Hello, everyone, and welcome to another episode of Retail Refined, a Market Scale podcast with your host, me, Melissa Gonzalez. Today, I'm excited to have Spencer Hewitt back on with us, who is CEO and founder of Radar. We had the pleasure of having him back in twenty twenty when he was kind of just getting started, and he has made tremendous impact with his team ever since. So today we get to have him on right off the backs of a recent one hundred and seventy million dollars Series B and one billion dollars valuation. But these things are not just funding milestones. I'm going to go ahead and dive into the conversation. Of course, starting with that congrats on the you know, it's both a one hundred and seventy million dollars raise and a one billion dollars valuation. So those two things are very significant. But it comes after a time where I think we've spent decades, right, about talking about what's the opportunity of really demystifying physical environments, bringing together technology, item level data, and what that really allows us to see. So why don't we start by digging into that? When you think about this announcement that just took place, why is now the moment for this kind of infrastructure? And what do you think that investors are seeing in physical retail, that maybe they've been misunderestimated in the last few years? Yeah. For sure. I think, you know, I I remember back when we were speaking the first time, pretty much every investor thought that, I think by now, that physical retail would basically be the vast minority of retail sales. And in reality, it's still over eighty percent of US retail sales. So I think the decline in physical retail hasn't really played out, in the way that the ecommerce bulls thought it would. And given its staying power, retailers now also realize the importance of continuing to invest in it and taking a lot of the learnings that they've gathered from measuring and instrumenting their online stores and trying to now actually apply them to the majority of their business, which which happens in the physical store. So I think that one of the enabling factors for us in our adoption has been just the continued decline of the cost of RFID tags becoming so that they can now be put on, you know, sub one dollar products in Walmart. And that has really, I think, cemented RFID as the winning technology for inventory and and, you know, and and therefore enables a solution like us. Yes. I remember these conversations for so long around RFID tags, sometimes it's like good to be ahead of the curve, but then, you know, the other opportunities that would have to come together from an economic standpoint so that the brands are making those investments at scale. Why don't you step back a little bit because there's gonna be varying degrees for the audience that understand RADAR and some that might be a little bit newer and really talking about what problem are you ultimately solving at an enterprise scale? Sure. So what we have built at Radar is a hardware and software platform. So we have sensors that go on the ceiling of the store. And, you know, the first purpose of these sensors is to count your inventory every second. So today, we are deployed across fifteen hundred stores in the US. We do over a hundred billion item counts per day. And what this allows you to do is have a real time view of your inventory and then predict and prevent out of stocks. So if you have customers walking into your store every hour of every day, you can assure that you will have the products that they're looking to buy when they're in the store ready to buy. That has a material impact on sales and margin, And that's kind of phase one. The other thing that we're able to do is measure the location of every item in the store in real time. And this allows you to now understand what people are picking up, what they're putting down, what is being tried on together. All the granular analytics you would have on a website, can now get from a physical store. And ultimately, you know, we believe this is just the beginning of the applications you'll build on top of knowing where every item in the store is in real time. So what kind of proof points did you have to go through? Right? Because I think there's gonna be varying degrees of who's listening to this. There's gonna be retailers. There's gonna be other founders. There's gonna be many who piloted and trying to understand, you know, what happens after the pilot. So what did you need to demonstrate before this kind of growth capital really made sense? Yeah. So we just need to prove, number one, the technology works. So, first, they want to know, is our system actually accurate? Because I think people have struggled historically to get, these always on systems on the ceiling to actually work, because they're trying to detect all these items from from from distance. So we've proven that we have ninety nine point seven to ninety nine point nine percent detect rate, at scale. So that's step one is proving we could detect and read all these RFID tags in the store. The next piece was we actually locate these products accurately enough. So we've now proven, yes, we can. We can locate these items within two feet on average, and this enables sources to find products quicker for customers, fulfill online orders out of the store more easily. They're actually the, you the the real champions of the product once once it's in their hands. And then from there, the we move to proving the actual business impact. So you have this technology and this location accuracy. How can you actually demonstrate revenue lift, margin expansion, reduction in shrink, etcetera? And we did that across pilot stores and control stores, which then ultimately resulted in these fleet wide rollouts, which then gave investors confidence to, you know, add more growth capital to Rare. So when you're working on this, and I'm sure it ranges, but what what are the kind of counterparts? Who are the stakeholders usually on the brand and retailer side that you're working with, especially when you're talking through the different proof points that you need to demonstrate? Yeah. Typically, we're we're dealing with the COO, the CTO, the CFO, and the the head of store operations. So, you know, it's a multi, stakeholder complex sale, and it touches every part of the business, which means you need, you know, buy in from all these leaders. But, ultimately, it it benefits, every single part of the business. So those are the those are the stakeholders we deal with. Yeah. The reason I asked that question too is because they have they all have different priorities, right, those stakeholders. And some of the metrics that you talked about, you know, whoever's heading up stores, might have a different kind of CapEx and ROI metrics that they're studying versus the head of marketing, for example. So, you know, strategically being able to bring those priorities together, I'm sure has that been part of kind of the journey that you've been on in working with these clients? Yeah. For sure. I think, like, ultimately yeah. I think we're selling, like, three things to them. We're selling them money, peace of mind, and and time. So we impact the revenue of the stores, the margin. We sell, you know, the money in that way. Peace of mind because they have confidence in their inventory and their decision making now. So they're not flying blind operating a retail chain with, you know, on the order of, like, sixty five, seventy five percent inventory accuracy. They're now operating a chain with ninety nine percent accuracy. And then lastly, time. Like, we we help the store associates just do their job more efficiently. They don't need to go to the back and search for something for fifteen minutes if it's not there. And if it is there, they know exactly where to find it. And, ultimately, that also saves the customer's Yeah. Well, I think what you're talking about is really closing that gap between digital and physical retail, which is the intellectual core of the conversation. So, you know, it's really understanding with that, like, level of data and precision and responsiveness so that you're empowering the store teams in a different way. From all these years of being in the data, right, what have been some of the blind spots that you think have been in the store that you that you really were able to illuminate and and and change the opportunity of that? Yeah. So I think you always have this tension between, like, store inventory control, like, so the corporate inventory control team, and then the the team on the ground in the store. So the team on the ground in store would like, hey. We're out of stock of this product. The inventory team would be like, we can see it's in stock. Like, you have, like, five of the units. Like, they're there. And then they don't send more. But in reality, they didn't have any of them. They had zero. They just didn't realize that they had been stolen or the shipment was short, etcetera. All these errors accumulate over time that eventually create these out of stocks and disappointments for customers. So, yeah, I would say that's a a big uncovering of a blind spot. It was just, like, their inventory is not as accurate as they thought it was. And I think it is kind of a it eliminates the guesswork. And I think it's a it's a moment of, like, I think, vindication for some of the operators that were operating under subpar conditions. So how do you then extend that to let's think about, like, what kind of information they get in the back end, some of the intelligence suggestions your platform's giving them. How is this then getting applied to merchandising, to labor planning, etcetera? We have an understanding of now the conversion funnel of every single SKU in the physical store. We believe that the in store browsing data is the highest intent and highest signal browsing data available, even more high in signal and high intent than ecommerce because we're actually physically in the store picking up a product. So based on how customers actually shop the store, we use AI to generate a dynamic floor plan that can remerchandise a particular category, and we can predict the percent increase in conversion of those particular units with this new approach. You can then, as a retailer, run a test on a single store location where the floor plan updates get pushed through to the associate app. Those changes are made, and then the test begins. And then if successful, you can actually scale that to more and more stores. This has been happening online for a very long time. It's just been difficult to do in the real world. It does. And so what I'm thinking about as you're talking about that is, like, what is a good partnership? Right? Because you're building this platform, And obviously, there's the opportunity of contextualizing with AI. But what does AI need to understand about the physical world in order for this data to be successful and useful in store? What does that partnership look like with you and and the client? Yeah. So I think that, ultimately, our system is ground truth for any AI. Like, not just our AI, but all AI systems any retailer is trying to apply to their business will only be as good as the input data quality. Right? So if you're training a model how to drive a car on people riding bikes, like, it's not gonna learn how to drive the car. So I think in the same way, if you're trying to run a business with AI and your data you're putting into your system is, like, sixty five percent accurate, you're not gonna get great results. So with RADAR, we bring the accuracy up to almost perfect, and that allows you to apply AI across your entire business, not just with our AI, but any other provider or service that you might be looking at using. Yeah. And so is there, like, a sweet spot, you know, when when is it a brand that has one store, ten stores? It doesn't matter. A hundred stores? Like, is there a multiplier impact that really benefits in a different way? If I was running a store, like, I would want to know my inventory in real time. Like, how how do you actually know when things are stolen? Right? The whole thing is if you you you probably don't know when they're stolen, so then how could you possibly know they're out of stock and you can't afford to do inventory counts every day? No one wants to do inventory counts every day even if they could afford to. So, yeah, I think it really would benefit any retailer of any size. Right now, we've been focused on larger chains, but we do believe that, you know, we will get to a more self serve product that will make its way down to, you know, the the the boutique level. That's exciting. I'm sure I mean, the smaller the team, more of a pain point it is in different ways too. So so would you think about this? Like, does the store begin to function more like an operating system as they partner with radar and can continuously understand product movement and availability? And you have some partnerships in the fitting room activity, fulfillment behavior. Like, how do you think of this? Do you would you categorize this as a store functioning more like an operating system? Yeah. Yeah. I definitely I definitely view this as like a, you know, OS for for retail. And I think that, really, it comes down to tight coordination between what we're sensing the data, the insights that we're surfacing, and then also the actions that we're surfacing for team members in the store to accomplish. So, really, none of this is possible without buy in and adoption from the store associates. We have you know, obviously, you can do a better job allocating to the store the right inventory, but you still need that inventory to be on the floor. You need the store associates to be able to find it for the customers easily. And we now have, you know, in holiday, north of a hundred thousand store associates using the Radar app, to do their daily job. And they're actually our number one fan because they feel like it makes their job that much easier. And we've heard from, you know, store managers that, you know, store associates that are maybe, like, shy or a little bit nervous to interact with customers. They're maybe, you know, used to using technology to help them in a lot of areas of their life. It's you know, working in a retail store is an area where, like, you actually don't have very much technology to help you. And when they have RADAR, they feel a lot more confidence interacting with customers because they don't feel like they're gonna disappoint them or let them down because they know exactly what they have, where to get it, and how to help them. I'm guessing too, the more they interact it with it as well, the more the system's also learning. So not just at the item level, but also through their interaction as well. Absolutely. So the system learns based on every action, you know, there's a feedback into the system. So if a item, was merchandised at a particular location, we know the impact of that relative to another store that has it in a different location, and we can feed that into the system that then becomes more intelligent, how to lay out the store, eventually, to staff the store at which times. Any anything in this physical store becomes measurable and therefore improvable. And so how do you separate useful intelligence from data noise, especially as you continue to scale? Yeah. I'm not a big fan of just, like, giving people data and be like, alright. Here you go. Like, here's a big, you know, spreadsheet with a bunch of data. So we've actually taken the approach of prescriptive analytics where we actually suggest actions in the moment. So based on what we're sensing in the store at that time, we can actually surface suggested actions to different team members in the store to take. So that might be recommending a particular product to a certain customer based on, you know, what has gone to the fitting room. It might be moving or substituting, you know, a a T shirt for a different one, because of, you know, weather data or, the fact that we understand it's, like, highly competitive with a T shirt next to it, and therefore they're going to cannibalize you. These are the types of suggestions we can make in a moment. So one of the strongest proof points you talk about is enabling the store associates. Right? You also do it to fulfill buy online, pick up in store, and there's other aspects of convenience, right? What does that speed of change look like in the store? How has Radar been helpful in that aspect of an associate's workflow? Yeah. So I you know, I just met with a store manager a few weeks ago, and she saw in her store an increase in online order fulfillment from, like, seventy eight percent without RADAR to ninety eight percent with RADAR. And the feedback from store associates is amazing because instead of having to search for, like, a hundred items to pick for buy online, pickup in store and BOSS orders without any location information, they know exactly where to go and what order to grab all the products. So their job just got so much easier instead of having to hunt down these products throughout the store, which, by the way, are moved around by customers constantly. So that's, I would say, a measurable impact on the online order fulfillment side. Yeah. It's ironic. Right? I mean, those moments of convenience, the less friction, the better. Did you say that? They're also able to accomplish in half in, like, less than half the time. Yes. Which is one of the key value propositions of flexible fulfillment. So let's go a little bit into like thinking about the future a bit too, right? I mean, obviously, again, coming on right after very significant raise, congratulations again. What what can you share with us that you're most excited about? What is this gonna enable today, and then the next year or two? Yeah. I think what this enables for us is to service more customers at the same time. I think in the past, we really were limited to one marquee customer per year. We now have the ability to scale a team to offer excellent support to many customers at the same time, deploy more stores per month, and then ultimately expand the products that we offer on top of this core data that we we we extract. Have there been surprises that have come up, you know, as you continue to grow and evolve and these light bulbs go off for you? I mean, you're a brilliant mind that you start to get really excited about, and you're like, okay. You know, maybe this is a even bigger opportunity for us to elevate the in store experience or add and an additive way to the customer experience. Yeah. For sure. I think, right now, we've been really focused on the operations of the store and inventory, and then analytics to, like, improve operations and store performance, etcetera. But I think there's a lot of really cool customer facing applications that we have ideas for that we will unlock as we, like, bring the latency of our system down. We currently, like, have the fastest RFID system in the world in terms of, like, how quickly you can update positions of items in the store. You know, today, we're, like, our media and update speed is, like, eight seconds, like, hundred thousand item store. But as that gets quicker and quicker, you'll be able to have, like, real time interactions. You know, if you're a customer, you can walk into a store, return something, drop it in a bin, get an email saying it'll be processed within twenty four hours. They're, like, waiting in line to do that return, which is super annoying, or get more information on a product that you're holding, you know, find products in your size, recommend a product for you, etcetera. Really bring a lot of the online experience personalization and convenience into the physical shopping space. Yeah. Which I think will be amazing as you you continue to enable that because I think there's been so many buzzwords for so many years around personalization, and people kinda just, like, you know, not laugh at it, but they think, Oh yeah, that's a buzzword, but we don't really get to experience that. And I think what you're leaning into, and especially at the scale it's getting deployed and the amount of data and information that you have, it's exciting. They could really become meaningful recommendations back that really feel personalized, and finding people at that moment of intent. Yeah. Absolutely. And then, you know, I always talk about yeah. I started the company to do autonomous checkout, and, yeah, I still find that very, very interesting. Interesting. Yeah. It'll all come together. Well, you're now deployed in fourteen hundred stores, is that right, across American Eagle and Old Navy? Yeah. Think now we're in fifteen hundred. Yeah. Close. So close to that. Right? So, you know, as you kind of gone through that deployment, what becomes harder, you know, as you go from, like, piloting to full fleets to enterprise adoption? There is, like, operational and change management required to adopt and leverage a system like this to its maximum. So I think it's maybe, you know, in a way, like, easier to do in a smaller subset of stores where you have, like, more team members dedicated effectively per store than you do at scale to drive some of that change in operating, you know, management in those pilot stores versus the fleet. So, there is there's an adoption curve. There's a trust, piece here where in the pilot, they blindly trust the data, that we provide and replenish based on it because it's a small subset. And then as you go to the fleet, the questions come, well, like, how do we know radar is really correct? And then you have to really work to, like, continue to audit, prove that we're accurate, in order to get that trust and then, you know, therefore, see see the benefit at scale, which which we've done. I think that I'm glad that you brought up change management because I think that that's a huge aspect of this. And I don't know how much you get involved in those conversations, but I would think it would make a partnership even stronger if you could help them think through and navigate those things because it's kind of like on two sides. Like right now, it's behind the scenes a bit to the customer, so it's not changing their behavior, but the way the store associate and how they're operating at the store level and then at the corporate level and how they're embracing this information. For those who might be going down this path, right, what are some of your recommendations to really position the a brand or retailer up for success if they're gonna embark on a large deployment like that with a company like yours? Yeah. I I think it's really just taking a step back and, like, rethinking everything you would do differently if you had real time inventory, if you had granular data to inform all these decisions that you're used to making without information. So it's it is, somewhat of a process of retail is still, like, an art and a science, but, you know, you're you are opening yourself to bring in a little bit more science into the art of retail. And I also think that there's being aggressive at, like, systematically hunting down all the areas for ROI. Like, if you have not an accurate inventory, do you need to have any, like, omnichannel order thresholds, or can you just display all the inventory to the customer online? Right? And why not? So being willing to try these new approaches that might seem a little bit, scary or risky based on, like, historical experience, and then seeing if they succeed or fail and then in scaling from from there. Yeah. I think it's so important to have those conversations. I love that you brought in, like, just looking for the ROI because things also kind of have a multiplier effect over time. That's also why earlier I was like, know, the impact it does to the individual store, but then we have a fleet of stores. I'm sure there's a little bit of that multiplier. Yeah. Like, one one thing I would say that's very interesting to me is demand planning. So, like Today, people buy inventory based on their perceived demand of that inventory. But the question I would have is, well, what percent of all time was that SKU available to the customer? Was it on the sales floor, like, ninety five percent of the time? Was it on the sales floor seventy percent of the How does that impact the total performance of that SKU relative to others? Right? So you might have winning items that were, for some reason, not available to customers by and large in the places that they should have been. And you could have losing items that were available the entire time. And effectively, you're making decisions about what to buy next year based on historical data that is fundamentally flawed. So I think, like The next expansion and focus on applying this data is into how do you improve your demand planning, when you do truly have the ability to granularly measure, like, what what I would call, like, true demand versus, like, your perceived demand based on inaccurate inventory. And that goes to kind of like that future question. Right? Like, what do you think the next generation of retail leaders will expect from their stores? Right? But they maybe they couldn't have done a decade ago. Is it real time visibility, predictive replenishment, automated insights? Like, what are the things you're thinking about that next gen leaders will expect or will have the capability to do? All the benefit of the thinking that has gone into ecommerce and how to optimize ecommerce actually very much applies now to to physical retail. Like, it's actually pretty copy paste. So, like, I think it's just gonna be expecting some of the same types of thinking and techniques that worked in ecommerce. Like, in ecommerce, there's a concept of, like, an attractiveness rating where you have certain areas of the page that are more attractive than others, quote unquote, because they get more attention and more mouseovers. So you put products in those in, you know, different locations to try to understand, is the product a winner? Is the location a winner? Is the location a loser? Is the product a loser? And you can now apply the same exact techniques in your store. So I I really think it's gonna be this, like omnichannel has been talked about, like, hey. We share inventory pools, but now it's gonna be, I think, sharing, like, ways of thinking and methodologies across both in store and online. I agree. I agree. And I think those will start to be the things that separate retailers and that lead the next chapter from those that don't is is really having that thought process. So for those who are, like, listening today, what's one metric that you think every retailer should be looking at more closely? Inventory accuracy. It's your, you know, number one asset, on your books. You know? How how accurate are you in knowing what you have and where it is? I think that is the number one metric that people should be looking at more quickly. And by that, I I think there's a there's a trend of talking about shrink. And, say that you are you know, have two percent shrink. What that means is that you could be dealing with a seventeen percent shortage and a fifteen percent overage when you do your physical inventory. You sum those together, and you get a negative two percent net shrink. And that's what gets reported out. But, really, you have a thirty two percent inaccuracy in your inventory. You have a product you didn't know you have, and you have a product that you think you have, you that you you don't. So I think focusing on that, that is, like, the foundation of every single other improvement. So many things cascade from that. And then what is one assumption about store technology that you think the industry needs to move past? Technology's gone to a place where it can, like, actually fundamentally affect the operations of the store in a way that it couldn't really have done ten years ago, or even five years ago. There's so much magic you're making happen behind the scenes that's not customer facing. And perhaps instead of thinking that it has to be that forward facing technology to make it elevate the store experience, maybe that's the thing that they need to look past. Is that a good summary? Yeah, I kind of think store technology should be there to you know, a lot of it should just, like, fade into the background and and make it easier for your store associates to do the one thing that only humans can do, which is, like, interact with customers and sell them and do a better job servicing them in the moment. So I think if you can invest in technologies that allow your store associates to get more valuable face time with your customers and give ultimately a better experience and a better feeling to people that walk in and shop in your store, then I think that is very valuable store technology. I agree. Well, I always love getting on with you. I think, you know, you're pushing so much forward in the industry. And I love how you joked how, in the early days, you were thinking about autonomous checkout, but it's all going to come together. And that's what happens too with RFID and the industry coming together. It's been great to see your continuous momentum and impact in the industry. And now that you've got this next round behind you, you can really focus on what you're going to continue to bring to the industry. But I think what this conversation makes clear is that this next chapter of physical retail, it's not just about better stores or better products or better experiences in isolation. It's really being defined by an intelligence layer that's connecting all of them. And I think there's going to be a closer bridge to the operating system mindset of the in store environment, like what we have in the e commerce world, and and that intelligence all coming together. Absolutely. Well said. Yeah. So thank you so much for for the time. Where should people follow you? You know, where where should they learn more about about Radar and what you're doing? You can follow me on LinkedIn, Spencer Hewitt, I guess, and then x. And then x. Okay. Alright. Good. Well, everybody, you heard it here. Spencer Hewitt, we are now yes. Six years later, so much Yep. You've accomplished. A billion dollar valuation is no easy feat, but appreciate all that you're doing to continue to improve the in store experience, and we'll continue to watch what you do next.
