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.