This week in the Accelerator: How should we measure the customer experience offline? What happens when a digital measurement expert turns his eye to the offline world? Author of Measuring the Digital World, Gary Angel, has done just that. How can data be used to help us plan for a better customer experience in real life? What could we see if we only apply digital measurement techniques to the offline world? In this episode I interview Gary Angel, CEO of Digital Mortar, an innovative technology for measuring the offline world.
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"Loyalty programs are not making it into the offline experience nearly enough."- Gary Angel
Book - Measuring the Digital World by Gary Angel
Articles - Leveraging Customer Analytics: The Airline Industry by Peter Fader
Blog Posts - In-Store Measurement Technologies: Finding the Right Technology for Your Space by Gary Angel
Key Concepts: Customer Lifetime Value, Marketing, Digital Data, Customer Centricity, Long-Term Customer Value, Marketing Leaders, Analytics, Creativity, Product Development, Audience Research
Who Should Listen: CAOs, CCOs, CSOs, CDOs, Digital Marketers, Business Analysts, C-suite professionals, Entrepreneurs, eCommerce, Data Scientists, Analysts, CMOs, Customer Insights Leaders, CX Analysts, Data Services Leaders, Data Insights Leaders, SVPs or VPs of Marketing or Digital Marketing, SVPs or VPs of Customer Success, Customer Advocates, Product Managers, Product Developers
Allison Hartsoe: 00:01 This is the Customer Equity Accelerator. If you are a marketing executive who wants to deliver bottom-line impact by identifying and connecting with revenue generating customers, then this is the show for you. I'm your host, Allison Hartsoe, CEO of Ambition Data. Each week I bring you the leaders behind the customer-centric revolution who share their expert advice. Are you ready to accelerate? Then let's go!
Allison Hartsoe: 00:32 Welcome everyone. We often talk about high value customers on this show, in the digital environment, but today's show is about measuring the customer experience offline. And to help me discuss this topic is my very dear friend, Gary Angel. Gary is the CEO at Snazzy Tech Company, Digital Mortar and the author of Measuring the Digital World. Gary, welcome to the show.
Gary Angel: 01:00 Thanks, Allison. Nice to be talking with you again.
Allison Hartsoe: 01:02 Can you tell us more about how you arrived at the concept for Digital Mortar and where did you start in your background? Because as you know, most people of our generation didn't actually get a degree in analytics in school. How'd you end up here?
Gary Angel: 01:19 Well, my degree is in philosophy, so that's a natural, past. You know, I was self taught computer programmer, like a lot of people back then I was kind of a hobbyist and I actually got into programming before I got into analytics and did a fair amount of fairly serious programming, but ended up for whatever reason, gravitating toward very analytic types of programs and I actually did a startup that focused on commodity and stock trading and went from there into more consumer analytics and eventually ended up focusing a lot more on the analytics and the programming. But we're really led to Digital Mortar was, you know, I, I spent nearly two decades I think in digital analytics and, and to love it. I mean, I think digital analytics is fascinating. It's been an incredible field. It's grown tremendously.
Allison Hartsoe: 02:04 I have to agree.
Gary Angel: 02:06 Yeah. It's, it's, it's, it really is a. I think it's funny because when I started in digital analytics, we really envied, the analytics that people were doing in sort of traditional offline ways. We looked at the direct response folks and how sophisticated they were and I think we, we really admired that, but over the course of the, the two decades or so I spent in digital analytics, I think digital analytics got to be the most sophisticated analytics area out there. And I think in a lot of ways offline folks look at what we do with digital and really admire it from my perspective. What really drove digital mortar was, you know, I was doing digital analytics. We had a very large client that brought us in actually on an offline shopper measurement problem. They'd, they'd hired a company, they'd done a bunch of measurement of shopper journeys in store and they were struggling to use it and they asked us to take a look at the data.
Gary Angel: 02:55 And when I took a look at it, I just found it fascinating. The idea that you could actually track shopper measurements in physical environments. Um, I thought was really interesting and really cool. And I looked at that space and saw a set of companies who in many respects felt to me like the early days of digital. Going back to the late nineties, engineering-driven driven companies that maybe understood the technology they were deployed but didn't have any really good sense of analytics or how to use it. So to me it looked like a place where I could bring a lot of experience about how to do customer behavioral analytics, but to a lot of green field and going back to my days as a programmer, I always wanted to create a technology company. The chance to really build software is super appealing to me. So that's what really led to Digital Mortar.
Allison Hartsoe: 03:43 That's a great story. Now I know from our previous background together how much time you spent in the book that you eventually wrote, which was how much time you spent and measuring the digital world. Can you talk a little bit about the some of the techniques that evolved in the digital space and how they are applying to the retail space?
Gary Angel: 04:07 Sure. Uh, well that's, that's a long, big conversation, but I think from my perspective, there are a couple real highlight points that people should understand about. The evolution of digital analytics I think are widely interesting and very applicable to the way things are going now in shopper measurement from a retail perspective. When we started in digital analytics, all the focus was on measuring pages on the website. You know, that's the standard report that people loaded up was the most popular pages on the website and all the metrics that we kicked around were about the website. The website had x number of years. The website had x number of hits. And I think over time, probably the single most important thing in the evolution of digital analytics was the realization that yes, the website is important. It's a tool, but what you're really measuring is shoppers and customers. You care about customers. That's what really matters from a marketing perspective. The website just a thing. It's just a frikkin' tool.
Allison Hartsoe: 05:05 Music to my ears,
Gary Angel: 05:08 Well, you know, I think we both feel that way and I think so much of what I saw is the critical evolution in digital analytics was that evolution from focus on the tool that you're measuring a to focus on the shopper and customer, and I think you know, along the way we figured out a lot of different ways to do that. One of the techniques that I got very well known for and and probably just bored people to death with on a regular basis was two kids segmentation. But the idea was really simple, and I think still really important, which is that to understand shoppers, you need to understand who they are and your relationship with them. That's the traditional kind of segmentation that people have done going back to my early days and did some direct response marketing for a long, long time. But what's interesting, and I think what really a goal is we started doing digital analytics and I think is just as true with what we do at Digital Mortar, it's not enough just to know the big broad categories of who the shopper is, how long they been a customer with you, what their demographics are, you know, what their income is, what their occupation is.
Gary Angel: 06:09 All that stuff's cool and interesting and nice and allows you to do a better job talking to them. But so much of what you need to know about them when you're a retailer or really any kind of company is what that person cares about right now. What they're doing. And so much of what we did with digital analytics was to understand what the person's behavior on the website meant for, what they cared about now, what they're looking at with this, thinking about what they're trying to decide about and helping them make that decision. Um, so the second tier of the segmentation and two-tier segmentation is a visit based segmentation. It's the activities that they're, what they're trying to do and what they're trying to accomplish. And I always thought, and I still believe that the really interesting things about, you know, thinking about how successful you are with a customer, come at the intersection of knowing who that customer is and what your relationship with them is, what they're trying to accomplish right now. And so I think that was a big evolution in digital analytics, was the realization that, that, a) that, that shoppers matter, not websites and, and b) that if you really gotta think about shoppers, you've got to think about segmentation and maybe have to change the way you think about segmentation a little bit. And those big, broad, traditional demographic categories aren't really what we work with anymore.
Allison Hartsoe: 07:22 Yeah. I think that behavioral segmentation is what we often see as quite predictive of future behavior. And we also see it when we match a frequently to who are the most valuable customers. We're seeing that people who are engaging frequently may also be valuable customers. So that's a, it's a good thing in so many ways in from the digital space to the offline space. In other words, if I knew who you are and I know what you're doing online, then maybe I can take that into the store. Can you tell us a little bit more about how you track people or how you might? I don't know if I want to start with tracking, but tell us a little bit more about how you understand behavior in the offline environment. Now that we framed it in the online environment.
Gary Angel: 08:08 Well, that's the thing. I mean in that online environment, you really have a rich understanding of what people care about. Right now. You're seeing every frigging web-page that they load, right? You can; you know how long they spent on that page and other navigational paths. No, everything they looked at, you know what they chose not to look at. You know where they went. You know which content loops produced action and which don't, and that's a really rich behavioral set, but what I realized when I came to the offline part of the world is that for most stores, what they know is what they sold and how many people came in the door and everything that happened in between is a complete black box and you know, I actually tell it this way too. Sometimes people on time talking to, I say, you know, imagine that you came into my office and you said, I have this wonderful digital analytics tool.
Gary Angel: 08:53 It measures how many conversions we got on the website, and it measures how many visits. I would laugh and kick you out, right? That's pathetic! You can't optimize the customer experience with the tool, that gives you two metrics and that's really what stores have. I think so. So the real point of what we bring to the table with Digital Mortar is measuring what happened inside where to shoppers go, where do they spend time, what do they look at, what do they pass by and don't look at? Do they have interactions with staff and ultimately what results in conversion and what doesn't and what does that conversion look like? The actual measurement of that turns out to be pretty tricky. I think one of the things that are maybe most challenging about our business is that unlike say a long, long time ago, probably before most listeners are even aware of in web analytics, we actually had two different collection technologies.
Gary Angel: 09:44 One was web logs, and the other was tags and overtime everyone shifts with the tags, and it made. It really did make things a lot better and a lot easier, but both those collection mechanisms will probably easier and more straightforward than what we deal with. On the physical side, we use are actually a whole array of different technologies. Some are appropriate to big, large spaces, some appropriate small spaces, some do, some are good for certain kinds of tracking, but not others, but by and large we have been happy to talk that through. We have like four or five different technologies that in different situations can be used to help them measure the shopper journey in store and a lot of times we find ourselves putting different pieces together, actually putting hardware in this store, which is kind of a pain in the ass, but it's the way you do it and what does that you do is actually give you. They give you data that's not dissimilar to what we're used to on the website, or they literally tell you at 10:33 AM in 15 seconds, the shopper was here in this store. Three seconds later, here's where they were in the stores. Three seconds later, here's where they were in the store, and you take that journey data, map that to the store and then hopefully you can figure out interesting things about what the shopper was interested in and how the store was meeting their needs.
Allison Hartsoe: 10:54 So let's talk a little bit about that tech stack. If I want to measure the offline experience and I want to use the rigor of what we've learned in the digital analytics space, what are the fundamental pieces that I need to have in the stack?
Gary Angel: 11:08 Yeah, so at the easiest level, although it's not really the best, is WiFi measurement. Every store, every facility that provides WiFi can actually do geolocation tracking. It's not super accurate. It misses a fair number of people, but most modern WiFi systems can actually Geo-locate people based on the signal from their phones. One Nice thing about that is it's super easy to deploy. You don't need any hardware. Um, it's remarkably inexpensive. It's just not a very good measurement, but that's one place to start. And particularly for larger facilities, if you have a stadium, if you have an airport, you have a shopping mall, that's not a bad place to do measurement because you don't need a lot of positional accuracies. Going back to the early days of digital, we always talked about trending data. Not necessarily looking at the actual counts, but looking at the trends. WiFi based measurement is kind of like that one step up from that.
Gary Angel: 11:56 We do a lot of more sophisticated electronic tracking with a device we call passive electronics snipers. These are made by a company down in Los Angeles called Eyeview. What they do is so very similar to WiFi. They track smartphones, but they do it with a much higher level of positional accuracy. They capture a lot more phones. You get significantly better measurement across the board. They're very inexpensive devices. They're not very visible. They're like credit card size. They're pretty easy to install in the store and make you pretty good journey tracking of the way shoppers are moving through the store if you need even more accuracy than that. If you want to capture 100 percent of your population, including people who either don't have smartphones or have their WiFi radios turned off. If you want to capture things like demographics if you want to capture display interaction.
Gary Angel: 12:40 Video camera is actually the most common technique for doing that on stores are heavily wired these days with cameras, mostly for security purposes. Sometimes those cameras can be re-purposed for measurement. There's also a whole bunch of vendors who make cameras that are entirely dedicated to measurement and work in a variety of, of conditions. One of the things I've been learning in the last year and a half is how complicated the real world as it. You know, you look at things like different lighting conditions and different crowding conditions and different ceiling heights, and all of those things can actually make a difference when it comes to camera, but that's a really interesting technology that, that, particularly for smaller spaces, are where you need 100 percent accuracy is really good way to go. Then there are mobile applications. Mobile applications are actually a great Geo-location platform. You can drop a little bit of code from a third party library into, uh, into your mobile app and you will actually have really good data about the way people have getting your physical spaces.
Gary Angel: 13:34 And that's super powerful. It's one of our favorite data sources, and it's very inexpensive to implement. And then lastly, RFID, which is used for a variety of purposes in retail. But you know, if you drop an active RFID tag on something like a shopping cart or a shopping basket, you can track that cart or basket through the space, which often is almost as good as tracking the shopper, except that's the range of technologies, and that probably gives you a sense how many different things there are. But the thing is, oh, these technologies work, none of them are perfect. Um, this is still kind of an immature space. They can be combined to do a pretty darn good job of actually measuring shopper movement through spaces.
Allison Hartsoe: 14:12 Okay. So that makes a lot of sense. I understand that there are ways that I can gather information and pull it together to understand the offline world with as much accuracy or perhaps getting close to the accuracy that I might get in the digital space. What would I do with that? What kind of examples would you have of applications of that analysis?
Gary Angel: 14:34 Yeah, that's really the crux, right? And I think one of the really interesting things about this space is because it's so new, people have those questions. You know, I, I know when we first started doing digital analytics, that's always what people wanted to know, okay, I can get all this data off my website, what can I do with it? And that question kind of went away. Right? And people figured it out, but it takes time to figure it out. It's, it's really the most important question is not a bad question because we've all seen people spend a lot of money on technology and then have no frigging idea what to do with it. I think there are maybe three major places that you can really drive value with this kind of analytics. One is a flat out operational improvement. Any grill world facilities turn out to be pretty complex, and there's a lot of ways that you can take this data just to make the basic operations better.
Gary Angel: 15:20 Whether that's around staffing or whether that's around the layout of the facility. There are just opportunities for understanding people movements and how that plays out and making things work more smoothly. Uh, the second part of it is customer experience. You know, these days retailers really care a lot more about experience than they did 10 or 15 years ago and they have to, I think one of the things that, the quote from retail analysts that really stuck with me and resonated and said the biggest change in retail these days is that you have to make people want to come to stores because they don't have to come to stores anymore. You know, 20 years ago if we needed something, we had to go to the store to buy it. Nowadays almost anything we can buy online. So if you want people to come to your stores, you have to make them want to go.
Gary Angel: 16:04 And that means customer experience is important. And if you think about what I said earlier about what you, the measurement that people have, well if they measure door counting and they're measuring point of sale, they know what they sold people. They don't know anything about the experience they delivered. So measuring that in-store experience, I think it's a big part of this. And then customer value. I know that's a huge deal to you, but I think it's critical, you know, if you think about it, if you are a on the channel business, a big piece of how you figure out customer value and how you understand what kind of value a person actually has is understanding the entire scope of their business with you and that certainly includes what they do in-store and obviously you know, so many things stores do around loyalty programs geared toward an understanding customer value in store, but you're always only capturing a fraction of that story in particular. If you want to understand customer value at earlier stages of the value chain before people are part of their loyalty program or if you don't have a loyalty program. This kind of in-store measurements is absolutely critical to really understanding which customers deliver value and how customer split your share between online and offline.
Allison Hartsoe: 17:14 I liked that you said it earlier stages, because I can think personally of times when I've walked into a store to understand what that brand was because I happened to be seeing it everywhere and to explore it, and then I either had an experience that wasn't very welcoming or I had an experience that was too welcoming, or I might've had something just right in the middle, and it does affect how I relate to that brand and whether I buy, whether I continue to interact with them, but in many cases the offline store is sometimes the first introduction. If you don't know it exists online unless perhaps maybe you're super active on social media and you're familiar with everything going on.
Gary Angel: 17:59 One thing that I think is really interesting and kind of gratifying to is you see some really good internet brands these days starting to open up stores and and I think that speaks to the fact that that in-person experience is an incredibly powerful branding experience. You know, when we used to do, we should do a lot of pretty sophisticated analytics, around a lot of different problems and retention insurance was one of the problems that I studied pretty frequently and invariably when we built churn models, the things that drove churn, we're often in-person interactions, the interactions you had with a terrible call center experience, or I went into the store, and they were rude to me, or I had to stand in line for 45 minutes, so I'm never going back there again. Those in-person experiences are tremendously impactful. Brand builders were good and ill, and I think it's both sides of that and I do agree to.
Gary Angel: 18:50 I think you know, people sometimes assume that high-value customers that they captured all of them? Well, that's not true. Obviously there's constant churn in that population. There's figuring out the new potential, high-value customers are and how you get them into your program so that you can make sure that they get the kind of personalized attention you want. And I think this kind of in-store measurement allows you to sort of expand down your reach in terms of understanding who shoppers are. Not just the people who are absolutely top tier loyalty program, credit card holding shoppers, but all the rest of the people delivering great experiences to those people obviously matters too, and I think it did, allows you really to understand much better where those people are, where they fit in that broader value chain.
Allison Hartsoe: 19:33 Now you said there were three pieces, so we talked about the operational improvement and the customer experience. Is there a third one? I don't want to miss a nugget here.
Gary Angel: 19:41 Yeah really? I was talking about value is the experience there. I mean I think it's about understanding customer value, understanding the actual experience of the customer. Those are two kinds of different things, right? I mean from my perspective, a lot of times you can understand the kind of experience the customer had at the store. What is it a good experience or was it a bad experience without necessarily understanding that there are a potential value or their value to you. I feel like those are two pretty separate analytic activities.
Allison Hartsoe: 20:07 Got It, got it. Okay. So what kind of examples, like specific examples of. Let's take the first one, the labor optimization, you know, how would I see that being applied in an actual store? What would happen?
Gary Angel: 20:22 Yeah. You know this, this really surprised us in some ways. I think one of the first analytics projects we ever did, as we got into this space, was around labor optimization and coming out of the digital world, you don't have that component, right, but in a store - a) labor is one of your largest variable costs. It's one of the things you can control. It's not a fixed sunk cost, but it's a big portion of your cost structure. It's also a huge deal in terms of the actual customer experience, right? I think we all have personal anecdotal, direct experience and how much difference it makes when you have a great associate versus you're getting ignored by associates or getting played by us as you mentioned that maybe a little too friendly associates who won't let you be. Well, one of the nice things about this kind of tracking technology is knowledge.
Gary Angel: 21:07 Is it likely to attract shoppers? It actually allows you to track your staff too, and you know, you can do that at a very detailed level, but you can also do a little more aggregate level. And in that first project, one of the things we did was basically just look section by section by the time of day, by day of the week, what the ratio of shoppers to associates was. There's a metric in retail called stars, shopper to associate ratio, um, but it's typically measured at a daily level. They have a door count, and they have the number of associates. They divide the two, and that's the shopper to associate ratio. But with this kind of measurement, you can take it down to a much finer grain level. You can look down at like a 10-minute interval at a particular store. And for this particular project, we were focused on which sections of the store were most vulnerable to under an over-staffing.
Gary Angel: 21:53 And probably not surprisingly, it's an athletic apparel store. One of the things we found was that the shoe wall was extremely vulnerable to under-staffing and there were a couple of threshold points were when the shopper to associate ratio climbed above those points. It got above. For instance, like the ten shoppers to one associate was one of the inflection points where the conversion rates drop precipitously and what we could see looking at shopper journeys was when the shoe wall got that crowded without being appropriately staffed. They just lost customers. People just freakkin' left. They didn't buy shoes, and there were really two inflection points in that curve and what was even more interesting was that there were some pretty consistent days of week and times of day on those days when this happened, so adjusting their staffing model so that they actually had the appropriate number of associates relative to those times of a day and those days of week really made a huge difference. I mean, this is one of those things where you.
Gary Angel: 22:49 Sometimes with measurement, it's hard to establish, but you find places where you are just flat out either losing customers because you can't service them or you have staff sitting around doing nothing. Those are easy places to find and identify opportunities to save money and drive real hard ROI with this and it's one of the things that convinced me that, you know, almost as important as the shopper measurement on this is the ability to measure what's your associates are doing and and put the two together in ways that really allow you to tune in store operations, and there are so many wins around that in the sense that not only are you improving your store performance, but you're sure as heck building customer satisfaction too. Nobody likes to be in an area and not be able to get the help they need. So I really think it's one of those fairly rare cases when you can actually improve your bottom lining and improved customer satisfaction too. And that that's pretty sweet.
Allison Hartsoe: 23:40 That's such a great, crisp example and I can see how they made the immediate fix and staffing. Were they also hungry to understand more about the type of associate or the behavior of the associates so that they could perhaps further classify whether not just whether the associate was physically present, but whether they were engaging and interacting versus they were standing there but not doing enough?
Gary Angel: 24:07 You know, it's funny, that project was very focused on a higher level, but we've done some work since then. It really takes that kind of analytics down to the point of looking at things like, what are your associates doing? It one really intriguing part of physical retail is that it's highly local. You can think you have great policies in place, but it's not like a website where pretty much every customer gets exactly the same experience. That's totally true, right? You can have bottlenecks and performance differences, but by and large, you are delivering a pretty seamless experience to people, but when you have 500 or 600 stores, you've got thousands and thousands of staff, many of whom have pretty high churn and may not have been adequately trained. And it's really important to realize how local the experience is. And with this kind of technology can measure what people are doing, you know, are they, are they getting distracted?
Gary Angel: 24:58 One of the really interesting challenges, a lot of storage space these days stores are becoming much more on the channel, meaning that people are often. People often buy things on the web and then pick up in store, or they buy things on the web and return in store. Well that takes staff, right? So associates are often getting their time taken up, handling Omni channel chores, which can impact the actual servicing of customers in the store. So we've done work around things like how often are staff interacting with people, which staff are best, which teams are best, how impactful on the channel experiences are in the store experiences. We used a ton of analytics here that crossed everything from operational efficiency to customer experience to Real Omni channel optimization, and all of it involves that ability to measure both staff and shoppers in the store.
Allison Hartsoe: 25:45 That's fascinating. Especially the point about the distraction of the in-store returns. That's one of those second-order operations. You don't always think of. It sounds on the surface like, oh, you can return things in store great, but you don't think about the time that the staff is no longer serving new buyers and because they're handling in-store returns.
Gary Angel: 26:06 Yeah, they tend to look at those staff hours as just available and of course in one sense they are and in one sense they're not. There are real impacts to that, and you know, that's such a fascinating area, and I think part of what makes it fascinating is people are still learning about it. People are still figuring out what the impacts are and what it means to bring somebody into the store. Can you deliver, you know, is that another potential shopping opportunity? How can you make it, but the impacts on staff are real and significant. I think a lot of stores are really just starting to discover there are costs to doing that as well. There are real benefits. I don't want to deny that. I think it's actually a great thing to do, but you do have to understand that you know, those hours aren't necessarily free and most times in stores, associates aren't just standing around twiddling their thumbs. So, um, would you give them another task to do? There are implications to that.
Allison Hartsoe: 26:51 So I'm going to ask you a question which is somewhat dangerous, and you can choose to answer it or not. The question is, years ago I saw a technology where you would walk up to a digital display, and it would read your sex and your age, and it would put or like approximate your age and the display would change to show you someone who looked like you and in the clothing. So it was actually your race, your sex, and your age. So the question is, do you find in the course of analysis when you're looking at staff and looking at the effectiveness of sales and conversion, do people indeed or do you measure that people buy from people who look like them?
Gary Angel: 27:42 You know, that's a really fascinating question. I have to say we haven't measured that and I'll all say two things about value right? I mean, we usually don't have the data down to that level. 2) Yeah. I've always got an Abercrombie and Fitch stores to buy from people who look like me.
Gary Angel: 27:57 Okay. I see.
Gary Angel: 28:00 What are the tricks to that is people have an idealized version of themselves, right? I mean we don't really know how we look. We have this idea of ourselves in a lot of cases, you know, for 20 or 30 years ago, it's still probably better that we looked 20 or 30 years ago. So I'm not sure how one sense. I'm not sure how effective. That's a fascinating thing. But no, we haven't looked at that, but I have a couple of things that I think are maybe more straightforward and there are some applications with that. I think we do work around things like digital signage and there are opportunities to do a much better job. I think customizing, personalizing and tuning the digital signage. Uh, but you know, a lot of people treat digital signage as if it's just another way to, to put a sign up in the store and they don't really take advantage of the capabilities to tune it.
Gary Angel: 28:49 And that can be done at the personal level, but it can also just be at the local level, you know, figuring out if you're in the San Francisco store that there's a likelihood that the types of clothing, the models, uh, the people you want to put up there are different for that. And I think people have under-explored the opportunities for technologies like digital signage. I also think that you know, if you think about things like staffing, some of the opportunities you have when you can start to measure what kind of person actually performs better, what kind of deans actually performed better. You know, if you think about a staffing model in some respects, we think about it as similar to a sports team. You probably don't want every single staffer to be the perfect extroverted customer support person because there are other tasks that staffers have to do.
Gary Angel: 29:37 And so you probably need a blend of people, but you can also look at the profiles of people who make a good associate. Right? I mean, when you look at the way people spend time, which people tend to be most effective, which people tend to interact the most with shoppers, you can profile those things and look for those kinds of personalities. I think there are things in hiring that people can do significantly better using this kind of information. Anytime you have a success metric, you can do a better job tuning and I think one of the challenges that people who have done hiring in retail is that they don't really have a success metric for what works in the long run and I think careful measurement can actually provide that in this case, so no, I think once you point out is really fascinating. I'm sure to some extent it's true. It's also really hard to measure, right? I mean that's one of those things where we generally don't have the data on either side to support that kind of measurement, but we focused on a lot of problems that I think are in some ways similar and probably a little more tractable.
Allison Hartsoe: 30:32 Yeah, that makes sense. So let's lean a little bit into the customer experience examples. We've talked a little bit about digital signage and staffing. In the customer experience side. Are there examples where a business in some form understands who a high-value customer is when they walk in the store? Or is that still a future dream in retail?
Gary Angel: 30:59 You know, it depends. A lot of times that is still a future dream. I think that some of the technologies I talked about give you the ability to do that. Probably the preeminent one for doing that kind of on the challenged joint is a mobile application. If you can give people a reason to download your mobile app and use it in store, you have the ability to do that kind of joy and when you do that you can collect some incredibly valuable information. You know, we've done that kind of work not just in retail actually, but even in some other cases, you know, we, we did some work for a sports team looking at, you know, people buy season tickets, but which games do they actually come for? What do they do when they show up in the stadium and um, a lot of times in sporting situations, you know, mobile apps are very popular.
Gary Angel: 31:42 People log into your WiFi, there's a lot of ways actually to de-anonymous people, and you actually can take the data down to the crime level, which is really, really cool when you have data that tells you about what someone looked at the store but didn't buy. That's obviously incredibly relevant. That's the kind of thing we do all the time on the website. Being able to do it in the store is even more powerful. So there are, there are places where you can do that, but the truth is that nine times out of 10 and except for the mobile app and maybe opted in WiFi, the data is anonymous. So in a lot of ways you can look at customer patterns, you can understand which of those customers have potentially interesting behaviors in store, which looked like high-value patterns but not part of a loyalty program. I didn't use your mobile APP. It's still essentially anonymous data
Allison Hartsoe: 32:30 So people don't have to worry too much about their identity leaking out as stores, try to customize the customer experience, but I think in some cases perhaps with loyalty programs, I've already authorized certain companies that I particularly like to know me and I want them to know me because I want certain benefits from that loyalty program. Are loyalty programs, making it into the customer offline experience.
Gary Angel: 32:58 You I don't think nearly as much as they should, and I'm a big fan of loyalty programs. I think almost everybody in the analyst community is. One of those programs is interesting because they're partly a blend of delivering better experiences to people and really optimizing customer value and there also very analytic. For certainly now in grocery and then for a lot of other industries, people do loyalty programs primarily for the data they can collect. So most of the analysts loved them, and I'm certainly in that camp. One of the things I love is when people take advantage of loyalty opportunities even outside of the loyalty program. So I'm a big advocate what I call surprise based loyalty concepts, which is you can take the same analytics about. Thinking about customers and what creates incremental lift in customers to deliver them incentives, surprises, delight experiences, rewards, uh, outside the context of a formal program. And I'm actually a big believer in that in some ways.
Gary Angel: 33:54 One of the drawbacks to the formal loyalty program, is that what we've seen when we measure that is a lot of times the incremental lift is a little disappointing because you tend to be rewarding people who are already 100 percent committed to you. And you know, I go to Starbucks like everybody else, and I cashed in my rewards but darn it after I've got all those points, I feel like I earned my reward, right? It doesn't, it doesn't feel like a surprise and delight moment to me, but you know, if I walk into a place and they say, Oh, Mr. Angel, thank you so much, have a croissant today on us, you know, that's a surprise and delight moment because they didn't feel like, you know, I had to go through this arduous march to get it. And because on the store side, it can be targeted analytically.
Gary Angel: 34:39 You can make sure that you're giving those rewards to people where you really think you can enhance their value, could introduce them to a new product, you can get them to try something they haven't done before, or you can enhance your share of wallet with them. Um, so there are, I think there are opportunities for people to take analytics and to take this kind of deep measurement and apply it to deliver experiences to people that are very much like loyalty experiences that are maybe outside the context of a traditional loyalty program. I think that's one of the biggest opportunities out there in loyalty is for people to expand those loyalty concepts beyond the people they've signed up in their programs. And I think this kind of measurement is really one big step along the way to doing that.
Allison Hartsoe: 35:18 Yeah, I really liked that application, and I've heard of only one example where that's actually being done where American Airlines is using eagles, and you have a certain number of eagles that appear when you are facing the customer service agent. The customer service agent can see these and you can't, but it guides them into what kind of surprise and delight they might give you. So for example, if you're a really good customer and you've had just a horrible run of delayed flights or other issues, then your score will will tell them that it's like, like this would be a good time to surprise and delight someone. Uh, but that's, that's generally within the context of is there an upgrade available and you've got 20 people to pick from. Who should you pick? Uh, so it's, it's arming the front line.
Gary Angel: 36:10 Well, that's the beauty of that too. There's literally no cost to that. And know airlines are a great example. And I think that airlines are a good example of loyalty programs can become a trap because I live in San Francisco, I have my hub airlines, right? There's, there's a couple of airlines that I have to fly a lot because they are based out of San Francisco. So I'm all, I always have a lot of status with those airlines and when I fly on another airline a lot of times not in their loyalty program. So I get treated awfully, and I think not only am I not an advocate of that, but I think one of the things that you can do a much better job of if you, if you can understand customer value of potential share a wall, is delivering those loyalty experiences to people who could be potentially high value but who aren't part of your loyalty program right now.
Gary Angel: 36:55 And I think the airline's consistently miss those opportunities. A) because they have a hard time tracking that, but even when they. I think a lot of times they could do a much better job understanding how frequent a flyer I am even if I'm not a frequent flyer with them. And I think that's the kind of. That's the kind of traveler you might want to target those surprise and delight experiences too as well. That's something I do not see a lot of companies do, but I think it's a tremendous opportunity for people from a marketing perspective. It's just a way to use analytics that's a little outside the box but delivers a lot of the same loyalty concepts but is designed not just to do it for the people that you've already proven or giving you 70, 80 or 90 percent of their share wall, but for people who have a large wallet and who might give you that kind of share.
Allison Hartsoe: 37:38 I completely agree and Gary, I think that is the point of a loyalty program is not to target the people who already love you but to target the people who you want to become high value, and I will include the link to that American Airlines story in the show notes for this podcast. I think they are fairly sensitive to not just rewarding high-value customers but calculating who's on the upswing and who do they want to become a really their partner, their, their long-term high-value customer. Because as you said, that group is always churning and it's such an incredibly important part for any brand because they support so much of the revenue stream for our brand.
Gary Angel: 38:20 Yeah, no question, and I think that's really good practice, and I think across the board there's a lot of industries that could take advantage of similar kinds of things. Certainly in our core, our core market retailers, that there's a tremendous opportunity to expand those kinds of loyalty concepts and beyond just your small group of core loyal shoppers and um, I, I believe that's always one of the, one of the areas that people under utilizing the analytics to really take advantage of what they know about shoppers to deliver better experiences.
Allison Hartsoe: 38:47 I agree, I agree. Okay. So we've talked about quite a few things here. Let's say that I am sold on offline retail, and I have some retail stores. What is the first thing that I should do and should I, should I be thinking strategically first or should I just get the hardware in there? Where should I start?
Gary Angel: 39:08 That's a really good question. I do think this is something where paid thinking strategically first, understanding your use cases. You know, we always talk about that and the analytics. It's true though. You really ought a understand what this is for and when we worked through some of the examples, that's a tiny subset of the things that might be of interest. I've been doing a lot of work recently, for instance, in queue control and queue management and if queues are an area that you're focused on. That's great. If the associate optimization is what you're focused on, that spine and store design is what you're thinking about. You're rolling out a concept store, and you want to understand if it's really working better. That's great. Understanding what you're about is probably the first thing, thinking about where you might get value from this. The second thing is technology matters.
Gary Angel: 39:51 In this case, it's not off the shelf. It's not standardized. It's not like the tagging has become in digital analytics. We do have to understand how technology applies to your business. I have a whole. There's a whole series of posts I've written about technology, but I think, you know, people might want to consume on the digital border blog. Yeah. So those are great, and I think one nice thing is, you know, we're not technology provider in the sense of the data collection. We don't make cameras; we don't make the digital electronic snippers we know you can do the mobile app, third-party license. So we're independent in that way and uh, we work with all those technologies, so I think we have pretty interesting things to say. Understanding the technology is probably the next thing to do. And, and then hopefully people will call us. I really think we do really cutting edge, analytically, driven business orient measurement here. I think we do it better than anybody in the world and to our seriously. If you're interested in this stuff, I think I hope people will check out our website and get us a call because we love this stuff. We love working on it, and I think we do it really well too.
Allison Hartsoe: 40:47 So Gary, how can people get in touch with you if they want to reach out?
Gary Angel: 40:51 Well, I think to check the website, digitalmortar.com. That's a good place to start. And, uh, like any website is loaded up with opportunities for real to click a contact button and call us, or hey, just if you're listening to this podcast, you can frigging just drop me an email. So Gary.Angel@digitalmortar.com.
Allison Hartsoe: 41:07 Okay. Um, and we'll include that email address again in the show notes. So let's summarize a little bit about what we talked about. First, we kind of dug into what has, this isn't so much what has changed, but why should you care about offline measurement? And what was really interesting here was that the offline measurement used to be the leading place where we would get inspiration on digital, but now that's reversed. And digital has grown so strong in measurement that the digital innovations are now influencing the offline measurement. And nowhere can you see that better than in the techniques we talked about on with Gary on the show. And also I highly recommend, if you really want to understand not just digital behavior, but understanding customer behavior. The two-tiered segmentation that Gary mentioned, uh, is, is really well outlined in his book, Measuring the Digital World.
Allison Hartsoe: 42:03 Again, we'll link to that in the show notes, uh, and a fantastic technique that we use a lot in that section. We talked about why am I caring about offline digital measurement and then in that piece we also got into the different technologies for the stack. We talked about WiFi measurement, doing geolocation, we talked about a company called Eyeview that's tracking smartphones and getting to a greater degree of accuracy. We talked about mobile applications being a fantastic source of really rich information and even being able to tie that back into your CRM. And then we also talked about RFID tagging on the cart or a basket which becomes like watching a visit through a website, being that the visitor is behind that. Did I miss anything in that first part of the summer, Gary?
Gary Angel: 42:54 The only thing is I think on the technology side, the camera is another huge portion of what people use to track that. There are companies out there that do the really nice job with measurement cameras, and there are even sometimes opportunities to combine what you're doing with security so that that is another core technology in this space.
Allison Hartsoe: 43:09 Excellent. Excellent. And then we talked about the impact, and there were a couple of examples. They really fell into two or three different flavors that we talked about, the operations of the business and staffing, of course, being the largest variable cost and how well the presence of your shoppers, how well the organization is staffing for shopper presence in that the presence of associates in key areas of the store is directly related to conversion. You talked about the shoe wall and the 10 to one shopper to associate ratio. Just saw conversions drop off a cliff. And I can imagine myself standing at a shoe wall with ten other people fighting for one associate. Sure. The first thing I'm going to do is, you know, go to the website or walk out. There's really no reason to stand in line like that anymore. And then you alluded to the queue measurement as well, which again I think is probably reflecting our current modern inpatients with lines.
Allison Hartsoe: 44:05 And we just want it now. It's so much easier to, just to buy now. So in that section, we then went on, and we talked about the loyalty programs. I thought that was very interesting, the application of loyalty programs were not just looking at how do you reward people who are already giving us consistent behavior, but how do you look for that surprise and delight opportunity, particularly with people who are not yet your high value customers and doing this through the perhaps through Gary. Did you say camera identification could do it or there was a tool that would help you understand who is coming in or do they actually need to be purchased for you to identify
Gary Angel: 44:49 You know that depends on what kind of techniques are going to use, sometimes that is purchased based. Um, sometimes you know, if you think about it in a store, if you understand we're someone shopped, if you know that they were shopping in some say, really high, high purchase value, high margin items. That alone can tell you that this is potentially a really high-value shopper. And so there are techniques that are even anonymously based. You know, if you have shoppers shopping in certain areas of the store without knowing anything else about him that may tell you that there are potential high-value customers. So you can get that even off of the electronic trail, so it depends, it depends on your store to shop or, but you know, there is a range of techniques. In some cases, you're going to have to de-anonymize the person that's usually mobile app or a tie at the point of sale, but in other cases, just their in-store behavior will be enough to give you a pretty good clue that there is a high-value shopper.
Allison Hartsoe: 45:39 Excellent. And I also want to reinforce that we are talking about retail stores a lot, but this also applies to stadiums, airports, any public space where people are moving through, and you want to give them a good experience. These same methodologies apply.
Gary Angel: 45:56 That's totally true.
Allison Hartsoe: 45:56 So Gary, did I miss anything in that section?
Gary Angel: 45:59 No, no, I don't think so. I, I just, I want to emphasize that last point about the stores are a core part of what we do, but there are lots of really complex experiences out there and most airports these days actually have at least some kinds of this measurement. Um, so they can understand their performance relative to things like queue control. Um, that's super interesting and of course what's interesting about airports too is they have a secondary application here, which I don't know if people know this, but retail in airports is booming. It's one of the healthiest sectors of retail around and one of the things most airports are really interested in it. Yeah. Yeah, it's real, it's really peculiar, right? But it's this huge captive population. People want it. People do kill a lot of time in airports and in airport retail is actually extremely healthy growth sector and one of the things a lot of airports are interested in is how can they build that experience, how can they get shoppers to the store, what's the design of the retail and how should the retail be configured to optimize people going in and using those retail experiences in airport.
Gary Angel: 47:01 So that's another place where it's kind of a blend of the people movement, but also some of the classic retail problems and airports have had that problem interestingly enough, not something I ever would have realized two years ago.
Allison Hartsoe: 47:13 Oh, very nice. Very nice. So in terms of what people should do next, we talked about understanding the use cases. You really have to go into what am I gonna use this for, how am I going to apply all this great information, what is it that I care about right now or what is it that my organization really cares about and then maybe branch out into some of the broader applications. And then we talked about the technology and then, of course, Hey, call Digital Mortar and, and uh, I want to restate your email address. I mean not, not to promote you to heavily get the um, the email for Gary was
Gary Angel: 47:47 Gary.Angel@digitalmortar.com.
Allison Hartsoe: 47:51 Fantastic. Okay. Gary, I really enjoyed talking to you and you know, every time we talk there's always a, a new level of understanding that I come to in terms of tracking and analysis and retail. So thank you so much for being on the show today.
Gary Angel: 48:08 Oh, my pleasure, Allison. Likewise. Is so fun to talk to you again. This has been great.
Allison Hartsoe: 48:15 Good, good. As always, everything we discuss is at ambitiondata.com/podcast or show notes for this show will be quite extensive, including a variety of links to things that we talked about and Gary, if you send me a couple of links to the technology that you mentioned on the Digital Mortar blog, we'll link to those as well. I think that'd be great for the audience.
Allison Hartsoe: 48:37 Remember, when you use your data effectively, you can build customer equity. It's not magic, just a very specific journey that you can follow to get results.
Allison Hartsoe: 48:48 Thank you for joining today's show. This is your host, Alison Hartsoe and I have two gifts for you. First, I've written a guide for the customer-centric CMO, which contains some of the best ideas from this podcast, and you can receive it right now. Simply text, ambitiondata, one word to 31996, and after you get that white paper, you'll have the option for the second gift, which is to receive The Signal. Once a month. I put together a list of three to five things I've seen that represent customer equity signal, not noise, and believe me; there's a lot of noise out there. Things I include could be smart tools I've run across, articles I've shared, cool statistics or people and companies I think are making amazing progress as they build customer equity. I hope you enjoy the CMO guide and The Signal. See you next week on the Customer Equity Accelerator.