Tag Archives: analytics

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!

Measuring Public Spaces: Customer Journey Tracking in Airports and other Public Venues

For this last stop in my whirlwind tour of customer journey tracking outside retail, I picked airports as a prototypical example of a public space. Airports are large, complex spaces with key chokepoints, broad wifi-coverage, and an interest in purely anonymous tracking. As with the vast majority of public spaces, there’s little or no interest in CRM type applications. We don’t need to know repeat visitor rates and don’t need to identify specific users.

For large public spaces, the business focus is almost exclusively on the proper layout and management of the physical space and supporting staff. In some cases, the full customer journey might be of significant interest. It would be useful, for example, to know how long it takes a typical flyer to check baggage, make it through security and get to a gate. It might also be interesting to see how many people get “lost” between key points – especially in international airports. But for a lot of location level analytics, point measurement will suffice. If you know the queue times at key chokepoints you can reasonably infer overall journey times and you can use the data to optimize each point.

For most of the applications I’ve considered – inside or outside retail – understanding the full customer journey is absolutely fundamental. But while I haven’t delved into the technology of tracking (yet), measuring the full journey complicates the tracking infrastructure. When you don’t need to track full journeys, it opens up point measurement solutions like camera and dedicated sniffers that might be too expensive to use if you needed to cover the entire facility. For any large space, this trade-off between point solutions and true journey measurement is important to consider.

Across all the use cases covered, I’ve concentrated on the measurement opportunities, and those opportunities in public spaces include better staff allocation, queue management, improved signage and facility design.

It isn’t just measurement, though. Customer-tracking technologies can help drive better crowd management strategies via real-time feedback. If you’ve been to Disneyland in the last five years, chances are you’ve used an app that tells you how long the lines are on every ride. It’s pretty useful (except when the park gets really crowded and the app can better be classified as ‘just depressing’) and it helps optimize the overall park experience for everyone. Most large airports have multiple gate entry points and some already display the wait times at each one – allowing passengers to self-steer and balance the lines. At others, I’ve been hand-directed by TSA staff or simply left to wonder if I’ve made a reasonable decision.

Incorporation of real-time crowd measurement into an App is potentially useful in almost any crowded environment – from event venues to resorts to sports arenas to large public spaces. And with so many companies looking for functionality to give their customers a real reason to download their app, this might not be a bad place to look.

For some people, customer experience is just a code word for maximizing sales. It’s not. And realizing that even purely public spaces have a real opportunity to use measurement to improve their operations is at least salutary.

Key Questions you can Answer with Customer Journey Tracking

  • Are staff allocated properly on a consistent basis to maximize customer experience?
  • Can information be publicly re-purposed to let individuals self-select journeys and optimize space usage?
  • Are key journeys confusing for visitors?
  • Are there unmet facility or concession needs based on area or day/time?
  • Are there exogenous factors that can be used to better optimize operations and usage?

Summing Up

There really are exciting use-cases for physical journey measurement outside of retail. If you have interesting customer journeys and reasonably complex physical spaces, this is a type of measurement you should consider. The exact nature of your problems and opportunities will determine what kind of collection technology is best – and for the most part you should realize that the off-the-shelf software dedicated to retail probably won’t serve your needs. With the right kind of data feed, however, you can take advantage of standard analytics warehousing and data analysis capabilities  to get the job done.

If you have questions about choosing the right technology, deploying it, or building customer journey analysis for your unique set of problems, drop me a line!

Ground Zero for the Retail Apocalypse: Mall Analytics

Overbuilt. Underused. Under-siege. Mall traffic has declined precipitously in the last decade and the need to aggressively drive traffic via better experience is a matter of plain survival. That need for traffic has led to dramatic changes in the way malls are designed and executed – making them more experiental and less anchored. But if you can’t measure the impact of an experience by segment, how you can possibly drive continuous improvement?

Malls are a hybrid case of physical location measurement: a large public space but one in which elements of store tracking are clearly present. Of course, most malls already have a basic counting infrastructure. They track the high-level flow of customers and can help individual stores evaluate their overall share as well as document the populations they deliver.

But with the way malls are changing, there are opportunities and new uses for customer journey tracking that can dramatically improve mall analytics. Not only are malls becoming more experiential, anchors are becoming more diverse and traffic patterns more complex. These days, it really isn’t good enough to understand broad traffic patterns without being able to segment and group customers meaningfully. To really optimize experience and design, you need to know more than how many customers passed through. You need to understand what customers did, in what order, and in what combinations.

Fortunately (because this is a big cost driver), Malls don’t require high positional accuracy in measurement. But they absolutely require the ability to track journeys and define segments. Zone counting just won’t cut it. It’s critical to be able to measure experience usage and tie that to actual store visits and to USE that knowledge to continuously tune experiences. It’s just as important to be able to track over-time usage of the malls. A lot of interesting store analytics happens at the visit level. Visitor is far more important for a mall evaluating experience drivers. If the key metric being optimized is repeat visits and you can’t track that, what’s the point?

Finally, malls are like stadiums in that they can expect reasonable rates of wifi access and have increasingly focused on building out CRM and digital marketing efforts to drive traffic. Adding tracking data to that equation delivers far better segmentation and relevancy (and segmentation and relevancy determine success) and makes it possible to bring straightforward remarketing techniques to bear on your customer marketing. It’s no surprise that we see so many re-marketing display ads these days. It’s the only form of display that even remotely works. Re-marketing based on store visit is a big shot in the arm to mall CRM relevancy and a great way to build partnerships and deliver added value from mall analytics. And, as an added bonus, you get dramatically better insight into whether or not those CRM efforts are actually working!

Key Questions

  • How do mall anchor experiences draw and how do their users interact with the rest of the mall?
  • How do changes in experience impact store usage and success by segment?
  • What shopping segments exist and how can segmentation be used to maximize CRM relevancy?
  • What experiences create return visits & increased over time consumption?
  • What experience data can be used to optimize digital communications?

For more information about in-store customer tracking and analytics, drop me a line.

Customer Strategy for Retail – Using Analytics and Customer Journey Tracking

I’ve detailed five different ways that in-store customer journey tracking drives store improvement: from optimizing store merchandising to improving in-store digital experiences and tuning omni-channel visits. All are important and each can drive measurable ROI. But in-store customer journey also tracking has broad implications at the strategic level of your organization.  Everyone wants to be more customer focused. I hear that all the time. Over and over. I even agree. And if you’re delivering a physical experience to customers without adequate measurement, you’re not just delivering a sub-optimal experience, you’re missing out on an opportunity to drive customer-centric thinking deeper into your enterprise.

In organizations that take customer focus seriously, the key question isn’t what will maximize sales. It’s what does the customer like/want. Getting an organization to think that way isn’t easy and it’s not even always clear that it’s the right thing to do. I’ve seen plenty of cases where operations and sales people just roll their eyes at a customer-centric proposal – sure that the bottom-line impact will be unsustainable. I tend to shy away from absolutes. The world is a complex place and not every problem demands absolute customer focus regardless of cost. But I do know this; unless you take that customer question to heart, your customer journey exercises will fail. You really do have to care about the customer’s experience and you have to get used to thinking about it that way.

Analytics in general and in-store measurement tracking in particular is a powerful tool for driving customer-centricity. Customer experience issues aren’t captured in traditional ERP data. They don’t show up in our BI reports on product sales by SKU. They aren’t illuminated by marketing studies. To bring customer experience into focus in the organization, you need a set of tools that help the organization map, track, and study real customer experiences.

In physical measurement, store tracking systems aren’t the only tool in your customer experience toolkit (just as digital analytics tools aren’t the only tools in the digital world). Voice of Customer data, in particular, is a critical part of building customer-centric thinking and fueling both strategy and continuous improvement. For years now I’ve championed the integration of VoC data with behavioral data so that decision-makers can see and balance the trade-offs between hard goals (sales optimization) and soft goals (experience, branding, satisfaction). That’s every bit as true in physical retail as it is in eCommerce with the additional requirement that Voice of Employee becomes almost equally important.

You can’t craft and hone an effective customer journey strategy on the back of a one-time customer journey mapping consulting engagement. That doesn’t work. Part of real customer-centricity is realizing that the work of understanding and optimizing customer journeys never ends. It’s a continuous process that requires tools and organizational commitment.

But by bringing real-measurement of the in-store customer experience to your enterprise, you drive a whole new set of customer-centric questions and a fundamentally different approach to staying customer-focused into the enterprise. I spent the last few years prior to Digital Mortar helping drive enterprise digital transformation. It’s hard. But customer measurement is both a hammer and wedge into the organization; it’s one of the most effective tools around to drive organizational transformation.

Use it.

Questions you can Answer

  • What types of customer shopping experiences are there in the store?
  • How do those experiences change in nature or distribution by store type and region?
  • How do my traditional customer segments map to in-store behaviors?
  • How do loyal customer visits in-store differ from casual or non-loyal visits?
  • Are there customers who aren’t well served by the store layout?
  • Are we finding the right type of sales associate and is there incentive structure encouraging both sales and customer satisfaction?
  • Have we setup the store and store operations to minimize customer frustration?

To find out how Digital Mortar can help you improve your in-store experiences and drive transformation, drop us a line.

Omni-channel Analytics and In-store Customer Tracking

While digital experiences are just beginning to penetrate the physical store, the customer’s integration of digital and physical shopping behaviors is already robust. If you have bricks & mortar, you have to figure out how to use that fact to your advantage in delivering experience. That’s what omni-channel is all about. There have been a number of omni-channel retail initiatives in the past couple of years that were undeniably successful. Online to in-store pickup, flexible return, and store localized supply chains have become key ingredients to omni-channel success. But there’s a long way to go before those experiences are mature and optimized.

Not surprisingly, retailers have discovered (sometimes to their chagrin), that omni-channel initiatives have a real downside when it comes to store operations. If you’re staff is spending more time processing online returns, what happens to customer service and sales?

It’s all too easy to steal from Peter to pay Paul. You may be delivering great service to one customer while you’re simultaneously ignoring another. And the two facts may be deeply related. Unless you can measure what’s actually happening in store, you’ll consistently miss these types of interactions.

With in-store tracking technology, you can explore how those omni-channel initiatives are actually impacting store operations AND customer experience. You can track what customers do after a return or before a pickup. You can track the over-time behavior of omni-channel customers to understand the impact on loyalty. You can measure whether sales interactions increase, decrease and are changed by omni-channel duties. And there are at least a couple strategies for beginning to join the in-store customer experience to the digital world. That join is hard, but it allows you do better analysis of almost every aspect of your business. Even better, it opens up a world of new marketing opportunities.

If there’s any area of online display advertising that works, it’s re-marketing. With the store to digital join, you have the opportunity to do digital re-marketing based on in-store behavior. That’s taking show-rooming to a new (and better) level!

If you’re looking for a deep-dive into the single hottest area in modern retail and in-store customer analytics, check out this video introduction I put together. It provides a crisp, easy introduction to the ins-and-outs of omni-channel analytics with in-store customer data including the all-important digital to store join.

Questions you can Answer

  • How much do omni-channel initiatives impact store operations and sales interactions?
  • Are omni-channel tasks being handled by the right staff?
  • Are omni-channel customers significantly different in their store behaviors?
  • What are the best cross-sell and personalization opportunities around omni-channel visits?
  • How much can a digitally sourced visit be steered to traditional shopping without damaging the experience?
  • How omni-channel initiatives change the way the store layout functions and are their opportunities to advantage some kinds of promotions or products as a result?