Tag Archives: store analytics

Evolve or Die: Analytics and Retail

In my last three posts, I assessed the basic technologies (wifi, camera, etc.) for in-store customer measurement and took a good hard look at the state of the analytics platforms using that measurement. My conclusion? The technologies are challenging but, deployed properly, can work at scale for a reasonable cost. The analytics platforms, on the other hand, have huge gaping holes that seriously limit the ability of analysts to use that data. Our DM1 platform is designed to solve most (I hope all) of those problems. But it’s not worth convincing anyone that DM1 is a better solution unless people get why this whole class of solution is so important.

Over about the same amount of time as those posts, I’ve seen multiple stories on the crisis in mall real-estate, the massive disruption driven in physical retail when eCommerce cross sales thresholds as a percentage of total purchases, and the historical and historically depressing pace of store closings in 2017.

It’s bad out there. No…that doesn’t really capture things. For lots of folks, this is potentially an extinction level event. It’s a simple Darwinian equation:

Evolve or die.

And people get that. The pace of innovation and change in retail has never been as high. Is it high enough? Probably not. But retailers and mall operators are exploring a huge number of paths to find competitive advantage. At a high-level, those paths are obvious and easily understood.

Omni-Channel is Key: You can’t out-compete in pure digital with “he who must not be named”…so your stores have to be a competitive advantage not an anchor. How does that happen? Integration of the digital experience – from desktop to mobile – with the store. Delivering convenience, experience, and personalization in ways that can’t be done in the purely digital realm.

Experience is Everything: If people have to WANT to go to stores (in a line I’ve borrowed from Lee Peterson that I absolutely love), delivering an experience is the bottom line necessary to success. What that experience should be is, obviously, much less clear and much more unique to each business. Is it in-store digital experiences like Oak Labs’ delivers – something that combines a highly-customized digital shopping experience integrated right into the store operation? Is it bringing more and better human elements to the table with personalized clienteling? Is it a fundamentally different mix of retail and experience providers sharing a common environment? It’s all of these and more, of course.

The Store as a Complex Ecosystem: A lot of factors drive the in-store experience. The way the store is laid out. The merchandising. The product itself. Presentations. In-store promotions. Associate placement, density, training and role. The digital environment. Music. Weather. It’s complicated. So changing one factor is never going to be a solution.  Retail professionals have both informed and instinctive knowledge of many of these factors. They have years of anecdotal evidence and real data from one-off studies and point-of-sale. What they don’t have is any way to consistently and comprehensively measure the increasingly complex interactions in the ecosystem. And, of course, the more things change, the less we all know. But part of what’s involved in winning in retail is getting better at what makes the store a store. Better inventory management. Better presentation. Better associates and better clienteling strategies. Part of winning in a massively disrupted environment is just being really good at what you do.

The Store in an Integrated Environment: Physical synergies exist in a way that online synergies don’t. In the friction free world of the internet, there’s precious little reason to embed one web site inside another. But in the physical world, it can be a godsend to have a coffee bar inside the store while my daughters shop! Taking advantage of those synergies may mean blending different levels of retail (craft shows, farmers markets) with traditional retail, integrating experiences (climbing walls, VR movies) or taking advantage of otherwise unusable real-estate to create traffic draws (museums, shared return centers).

In one sense, all of these things are obvious. But none of them are a strategy. They’re just words that point in a general direction to real decisions that people have to make around changes that turn out to be really hard and complex. That’s where analytics comes in and that’s why customer journey measurement is critically important right now.

Because nobody knows A) The right ways to actually solve these problems and  B) How well the things they’re trying to do are actually working.

Think about it. In the past, Point of Sale data was the ultimate “scoreboard” metric in retail and traffic was the equivalent for malls. It’s all that really mattered and it was enough to make most optimization decisions. Now, look at the strategies I just enumerated: omni-channel, delivering experience, optimizing the ecosystem and integrating broader environments…

Point-of-Sale and traffic measure any of that?

Not really. And certainly, they don’t measure it well enough to drive optimization and tuning.

So if you’re feverishly building new stores, designing new store experiences, buying into cutting edge digital integrations, or betting the farm on new uses for your real-estate, wouldn’t it be nice to have a way to tell if what you’re trying is actually working? And a way to make it work better since getting these innovative, complex things right the first time isn’t going to happen?

This is the bottom line: these days in retail, nobody needs to invest in customer measurement. After all, there’s a perfectly good alternative that just takes a little bit longer.

It’s called natural selection. And the answers it gives are depressingly final.

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.

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.

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?

The Strategic Uses of In-Store Customer Journey Measurement

Store layout, promotion and staff optimization are the immediate and obvious ways to use the core data from customer journey analytics. Together, they comprise the “you” part of the equation – optimizing your operational and marketing strategies. But the uses of in-store tracking don’t end there. There’s tremendous strategic value in being to understand customer journeys – a lesson we’ve learned over and over again in digital. When it comes to omni-channel, store and experience design, and the integration of new technologies to the store, you simply can’t do the job right without in-store journey measurement.

I cover the fundamentals of why the in-store journey matters and how to build in-store customer journey data in this new post on Digital Mortar.

 

Using In-Store Customer Journey Data: Associate Optimzation

If store layout/merchandising and promotion planning are the core applications for in-store customer journey measurement, staff optimization is their neglected and genius offspring. For most retail stores, labor costs are a huge part of overall operating expenses – typically around 15% of sales. And staff interactions are profoundly determinate of customer satisfaction. In countless analytic efforts around customer satisfaction and churn, the one constant driver of both is the quality of associate interactions. People matter.

The human factor is a huge part of the customer journey. Some in-store measurement solutions treat staff interactions the way digital solutions treat employee visits – as data to be culled out and discarded. The only thing worse is when they leave them in and don’t differentiate between customer and staff!

No part of the customer journey and no part of the store has a bigger impact on the journey, on the sale, and on the brand satisfaction than interactions with your sales associates. And, of course, labor costs are one of the biggest cost drivers at the store. So optimizing staff is critical on every front: revenue optimization, customer satisfaction and cost management. It’s rare that a single point of analysis drives across all three with so much impact, highlighting how important associate optimization really is.

With staff data integrated into customer journey measurement, you know how often associate interactions occurred, you know how long they lasted, and you know how often they resulted in sales. Some stores will already track at least some of this as part of their incentive programs, but customer journey data provides a true measure of opportunity and productivity. Some of these data points are straightforward, but there are interesting aspects to staffing data that go beyond basic conversion effectiveness. It’s possible, for example, to isolate the number and impact of cases where staff interactions should have happened but didn’t. It’s also possible to understand optimal contact strategies, answering questions like ‘how long should a customer be in a section before a contact becomes desirable or imperative? ‘  Even more interesting is the opportunity to bring sports-driven team and player metrics to bear on the problems of staffing. You can understand which associate combinations work best together, how valuable team cohesion is, and the value spread between a top associate and an average hire. This is all invaluable data when it comes time to plan out schedules and staffing levels and, when paired with weather data, can also be used to optimized staffing plans on a highly local basis.

Finally, there are deep opportunities to use this data to optimize broader aspects of staff optimization. By integrating Voice of Employee (VoE) data with associate effectiveness, you can hone in on the golden questions that will help you identify the best possible hires. Creating a measurement-driven, closed loop system to optimize associate hiring decisions isn’t what people generally think of when they evaluate in-store measurement. But it’s a unique and powerful use of the technology to drive competitive advantage.

 

Questions you can Answer

  • Are there days/times when a store is over/under staffed?
  • Are there better options of positioning staff?
  • What’s the best way to optimize staffing teams and placements?
  • How much does training impact staff performance?
  • What questions should I ask when I hire new staff to identify potential stars?
  • How successful is any given associate in converting opportunities?
  • What’s the right amount of dwell-time to allow a customer prior to an associate interaction?

To find out more about retail analytics and in-store customer journey tracking, check out my new company’s site: DigitalMortar.com