Joe Shasteen: Welcome everyone to today's fireside chat at NRF 2026. I'm Joe Shasteen, the Global Manager of Advanced Analytics at RetailNext. And for those of you who might be unfamiliar with RetailNext, we're the leading retail intelligence platform that translates foot traffic data into profitable insights across the entire customer journey. Our AI-backed solution consolidates multiple retail analytics capabilities into one unified platform, delivering strategic insights with measurable ROI. And today, as a part of our fireside chats, we're going to be sharing a lot of real-world insights from our customers and how they use the data. And today, I'm very excited to have David Lizak with us from Shoe Palace. David, would you mind giving us a little bit of an introduction of yourself and some of your background at Shoe Palace and before?
David Lizak: Sure. Good morning, Joe. Thank you for having me. I'm David Lizak with Shoe Palace. I've been with Shoe Palace for about six years. I get the opportunity to oversee a handful of platforms that our stores face, that they use, whether it be through the POS systems, the workforce labor planning — any system that the stores really touch, I get an opportunity to sort of manage that. And in my previous years, I've been in sneaker retail. I think I've had a pretty cool journey. I've been able to be in sneaker retail my entire career. Once upon a time, I was the general manager for the Foot Locker right here in Times Square. So long story short, I've had an opportunity to roll out and explore various platforms and technologies throughout my career, and now I get to do that with Shoe Palace.
Joe Shasteen: Awesome. To kind of go back in time a little bit — RetailNext and Shoe Palace have been partners for a very long time. So going back to around 2018, when Shoe Palace was making the decision to roll out a traffic solution, could you walk us through a little bit of some of the thinking behind that, and what was the impetus for actually driving the move from maybe traditional retail analytics into some of the more traffic-based solutions as well?
David Lizak: You know, it's hard to imagine doing this business without having traffic, right? But the reality is there's a lot of retailers that don't have it. At one time, we were that retailer. And even to this day, we've absorbed a few stores that also were kind of going through that process again. The why, though, at that time — think about driving a car with no speedometer. The only way you know how fast it's going, the only way you know how sales are, is "I feel it," or the car next to you, or you get pulled over. But nonetheless, you don't really have a true gauge of how you're performing, right? So that first layer of being able to see traffic gives you that visibility, and that's something that our organization really, really needed to have: what's that baseline to compare it to? We had this result — how are we actually doing?
Joe Shasteen: And you were talking a little bit too about how, when you started, Shoe Palace had already started rolling out some of the traffic solutions. So what about when you joined Shoe Palace in 2019? What was some of the transition that they were making as they had already started to implement some of the traffic solutions, and how did you and your role impact what was being done at Shoe Palace in that 2019 time here?
David Lizak: Yeah, that's a learning curve for the whole organization at the time, because you're just now going year over year with traffic data. So you have to really teach that to your store teams and to your field leadership — what this metric is. And it's not really until — and fortunately, I was able to join during that time — it's not really until that time when you see it in comparison to the previous years that you know how you're actually doing with this data. And then it gets into the educational process of, okay, well then how do we improve it? And it's a teaching process the whole way along the line. The most tricky part, I feel like, is we get so savvy with some of the technology and how elaborate we want to make it. And the idea is we've got to be able to keep it simple for our store teams. And I think that was really the baseline for that 2019 when I joined: keep it simple for our stores. You can't control the traffic number. It's a good baseline to tell the story, but it took us away from the management team saying, "Oh, boss, it was just slow yesterday." Well, now you have facts to back that up, and it's a different story, right? And trying to simplify that for our store teams was really the biggest thing.
Joe Shasteen: You know, along those lines, when you've been implementing the traffic, what was that process like to implement and work with the store team? You said keep it simple — so how did you do that? Because it can be very daunting early on for people who might not be used to retail analytics, especially early on in that process. So what was that like working with them, getting them up to speed and comfortable with the metrics so they're not afraid? I mean, we've seen instances where store teams are blocking our sensors so you can't see traffic. So how do you get through that and kind of remove that fear behind the data?
David Lizak: I'll come back to the how for a second, but the instant that a particular metric becomes the primary thing that you're trying to improve, you lose sight of what the overarching goal is of an organization. So while you get this new metric and that's super, super solid and that's what we want to improve — any number can be manipulated at some point or another. So it's important that while we make this really important, let's not take a side step to what the overarching goal is. We want to perform better sales in the business, right? So back into how do we teach that. At that level, it's keeping it simple. "Well, you ran ten percent yesterday in conversion. We want you to run twelve percent." Well, do we expect the manager or the assistant or this part-timer to be able to divide the amount of transactions into traffic? Probably not. So let's simplify it for them. You converted this many customers in this time frame yesterday — I need you to do one more. So that sort of logic of breaking it down and keeping it a little more simple depending on who the audience is at that time was really our strategy then.
Joe Shasteen: Great. And you've also mentioned too previously how you were able to use some of the data with your STAR ratio. Could you talk a little bit about that STAR ratio that you developed using the traffic as well?
David Lizak: Yeah. You could really see your associates and management teams come alive once you start talking about what these healthy numbers are. Again, try not to overcomplicate it, but you tell somebody, here's a STAR. This is the amount of customers that are going to come in while you and I are working. Can you handle it? Can you not? And you start to paint a picture and say, okay, well, if that ratio was twenty, can we handle forty customers right now? In an hour? Sure, I think we can. Now you tell that same person you're explaining it to — okay, if that number was fifty, do you feel confident that we can get to a hundred people, you and I? And they kind of go, "Oh, that's probably too high. That's too much." Right? And then they get sort of an aha moment. And then you say, on the flip side, if that ratio was ten — okay, well, that means there's twenty people that come through the door in this hour while you and I are working. And they kind of go, "Oh, that's sort of low." Well, the idea there is we need to cut back on some of the hours or labor costs that we're abusing there. So having traffic, being able to tell that story, really is an eye-opener for how the store teams are able to manage their day-in and day-out business.
Joe Shasteen: So kind of taking it a little bit to a higher level from the store teams — I know oftentimes there's different cross-functional teams maybe at the corporate level that are trying to use the data. So how do you get buy-in from the store level, but then also with different cross-functional teams within Shoe Palace, to make sure that they're leveraging the data and making the correct decision based off of all the traffic data that you've implemented as well?
David Lizak: Yeah. You know, immediately in our world, there's sort of three pillars that come to my mind: the marketing teams, the product teams, and then our operations teams. I'll save the operations one for last, because that's probably the one that I'm most familiar with. But when you think about it through a product standpoint, we make a lot of big assumptions before having traffic about where our busiest markets are. We talked a little bit about how many stores we have in Vegas right now — we've got about twenty stores there. And I can make assumptions based off of sales where we should push product down to, or what that demographic looks like, but there's a little bit of blindness that if we don't actually know the information that RetailNext is providing to us, maybe there's some better leverage in there that our marketing teams can pull. Or if product — when it comes to a product case, the same way when we talk about pair counts or styles of matching apparel. Whatever the case is, there's a lot of different levers that our teams use traffic analysis for, and a little bit of demographic details for it.
Joe Shasteen: And then you mentioned kind of the operations side that you're most familiar with. So, STAR ratio as well, but any other aspects too on the operations side?
David Lizak: When you start to use STAR in comparison to an entire fleet of stores — obviously we're heavily saturated in California and then a lot in the South: Texas, Arizona, Nevada, Florida. Those are all very different fields of stores and different kinds of environments. But when you're able to sort of neutralize the traffic to the labor that you're using within a store, it sort of equalizes or normalizes that conversation between those markets. Then you get to get a little more granular between your outlets, your street stores, your mall doors. But our operations team being able to identify sort of that healthy STAR ratio again really helps us in the labor planning to say, okay, this is a healthier number for a street store, or a realistically healthy number for a street store. We expect our malls to run a little bit of a higher number, and that allows our teams to plan for it. And then you start to identify those anomalies on the outside of it to increase productivity.
Joe Shasteen: I think that's really interesting around the different store types and how you're using that data. Is there anything else with the expectation side that you're doing when you're coaching differently — is it a mall-side store or a street-side store? Any differences there as you're implementing the data and trying to get them to action on it in a different store environment?
David Lizak: It continuously comes back to just speaking to it and coaching to it the right way. You don't ever want to compare your street store — I think about it all the time. We have our very first store, Bascom, that sits right in the middle of San Jose, and Valley Fair. It's a couple miles away from the store. They have absolutely nothing in common with each other as far as the demographic, the traffic type, or anything of that nature. Right? So it wouldn't be fair to compare those two based off their STAR ratio, based off of the sales per hour, based off of a wage percent. None of that makes sense, right? You need to be able to make those comparisons to other, like, similar stores. And that's what we've been able to do across our organization. I can take a street store in San Jose and compare it to one in Houston and be able to have a cohesive conversation around what that labor should look like in the store compared to traffic.
Joe Shasteen: Yeah. And kind of establishing those different baselines that can drive that continuous improvement in the store is crucial as well. So are there any examples that you implemented recently where using these baselines has kind of driven that continuous improvement in Shoe Palace locations? And what does that look like for Shoe Palace stores as well?
David Lizak: I would say the biggest improvement and gain that we have the ability to do now is when we use the historical traffic data that we have in comparison. Like, we make a lot of assumptions around our managers. Okay? And rightfully so. We know our stores. We think we know our stores. We make a lot of assumptions about what traffic flows look like within a week, when our power hours are, our peak times are. Right? They would make these assumptions, but we go back to the data, go back to the ticket, go back to film. And it shows us there — hey, maybe we had an opportunity. This was a higher traffic time. This is a place where we had more opportunity. And it allows us to say, hey, let's stick with the facts. Numbers don't lie, right? And really, it goes back to that adaptation in 2018 to 2019, 2020 — you're teaching it the first time. Now it's not so much a teaching conversation as much as, hey, we know where to look and where the truth is.
Joe Shasteen: And then one final question, because we talked about this as well — integrating the data. So what's been your process like for integrating the data into other data sources, leveraging and combining it with other metrics that you have? How has that been for you as well, using that and getting into different tools and platforms?
David Lizak: Yeah. Well, RetailNext obviously has some great partnerships with a handful of our other partners out there, one of which being StoreForce — I think they're here today. But we've been able to really leverage that across all doors. Again, going back to being able to identify power hours and peak times and opportunities that management really misses out on often, it goes back to allowing us to bring in that STAR, combine it to a sales per hour, going back to a square footage of a store, and being able to identify where customers are running through in that particular location. Bringing all that data into one sort of centralized place really has allowed us to have significant wins.
Joe Shasteen: Awesome. Well, thank you so much, David, for walking us through kind of the seven-year process of Shoe Palace to today, and that commitment to data excellence, and what that evolution looks like, and how that can really drive continuous performance and improvement in your locations as well. So thank you very much for your time today.
David Lizak: Glad to be here. Thank you.
