Tag Archives: store analytics

Creating a Measurement Language for the Store

Driving real value with analytics is much harder than people assume. Doing it well requires solving two separate, equally thorny problems. The first – fairly obvious problem – is being able to use data to deepen your understanding of important business questions. That’s what analytics is all about. The second problem is being able to use that understanding to drive business change. Affecting change is a political/operational problem that’s often every bit as difficult as doing the actual analysis. Most people have a hard time understanding what the data means and are reluctant to change without that understanding. So, giving analysts tools that help describe and contextualize the data in a way that’s easy to understand is a double-edged sword in the best of ways – it helps solves two problems. It helps the analyst use the data and it helps the analyst EXPLAIN the data to others more effectively. That’s why having a rich, powerful, UNDERSTANDABLE set of store metrics is critical to analytic success with in-store customer tracking.

Some kinds of data are very intuitive for most of us. We all understand basic demographic categories. We understand the difference between young and old. Between men and women. We live those data points on a daily basis. But behavioral data has always been more challenging. When I first started using web analytics data, the big challenge was how to make sense of a bunch of behaviors. What did it mean that someone viewed 7 pages or spent 4.5 minutes on a Website? Well, it turned out that it didn’t mean much at all. The interesting stuff in web analytics wasn’t how many pages a visitor had consumed – it was what those pages were about. It meant something to know that a visitor to a brokerage site clicked on a page about 529 accounts. It meant they had children. It meant they were interested in 529 accounts. And depending on what 529 information they chose to consume, it might indicate they were actively comparing plans or just doing early stage research. And the more content someone consumed, the more we knew about who they were and what they cared about.

Which was what we needed to optimize the experience. To personalize. To surface the right products. With the right messages. At the right time. Knowing more about the customer was the key to making analytics actionable and finding the right way to describe the behavior with data was the key to using analytics effectively.

So when it comes to in-store customer measurement, what kind of data is meaningful? What’s descriptive? What helps analysts understand? What helps drive action?

The answer, it turns out, isn’t all that different from what works in the digital realm. Just as the key to understanding a web visit turns out to be understanding the content a visitor selected and consumed, the key to understanding a store visit turns out to be understanding the store. You have to know what the shopper looked at. What was there when they stopped and lingered. What was along the corridor that they traversed but didn’t shop. You have to know the fitting room from the cash-wrap and an endcap from an aisle and you have to know what products were there. What’s more, you have to place the data in that context.

Here’s what the data from an in-store measurement collection system looks like in its raw form, frame by frame:

TimeXY
04:06.03560
06:50.0966
09:10.02374
11:02.01892
11:35.03398
13:15.02874
14:25.0781
16:16.04175
19:09.04962
21:03.04572
23:23.05583
23:58.05490
24:09.04086
25:05.01590
27:24.0779
27:45.04399
28:42.03797
29:25.04580
32:07.04775
33:05.01677
35:31.03765
36:08.03475
36:33.0973
39:16.03576
40:07.01397

That’s a visit to a store. A little challenging to make sense of, right?

It’s our job to translate that into a journey with the necessary context to make the data useful.

That starts by mapping the data onto the store:

store journey analytics

By overlaying the measurement frames, we can distinguish the path the user took through the store:

StoreFrame1

With simple analysis of the frames, we can figure out where and when a customer shifted from navigating the store to actually spending time. And that first place the shopper actually spends time, has special significance for understanding who they are.

In DM1, the first shopping point is marked as the DRAW. It’s where the shopper WENT FIRST in the store:storeFrame2

In this case, Customer Service was the Draw – indicating that this shopping visit is a return or in-store pickup. But the visit didn’t end there.

Following the journey, we can see what else the customer was exposed to and where else they actually spent time and shopped. In DM1, we capture each place the shopper spent time as a LINGER:

storeFrame3

Lingers tell us about opportunity and interest. These are the things the shopper cared about and might have purchased.

But not every linger is created equal. In some places, the shopper might spend significantly more time – indicating a higher level of engagement. In DM1, these locations are called out on the journey as CONSIDERS:

storeframe4

Having multiple levels of shopper engagement lets DM1 create a more detailed picture of the shopper and a better in-store funnel. Of course, one of the keys to understanding the in-store funnel is knowing when a shopper interacts with an Associate. That’s a huge sales driver (and a huge driver – positive or negative – to customer experience). In DM1, we track the places where a shopper talked with and Associate as INTERACTIONS. They’re a key part of the journey:

storeFrame5

Of course, you also want to know when/if a customer actually purchased. We track check-outs as CONVERSIONS – and have the ability to do that regardless of whether it’s a traditional cash-wrap or a distributed checkout environment:

storeFrame6

Since we have the whole journey, we can also track which areas a customer shopped prior to checkout and we’ve created two measures for that. One is the area shopped directly before checkout (which is called the CONVERSION DRIVER) and the other captures every area the customer lingered prior to checkout – called ATTRIBUTED CONVERSIONS.

StoreFrame8

To use measurement effectively, you have to be able to communicate what the numbers mean. For the in-store journey, there simply isn’t a standardized way of talking about what customers did. With DM1, we’ve not only captured that data, we’ve constructed a powerful, working language (much of it borrowed from the digital realm) that describes the entire in-store funnel.

From Visits (shopper entering store), to Lingers (spending time in an area), to Consideration (deeper engagement), to Investment (Fitting Rooms, etc.), to Interactions (Associate conversations) to Conversion (checkout) along with metrics to indicate the success of each stage along the way. We’ve even created the metric language for failure points. DM1 tracks where customers Lingered and then left the store without buying (Exits) and even visits where the shopper only lingered in one location before exiting (Bounces).

Having a rich set of metrics and a powerful language for describing the customer journey may seem like utter table-stakes to folks weaned on digital analytics. But it took years for digital analytics tools to offer a mature and standardized measurement language. In-store tracking hasn’t had anything remotely similar. Most existing solutions offer two basic metrics (Visits and Dwells). That’s not enough for good analytics and it’s not a rich enough vocabulary to even begin to describe the in-store journey.

DM1 goes a huge mile down the road to fixing that problem.

[BTW – if you want to see how DM1 Store Visualization actually works, check out these live videos of DM1 in Action]

Segmentation is the Key to Marketing Analytics

The equation in retail today is simple. Evolve or die. But if analytics is one of the core tools to drive successful  evolution, we have a problem. From an analytics perspective, we’re used to a certain view of the store. We know how many shoppers we get (door counting) and we know what we sold. We know how many Associates we had. We (may) know what they sold. This isn’t dog food. If you had to pick a very small set of metrics to work with to optimize the store, most of these would belong. But we’re missing a lot, too. We’re missing almost any analytic detail around the customer journey in the store. That’s a particularly acute lack (as I noted in my last post) in a world where we’re increasingly focused on delivering (and measuring) better store experiences. In a transaction-focused world, transactions are the key measures. In an experience world? Not so much. So journey measurement is a critical component of today’s store optimization. And there’s the problem. Because the in-store measurement systems we have available are tragically limited. DM1, our new platform, is designed to fix that problem.

People like to talk about analytics as if it just falls out of data. As if analysts can take any data set and any tool and somehow make a tasty concoction. It isn’t true. Analytics is hard work. A really great analyst can work wonders, but some data sets are too poor to use. Some tools lock away the data or munge it beyond recognition.  And remember, the most expensive part of analytics is the human component. Why arm those folks with tools that make their job slow and hard? Believe me, when it comes to getting value out of analytics, it’s hard enough with good tools and good data. You can kid yourself that it’s okay to get by with less. But at some point you’re just flushing your investment and your time away. In two previous posts, I called out a set of problems with the current generation of store customer measurement systems. Sure, every system has problems – no analytics tool is perfect. But some problems are much worse than others. And some problems cripple or severely limit our ability to use journey data to drive real improvement.

When it comes to store measurement tools, here are the killers: lack of segmentation, lack of store context, inappropriate analytics tools, inability to integrate Associate data and interactions, inability to integrate into the broader analytics ecosystem and an unwillingness to provide cleaned, event-level data that might let analysts get around these other issues.

Those are the problems we set out to solve when we built DM1.

Let’s start with Segmentation. Segmentation can sound like a fancy add-on. A nice to have. Important maybe, but not critical.

That isn’t right. Marketing analytics just is segmentation. There is no such thing as an average customer. And when it comes to customer journey’s, trying to average them makes them meaningless. One customer walks in the door, turns around and leaves. Another lingers for twenty minutes shopping intensively in two departments. Averaging the two? It means nothing.

Almost every analysis you’ll do, every question you’ll try to answer about store layout, store merchandising, promotion performance, or experience will require you to segment. To be able to look at the just the customers who DID THIS. Just the customers who experienced THAT.

Think about it. When you build a new experience, and want to know how it changed behavior you need to segment. When you change a greeting script or adjust a presentation and want to know if it improved store performance you need segmentation. When you change Associate interaction strategies and want to see how it’s impacting customer behavior you need segmentation. When you add a store event and want to see how it impacted key sections, you need segmentation. When you want to know what other stuff shoppers interested in a category cared about, you need segmentation. When you want to know how successful journeys differed from unsuccessful ones, you need segmentation. When you want to know what happens with people who do store pickup or returns, you need segmentation.

In other words, if you want to use customer journey tracking tools for tracking customer journeys, you need segmentation.

If your tool doesn’t provide segmentation and it doesn’t give the analyst access to the data outside it’s interface, you’re stuck. It doesn’t matter how brilliant you are. How clever. Or how skilled. You can’t manufacture segmentation.

Why don’t most tools deliver segmentation?

If it’s so important, why isn’t it there? Supporting segmentation is actually kind of hard. Most reporting systems work by aggregating the data. They add it up by various dimensions so that it can be collapsed into easily accessible chunks delivered up into reports. But when you add segmentation into the mix, you have to chunk every metric by every possible combination of segments. It’s messy and it often expands the data so much that reports take forever to run. That’s not good either.

We engineered DM1 differently. In DM1, all the data is stored in memory. What does that mean? You know how on your PC, when you save something to disk or first load it from the hard drive it takes a decent chunk of time? But once it’s loaded everything goes along just fine? That’s because memory is much faster than disk. So once your PowerPoint or spreadsheet is loaded into memory, things run much faster. With DM1, your entire data set is stored in-memory. Every record. Every journey. And because it’s in-memory, we can pass all your data for every query, really fast. But we didn’t stop there. When you run a query on DM1, that query is split up into lots of chunks (called threads) each of which process its own little range of data – usually a day or two. Then they combine all the answers together and deliver them back to you.

That means that not only does DM1 deliver reports almost instantaneously, it means we can run even pretty complex queries without pre-aggregating anything and without having to worry about the performance. Things like…segmentation.

Segmentation and DM1

In DM1, you can segment on quite a few different things. You can segment on where in the store the shopper spent time. You can segment on how much time they spent. You can segment on their total time in the store. You can segment on when they shopped (both by day of week and time of day). You can segment on whether they purchased or not. And even whether they interacted with an Associate.

If, for example, you want to understand potential cross-sells, you can apply a segment that selects only visitors who spent a significant amount of time shopping in a section or department. Actually, this undersells the capability because it’s in no way limited to any specific type of store area. You can segment on any store area down to the level of accuracy achieved by the collection architecture.

What’s more, DM1 keeps track of historical meta-data for every area of the store. Meaning that even if you changed, moved or re-sized an area of the store, DM1 still tracks and segments on it appropriately.

So if you want to see what else shoppers who looked at, for example, Jackets also considered, you can simply apply the segmentation. It will work correctly no matter how many times the area was re-defined. It will work even in store roll-ups with fundamentally different store types. And with the segment applied, you can view any DM1 visualization, chart or table. So you can look at where else Jacket Shoppers passed through, where they lingered, where they engaged more deeply, what else they were likely to buy, where they exited from, where they went first, where they spent the most time, etc. etc. You can even answer questions such as whether shoppers in Jackets were more or less likely to interact with Sales Associates in that section or another.

Want to see if Jacket shoppers are different on weekdays and weekends? If transactors are different from browsers? If having an Associate interaction significantly increases browse time? Well, DM1 let’s you stack segments. So you can choose any other filter type and apply it as well. I think the Day and Time part segmentation’s are particularly cool (and unusual). They let you seamlessly focus on morning shoppers or late afternoon, weekend shoppers or even just shoppers who come in over lunchtime. Sure, with door-counting you know your overall store volume. But with day and time-part segmentation you know volume, interest, consideration, and attribution for every measured area of the store and every type of customer for every hour and day of week.

DM1’s segmentation capability makes it easy to see whether merchandise is grouped appropriately. How different types of visitor journeys play out. Where promotional opportunities exist. And how and where the flow of traffic contradicts the overall store layout or associate plan. For identified shoppers, it also means you can create extraordinarily rich behavioral profiles that capture in near real-time what a shopper cares about right now.

It comes down to this. Without segmentation, analytics solutions are just baby toys. Segmentation is what makes them real marketing tools.

The Roadmap

DM1 certainly delivers far more segmentation than any other product in this space. But it’s still quite a bit short of what I’d like to deliver. I mean it when I say that segmentation is the heart and soul of marketing analytics. A segmentation capability can never be too robust.

Not only do we plan to add even more basic segmentation options to DM1, we’ve also roadmapped a full segmentation builder (of the sort that the more recent generation of digital analytics tools include). Our current segmentation interface is simple. Implied “ors” within a category and implied “ands” across segmentation types. That’s by far the most common type of segmentation analysts use. But it’s not the only kind that’s valuable. Being able to apply more advanced logic and groupings, customized thresholds, and time based concepts (visited before / after) are all valuable for certain types of analysis.

I’ve also roadmapped basic machine learning to create data-driven segmentations and a UI that provides a more persona-based approach to understanding visitor types and tracking them as cohorts.

The beauty of our underlying data structures is that none of this is architecturally a challenge. Creating a good UI for building segmentations is hard. But if you can count on high performance processing event level detail in your queries (and by high-performance I mean sub-second – check out my demos if you don’t believe me), you can support really robust segmentation without having to worry about the data engine or the basic performance of queries. That’s a luxury I plan to take full advantage of in delivering a product that segments. And segments. And segments again.

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?