. It’s not too hard because the data streams are pretty much identical – identifier, timestamp, x, y coordinates. There really isn’t much else to it.

 

The measurement costs are trivial compared to what you spend on App development and small compared to what you spend on digital analytics app measurement and analysis. The data is extremely robust and – in a field plagued by bad data – quite accurate. The omni-channel join possibilities are like adding hot fudge sauce to an already delicious sundae. Paired with DM1, you can measure and optimize exactly how this critical and growing customer segment uses the store. You can study how digital and store behaviors interact. And you have an excellent data source for overall store navigation and store usage that you can pair with other data sources or use as is.

 

Okay…it may not be the biggest no-brainer in the history of earth. But adding geo-location and DM1 analytics to your mobile app is definitely the biggest no-brainer in shopper measurement.

Using Your Existing Store WiFi for Shopper Measurement

The most daunting part of doing shopper measurement isn’t the analytics, it’s the data collection piece. Nobody likes to put new technology in the store; it’s expensive and it’s a hassle. And most stores feel like they have plenty of crap dangling from their ceilings already.

 

If you’re in that camp, but would love to have real in-store shopper measurement, there are three technologies you should consider. The first, and the one I’m going to discuss today, is your existing WiFi access points.

 

Most modern WiFi access points can geo-locate the signals they receive. Now you may be thinking to yourself that the overwhelming majority of shoppers don’t connect to your WiFi. But that’s okay. Phones with their WiFi enabled ping out to your access points on a regular basis even when they don’t connect to your WiFi. And, yes, it’s both possible and acceptable to use that for anonymous measurement.

 

What that means, is that you can use your store’s WiFi to measure the journeys for a significant percentage of your shoppers. Access point tracking is incredibly convenient. Since it’s based off your existing customer WiFi system, you already have the necessary hardware. If your equipment is modern, it’s usually just a matter of flipping a software switch to get geo-location data in the cloud.

 

Providers like Meraki have been gradually improving the positional accuracy of the data and they make it super-easy to enable this and get a full data feed. And if you’re equipment is older or from a vendor that doesn’t do that? It’s not a lost cause. Every reasonably modern WiFi Access Point generates a log file that includes the basic data necessary for positional triangulation. It’s not as convenient as the cloud-based feeds that come with the best systems, but if you don’t mind doing a little bit of traditional IT file wrangling, it can work almost as well. We’ll do the heavy lifting on the positioning.

 

The biggest downside to traditional WiFi measurement has been the lack of useful analytics. Working from the raw feed is very challenging for an enterprise (harder than just installing new devices) and the reporting and analytics you get out of the box from WiFi vendors is…well…about what you’d expect from WiFi vendors. Let’s just say their business isn’t analytics.

 

That’s where our DM1 platform really makes a huge difference. DM1 is an open, shopper analytics platform. It’s built to ingest ANY detailed, geo-located data stream. It can take data from your mobile app users. It can take data from dedicated measurement video cameras. It can take data from iViu passive network sniffers. Really, any measurement system that creates timestamped shopper/device and x,y coordinates can be easily ingested.

 

Your existing WiFi Access Point data fits that bill.

 

Imagine being able to take your WiFi geolocation data and with the flip of switch and no hardware install be able to do full-store pathing:

DM1 Digital Mortar store analytics full shopper path analytics

 

Full in-store funnels:

Digital Mortar Store Analytics DM1 Funnel Analysis for retail analytics and shopper tracking

 

Even cooler, because DM1 uses statistical methods to identify Associate devices, we’ll automatically parse that WiFi data to identify shoppers and associates. That lets you track associate presence and intraday STARs for any section of the store. No changes to store operations. No compliance issues. You can even do a path analysis on the shopper journey by salesperson or sales team:

DM1 Retail Analytics digital mortar full store path analytics and associate interactions

 

How cool is that!

 

And remember what I said about other data sources? DM1 can simultaneously ingest your mobile app user data and your WiFi data and let you track each as separate segments. You get the extra detail and positional accuracy for all your mobile shoppers along with the ability to rapidly swap views and see how the broader population of smartphone users is navigating your store.

 

Coupling DM1 to WiFi geo-location data really is the easiest, cheapest way to give serious, enterprise-class in-store shopper measurement a try.

 

And the Fine Print

If you’re wondering if there are drawbacks to WiFi measurement, the answer is yes. We see it as a great, no-pain way to get started with shopper analytics. But there are strong reasons why, to get really good measurement, you’ll need to migrate at least some stores to dedicated measurement collection. WiFi’s positional accuracy suffers in comparison to dedicated measurement devices like iViu’s or camera-based solutions. And it also measures fewer shoppers. Even compared to other means of electronic detection, you’ll lose a significant number of phones – especially IOS devices.

 

If you were reading closely, you’ll remember that I said there were three technologies to consider if you want to do shopper journey measurement without adding in-store hardware. WiFi is the easiest and the most widespread of these. But there are slam-dunk solutions for mobile app measurement that I’ll cover in my next post. And if you have relatively modern security cameras, there’s even a software-based solution that can help you turn that data into grist for the DM1 mill. That’s a solution we’ve been hoping for since day 1 – and it’s finally starting to become a reality.

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

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

 

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

 

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

 

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

 

Why Users Level of Sophistication Matters

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

 

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

 

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

 

 

Why Some Problems Benefit from Bespoke

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

 

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

 

 

Data Clean-up and Associate Identification

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

 

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

 

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

 

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

 

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

 

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

 

 

Store Layout Visualization

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

 

Contrast that to doing the same tasks in DM1.

 

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

 

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

 

 

Path Analysis

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

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

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

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

 

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

 

 

Funnel Analysis

Digital Mortars DM1 funnel analytics for retail and shopper tracking

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

Digital Mortar's DM1 retail analytics shopper tracking funnel analytics

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

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

 

That’s powerful!

 

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

 

 

Finally, Don’t Forget to Consider the Basic Economics

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

 

Bottom Line

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

 

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

 

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

A Year in Store Analytics

It’s been a little more than a year now for me in store analytics and with the time right after Christmas and the chance to see the industry’s latest at NRF 2018, it seems like a good time to reflect on what I’ve learned and where I think things are headed.

Let’s start with the big broad view…

The Current State of Stores

Given the retail apocalypse meme, it’s obvious that 2017 was a very tough year. But the sheer number of store closings masked other statistics – including fairly robust in-store spending growth – that tell a different story. There’s no doubt that stores saddled with a lot of bad real-estate and muddied brands got pounded in 2017. I’ve written before that one of the unique economic aspects of online from a marketplace standpoint is the absence of friction. That lack of friction makes it possible for one player (you know who) to dominate in a way that could never have happened in physical retail. At the same time, digital has greatly reduced overall retail friction. And that reduction means that shoppers are not inclined to shop at bad stores just to achieve geographic convenience. So the unsatisfying end of the store market is getting absolutely crushed – and frankly – nothing is going to save it. Digital has created a world that is very unforgiving to bad experience.

On the other hand, if you can exceed that threshold, it seems pretty clear that there is a legitimate and very significant role for physical stores. And then the key question becomes, can you use analytics to make stores an asset.

So let’s talk about…

The Current State of In-Store Customer Analytics

It’s pretty rough out there. A lot of companies have experimented with in-store shopper measurement using a variety of technologies. Mostly, those efforts haven’t been successful and I think there are two reasons for that. First, this type of store analytics is new and most of the stores trying it don’t have dedicated analytics teams who can use the data. IT led projects are great for getting the infrastructure in the store, but without dedicated analytics the business value isn’t going to materialize. I saw that same pattern for years in web analytics before the digital analytics function was standardized and (nearly always) located on the business side. Second, the products most stores are using just suck. I really do feel for any analyst trying to use the deeply flawed, highly aggregated data that gets produced and presented by most of the “solutions” out there. They don’t give analysts enough access to the data to be able to clean it, and they don’t to a very good job cleaning it themselves. And even when the data is acceptable, the depth of reporting and analytics isn’t.

So when I talk to company’s that have invested in existing non Digital Mortar store analytics solutions, what I mostly hear is a litany of complaints and failure. We tried it, but it was too expensive. We didn’t see the value. It didn’t work very well.

I get it. The bottom line is that for analytics to be useful, the data has to be reasonably accurate, the analytics platform has to provide reasonable access to the data and you must have resources who can use it. Oh – and you have to be willing to make changes and actually use the data.

There’s a lot of maturing to do across all of these dimensions. It’s really just this simple. If you are serious about analytics, you have to invest in it. Dollars and organizational capital. Dollars to put the right technology in place and get the people to run it. Organizational capital to push people into actually using data to drive decisions and aggressively test.

Which brings me to….

What to invest in

Our DM1 platform obviously. But that’s just one part of bigger set of analytics decisions. I wrote pretty deeply before the holidays on the various data collection technologies in play. Based on what I saw at NRF, not that much has changed. I did see some improvement in the camera side of the house. Time of Flight cameras are  interesting and there are at least a couple of camera systems now that are beginning to do the all-important work of shopper stitching across zones. For small footprint stores there are some viable options in the camera world worth considering. I even saw a couple of face recognition systems that might make point-to-point implementations for analytics practical. Those systems are mostly focused on security though – and integration with analytics is going to be work.

I haven’t written much about mobile measurement, but geo-location within mobile apps is – to quote the Lenox mortgage guy – the biggest no-brainer in the history of earth. It’s not a complete sample. It’s not even a good sample. But it’s ridiculously easy to drop code into your mobile app to geo-locate within the store. And we can take that tracking data and run it into DM1 – giving you detailed, powerful analytics on one of the most important shopper segments you have. It costs very little. There’s no store side infrastructure or physical implementation – and the data is accurate, omni-joinable and super powerful. Small segment nirvana.

The overall data collection technology decision isn’t simple or straightforward for anyone. We’ve actually been working with Capgemini to integrate multiple technologies into their Innovation Center so that we can run workshops to help companies get a hands-on feel for each and – I hope – help folks make the right decision for their stores.

People is the biggest thing. People is the most expensive thing. People is the most important thing. It doesn’t matter how much analytic technology you bring to the table – people are the key to making it work. The vast majority of stores just don’t have store-side teams that understand behavioral data. You can try to create that or you can expand the brief of your digital or omni-channel teams and re-christen them behavioral analytics teams. I like option number two. Why not take advantage of the analytics smarts you actually have? The data, as I’ve said many times before, is eerily similar. We’ve been working hard to beef up partnerships and our own professional services to help too. But while you can use consultants to get a serious analytic effort off the ground, over time you need to own it. And that means deciding where it lives in your organization and how it fits in.

Which I know sounds a lot like…

Everything old is new again

I make no bones about the fact that I dived into store measurement because I thought the lessons of digital analytics mostly applied. In the year sense, I’ve found that to be truer than I knew and maybe even truer than I’d like. Many of the challenges I see in store analytics are the ones we spent more than decade in digital analytics gradually solving. Bad data quality and insufficient attention to making it right. IT organizations focused on collection not use. A focus on site/store measurements instead of shopper measurement.

Some of the problems are common to any analytic effort of any sort. An over-willingness to invest in technology not people (yeah – I know – I’m a technology vendor now I shouldn’t be saying this!). A lack of willingness to change operational patterns to be driven by analytics and measurement and a corresponding challenge actually using analytics. Far too many people willing to talk the talk but unable or unwilling to walk the walk necessary to do analytics and to use it. These are hard problems and it’s only select companies that will ever solve them.

Through it all I see no reason to change the core beliefs that drove me to start Digital Mortar. Shopper analytics is critical to doing retail well. In a time of disruption and innovation, it can drive massive competitive advantage if an organization is willing to embrace it seriously. But that’s not easy. It takes organizational commitment, some guts, good tools and real smarts.

Digital Mortar can provide a genuinely good tool. We can help with the smarts. Guts and commitment? That’s up to you!

The State of Store Tracking Technology

The perfect store tracking data collection would be costless, lossless, highly-accurate, would require no effort to deploy, would track every customer journey with high-precision, would differentiate associates and shoppers and provide shopper demographics along with easy opt-out and a minimal creep factor.

We’re not in a perfect world.

In my last post, I summarized in-store data collection systems across the dimensions that I think matter when it comes to choosing a technology: population coverage, positional accuracy, journey tracking, demographics, privacy, associate data collection and separation, ease of implementation and cost. At the top of this post, I summarized how each technology fared by dimension.

In-store tracking technologies rated

As you can see, no technology wins every category, so you have to think about what matters most for your business and measurement needs.

Here’s our thinking about when to use each technology for store tracking:

Camera: Video systems provide accurate tracking for the entire population along with shopper demographics. On the con-side, they are hard to deploy, very expensive, provide sub-standard journey measurement and no opt-out mechanism. From our perspective, camera makes the most sense in very small foot-print stores or integrated into a broader store measurement system where camera is being used exclusively for total counting and demographics.

WiFi: If only WiFi tracking worked better what a wonderful world it would be. It’s nearly costless and there’s almost no effort to deploy. It can differentiate shoppers and Associates and it provides an opt-out mechanism. Unfortunately, it doesn’t provide the accuracy necessary to useful measurement in most retail situations. If you’re an airport or an arena or a resort, you should seriously consider WiFi tracking. But for most stores, the problems are too severe to work around. With store WiFi, you lose tracking on your iPhone shoppers and you get less coverage on all devices. Worse, the location accuracy isn’t good enough to place shoppers in a reasonable store location. It’s easy to fool yourself about this. It’s free. It’s easy. What could go wrong? But keep two things in mind. First, bad data is worse than no data. Making decisions on bad data is a surefire way to screw up. Second, most of the cost of analytics is people not technology. When you give your people bad tools and bad data, they spend most of their time trying to compensate. It just isn’t worth it.

Passive Sniffer (iViu): There’s a lot to like with this system and that’s why they are – by far – our most common go to solution in traditional store settings. iViu devices provide full journey measurement with good enough accuracy. They cover most of the population and what they miss doesn’t feel significantly biased. The devices are inexpensive and easy to install, so full-fleet measurement is possible and PoC’s can be done very inexpensively. They do a great job letting us differentiate and measure Associates and they provide a reasonable opt-out mechanism for shoppers. Even if this technology doesn’t win in most categories, it provides “good-enough” performance in almost every category.

Combining Solutions

This isn’t necessarily an all or nothing proposition. You can integrate these technologies in ways that (sorta) give you the best of both worlds. We often recommend camera-on-entry, for example, even when we’re deploying an iViu solution. Why? Well, camera-on-entry is cheap enough to deploy, it provides demographics, and it provides a pretty accurate total count. We can use that total to understand how much of the population we’re missing with electronic detection and, if the situation warrants it, we can true-up the numbers based on the measured difference.

In addition, we see real value in camera-based display tracking. Without a very fine-grained RFID mesh, electronic systems simply can’t do display interaction tracking. Where that’s critical, camera is the right point solution. In fact, that’s part of what we demoed at the Capgemini Applied Innovation Exchange last week. We used iViu devices for the overall journey measurement and Intel cameras for display interaction measurement.

Similarly, in large public spaces we sometimes recommend a mix of WiFi and iViu or camera. WiFi provides the in-place full journey measurement that would be too expensive to get at any other way. But by deploying camera at choke-points or iViu in places where we need more accurate positional data, we can significantly improve overall collection and measurement without incurring unreasonable costs.

Summing Up

In a very real sense, we have no dog in this hunt. Or perhaps it’s more accurate to say we back every dog in this hunt We don’t make hardware. We don’t make more money on one system than another. We just want the easiest, best path to getting the data we need to drive advanced analytics. Both camera systems and WiFi have the potential to be better store tracking solutions with improvements in accuracy and cost. We follow technology developments closely and we’re always hoping for better, cheaper, faster solutions. And there are times right now when using existing WiFi or deploying cameras is the right way to go. But in most retail situations, we think the iViu solution is the right choice.

And the fact that their data flows seamlessly into DM1 in both batch and – with Version 2 – real-time modes? From your perspective, that should be a big plus.

Open data systems are a huge advantage when it comes to planning out your data collection strategy. And finding the right measurement software to drive your analytics is – when you get right down to it – the decision that really matters.

And the good news? That’s the easiest decision you’ll ever have to make. Because there’s really nothing else out there that’s even remotely competitive to DM1.

Data Collection for Store Location Analytics: Picking a Technology Winner

Data collection technology is at the heart of in-store customer location analytics. In my past two posts, I’ve described some of the cool analytics and measurement that our second release of DM1 brings to the enterprise. And in a way, this is the only stuff that matters. It’s what you use to solve problems. But you can’t solve those problems and DM1 can’t give you the measurement you need, without a workable data collection technology: a technology that’s reliable, accurate, and cost-effective to deploy. In digital analytics, tagging was that technology. And while tagging can seem mysterious, a basic tag is really nothing more than 20 lines of javascript code that any competent programmer could write in a day. For in-store location analytics, it’s a lot more challenging. It’s so challenging, in fact, that we’ve struggled to find data collection technologies that meet our needs. We’ve engineered the DM1 platform to be hardware and collection neutral. We take data from a variety of sources and we’ll engineer the best possible measurement from that source. But we do have a favorite – and it’s a solution that’s become our go to suggestion for MOST clients. It’s called iViu.

The iViu technology uses passive network sniffers. These little devices track smart-phones (and potentially other electronics like Smart Watches). They triangulate on the signal the device sends out to position the phone. And because they can identify the phone, they are able to track the full shopper journey from just outside the store to cash-wrap or exit. Like almost all in-store tracking devices, they can’t identify who somebody is, though they can track the same device over time. So the iViu data does let us track same-store usage (at least outside of the EU where, to be fully compliant with EU guidelines we throw away device signatures at the end of each day) and even the same shopper at different locations under your real estate portfolio. But it doesn’t tell us who the shopper is or give us a natural join key to household or digital data.

In laying out the basics, I’ve glossed over all the complexity involved – and there’s a lot. In store location analytics, data collection technologies compete along several critical dimensions: coverage, accuracy, journey measurement, demographics, privacy, associate tracking, ease of implementation and cost. Each technology and each vendor has its own unique strengths. I’m going to cover each of these factors, explain where iViu fits in, and summarize why we usually end up choosing their technology. The three main location analytics technology contenders are Camera, Passive Network Sniffers (iViu) and off the shelf WiFi access points.

Population Coverage: Ideally, a counting system will measure every shopper who comes in the store. If a counting system doesn’t collect everyone, it’s important to understand the breadth of its coverage and whether it introduces any deep bias into the measurement. In terms of population coverage, you can’t beat camera systems. They are the best technology around for getting 100% coverage. They rarely miss anyone and if anything, their pitfall is that they can be prone to overcounting.  All electronic mechanisms are limited to tracking shoppers with smart-phones. In the U.S. (and most of the world), that isn’t much of a problem. It does mean you likely won’t be counting smaller kids. Of more significance, however, is that the phone must be an emitter – with either its Bluetooth or WiFi turned on. Best estimates are that about 15% of people don’t enable those signals. So an iViu device will typically get signals from about 80-85% of the population. And we think that’s a relatively unbiased group – probably a more accurate sample than what we get in the digital world using cookies. One big advantage to the iViu system versus other electronic systems is that iViu does a much better job tracking iPhones or other devices that employ MAC randomization. Why are iPhones an issue? Beginning with iOS8, Apple started randomizing the MAC address of the device when it pings out to the world. WiFi access points and most electronic detectors use the MAC address as the device identifier. So every time an iOS device pings a typical collector, it will look like a different device. In practice, this means that WiFi based coverage will lose all iOS devices that aren’t connected to your network. That’s a pretty huge problem. In addition, iViu devices are dedicated to measurement and they do a much better job of listening than standard WiFi access points. In side by side tests, iViu devices pick up more shoppers, more consistently.

So for Population Coverage, we see it this way:

Camera: Best

iViu: Good

WiFi: Poor

Accuracy: There are lots of different ways to think about accuracy and many different use-cases and data quality problems. I’m going to focus here on basic positional accuracy – the ability to locate the shopper at particular place in the store. Positional accuracy isn’t vital for applications like door-counting – but our DM1 platform absolutely depends on it. We map location to the store and report and segment based on shopper interest. If the mapped location is wrong, our analytics are wrong. With camera systems, each camera covers a specific area of the store and it’s relatively easy to map the location of the person to the specific part of the area the camera covers. For electronic tracking, it’s more complicated. Most electronic systems work by using either (or both) the relative signal strength detected and a triangulation of the signal across devices. But while the methods used are similar, the end result is quite different depending on the implementation and the vendor. Using the iViu devices, we can usually get an accuracy of location down to about 1.5 meters. That’s almost always good enough for the type of measurement we do with DM1. With off-the-shelf WiFi systems, the accuracy is more like 10 meters (and that’s often best case). We can live with that level of accuracy if we’re doing measurement on a mall, stadium, airport or a resort. But for a store, it just isn’t good enough to work with.

So for accuracy we see it this way:

Camera: Good

iViu: Good

WiFi: Poor

Journey Measurement: All the interesting questions in shopper and store optimization involve the journey – the ability to track the shopper visit across the store. It’s fundamental to DM1 and big part of what our analytics bring to the table. In theory, all the data collection technologies should be able to track the journey. In practice, however, we’ve found that electronic systems do this vastly better than the current crop of camera systems. Electronic systems have the fairly easy task of distinguishing one phone from another. Camera systems have to track people. And while there have been dramatic improvements in facial recognition, those improvements are challenged by real-world measurement situations and often haven’t found their way into workable/available technology. A typical camera only covers a 20×20 foot area. So even a modest mall store will require a goodly number of cameras to track its full footprint. As shoppers move from zone-to-zone, the system has to be able to determine that it’s the same person. Camera systems suck at this. Suppose a camera system gets a zone crossing right 90% of the time. That sounds pretty good, until you realize that you have about a 50% chance of following a shopper across 100 feet of your store. It’s because of this limitation that so many camera-based systems are, essentially, zone counters. They count the shoppers in an area and their linger time. They don’t count journeys. It’s not a software problem. It’s a collection problem.

Camera: Poor

iViu: Good

WiFi: Good

Demographics: We’re behavioral analysts, but while we tend to believe that real behavior trumps demographics, it doesn’t mean we think demographics don’t matter. Age and gender are nearly always interesting analytic variables and it’s a distinct advantage to be able to collect them. The scorecard here is simple. Camera does a pretty good job of this. No electronic system does this at all.

Camera: Good

iViu: No

WiFi: No

Privacy: There’s an undeniable creep factor involved with in-store tracking and it can be a legitimate barrier to measurement. All of the technologies involved here do essentially the same thing and all of them do it anonymously. Some of our clients have preferred video to electronic measurement on privacy grounds. I frankly don’t understand that thinking, but privacy is an area where the arguments turn more on perception than reality. Both technologies are providing the same basic measurement and the only significant difference is that electronic measurement provides an opt-out mechanism and video doesn’t. For electronic measurement, there’s a national, online opt-out registry (and, of course, you can always turn off phone WiFi too). There is no equivalent system to opt-out of video measurement and if there was, I imagine it would involve sending in your face – which kind of sucks. I do think video benefits from the fact that most stores have already deployed it for security purposes (though the camera’s you use are usually different), but it’s hard to understand why it’s better to measure people one way than another when the implications for them are identical. I’m going to call this one a wash.

Camera: Ok

iViu: Ok

WiFi: Ok

Associate Tracking: Coming from the digital world, our focus when we started Digital Mortar was all about shoppers. But we quickly realized that tracking associates was critical. First, because associates are a huge part of the customer experience. You can’t really measure shopper journeys unless you can measure when and if they talked to an associate. But there’s also real value in understanding whether you had enough staff on the floor. If they were in the right places. If the type of associate, their training or experience or tactics, made a substantial performance difference. With electronic tracking, it’s pretty easy to measure associates (they just have to carry a device). In most cases, you don’t even have to register that device. DM1 automatically detects devices with employee behaviors and classifies them appropriately. We do that to minimize compliance issues. With camera, it’s a different story. Camera systems either conflate employees with shoppers (which is a disaster) or use supplementary electronic means to remove them from the data. Not only does this introduce complexity into the system, it makes it much harder to track and measure interactions. We’ve also seen minor compliance issues (a few associates forgetting to pick up their tags occasionally) have significant negative implications on measurement quality. It’s worth mentioning here that if you only want to measure associates, there are other technologies worth considering that require code on a mobile device but which will provide VERY accurate and detailed associate tracking.

Camera: Poor

iViu: Good

WiFi: Fair

Ease of Implementation: Let’s face it, having to put hardware into stores is a hassle. One of the big benefits to WiFi based measurement is that it can take advantage of existing access points that were put there to provision WiFi to customers or to support store functions. Every other form of measurement takes new hardware and store installation. But there is a pretty big difference in the level of effort required. It takes a lot of cameras to cover a store. The cameras have to be in the ceiling and they have to precisely placed. It’s real work to get right and it’s often expensive, involves some degree of retrofitting and is time consuming. An iViu device will cover something like 10x the area of a camera – so you need a lot less of them. It’s easier to install. It doesn’t have to precisely placed and it doesn’t have to ceiling mounted. We’ve put iViu devices under tables, on top of or behind displays, on pillars and even in drawers. They do require power (plug or PoE), but they’re a snap to setup – it’s pretty much plug and play – and we’ve found that we can install in most locations without huge difficulty.

Camera: Poor

iViu: Fair

WiFi: Good

Cost: You know how athletes who sign huge new contracts always say “It wasn’t about the money” and you’re thinking – “Of course it was about the money”? Money matters. Realistically, a store can only afford to spend so much on measurement. Worse, the more you sink into the hardware, the less you can spend on the stuff that actually makes a difference – the analytics software and the people to drive it. At Digital Mortar, we’re believers in comprehensive measurement. We want to measure every store – not one store out of a hundred. And if you’re trying to measure Associates, optimize locally, or do real-time interactions, measuring a single store just doesn’t cut it. So having a measurement technology that’s cheap enough to go fleet-wide? We think that’s priceless. From that perspective, you can’t beat WiFi since it’s usually already in place and even if you have to provision it, the cost is reasonable and there are extra benefits. But, as noted, there’s pretty limited analytics you can do with a 10 meter margin of error. For a system that provides robust measurement, we like the fact that iViu devices are very cost-effective. The hardware for most stores costs less than $5k (that’s to buy the devices not an annual cost). Even very, very large stores will cost less than $20K per store. Camera systems are often far more costly – to the point that they are generally impractical for very large stores and make deploying to a large number of stores impossible.

Camera: Poor

iViu: Good

WiFi: Very Good

As you can see, there isn’t one solution that’s perfect in every respect. And in the real world, we often find reasons to deploy each technology. In Part 2 of this post, I’ll summarize the findings, explain why – given it’s overall profile – iViu makes sense for most retail stores, and also talk a little bit about the ways that you can blend technologies to get the best of each world.

A Peek Into the Future of Store Analytics

We just did our first non-incremental release of the DM1 store analytics platform since we brought it to market. It brings new analytics views to the Workbench, a host of UI and analytic tweaks, new cloud options and, best of all, real-time and full-store playback functionality to the product. Real-time creates a bevy of opportunities to operationalize measurement in both operations and marketing. So DM1 can drive more value, faster. What’s next? At the end of my last post, I described some of the juicier features slated for upcoming release: a real-time, dedicated Store Managers console, full pathing and even some machine learning applications. But I want to step back from a feature list and talk a bit about where we see the DM1 platform headed and how we try to balance and prioritize new functionality as we shape the product. It’s hard to do because we love all the new features.

From a personal perspective, no part of building Digital Mortar is more interesting or more intellectually challenging than building DM1. On the one hand, building SaaS systems in the cloud today is incredibly gratifying. You can build powerful, beautiful stuff so much faster and easier than back in the ‘80s when I first started programming or even in the late ‘90s when Semphonic took an abortive shot at building a web analytics tool. But an embarrassment of riches is still an embarrassment. Throwing stuff at a wall doesn’t make for a coherent product road-map. So when we think about new feature prioritization for DM1, we start with our core product vision.

DM1 is designed to be the measurement backbone of the store. We see the store as a learning machine with the core methodologies we brought from digital: continuous improvement through test & measure driven by analytics based on behavioral segmentation (what people actually do) and the ability to break-down shopper journeys into discrete, analyzable steps.

That core vision shaped our initial DM1 release (what Valley folks love to call the MVP – an acronym that is surely designed to suggest the sporting world’s Most Valuable Player award but actually stands for Minimally Viable Product). When DM1 went live, just about every piece of it was specifically targeted to this core vision. It provided direct access to a bunch of journey metrics that described how the store performed, it included basic shopper segmentation to analyze cross-sell patterns and do simple day-time parting, and it included a pretty robust funnel tool for breaking down shopper journeys into individual (step) components.

Let’s call this basic shopper-journey, store measurement system DM1’s core. It’s the engine that drives and integrates every other aspect of what the product might eventually do. Coming out of the digital analytics world, we tend to map a lot of our thinking into that model. The DM1 core is the equivalent of Adobe Analytics in the broader Adobe Marketing Suite. It’s the analytic and measurement engine.

Right now, most of our focus will continue to be on building that core engine.

Of the significant features we have slated for short term development, here are the ones that contribute directly to the core function of the program:

New and More Comprehensive Associate Reporting: Track individual and team performance on the floor with optional integration to VoE, employee meta-data, and VoC from in-store visits. DM1 already includes a lot of generalized Associate analytics, but this report will distill that into a set of reports that are much easier to digest, understand and act on.

D3 Integration: DM1’s current charting capabilities are pretty basic. We use an off-the-self package and we provide straightforward bar and line charting. Probably the best part of the charting is how seamlessly DM1 picks the best chart types, intelligently maps to separate axes, and lets you easily combined “like indices” in a single chart. But we’re far from pushing the envelope on what we can do visually and by using D3 for our charting package, we’ll be able to considerably expand the range of our visualizations and support even deeper on-chart customization.

Full Pathing: We’ve been tinkering since day 1 with ways to bring full pathing to store analytics. On the one hands, it’s not really all that hard. The amount of data is much less than we’re used to in digital. Our engine passes the data exhaustively with every query, so full pathing isn’t going to strain us from a performance perspective. But stores don’t have discrete waypoint like pages on a Website which makes each shopper’s path potentially a snowflake. We’ve tried various strategies to meaningfully aggregate paths within the store and I think we’ll be able to produce something that’s genuinely interesting and useful in the next few months. This will supplement the funnel analytics and provide richer and more varied analysis of how shoppers flow through the store.

Segmentation Builder: DM1’s current segmentation capabilities are limited to basic filtering on a set of pre-defined types. It does provide a pretty nice ability to segment on uploaded meta-data, but you can’t build more complex segments using Boolean logic or Regex. Not only do I think that’s important for a lot of analytic purposes, it’s also something we can support fairly easily.

Machine Learning for Segmentation: On that same theme, I’m a believer in data-driven segmentation. Data-driven segmentation uses more data, is richer, more reflective of reality, and usually more interesting than rule-based segmentations even if produced in a fairly rich builder. Both GCP and Azure offer pretty amazing ML capabilities that will allow us to build out a good data-driven segmentation capability for DM1. I think the harder part is doing the UI justice.

Store Groups: DM1 handles lots of stores, but right now, the store is the ultimate unit of analysis. We don’t support regions or fleet-wide aggregations. There are a lot of analytic and reporting problems that would be solved or made much easier with Store Groups. It’s a capability we’ve considered since Day 1 and sooner rather later I except it to be in the product.

Fully Integrated Dashboard: V2 didn’t do much to evolve the dashboard capability of the product, but we have a pretty clear direction in mind. In the next release, I expect the Dashboard to be capable of containing ANY Workbench view. That’s a simple elegant way to let analysts customize the dashboard to their taste and produce exactly what they need for the business. I remember a computer scientist from the original deep-blue chess program saying something to the effect that “Exhaustive search means never having to say your sorry”. No matter how much capability we build out in the dashboard, analysts are always going to want something from the Workbench if it does more. So I think it just makes sense to unify them and let the Dashboard do EVERYTHING the Workbench does.

Not everything we have in mind is about the core though. In the next few months, we plan to release a Store Manager Console based on the new real-time capabilities. The Store Manager Console is a whole new companion capability for DM1 targeted to a fundamentally different type of user. DM1 core is for the corporate analyst. It’s a big, powerful enterprise measurement tool. It’s definitely more than most Store Managers could handle.

But while the centralized model works really well in digital analytics (since Websites are wholly centralized), it’s less than ideal in the store world. There are a lot of decisions that need to happen locally. DM1’s Store Manager console will continuously monitor the store. It will keep track of shopper patterns, monitor queue times, alert if shoppers aren’t getting the help they need, and make it easy for Store Managers to allocate staff most effectively and message them when plans need to change.

It’s a way to bring machine smarts and continuous attention to the Store Managers iPad. Most of the capabilities we’re baking into the Store Manager Console (SMC) were actually delivered in V2. The real-time store tracking, simulator and Webhooks for messaging are the core capabilities we needed to deliver the SMC and were always a part of that larger vision.

As I hope our rate of progress has already made clear, we’re ambitious. Software design usually embodies deep trade-offs between functionality and ease-of-use or performance. Those trade-offs are challenging but not inevitable. We’ve seen how digital analytics tools like Google Analytics and data viz tools like Tableau have sometimes been able to step outside existing paradigms to deliver more functionality side-by-side with better usability. Most of what we’ve done so far in DM1 is borrow creatively from two decades worth of increasing maturity in digital analytics. Still, tools like our Funnel Viz and – particularly – our Store Layout Viz have tackled location/store specific problems and genuinely advanced the state-of-the-art. As we tackle pathing and machine learning, I hope to do quite a bit more of that and find ways to bring more advanced analytics to the table even while making DM1 easier to use.