The Kroger Co. (KR) Earnings Call Transcript & Summary

All right. Well, welcome, and let's talk a little bit about AI in retailing and starting with in the asset protection, loss prevention space, but again, the beauty of AI is it's allowing us to do multiple things at once and do things that can be dull, dangerous, and disgusting and things like that. So -- but it does things to give us a heads up. And so what I'd like to do is kind of look at the scope and scale of retail crime. Now there's a little controversy out there, not with us and not with the 94 retail corporations that we work with week in and week out, but with others. And part of the issue is what's going on? Is it measured and how is it measured coded and used and there's a difference and we all know this between what law enforcement knows and the police and sheriffs, they only know what a crime victim lets them know and so it's tough. And our data show in our surveys that law enforcement only knows about 40%, maybe 50% of what's going on from a crime event standpoint. That's what we're interested, how many crime events because that's what leads to everything, all the victimization of harm that we all incur. But what we do, in addition to using official crime data is work with the retailers themselves and understand what's going on across their 300,000 stores that we're working with them on to understand what are they experiencing and what's happening? Well, what's happening is growing in scope and scale. And even in the life safety standpoint, it's becoming more dangerous and aggressive and they can look at year-over-year growth sometimes as many as triple digits. So it's an issue, it's a problem, and that means it's an opportunity. So we're going to talk a little bit about that. And I'm going to first turn to Mike Lamb, long-time friend and colleague from the Home Depot, from Walmart and lately from Kroger Company to talk a little bit about what are you experiencing? What's that look like? And why is the big fallout for a little more technology, if a lot -- not a lot more AI support?

Yes. Well, thank you, Read. And it's certainly a pleasure to be with you this afternoon. My experience is in 40-plus years of being in the space of trying to keep the red actor at bay. In the last, I'd say, 3 to 5 years, the escalation just in the epidemic of habitual organized theft has really astounded me, even for the years that I've been doing this. And I was with Kroger initially is to kind of step back a moment from May of 2017. And then I retired from Kroger in November of 2020. And -- so I was going for about 18 months, ultimately before I returned to Kroger. And I was astounded with what I saw in just an 18-month period with really what I would call the epidemic of organized retail crime. So for the Kroger company, one of our main core values, and I'm sure it is for any retailer that may be in this room is safety -- life safety, right? So what we're seeing with regard to organized theft brings about today an element of violence that I've never witnessed in 45 years. I was talking to a dear friend and colleague at Wolf just last night. Years ago, bad guy take your stuff and if you approach them they drop and run. Today, they dare you to do something about it, literally dare you to do something about it. And we have certain markets across our chain where this problem is worse than others, obviously, but it's led us to many changes and perhaps I'll mention it later, about how we go to market to understand better the organized retail crime operator. And more importantly, how we leverage technology, AI. We have solution providers that support, but not solution providers, our strategic partners in this room today. I see a few of them that have supported Kroger over the years. And in the absence of this technology and the advancement of AI and a lot of the work that's being done holistically, I would suggest to you that we'd be in a far worse place. Some of the media, some folks say, "Well, is it really that bad?" Well, I've never been one to rely purely on a motion or eyesight more on data and history. And our trending suggests to us, it is indeed a problem, and it's a growing problem. We don't try to pinpoint it to a specific number. But we are, like most retailers, we're seeing this rise in organized theft. And we know and realize we have to do more about it at the Kroger Company. And my organization that I work for today, I tip my hat because at the very top of the priority list is life safety. We preach constantly there is nothing in that store more important than the safety of our customers and associates. So any technology that helps complement that, help us mitigate that risk is always going to be a priority for the Kroger Company.

Excellent. And I appreciate that, Mike. And life safety and life safety, we have the major retailers come through our labs almost on a weekly basis now. It's a white board. And that's where everybody is starting with the major department store chain last night when Mike and his team were there a couple of months ago were in the labs, same thing, life safety, but it's always going to be about that experience and that outcome by the green and the red shopper, right, the good and bad. Let me go over to you and maybe get a little bit of an understanding, Robert, what's going on in your realm and how do you look at it? How is that affecting your business?

Thank you, Dr. Hayes. I just first want to point out this article that came out just a few days ago from the National Association of Convenience Stores, it's a convenience.org highlighting that since January 2020, the per loss -- per store loss is more than doubled, which is significant and why there's a lot of focus on this today. And then you can also see further in the article talks about the largest spending is now in technology and video analytics solutions. So to kind of echo what Mike said, certainly, there's -- top priority is the safety of the folks in the store, whether that's associate or the customers and so with that, with partners, like Mike said in this room even we're looking at things that can detect slip and falls, detect crowds gathering outside the store, maybe at night that the associate inside can't see and other things. So I characterize the problem a little differently from some of Mike's earlier examples like with Home Depot, Walmart and convenience, you don't have groups of people rushing the store and arm sleeping 100 bags of M&Ms, it just doesn't make sense. But where we do see some of the problems are around fuel theft. And so with computer vision technology, we're able to detect -- help us detect some things that are happening to help prevent fuel theft. Also, we see some of the things around gift card scams and just regular theft through self-checkout, but we'll talk about some of those later.

So I mean, what's interesting is they're very different experiences for each of your businesses. And at NVIDIA, we put out a survey recently and surveyed over 400 retailers. And what came back loud and clear is retailers are taking ORC seriously, loss prevention seriously, safety seriously. 35% have already invested in solving this problem and are continuing to expand on that. So I thought that was really emphasized just the criticality and urgency around this problem. And then, Matt, I think you're also seeing.

Absolutely. So Cynthia, I'd agree with you 100%. We're really validating the comments that you made, we're seeing that on a daily basis with our customers. Look, we started out in kind of a decomposed pieces parts environment. We were looking at license plate standing. We're looking at cameras and so on and so RFID tags and things like that. Well, now we've got to look at it more holistically. We've got to pull it back in and then pull it more down earth really working with partners, and you'll hear a number of the names up here today ever seen and Radius AI and so forth, where we're looking at that AI platform to try to control these bad actors. Now in addition to that, though, we are adding a bit of complexity, spoilers. We can help you with that because, of course, we're going to create a massive amount of data. Do you have a platform? Do you have a server room? Do you have an IT person who can manage that server? Or are you looking at doing something on the edge with something as small as a phone, right? So again, we're seeing a lot of these technologies really come to the floor right now. And in real time, we're realizing how we manage them and how we help you deploy them. And ultimately, at the endpoint in the store, how we assist your associates in making sure they can utilize them effectively.

Excellent. So I want to go back to Mike real quickly, and we hear a lot about overseas organized retail crime. And by the way, our team had done a study for RILA, Retail Industry Leaders Association, I don't know if I have a lot to mention them in RF, but several years ago, 20 years ago. And based on a ton of interviews across the country, we recommended let's change it to ORC because it's much more going on, right, than just stuff that can be organized in some way. Partly, though Mike and I had been talking about this and some other retailers in our team, we're also using coordinated crime, right? Because some of them are organized, some of them aren't, but they are definitely coordinating more and more maybe before they get there and maybe not during there and then sometimes after all of the above, right? So think about coordination of offenders that are looking at places to go, what they want to take, why they want to take it, how they're going to take it, who is going to work with them and so on. Mike, can you tell us just an example, maybe the difference between organized retail crime events or coordinated crime compared to, say, what you see all day in and day out, the opportunistic type.

Yes. Yes. Well, thanks, Read. One of my personal takeaways in learnings and coming back to Kroger in May of '22, at every month, we made a trip out to one of our markets where we had a significant issue with homelessness and drug affliction. And what we saw there was quite astounding to my team, to anyone that was not living in that moment as a store leader, as a store associate, as an asset protection team. Just this absolute blatant level and brazing level of theft. There was a period of time when we were there that we didn't walk into a store where we didn't see activity that was really alarming. And quite frankly, violent behavior. So as an organization, we came away from that and said, what have we learned? And more importantly, what are we going to do about it? And we had a very simple model of risk rating at Kroger. We still do simple, not in the fact that it's not effective, but we don't have 16 levels of variety of risk ratings. We simply have low, medium, high and max. But we walked away, Read and team with some learnings that there's another group of stores that we dubbed extreme stores because they're just that. In many cases, these are stores where our position was we have to take our stores back. And we knew that we can only get there through 2 things: people and technology. And then the other learning is not all shop lifters are created equal, right, Read? I mean for us, we used a very simple model to say we have a Level 1, Level 2 and Level 3 in the organization. The Level 1 is the more where temptation meets opportunity. It's kind of the more spontaneous theft that occurs in the business. And we got great solution provider partners that help us mitigate that. We had another characteristic that was mobility. How mobile are these people? What's their ability to get from one store or one area or one market to another. And then third and finally, most importantly, what was the propensity for violence? What was that propensity for violence because as I said it before, I'll say it repeatedly, nothing in our store is more important than the safety of our associates and customers. So we created a fifth tier at Kroger called Extreme. Thank goodness, we don't have a lot of them, but they're out there. So as you look at Level 1, more of the low-level first-time offender. Trip wires that we have in the store, anti-theft to benefit denial tools work. Level of violence, potential for violence, we see is low or at the very best medium. Mobility. Well, they're all over the country. We operate a lot of stores. Total loss in the shop lifting bucket, we think is somewhat minimal to the other two. Second level, and I've talked about this, and we did it your OPRC-impact meeting, is the more coordinated and organized. They may limit the number of boosters because boosters can reveal the head operators, which lead to problems for prosecution for them. So you got the Level 2, very well organized, plan their stores, know where they're going, have their shopping list, have teams at the track. Mobility is significant. They can be running the I-95 corridor on the East Coast. They can be running the I-75 corridor, well organized, very efficient. Characteristics related to violence, we view as medium to high. The reason we say medium is they're very well organized. They think about this. We've had them come in and speak to our AP teams, some of these people that have converted. And -- but we say medium to high and high because maybe some of these folks have been arrested a few times. Perhaps they have a criminal record. This next one is going to be the one that may result in some significant jail time. So there's no absence of violence there, but there is a violence component. Then we have Level 3. And for us, that's more unfortunately, the drug afflicted, the homeless. We see them with limited mobility, and you probably know what markets across the country, I'm talking about. Every retailer in America, I think faces similar issues. The violence level potential propensity, we see as extreme. So in these stores, we've set up an extreme store strategy. And we're doing a number of things within those body of stores that say we will take our stores back. We're here to do business. We're a grocery store and it's not -- it's going to be business as unusual in some of these stores, but no longer are we going to allow that population to rule our stores. We will take this bull by the horns and we will leverage technology and we'll use things like AI. We use a number of vendors in this room. We have camera towers that I'm happy to discuss all stage. We have the Everseen Technology at scope. And we've created what we're calling an elite guard. Guarding in retailing can be complex. And this is not just about having a guard, it's about having the right guard with the right equipment, whose job it is, is to make sure they safeguard our stores first and foremost from a safety point of view and then secondarily from a shrinkage point of view. And we're on that journey. We've been on that journey for about 6 months, and we're very excited about it. We've got great senior leadership buy-in for this program. We've got great solution partner buy-in for this program. And only we get to an expansion of your team, but because I know this is kind of my passion. We're looking at this thing, and I heard you referenced it earlier, its ecosystem. So how do we take our great strategic partners like Everseen, like LiveView Technologies, like PerCheck, like Ora, who I see in the back of the room. If you miss these guys, I don't know how you could, they're wearing yellow shirts, but how do you stitch that together? How do you integrate that in such a way that, that becomes a force multiplier. Every one of these solutions adds value to our organization. But to the extent that we can have sort of a spoken hub and a data aggregator, and we're feeding that information in on the very value-added return that we get on these investments. Now it becomes more of a value proposition for the organization. So we've been on that journey for a while at Kroger. We're very excited about it, got outstanding senior leadership support of it. And if the passion is not coming through, it probably won't. That's a real big one for us. There's a gentleman on the front row, he's got a widget or a gadget probably won't show it, but it's really about exploiting technology, right? Let us test, let's learn. If we fail, let's fail fast, and let's keep moving. And I've always been a strong advocate for that.

Perfect. So let's now leverage. We've talked a little bit about the scope and scale that damage the impact that's going on from the theft, fraud and violence out there. Look a little bit at theft primarily; but we're also interested in violence, right, the mass attacks you see, whether it's in Philadelphia with looting, multiple stores are shut down and will never open again. Those neighborhoods. They're out of luck now, right? They've been affected as well. We also are talking about what we all saw, I would say, Nord Stream 1 and 2 mass attacks there. Individual shooters, right, who are working on active shooters. We've got things going on in the works right now. But we're going to talk about leveraging AI because we can use it for both of those extremes, but we're also going to talk about how. So Cynthia, if we can kind of take a look at how we're thinking about the world, how things are being implemented. And in our particular ecosystem, and really, this is about a 23-, 24-year-old concept, evidence, research-based and [indiscernible] and so on, but we all use it in the industry and these retailers that are here. And really, at the end of the day, a crime of attempt, event, incident starts with an individual or maybe a crew of individuals that move through place and time and they victimized, right? Their decisions may have happened in a moment or could have been over months or years, but they have to move in place in time, whether they're working there or visiting or otherwise. So it helps us, though, create and understand aiming points, think of a cancer cell of virus. It has to initiate and it has to progress and it has to progress in steps or stages. It's not linear. They go all over the place. But they have to kind of check some boxes. Those are aiming points for us to sense them, to affect them and they coordinate to do both of the above, right? It gives us a logic model, that's what science is. It's a logic model and evidence or observations. So Cynthia next one, what we've done now is we realize too, not only this person have to move there and do something to harm us, to victimize us, but unless they're caught or jailed during the event, they're moving on. They're moving on it. Those -- we want to think about what happens before, during and after that point, again, opportunities for AI, right, whether it's visual, digital, oral, textual that's how we look at the world. And whatever we have retailers in, whatever we do with all our research, everything is aligned, okay, where does that fit in to detect, affect or help us connect in this area. So Cynthia if you go to the next one. Now again, if it's a serial offender and there are millions of serial offenders, they're coming back or they're going somewhere else, right? So what we didn't prevent the first victimization in that first instance. But what we collected, again, leveraging AI and so on might be helpful either in the next moment or over time to prevent and safe part of victim 2. Does that make sense? Again, the victim could be a person, place, merchandise, money or whatever, but that's how we look at the world. And this logic model is how all of this come together, line out everything that we're doing, trialing and have what's called the profession, common operating language right [indiscernible], which we have a lot of that, too. So that's kind of how we align what AI play. And that means what signature or signals are we looking for from that offender, that crew that we need to pick up on what sensors do we need to do that? What AI models do we need to use to maximize that? And then how do we get that to the right people, the right time to make the call. Still no matter what the AI play, human still makes the call, right? AI has given us a heads up. And the more sensors we have telling the same thing, the better decision that we can make, okay? So that's just kind of framing up from that standpoint, there is a method of madness. We're not just hopefully just doing stuff, we have kind of a model, so we're all kind of working together. And at LPRC, with 240 corporations, we can get some stuff done and that does include all these retail associations, all law enforcement agencies, P&G and the other manufacturers, right? We can get things done, but we have to have this model. And so whether we're meeting with Mike and his team or anybody else, we can do that. Not just showing affecting offender choices, detecting them and then connecting within a company or within a store within a company between companies, between companies and their law enforcement partners. All this happens in the ecosystem. Just to give you an idea also of what we can do. We have 6 really cool interior labs, one that is an amazing store set up with 300 technologies in there, by the way. But we also need to work in a wild, not just in-store but with groups of stores. So we have stores that are co-located in enclosed malls, open centers, non-shopping centers. In this case, on the east side, Walmart, on the north side, Family Dollar and Dollar General, Advance and AutoZone, Circle K and Wawa, Walgreens and so on. How do we help them individually get better? How do we help them work together to get that community immunity that they need and then you'll notice red dots there, by the way, are what are called crime generators, attractors, maybe even a radiator, liquor stores, bars. What happens at your place, again, didn't start, may not end there, but it's also largely affected by what's going on around the world. So we're working with sensors outside of that particular -- that place that we're interested in protecting within that place, but between something that can inform all the places, right, and they [indiscernible] on. So that's a method demands there.

Yes. So Robert, we didn't hear from you as well. And I know you have put a lot of thought into Jacksons Food, what you're doing and what the problem is? Because your problem is very different than Mike's problem at Kroger. You have a different environment. Can you give us a little information on that?

Sure. So as I mentioned earlier, we don't have the same type of problems as big-box stores, a lot of ours is around fuel theft and we're leveraging Radius AI, along with Lenovo and NVIDIA to help with that. That's kind of our partner ecosystem. But some of the other things that I alluded to earlier, such as gift card fraud, that's the whole reason I've actually started diving into this as we were having issues with gift card fraud people calling up the stores and doing social engineering and trying to get gift cards activated. And so I was looking at a product called in-store AI, and I believe they're here at the show as well where it's listening to certain keywords and when we are hearing certain keywords about activate a number of cards and things like that, that could tell us in real time that, hey, perhaps a theft is going on and looking at ways to stop that from even at the easiest ways like dropping the phone line, so it just cuts them off. So things like that, the scanning, the self-checkout, we don't really have as much problem as somebody like Mike at Kroger, where people are taking products and scanning something else because everything is real close and the cashiers are there. But I think part of the big thing is that we need to keep in mind and Matt alluded to this is making sure the ecosystem is consolidating all this data. Because there's a ton of data that we now have the availability to and we're just going to overrun the operators, and they need a way to consolidate this down into something actionable, like, hey, here's 5 things you need to look at or we're seeing trends and sometimes some of the generative AI or the artificial intelligence can help us determine what those trends are and give the operators actionable information so they can go and be more proactive rather than reactive.

Excellent. So let's -- I want to talk a bit about self-checkout because we don't want to just think about the inbound, we want to think about the outbound as well. Mike, can you talk just a minute about what you experienced at, I don't know if you want to say Walmart, but certainly a Kroger Company before you started instituting that AI on the front end. And we talked a little bit about this outside some of the adaptations that were made there behaviorally to help better impact and better safeguard your associates?

Yes. Well, first and foremost, we love self-checkout at Kroger. It is what we do. We will continue to do it. But we also know there's individuals that take advantage. I mean, and I know I'm preaching to the choir here, but -- and what we've learned over the years in working with our solution partner and the great work they're doing in that space is there's the evolution of change. Initially, it was just skip scan it, right? Whether purposely or not, the items we're getting ranked. And look, we're in the business to sell grocery. So if you're not scanning it, you're not selling it and you're shrinking it. So it's a bit of a double whammy. And then as I use the words of the CEO of this company as we slap them in the face a bit, I know you can't do that, then they adjust behaviors, and they start coming out wider around the scanning bid. So we have to come out wider with them. So -- and then from there, we introduced ticket switching. And I've got videos here that I'd like to tell you they're entertaining. They're really not. But I guess, in some respects, you'll find some humor in them. somebody will get a pack of Kool Aid, right? And you know this, Christian Keith, put it over a $100 brisket, you got a Kool Aid flavor, $100 brisket going out the door for $0.79. So then he was the evolution of ticket switching. So as we dial that in and got efficient and picked the small percent that we're taking advantage, it's sort of that little number, at times a big number is a big number, both in sales and shrink. Then -- and we smacked them there and then they said, "Oh, I'll just leave it in the cart." So now this solution provider, they're in the room, said, we're going to give you a cart-based loss. So to the extent that, that product remains in the cart and has never even introduced at point of sale. And our own mystery shops told us we had an opportunity with that. They're now capturing that basket. And it queues that self-checkout clerk over to that robot, the self-checkout unit, and we literally show the video in all these matters of the adamant question. So it's no longer the Easter egg hunt. There's been some critics that say, well, well, you're really slowing down the customer. We think we're speeding up the process. There's been weight scales in the industry for years, a lot of friction with that. We think we're optimizing the experience and goodness knows where we'd be on shrink if we didn't have that technology. So -- and that's what we look for Dr. Hayes and our solution providers. That can help us think and look forward to say, there's a bit of a shelf life in what you're doing and you need to constantly reinvent yourself. So that's one point. And then in the ecosystem. And I'll reference self-checkout. I do believe and what we're seeing and witnessing that today at Kroger, you're getting more coordinated theft through self-checkout because it's a bit to guiltless crime. Is it not blaming on, "Oh, sorry, I didn't realize they didn't ran it, whatever it is." So if you look at these coordinated teams of organized operators in theft, self-checkout is the land of opportunity. So we got to stay a step ahead and we're going to accomplish that through AI and with our teams. Now let me add on. We're concerned about safety. We want our SCO attendance safe in the course of doing their jobs. And we'll opt over safety for them, then we will more so the prevention of Read going out with that brisket. But it's important that we do both. We mitigate safety risk and we look at the loss impact to the organization and the opportunity for missed sales. So -- and then you think ecosystem. So how do you take these red actors that we so commonly referred to them as and how do you connect them to something else. It's a solution in your store like LiveView Technologies where we have towers that are software-driven. We literally had a -- we stitched together sort of with bubble gum and bandage in the beginning. The notion that if we trigger a gatekeeper or per check system at the door, that tower is going to pan to the door, zoom in and give us the image of that individual or individuals and where they're going. And if they get into a vehicle, to the extent that we want to leverage license plate recognition, we're now starting to create this integrated ecosystem that positions us much better. I was at a conference recently, Read, I don't know if you were there or anybody on stage who was there. Sure, some of the audience members might have been. We had 2 attorneys general come in and talk about, look, we care about ORC, we want to be more involved. But the things you're doing with AI, machine learning and technology that allow you to better package our case, make us more efficient in assisting you. So rather than they have to do a lot of the investigative work, the retailer themselves is doing it for them. So it allows for your more nimble, your ability to prosecute these bad actors becomes much more efficient. And so that's a lot of the work that Kroger is undertaking and that work continues. And I just want to make one final comment here on that. The collaboration that we're seeing today in my 40 years plus doing this across retailers, across law enforcement, across many of the cities in which Kroger operates, is growing in a much favorable way. Still a lot of legislative work to be done. I think we all recognize that. But there is the coming together, the retailers for the common cause of life safety and safeness and then how do we eradicate this organized problem because we all know it's got a very egregious underbelly, drug trafficking, human trafficking. So it is the challenge of the day. But again, I'll repeat, safety first, shrinkage thing second.

So -- and I think you set the stage too, interoperability more and more, whether we're talking with Bunnings down in Australia and New Zealand to obviously, throughout the United States and Europe, we got to be integrated, integrated, integrated, we came of all these disparate things we want to align, integrate them for maximum effectiveness, go from that mono to probably treat the concept, but going back how can we know the threats coming our way. We look at things online, texting and other things that there are threats going on to indicate somebody inside or out is coming to harm us in some way. That's worked on the parking area, knowing that, that vehicle that's now hit 2 or 3 other sites is just pulling up to and entering our parking area and understanding that's here to give again our manager and the team, another few seconds heads up in that case, detecting certain weapons, screaming, obviously, tire screeching, glass breaking, metal banging, things that might be significant, yelling and so on. Those are important AI plays for us way before they even get into that Zone 3, the interior space, as they transition in, again, the same thing, we are trying to leverage AI. And I thought you wanted to show maybe a quick example.

I would actually think Robert has some interesting examples.

Yes. Because Robert, you're dealing with gas drive offs and you're dealing with tankers pulling in, right, and fuel [indiscernible] retailers and deal with that. Can you take us to an example here?

Sure. This -- what you see here on the slide is one of our fuel islands, we can -- with computer vision, we can see vehicles, we can look at each dispenser. There is LPR so we can get license plates and we can see, like on the screen here, you see pump 11 occupied faults, active faults. But when somebody engages the dispenser, puts the nozzle in and starts it, it will switch to active. It starts a timer, so we see how long the nozzle is active in a vehicle and fuel is flowing. So why that's important is for fuel theft, if somebody has a specific type of vehicle that maybe has a bladder in it and taking more the amount of fuel that should be required for that type of vehicle, it can set off an alarm. And even with the right integration, we haven't done this today, but it can even shut off the dispenser so that the fuel will stop flowing. It can alert police department, law enforcement. We have the license plate. And even more importantly, in the next slide, we see right here, it will identify different types of vehicles. So the important thing there is to realize gasoline weighs about 6 pounds a gallon and diesel weighs just over 7. So if somebody is going to come in and steal 400 or 500 gallons worth of fuel, they can't put that in the Toyota Prius, right, because it just -- it won't hold it, as the weight is too much. But if they have like Ford F-250, 350, something like that, they can hold that much weight than you know. So it could just be Prius is pulling up. We're not so concerned about it, but if you have a very large truck pulling up and they're there with the dispenser engaged for a certain amount of time or over a certain amount of time, then hey, maybe something is a miss, we can alert the people inside the store, make sure to preserve the video camera footage, things like that, alert law enforcement if it's a repeat offender based on the license plate. So this is just one example using technology that's available today. The first slide that I showed that was -- we didn't change anything with our cameras. That was existing cameras that we had at the location, just pointing out of the dispenser. We didn't even align it correctly to capture all of it. We're just doing a test with the folks at Radius and that's what we came back with. So it's available today. It's relatively cost effective and so it can really help because when the problem we were seeing when fuel in certain parts of the country were hitting $9 a gallon that's when people got creative on figuring out how to steal fuel, whether it's from the dispenser or maybe parking a truck over the valve where the fuel gets delivered and taking it that way.