Tag Archives: Digital Mortar

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!

Clienteling in Hospitality and the Lessons for Retail

Ah to be at a resort. And not just for the sun, the swimming, the beaches and the drinking…it’s the measurement opportunities that really appeal. When it comes to physical journey measurement, resorts have it all. Large, complex physical spaces with an absolute premium on experience. Complicated staffing and numerous interactions. Overwhelmingly high opt-in rates for wifi. CRM uses out the wazoo. Resorts include a retail component, a dining component, a service component and whole bunch of physical spaces all with important experience optimization potential.

The opt-in component drives countless CRM opportunities – both in near real-time and for long-term follow-up and customer database enrichment. With on-property journey tracking you know how much time customers spend at the resort, how often they go out, where they like to spend their time and even what sort of pool they prefer. You can know whether a family likes to stick together or split up, when they like dine, and when and where they had to wait. All of this is incredibly useful stuff for personalization and messaging in EVERY channel.

Resorts are also a place where the intersection of CRM and navigation analytics can deliver deep insight into segmented experience optimization. For years, I’ve preached the simple and straightforward gospel that there isn’t a single best customer experience. There are countless different types of customers on countless journeys. Without bringing segmentation into your experience analysis, you’ll never be able to achieve true optimization across a population of any size.

And, of course, resorts are the ultimate example of clienteling. What retailers are trying to adopt, resorts and hospitality and have made core to their business.

Clienteling is a huge opportunity space for customer location tracking. But to understand why, a bit of digression into the background analytics is necessary. Years of work in digital have made me rather skeptical of real-time analytics. There was a time when digital analytics solutions were sold heavily on the basis of their real-time capabilities. Except in media, though, not many people found ways to take advantage of that capability. At the enterprise level, business decisioning doesn’t happen in real-time.

On the other hand, real-time does matter in personalization. It’s not that you can’t personalize without real-time analytics. You can (and should – it’s the right way to start). But there’s no doubt that what the customer is doing right now matters! A lot of companies that do in-store customer tracking see that as the real opportunity. Real-time couponing and messaging to customers as they move through your space. And there are plenty of solutions for delivering that.

But here’s the thing….it turns out that most people don’t want to be harassed as they walk around your store. And unlike resorts that have a lot of experiences guests might actually want to hear about, the sale in Aisle 6 probably doesn’t qualify. For a lot of folks, text messages are for friends and family. Not promotions. So we’ve seen consistently low opt-in rates for those systems – rendering them close-to or flat-out useless. And, of course, the lower the opt-in rates the worse the lift performance. You end up subsidizing that tiny population of coupon clippers who drive you crazy in every channel.

Which is the the long way around to why at Digital Mortar, not only have we chosen to focus on measurement not messaging, it’s why we believe that the best use of real-time analytics in both hospitality and retail isn’t to drive customer SMS – it’s to support staff with clienteling. You don’t have opt-out issues with Associates and staff. What you have is the ability to use analytics to drive better conversations in the RIGHT NOW – using what you know about customer including what you’ve just learned in the last 10 minutes.

Key Questions

  • How do new visitors get to “know” the resort and what parts of the experience stick?
  • What experiences tend to group together and are their flow issues or bottlenecks that damage the experience?
  • Are there under-served experience areas from a staffing perspective?
  • What data is most valuable from a CRM perspective?
  • What real-time data can drive better real-time conversations at every touchpoint?

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.

Taking In-Store Measurement…Out of the Store

In my last few posts, I explained what in-store journey analytics is, described the basics of the technology and the data collection used, and went into some detail about its potential business uses. Throughout, and especially in that last part around business uses, I wrote on the assumption that this type of measurement is all about retail stores. After all, brick & mortar stores are the primary focus of Digital Mortar AND of nearly every company in the space. But here’s the thing, this type of measurement is broadly applicable to a wide variety of applications where customer movement though a physical environment is a part of the experience. Stadiums, malls, resorts, cruise ships, casinos, events, hospitals, retail banks, airports, train stations and even government buildings and public spaces can all benefit from understanding how physical spaces can be optimized to drive better customer or user experiences.

In these next few posts, I’m going to step outside the realm of stores and talk about the opportunities in the broader world for customer journey tracking. I’ll start by tackling some of the differences between the tracking technologies and measurement that might be appropriate in some of these areas versus retail, and then I’m going to describe specific application areas and delve a little deeper into how the technology might be used differently than in traditional retail. While the underlying measurement technology can be very similar, the type of reporting and analytics that’s useful to a stadium or resort is different than what makes sense for a mall store.

Since I’m not going to cover every application of customer journey tracking outside retail in great detail, I’ll start with some general principles of location measurement based upon industry neutral things like the size of the space and the extent to which the visitors will opt-in to wifi or use an app.

Measuring BIG Spaces versus little ones

With in-store journey tracking, you have three or four alternatives when choosing the underlying measurement collection technology. Cameras, passive wifi, opt-in wifi and bluetooth, and dedicated sniffers are all plausible solutions. With large spaces like stadiums and airports, it’s often too expensive to provide comprehensive camera coverage. It can even be too expensive to deploy custom measurement devices (like sniffers). That’s especially true in environments where the downtime and wiring costs can greatly exceed the cost of the hardware itself.

So for large spaces, wifi tracking often becomes the only realistic technology for deploying a measurement system. That’s not all bad. While out-of-the-box wifi is the least accurate measurement technology, most large spaces don’t demand fine-grained resolution. In a store, a 3 meter circle of error might place a customer in a completely different section of the store. In an airport, it’s hard to imagine it would make much difference.

Key Considerations Driven by Size of Location:

  • How much measurement accuracy to do you need?
  • How expensive will measurement specific equipment and installation be and is it worth the cost?
  • Are there special privacy considerations for your space or audience?

Opt-in vs. Anonymous Tracking

Cameras, passive wifi and sniffers can all deliver anonymous tracking. Wifi, Bluetooth and mobile apps all provide the potential for opt-in tracking. There are significant advantages to opt-in based tracking. First, it’s more accurate. Particularly in out-of-the-box passive wifi, the changes in IoS to randomize MAC addresses have crippled straightforward measurement and made reasonably accurate customer measurement a challenge. When a user connects to your wifi or opens an app, you can locate them more frequently and more precisely and their phone identity is STABLE so you can track them over time. If your primary interest is in understanding specific customers better for your CRM, tracking over-time populations or you have significant issues with the privacy implications of anonymized passive tracking, then opt-in tracking is your best bet. However, this choice is dependent on one further fact: the extent to which your customers will opt-in. For stadiums and resorts, log-in rates are quite high. Not so much at retail banks. Which brings us to…

Key Considerations for Opt-In Based Tracking

  • Will a significant segment of your audience opt-in?
  • Are you primarily interested in CRM (where opt-in is critical) or in journey analytics (which can be anonymous)?

How good is the sample?

Some technologies (like camera) provide comprehensive coverage by default. Most other measurement technologies inherently take some sample. Any form of signal detection will start with a sample that includes only people with phones. That isn’t much of a sample limitation though it will exclude most smaller children. Passive methods further restrict the population to people with wifi turned on. Most estimates place the wifi-activated rate at around 80%. That’s a fairly high number and it seems unlikely that this factor introduces significant sample bias. However, when you start factoring in things like Android user or App downloader or wifi user, you’re often introducing significant reductions in sample size AND adding sample biases that may or may not be difficult to control for. App users probably aren’t a  representative sample of, for example, the likelihood of a shopper to convert in a store. But even if they are a small percentage of your total users, they are likely perfectly representative of how long people spend queuing in lines at a resort. One of the poorly understood aspects of measurement science is that the same sample can be horribly biased for some purposes but perfectly useful for others!

Key Considerations for Sampling

  • Does your measurement collection system bias your measurement in important ways?
  • Are people who opt-in a representative sample for your measurement purposes?

The broad characteristics that define what type of measurement system is right for your needs are, of course, determined by what questions you need to answer. I’ll take a close look at some of the business questions for specific applications like sports stadiums next time. In general, though, large facilities by their very nature need less fine-grained measurement than smaller ones. For most applications outside of retail, being able to locate a person within a 3 meter circle is perfectly adequate. And while the specific questions being answered are often quite specific to an application area, there is a broad and important divide between measurement that’s primarily focused on understanding patterns of movement and analysis that’s focused on understanding specific customers. When your most interested in traffic patterns, then samples work very well. Even highly biased samples will often serve. If, on the other hand, you’re looking to use customer journey tracking to understand specific customers or customer segments (like season-ticket holders) better, you should focus on opt-in based techniques. In those situations, identification trumps accuracy.

If you have questions about the right location-based measurement technology solution for your business, drop us a line at info@digitalmortar.com

Next up, I’ll tackle the surprisingly interesting world of stadium/arena measurement.