Tag Archives: location analytics

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.

Four Fatal Flaws with In-Store Tracking

I didn’t start Digital Mortar because I was impressed with the quality of the reporting and analytics platforms in the in-store customer tracking space. I didn’t look at this industry and say to myself, “Wow – here’s a bunch of great platforms that are meeting the fundamental needs in the space at an enterprise level.” Building good analytics software is hard. And while I’ve seen great examples of SaaS analytics platforms in the digital space, solutions like Adobe and Google Analytics took many years to reach a mature and satisfying form. Ten years ago, GA was a toy and Adobe (Omniture SiteCatalyst at the time) managed to be both confusing and deeply under-powered analytically. In our previous life as consultants, we had the opportunity to use the current generation of in-store customer journey measurement tools. That hands-on experience convinced me that this data is invaluable. But it also revealed deep problems with the way in-store measurement is done.

When we started building a new SaaS in-store measurement solution here at Digital Mortar, these are the problems in the technology that we wanted to solve:

Lack of Journey Measurement

Most of today’s in-store measurement systems are setup as, in essence, fancy door counters. They start by having you draw zones in the store. Then they track how many people enter each zone and how long they spend there (dwell time).

This just sucks.

It’s like the early days of digital analytics when all of our tracking was focused on the page view. We kept counting pages and thinking it meant something. Till we finally realized that it’s customers we need to understand, not pages. With zone counting, you can’t answer the questions that matter. What did customers look at first? What else did customers look at when they shopped for something specific? Did customers interact with associates? Did those interactions drive sales? Did customer engagement in an area actually drive sales? Which parts of the store were most and least efficient? Does that efficiency vary by customer type?

If you’re not asking and answering questions about customers, you’re not doing serious measurement. Measurement that can’t track the customer journey across zones just doesn’t cut it. Which brings me to…

Lack of Segmentation

My book, Measuring the Digital World, is an extended argument for the central role of behavioral segmentation in doing customer analytics. Customer demographics and relationship variables are useful. But behavior – what customers care about right now – will nearly always be more important. If you’re trying to craft better omni-channel experiences, drive integrated marketing, or optimize associate interactions, you must focus on behavioral segmentation. The whole point of in-store customer tracking is to open up a new set of critically important customer behaviors for analysis and use. It’s all about segmentation.

Unfortunately, if you can’t track the customer journey (as per my point above), you can’t segment. It’s just that simple. When a customer is nothing more than a blip in the zone, you have no data for behavioral segmentation. Of course, even if you track the customer journey, segmentation may be deeply limited in analytic tools. You could map the improvement of Adobe or Google Analytics by charting their gradually improving segmentation capabilities. From limited filtering on pre-defined variables to more complex, query-based segmentation to the gradual incorporation of sophisticated segmentation capabilities into the analyst’s workbench.

You can have all the fancy charts and visualizations in the world, but without robust segmentation, customer analytics is crippled.

Lack of Store Context

When I introduce audiences to in-store customer tracking, I often use a slide like this:

In-store Customer Analytics

The key point is that the basic location data about the customer journey is only meaningful when its mapped to the actual store. If you don’t know WHAT’S THERE, you don’t have interesting data. The failure to incorporate “what’s there” into their reporting isn’t entirely the fault of in-store tracking software. Far too many retailers still rely on poor, paper-based planograms to track store setups. But “what’s there” needs to be a fundamental part of the collection and the reporting. If data isn’t stored, aggregated, trended and reported based on “what’s there”, it just won’t be usable. Which brings me to…

Use of Heatmaps

Heatmaps sure look cool. And, let’s face it, they are specifically designed to tackle the problem of “Store Context” I just talked about. Unfortunately, they don’t work. If you’ve ever tried to describe (or just figure out) how two heat-maps differ, you can understand the problem. Dialog like: “You can see there’s a little more yellow here and this area is a little less red after our test” isn’t going to cut it in a Board presentation. Because heat-maps are continuous, not discrete, you can’t trend them meaningfully. You can’t use them to document specific amounts of change. And you can’t use them to compare customer segments or changed journeys. In fact, as an analyst who’s tried first hand to use them, I can pretty much attest that you can’t actually use heat-maps for much of anything. They are the prettiest and most useless part of in-store customer measurement systems. If heat-maps are the tool you have to solve the problem of store context, you’re doomed.

These four problems cripple most in-store customer journey solutions. It’s incredibly difficult to do good retail analytics when you can’t measure journeys, segment customers, or map your data effectively onto the store. And the ubiquity of heat-maps just makes these problems worse.

But the problems with in-store tracking solutions don’t end here. In my next post, I’ll detail several more critical shortcomings in the way most in-store tracking solutions are designed. Shortcomings that ensure that not only can’t the analyst effectively solve real-world business problems with the tool, but that they can’t get AT THE DATA with any tools that might be able to do better!

Want to know more about how Digital Mortar can drive better store analytics? Drop me a line.

In-Store Customer Journey Tracking: Can You Really Do This?

When I describe my new company Digital Mortar to folks, the most common reaction I get is: “Can you really do this?”

Depending on their level of experience in the field, that question has one of two meetings. If they haven’t used existing in-store customer tracking solutions, the question generally means: is the technology practical and is it actually OK to use it (i.e. does it violate privacy policies)? If they have experience with existing in-store customer tracking solutions what they mean is: “does your stuff actually work as opposed to the garbage I’ve been using?”

I’m going to tackle the first question today (is the technology practical and legal) and leave the second for next time.

Is the Technology Practical?

Yes. As my post last week made clear, the various technologies for in-store customer tracking have challenges. Data quality is a real problem. There are issues with positional accuracy, visitorization, journey tracking, and even basic reliability. This is still cutting or even bleeding-edge technology. It’s like digital analytics circa 2005 not digital analytics 2017. But the technologies work. They can be deployed at scale and for a reasonable cost. The data they provide needs careful cleaning and processing. But so does almost any data set. If chosen appropriately and implemented well, the technologies provide data that is immediately valuable and can drive true continuous improvement in stores.

How Hard is it to Deploy In-Store Tracking?

Unfortunately, the in-store customer tracking technologies that don’t take at least some physical in-store installation (Wi-Fi Access Point based measurement and piggybacking off of existing security cameras) are also the least useful. Wi-Fi measurement is practical for arenas, airports, malls and other very large spaces with good Wi-Fi opt-in rates. For stores, it just doesn’t work well enough to support serious measurement. Security cameras can give you inaccurate, zone based counts and not much else.  Good in-store measurement will require you install either measurement focused cameras or passive sniffers. Of the two, sniffers are lot easier. You need a lot less of them. The placement is easier. The power and cabling requirements are lower. And they are quite a bit cheaper.

Either way, you should expect that it will take a few weeks to plan out the deployment for a new store layout. This will also involve coordination with your installation partner. Typically, the installation is done over one or two evenings. No special closing is required. With sniffers, the impact on the store environment is minimal. The devices are about the size of a deck of playing cards, can be painted to match the environment and any necessary wiring is usually hidden.

After a couple week shake down, you’ll have useable measurement and a plan you can roll out to other stores. Subsequent stores with the same or similar layout can be done as quickly as your installation partner will schedule them. And the post-install shake-down period is less.

So if you’re planning a Pilot project, here’s the timeline we use at Digital Mortar:

Month 1

  • Select Store Targets: We typically recommend 3 stores in a Pilot – one test and two control stores with similar layout and market.
  • Select Initial Store
  • Design Implementation for the Initial Store
  • Train Installation Partner
  • Do initial 1 store installation

Month 2

  • Test the initial installation and tune plan if necessary
  • Rollout to additional stores
  • Provide initial reporting
  • Targeted analysis to develop store testing plan

Month 3

  • Run initial test(s)
  • Analyze control vs. test
  • Assess findings and make optimization recommendations
  • Evaluate pilot program

This kind of Pilot timeline gets you live, production data early in Month 2 with initial store findings not long after. And it gets you real experience with the type of analysis, testing and continuous improvement cycle that make for effective business use.

Is it Ok to Use Location Analytics?

Yes. In-store tracking technology is already widely used. The majority of major retailers have tried it in various forms. There is an established community of interest focused on privacy and compliance in location analytics (the Future of Privacy Forum) that is supported by the major technology players (including giants like Cisco who do this routinely), major retailers, most of the vendors specific to the space, and plenty of heavy-hitters from a political standpoint. They’ve published guidelines (with input from the FTC) on how to do this. In many respects, the landscape is similar to digital. To do this right, you must have a documented and published privacy policy and you MUST adhere to your own privacy policy. If you offer an online opt-out, you must provide and honor an online opt-out. If you offer an in-store opt-out, you must provide it. To abide by the privacy standards, you must treat the visitor’s phone MAC address as PII information. You must not keep and match the visitor’s MAC address without opt-in and you should make sure that is hashed or transformed when stored.

And, of course, in the EU the tracking guidelines are significantly more restrictive.

In almost all respects, this is identical to the use of cookies in the digital world. And, as with the digital world, it’s not hard to see where the blurry lines are. Using in-store customer journey tracking to improve the store is non-controversial – the equivalent of using first-party cookies to analyze and improve a website. Using appropriately described opt-ins to track and market to identified customers is fine as long as the usage is appropriately disclosed. Selling customer information begins to touch on gray areas. And identifying and marketing to users without opt-in using any kind of device fingerprinting is very gray indeed.

Bottom line? In-store customer tracking and location analytics is ready for prime-time. The technologies work. They can be deployed reasonably and provide genuinely useful data. Deployment is non-trivial but is far from back-breaking. And the proper uses of the data are understood and widely accepted.

In my next post, I’ll take up the analytic problems that have crippled existing solutions and explain how we’ve solved them.

An Overview of In-Store Tracking Technology

How does it work? Can you really do this? Is it legal? Those are the questions that I get asked the most about in-store customer journey tracking. The same kind of questions, to be honest, I used to get fifteen years ago in digital analytics. And when you have to answer questions like these, you know it’s still pretty raw out there. Collection technologies are a core part of measurement – whether it’s tags in digital analytics or PCAP files for in-store customer tracking. Technology matters. And with in-store tracking, the data collection technologies aren’t half-baked, but they aren’t well-cooked either.

Here’s what you need to know:

Collection Technologies

There are four (!) common approaches to in-store customer tracking: camera, wifi, passive network and mobile apps. Each has distinct characteristics and at least some advantages and disadvantages. Camera is pretty easy to understand. The cameras used for in-store measurement are video. Each camera has on-board processors that identify people, “blob” them, and then track them across their field of view. This yields a stream of data that is positionally very accurate and can also identify basic demographics around each visitor. The anonymized data is then passed to a central server where systems like ours can use it.

Your existing WiFi system can also be used to track customer journey data. This works whether or not people login to your access points. Phones regularly ping out looking for a network and those pings – anonymized – can be triangulated to figure out the position. Put those pings together, and you have a journey. One of the best things about WiFi tracking is that almost everybody already has the necessary hardware in place. That means there’s no new installation; and most of the top-tier providers of internet access points make it super easy to route the data directly to your cloud-based system. Often at no additional cost.

Passive network sniffers are small WiFi-like devices designed explicitly for in-store measurement. They work on principles similar to WiFi but they solve some problems that WiFi doesn’t. They track multiple bands, not just passive WiFi pings, and they can deliver better positional accuracy because they can be deployed in very large numbers quite cost-effectively.

Lastly, you can use code inside a mobile application to track the customer journey. Mobile apps can deliver a steady stream of positional data and have the unique benefit of being able to tie that data to the customer’s digital in-app experience.

So what’s not to like?

Well, each technology has some significant issues.

Cameras are expensive, installation is a challenge, each camera only covers a small zone, and camera systems do a remarkably poor job stitching together the customer journey. So as typically delivered, camera systems cost a lot and deliver limited measurement.

WiFi isn’t very accurate positionally – meaning it can’t be used effectively for much beyond door-counting in smaller and mid-size retail spaces. Worse, changes in MAC randomization in the IoS world have essentially eliminated the ability of WiFi systems to passively track customers with Apple devices. That means you either depend on users to connect to your WiFi (which does yield stable measurement) or you only measure your Android customers. Two bad solutions don’t add up to a good one.

Passive network sniffers improve on WiFi in terms of positional accuracy and their ability to fingerprint devices. But they don’t solve those problems perfectly and, of course, they don’t give you the no-installation, no hardware cost convenience that WiFi did.

Measurement using mobile apps? That’s great, just like everything with mobile apps provided you can get customers to actually download the app. Depending on customer app downloads for measurement is inherently a limiting factor.

Bottom line? There are places and times for every technology and there are ways to combine the technologies to yield better results (we do that). But this isn’t measurement nirvana. No solution is perfect and you’ll find plenty of things to hate in any direction you choose.

To get more detail on the ins-and-outs of in-store customer journey tracking technology (and it’s complicated), ping me. I’ll send you a DM whitepaper that gives you everything you need to know to choose wisely!

I’ll tackle practicality and legality next time!