Tag Archives: retail analytics

In-Store Customer Analytics: Broken Inside and Out

In my last post, I described four huge deficiencies in the current generation of in-store tracking solutions. The inability to track full customer journeys, do real segmentation, or properly contextualize data to the store make life very hard on a retail analyst trying to do interesting work. And over-reliance on non-analytic heatmaps – a tool that looks nice but is analytically unrewarding – just makes everything worse.

Of course, you don’t need to use one of these solutions. You can build an analytics warehouse and use some combination of extraordinarily powerful general purpose tools like Tableau, Datameer, Watson, and R to solve your problems.

Or can you?

Here are three more problems endemic to the current generation of in-store tracking solutions that limit your ability to integrate them into a broader analytics program.

Too Much or Too Little Associate Data

In retail, the human factor is often a critical part of the customer journey. As such, it needs to be measured. In-store counting solutions have tended toward two bad extremes when it comes to Associate data. Really, really bad solutions have just tracked Associates as customers. That’s a disaster. In the online world, we worked to screen-out the IP addresses of employees from our actual web site counting even though it was a tiny fraction of the overall measurement total. In the store world, it’s not a tiny fraction – especially given the flaws of zone-counting solutions. We’ve seen cases where a small number of associates can look like hundreds of customers. So including associate data in the store customer counts is pretty much a guarantee that your data will be garbage. On the other hand, tracking associates just so you can throw their data away isn’t the right answer either. Those interactions are important – and they are important at the journey level. Solutions that throw this data away or aggregate it up to levels like hour or day counts are missing the point. Your solution needs to be able to identify which visits had interactions, which didn’t, and which were successful. If it can’t do that, it’s not going to solve any real-world problems.

Which brings me to…

Lack of Bespoke Analytics

One of the obvious truths about analytics in the modern world is that no bespoke analytics solution is going to deliver everything you need. Even mature, enterprise solutions like Adobe Analytics don’t deliver all of the visualization and analytics you need. What bespoke analytics tools should deliver is analytics uniquely contextualized to the business problem. This business contextualization is hard to get out of general purpose tools; so it’s the real life-blood of industry and application targeted solutions. If a solution doesn’t deliver this, it’s ripe for replacement by general purpose analytic platforms. But by going exclusively to general purpose solutions, the organization will lose the shorter time to value that targeted analytics can provide.

Unfortunately, the vast majority of in-store customer tracking tools seem to deliver the sort of generic reports and charts that you might expect from an offshore outfit doing $10/hour Tableau reports. The whole point of bespoke solutions is to deliver analytics contextualized to the problem. If they are just doing a bad job of replicating general purpose OLAP tools you have to ask why you wouldn’t just pipe the data into an analytic warehouse.

Which brings me to my final point…

Lack of a True Event Level Data Feed

No matter how good your bespoke analytics solution is, it won’t solve every problem. It isn’t going to visualize data better than Tableau. It won’t be as cognitive as Watson. Or as good a platform for integration as Datameer. And its analytics capabilities are not going to equal SAS or R. Part of being a good analytics solution in today’s world is recognizing that custom-fit solutions need to integrate into a broader data science world. For in-store customer journey tracking, this is especially important because the solution and the data collection mechanism are often bound together (much as they are in most digital analytics). So if you’re solution doesn’t open up the data, you CAN’T use that data in other tools.

That should be a deal killer. Any tool that doesn’t provide a true, event level data feed (not aggregated report-level data which is useless in most of those other solutions) to your analytics warehouse doesn’t deserve to be on an enterprise short-list of customer journey tracking tools.

Open integration and enterprise data ownership should be table stakes in today’s world.

Summing it Up

There’s a lot not to like about the current generation of in-store customer journey solutions. For the most part, they haven’t delivered the necessary capabilities to solve real-world problems in retail. They lack adequate journey tracking, real segmentation, proper store contextualization, bespoke analytics, and open data feeds. These are the tools essential to solving real-world problems. Not surprisingly, the widespread perception among those who’ve tried these solutions is that they simply don’t add much value.

For us at Digital Mortar, the challenge isn’t being better than these solutions. That’s not how we’re measuring ourselves, because being better isn’t enough. We have to be good enough to drive real-world improvement.

That’s much harder.

In my next post(s), I’ll show how we’ve engineered our new platform, DM1, to include these capabilities and how that, in turn, can help drive real-world improvement.

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!

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.

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.