Connected Podcast Episode 119: Bringing Modern Data Science to Automotive

Greg Uland: I am Greg Uland with Reynolds. Reynolds and this is Connected. Today is an exciting one. I get to sit down and talk with A.J. McGowan. A.J. is the president of AutoVision, which is the newest acquisition for us at Reynolds. We couldn't be more excited to have him and his team on board and to really get to work in this this area of used vehicles. And A.J., thanks so much for joining me.

A.J. McGowan: Thank you for having me.

Greg: Been a busy week for you.

A.J: Oh, it's been a crazy busy week, but a lot of exciting stuff happening.

Greg: Yeah, absolutely. So, A.J. if you don't mind, we could start maybe with with a little background on AutoVision  as a company. And then I want to get into your background too. But, you know, your company is not a huge company yet. We're very, very excited to have you on board, you and your team, and to get this product built into, you know, the Reynolds Retail Management System. But for those that maybe aren't familiar with AutoVision, can you give a little background, kind of you know, when the company started and I'm sure there's there's always a fun, a fun story with any start up, right?

A.J: Yeah, sure. So we we started the company about about five years ago, so 2018, really with the notion that if you could bring modern data science into the, into the car realm, there's a lot of- you get really, really good at pricing cars that there's a lot of interesting things that you could do with that technology. And so, you know, talking a little bit about the fun story, you know my, my co-founder. Well so I have two co-founders, Lyle and a friend and we are actually all related. So my co-founder, Lyle, his wife and my wife are cousins and then our CTO, a friend, he is cousins with our wife. So it was a little bit of a little bit of a family thing. And, you know, Lyle came to me one day and said, "How much you know about the wholesale part of the used car market?" And, you know, my my background is primarily in tech startups, kind of pure play tech cloud, you know, big data type stuff. Yeah and he said, how much you know about the wholesale part of the car market? And I said, "Well, I guess it makes sense that there's a wholesale part of the car market if there's a retail part of the car market." And he said, "Well, I've got a lot of things to show you." So, you know, as we dug into it, you know, it just became really, really evident that there was an opportunity to do, you know, some really interesting things. Like I said, if you can get good at pricing cars. And so that's, you know, why we decided to take that and build the company. And then about two and a half years ago, why we built the AutoVision products on top of the platform that we had built to process new and used car data, both retail and wholesale. And like I said, to get really, really good at pricing cars. So we spent a few years, you know, really down in the trenches and doing all the hard work in the R&D of getting good at that, and then built AutoVision on top of it. And you know, we've been running hard since then and really excited to be, you know, building into Reynolds now.

Greg: Yeah. No, that's great. That's great. And congratulations on on the acquisition. And like I said, we're really happy to have you here. So what about you? You said, you know, your your backgrounds mostly in kind of pure tech and Silicon Valley type big data startup world. So I guess what what does that background look like, if I'm understanding correctly, you're not a tried and true car guy, but we all we all end up in this industry in some way, shape, or form. So what what does your kind of path look like?

A.J: So. So, you know what's funny is I, I didn't - I guess I'm sort of the typical tech entrepreneur that didn't finish college, but I did briefly attend college. And while I was there, my job to get through school was selling used cars at a Chevy lot in Tempe, Arizona. So, okay, so I started out early on the car side, but I had always kind of been a tech guy and I got involved in the startup scene in Phoenix. I was lucky enough to be involved with a company called Limelight Networks and the sort of big data cloud compute before we called it that. So, you know, we took that public back in 2007. I started another company where I actually invented the ability to inject advertising directly into video streams to make it work on mobile devices for the first time, and then sort of sold that. And then I did a travel logistics company that so I've kind of, you know, been all over the map. But the the common theme for me, I guess, has been, you know, finding problems that, you know, were really hard to solve and using data and using compute in, you know, sort of innovative ways to fundamentally change the business model. And that's that's really the thing that, you know, keeps me interested and got me so interested when Lyle came to me and started walking me through how, you know, the used car marketplace works and how the car industry works in general, you know, sort of, if you'll pardon the pun, under the hood. Yeah. So, you know, that's that's been my path to get here.

Greg: Okay. Now, that's great. That's great. So you mentioned something there about, you know. Using data in innovative ways and finding ways, you know, ultimately with where we're at today to price vehicles appropriately, you’ve said it a couple of times. Right. And there's there's two pieces that equation. There's there's how you buy it and how you sell it. Right. And also, I guess three pieces, how much you put into it to get it get it sold. Right. So you have the acquisition, you have the recon piece of it, and then you have the sale sales price of it. And dealers have been doing this for a long time. Right, on a lot of different levels. Been been buying cars and selling cars and making pretty decent money, especially over the last handful of years. And honestly, margins are staying pretty decent right now. So what you know, when you think about the problem. Right, And you mentioned like solving problems, what is the problem with pricing right now? I mean, what what are what are you able to fix or what did you set out to fix, I guess, with AutoVision and what you're doing?

A.J: Well, So you really hit the nail on the head when you said that, you know, dealers know what they're doing and know how to price cars. And, you know, we looked at, you know, when we looked at the industry, what we saw was that there were, there were a lot of folks that were bringing data to bear in automotive, but they were very opinionated. So, you know, if you look at the way the cars are traditionally priced, you know, you can get down to specifics in terms of, you know, what zip code is it in and, you know, what options does it have on it? And you know, some of those sorts of things. But the thing that's never really taken into account is, you know, that dealer's, you know, specific view of the market. You know, a buy here pay here lot looks very differently at a car than a franchise dealership who looks at it very differently than, you know, a big block retailer on the on the used side. And a large franchise group looks at it much differently than, you know, 2 or 3 rooftop group. And, you know, that perspective of really understanding your local market is something that is, you know, wildly invaluable and sort of the secret sauce in a lot of ways to, you know, many dealerships operations. And so, you know, our goal with AutoVision was to use, you know, technology that's available now to do real time processing, which allows dealers to really set the their view of the market into AutoVision. And then we use our tools to analyze the data that's there and show them this is what this vehicle's worth to you. So we we, you know, try very hard not to have an opinion about what a vehicle's worth. What we try to do is leverage, you know, these big tech tools to do a lot of analysis of data in real time, but then really just to automate and help to streamline and turn into a process the things the dealers already know.

Greg: Yeah. So go a little deeper for me then. What, what I guess, maybe give me an example, right? How would a vehicle be priced differently either when being purchased or being sold for, you know, pick, pick your example, but, you know, buy here pay here lot versus, you know, a 20 store franchise dealer group. Like what, what are those differences when you say their view of the world or their view of that vehicle. What variables come into play?

A.J: Sure. Yeah. So let's, so it's kind of step down from - so if you're talking about like a big, you know, big dealer group that has geographic reach, when they're looking to buy a vehicle, they may not, you know, not all - dealer groups operate a little bit differently, but they may not necessarily need to retail out that vehicle at the store in which it was picked up. Right? They've got a store in Colorado. They've got a store in Los Angeles, you know, a four wheel drive Jeep that they pick up in Los Angeles is probably going to sell a lot better in Colorado, or it might not if that particular model is, you know, sort of flooded in the marketplace there. And so being able to analyze, you know, in real time what are the geographic conditions and, you know, where where might I be able to best retail out of this based on the local market conditions and how much it's going to cost to actually move the vehicle. You know, being able to look at all of those factors are really important. If you have the geographic reach, take advantage of it.

Greg: What are some other examples? I mean, that's a good example, right? That's a good realistic variable. So what as you're as you're going down that path, where else where are you headed?

A.J: Sure. So if you look at and this is true of really any franchise, you know, let's say that you're a Toyota dealership and, you know, you get a 2020 Prius that comes in as a trade. Well, you may look at that very differently than the Ford dealer across the street. You know, you might only want to see when you're looking at what market pricing is, you might only want to see other CPO cars from other Toyota dealerships, or you might only want to see other franchise dealerships. And you don't care as much about, you know, what some of the independents are doing. So your view of that vehicle being the Toyota dealership is very different than other people's will be. And our tech, again, allows you to, you know, look at that and sort of slice and dice it and say, for my dealership what I'm looking at Toyotas that are within a couple of years old with these parameters, I want to look at those differently. I want to look at a different set of competitors and maybe for other vehicles. You know, being a franchise store, I don't care about what some of the online retailers are doing or some of the, you know, sort of nationwide franchises. And we can even allow you to exclude or specifically include specific competitors automatically. Again, as we're calculating prices for vehicles and everything that we talk about, we don't we don't ever make a distinction between appraised time and price time. So you can set up different parameters if you want within the system, but all the capabilities of the same. So you can look at that vehicle, whether you're appraising it or you're following that, you know, down the chain and pricing it later and see how is the market actually shifting around me? And so one of the other things that we do it's very different is we add a lot of automation because we can get to the point where we're really encapsulating that dealer's view of the universe, and that allows us to then automate processes like when we go to price cars, we can update that retail market on a daily basis because we know all the parameters that go into making that market and then sort of stepping down from there from a franchise dealership or really sort of any franchise dealerships, he starts to talk about independent dealerships. You know, there's a huge variety of independent dealerships and they all have different needs. You know, a, you know, a large indie that's carrying 200 or 300 cars, very different than, you know, somebody that's got 50 cars, very different than one that has ten buy here pay here different than non buy here. And again, when you look at all of that customization that goes into it, they might have the opposite view where they don't care what the franchises are retailing it for or, you know, for an on brand late model Toyota. They don't want to see what the CPO cars are retailing for because that's not really their market, that's not who they're appealing to. And so just, you know, kind of using those, you know, kind of three examples. Yeah. You know, I think hopefully that gives you a little bit of context. Those are three, you know, kind of high level examples of how you can use AutoVision to define, hey, here's how I look at the universe or here's how I look at the universe for these specific cars. And then that's now baked into your appraising your pricing that you can control that consistently throughout your store.

Greg: Yeah, that's great. And those are great examples. So what I'm what I'm hearing is a lot of flexibility, right, to tailored to each dealership, each dealership group. But you know, if I'm a dealer, I hear that. And those are great things like that's a positive. But it also historically at least, has met some complexity. Right. And and some time to get things set up properly, some decent training. So I'm bringing these up because again, just bringing you on. I mean, we - you and I, this is the first time we're talking. And so I'm excited and intrigued here. But how how do you handle how does AutoVision handle? Or taking what would seemingly be the complex and making it simple.

A.J: Sure. Yeah. So, you know, I think a lot of it starts from starts from what we're talking about, which is, you know, we do a pretty thorough analysis with dealers when we're turning them on to make sure that we've got all of their settings set up correctly. So that's not something where, you know, we sort of throw people in on the deep end of the pool and, you know, see whether or not they swim. So we'll go through, figure out what their needs are, how they view the universe, and we'll set up their settings for them the first time. From there, a lot of it comes down to good UI. You know, we've, we spent a lot of time and care and attention to detail on making our UI two things. One is simple if that's what you want it and you want to get, you know, just a quick answer. But then we're also always focused on being able to show our work. So there's always a do you want to learn more? Do you want to go a level deeper? And so we can give you that quick answer of here's where we think you should retail it based on your settings. But if you want to drill down and see exactly how we got there, will expose all of the data for you and let you slice and dice it, you know, six ways from Sunday. Yeah. And so that philosophy of being, you know, kind of simplest answer first and then drill down and then making everything really customizable where if you don't want to see everything on your screen, you just want five boxes that tell you, you know, what's my book, what's the wholesale value, what's the retail value? We can make it literally that simple as well. So, you know, we spent we spent a lot of time on that. And, you know, what we hear from dealers is that the training is actually pretty straightforward and it's less than they would have thought switching over, you know, even for switch over of their inventory management system, you know, they tend to become, you know, pretty proficient with the system in the first 48-72 hours. And, you know, and then we start getting more questions, you know, as people have been using it for a week or two, they start to say, wow, it's really powerful. But what if I wanted to tweak this? What happens if I say I want, you know, more comps or I want a little wider radius? How is that going to impact me? But you know, that, that learning process is something that we've really spent a lot of time on so that it's easy to get started. But then you can go as deep as you want.

Greg: Yeah. Yeah. Do you see much variability once somebody gets set up and installed? Do you see a lot of variability in how they use the tool or do they continue to add to how they use it? Just kind of out of curiosity, because it does sound like there are a lot of layers just listening to you kind of describe it. So do you see change in behavior over time with users there?

A.J: No. What we see is typically users kind of self-classifying. So, you know, some users are just looking for the answer and they tend to kind of stay at that surface level and they're happy that it's fast and it looks nice and they're getting accurate answers. Yeah. And then we've got, you know, the folks that like to tinker and, you know, like to get in and drill down into the weeds. Yeah. And they'll usually start out that way and and nothing's going to change that, right? So, so we usually we see them kind of bifurcate there. Traditionally, we haven't seen a lot of folks that we're in the middle, which has been kind of interesting as we've looked at the usage data that we usually see people that just want, hey, the tools at all. I just want the tool that works. It's great. You can get me more accurate data. I love it. Let's do it. Yeah. And then you've got the guys that really want to drill in and, you know, they want to look at the retail data, they want to tinker, they want to tune in, and they tend to do that over time.

Greg: No, that's great. That's that is interesting to look at it for sure. You mentioned something there that that just struck me for some reason and maybe you can go a little deeper on. You said more accurate data, sure, and I'm curious because from without knowing, obviously, the level of detail that you do like, on the surface, it seems like the data is the data, right? And if I don't see a way that it's inaccurate. But are there more layers to the data that can make it more precise and maybe maybe precise, and we're splitting hairs here, but I don't know. You said more accurate. I'm hoping you can go a little deeper on what you mean by that?

A.J: Sure. Yeah. Well, so it's interesting that you say that. So I mean it both ways, so I guess is - okay I'm sorry to say it too. So we're really, really good at looking at retail market data or wholesale data and teasing out the exact configurations of vehicles and then being able to isolate and specify the exact comparable set. Yeah. You know, we're not we're not perfect by any stretch of the imagination. But what we hear consistently over and over again from folks switching from competitors is that our competitive sets are more accurate. We don't have ghost cars. You know, the cars that we say in the market are there and they tend to match up pretty well with what they're actually looking at, what they're actually comparing against. So from an accuracy standpoint, you know, we think we do a really, really good job there. We're constantly striving to get better, but we think we do a great job. The second the second piece of it is precision, to your point is, you know, once you know, once you have that, we've got that kind of set of accuracy. What that allows you to do is take the training wheels off to a certain extent where if you're not 100% sure that you're accurate or at least, you know, high 90s, that you're accurate on that competitive set, then you really want to have those guardrails in and be a little bit more general about the cars that you're looking at. Yeah. When you can get more accurate and more confident in the data, that allows you to then become much more specific and say, You know what? I only want to see for this particular truck, I want to match it all the way down to the engine fuel type, because I know that if it's biodiesel, it's going to be a completely different price points than the regular diesels, right? Yeah. And when we know when we're confident about that or we're confident that white cars are going to do differently than, you know, sell better or worse than red cars in this market, when we get confident around those individual aspects, then that allows you to become both more precise and more accurate.

Greg: All right. That makes sense. That makes sense. So you mentioned a couple of features there. And this is something that forever is just so interesting to me. And maybe, you know, if you have, maybe you don't. But I'm curious, what does the data say are the most valuable features in a vehicle? Right. Sunroof, heated seats, cooled seats, heated steering wheel. It probably varies by market, I'm guessing. But does anything stand out and maybe any surprises where you you know what, for whatever reason, that thing, consumers absolutely love it?

A.J: Yeah. You know, it really depends on the vehicle what's going to do - so vehicle by vehicle it's different and so it's really important to tease out the options and the packages and those sorts of things being able to compare between them. The thing that's been most surprising to me that I think, you know, I came from tech, you know, I'm not a a tried and true car guy, so to speak. But the thing that's been made a real impression on me is how much of a difference the color of the vehicle actually makes. Where you will see. Well, because one of the really unique stats that we track is what we call PTM 30. Yeah. Where rather than just looking at, hey, what's 100% of market of this vehicle we'll actually look at as the vehicles are selling in the market, what percent of market were they when they sold? And so we can show, you know, white cars of, you know, this particular make and model and year and so on. So all the level of detail, if they're white, they're selling at 92% of market. And if they're black, they're selling at 105% of market. And a lot of times that gets lost in the noise and people, you know, I think every dealer sort of intuitively knows how important color is to the consumers. But to be able to quantify it, really like look at the data and see, you know, sometimes you're talking about a $40,000-50,000 car, you're talking about $3,000, $4,000, or  $5,000 price swings on these vehicles based on what color it is. And so that's been that's been really, really interesting to me where I wouldn't have thought I would have thought it would impact how quickly they would sell and, you know, some some other stats. But the fact that it impacts the actual price that much and where they're actually selling out has been really interesting to me.

Greg: Huh. Are there any commonalities between you mentioned, you know, the vehicle matters for sure, but when you use color as an example, like you said, black over white, I assume that's pretty common. Are there other colors that stand out? I think back, you know, I bought a used vehicle not too long ago and there are only three colors I was going to buy. Right? It was going to be it was going to be black, it was going to be silver or it was going to be like a deep metallic blue. Those were, that was it, right? It wasn't buying a red car. I wasn't buying a white car. So I don't know. Are there any other ones that that stand out? Just out of curiosity?

A.J: Nothing that probably wouldn't be intuitively obvious. Right. Okay. If you look at the sports cars, then the reds and yellows are going to sell a lot better than if you're looking at sedans. Yeah. Yeah. So, you know, and there's some things like that. But no, no earth shattering news that I think anybody watching this doesn't already know.

Greg: Yeah, but but it is good to have the data behind it. Right. And to know just how much more valuable it is, right? Because it's one thing to know that it's more valuable. It's another to know that that, you know, based off of the last, you know, 30 days worth of of data, this is exactly how much more valuable it is.

A.J: Yeah. Well, and so the key thing there is it's it is you're absolutely right. It's valuable to know what the data is. But the way that we look at it is the most important thing is being able to fuel the automation, right? It's one thing if the dealer goes and looks at it and says, Well, it's red, it's got good eyeball, I know it's going to sell. And then they're having to manually adjust their pricing. But if you're going to trust our automation, then our system has to be able to factor those types of things and that might be intuitive to a person, but get lost when you're looking at a lot of vehicle valuation systems. So that ability to tease out those nuances, it's great. You know, the data is interesting, it's cool to look at. But the really important part is if you're going to have a system that's going to do this stuff and make you more efficient, where you're not having to go in every day and manually adjust your radius and, you know, change all those things and reprice all your cars. If you can start to trust our data, if you're going to trust our automation, then you have to know that we're looking at all the factors that really matter.

Greg: Yeah. Yeah. Do you do users trust that?

A.J: They do want to use it for a little bit. You know, once they once they buy their first couple of cars that, you know, they got, you know, a couple thousand dollars more than they would have expected. Yeah, then they start to go all in. That's probably the - you were asking earlier about changes in behavior - that's probably the biggest change of behavior that we see is - you'll see people sort of tentatively use it, start appraising cars once they've bought a few and sold a few and made more money than they were making before, then they'll start to go all in on the automation.

Greg: Yeah. Yeah. No, that's interesting because. Yeah, I mean, it is. Trust is the perfect word for it, too. Right. It's you don't, I don't know. Inherently, necessarily. Especially when. When you're changing to something. Right. It's really difficult to inherently trust. Software. You want to test it, right? You want to understand what it's doing. You want to see the results. And once you do, then you can start to then you start to trust it. And it's it's such a unique word to use and an interesting word to use when talking about a basically a relationship between a person and the software they're using. But it's an accurate one. And that's that's really what you're you're getting to. So you have to you you have to get it right, or and if you break that trust early on, you know, it's going to be really hard to build back up.

A.J: Yep. No, you're you're absolutely right. You know, I think the the unfair advantage that we have there is going back to not having an opinion. Right? So yeah, once we once we've got that dealer to take, you know, their know how their brain, you know everything that they know about their market and built over 10, 20, 30 years of experience and put it into the software then it's just trusting us to do our job right. But the opinion is theirs, right? All we're doing is amplifying them right and their view of the market and hopefully along the way helping them, you know, through automation and through some of the tools that we provide for, you know, surfacing opportunities help them to find things that otherwise they wouldn't have had the time to go and find on their own.

Greg: Yeah. Yeah. Well, you know what? The timing of all this, too. You said you started the company in 2018, obviously, but, oh, April of 2020, in the entire world change, right? Especially when it comes to vehicle pricing in general. But used vehicles. Yeah. And it was really probably July or August when everything started to spike that year. Right. And and there was just this there's intense focus on used vehicles because, you know, it was in some ways what you could control as a dealer. Right. And and used vehicles continue to outpace new vehicles and sales margins are strong. I was just looking at numbers this morning. Last month on average across the U.S. used vehicles front back 30, 130, 100 profit and gross front and back. Those are solid numbers. Right. And and granted, prices are going up. So obviously, your your dollars in profit are going to go up, too. But margins are pretty decent. And there's really not that I see. I don't know, maybe you have a different view or you've heard differently, but that I see there's not anything that's going to change dramatically, I don't think, in the next 6-12 months.

A.J: No, certainly not in the next 6-12 months. You know, I think we'll continue to see, you know, a little bit of oil in the market and, you know, regional changes here and there. But yeah, I think I think in the next 6-12 months, we're looking at more of the same. We're starting to do some things that are really interesting on the predictive side with looking at fundamental, you know, vehicle value. Yeah, I mean a little bit more observable way. So it's what we called market insights. Yeah, we're we're doing some trending analysis, you know, kind of week over week, month over month, again, using all of that kind of, you know, real time slicing and dicing of the data. But we're starting to tease out some really interesting data points there where, you know, we can look at how the market, not only how the market is reacting, but what the value of the vehicle seems to actually be as opposed to just what the prices are listed out. So, you know, a lot a lot more of that to come, I think, in the coming months. But, you know, we we think that if you can get if you can get a good view of the market, you can automate this stuff that you can start to, you know, at least maybe not see into the future, but at least see around the corner.

Greg: Yeah. Yeah, that makes sense. Talk a little bit too about the, the variability there is regionally or locally even, right? So you look across the country, you have visibility to dealers across the country. How much variability, variability is there in reality? Right. We all know that each market's different. But is that is that a, you know, $200 difference? Is that a $2,000 difference? You know, like what is that variability?

A.J: I mean, it's massive and it's it it's really market and vehicle specific. So one of the things that we do that dealers really like is like when you're searching for cars at auction on our platform, we'll show you projected profitability analysis that's based on, you know, those 30 numbers that we were talking about and some other factors. One of the things that we'll do is calculate in transport costs. And so what it allows dealers to do is actually start casting a wider net inside of the the auctions. Then, you know, sort of the old days of, you know, they just show up at the lane and, you know, what's for sale is for sale within, you know, 50 miles of their dealership or wherever they're willing to drive. And you start to see some really interesting opportunities arise there for. Buying vehicles where a, you know, may cost thousand dollars to move it from from somewhere in Nevada to, you know, here in the the San Francisco Bay area. But you're gonna be able to sell it for $3,000 more than what that dealer is going to be able to resell it for in Nevada. And so, you know moving it's even a relatively long distance sometimes can make can make a lot of sense. And so, you know, the ability to use data that's created to kind of maximize those arbitrage opportunities we think is is huge and is really just is just getting going. Yeah. Yeah. And what's really interesting about that is, you know, you'll see it very clearly when you're talking about higher line cars like, you know, if you look at, you know, super high end Shelby, you know, Mustang, you'll see $4,000 or $5,000 differences between market like Phoenix in a market like Los Angeles, which are relatively close geographically, but you can see huge swings in price. But what's been really interesting to me is to see that same effects, you know, like I used an example earlier of a 4x4 vehicle that you might be selling in L.A., you know, 4x4 Jeep that you might be selling in L.A. versus in Colorado. You know, you will see big swings in price there and you'll see, you know, changes based on the number of vehicles that are in the market as well. Right. You take a market like L.A. where there's just a ton of cars and there's a ton of, you know, a ton of inventory, It sort of suppresses prices across the board on a lot of different units. And you can then farm out of a market like that into some of these surrounding feeder markets and create really interesting opportunities. So it's up and down the stack. And it's it's more than you think it is when where you don't see it are kind of the, you know, the dime a dozen the fleet cars and you know, things like that where you've got a ton of them in every market. But we do see, you know, for things that are even a little bit more rare, you'll see large geographic discrepancy.

Greg: Hmm. Interesting. So do you think, I guess as the world shrinks, right as that, or at least the US shrinks and you can start factoring in these transportation costs and you can start really seeing different opportunities to purchase vehicles. Do you see that having an impact on the wholesale market in general and kind of flattening that out at all over time?

A.J: That's a really interesting question. So, you know, I think I think in general that's that you'll see some of the wholesale markets start to flatten out. But I think you also see a point where you start to see diminishing returns. And it'll be interesting to see how dealers respond to that. Right. Because the other obvious, you know, big thing that's happening in our industry is a ton of consolidation. So if you're a dealer group that's got, you know, 50 or 100 rooftops, then the tech that we have can help you to place that inside of your group. But it isn't necessarily going to impact the wholesale market that much. Right? So it's there's there's too many variables, I think, to really say with certainty how it's going to impact the wholesale market. But, you know, I think it'll be interesting to watch.

Greg: Yeah. Yeah. That's good. That's good. All right. Well, it's been it's been a really fun conversation. Good to get to know you a little bit. Again, I can't wait to get to work with you. And we're super excited to kind of finish building this out into, you know, the Reynolds Retail Management System. And, you know, obviously a lot more exciting news to come as we do that. But anything we haven't talked about or haven't touched on today that you want to make sure we do before we wrap up?

A.J: No, I think the only other thing that I will say is we're really, really excited, obviously, to be part of the brands family and, you know, a lot of amazing tools and legacy that that's been built here. And, you know, our hope is to be able to to help better connect things and, you know, provide another layer of data that, you know, just continues to build value for our customers. So we're we're really excited about it.

Greg: Great. Hey, we are to AJ McGowan, president of AutoVision. Thank you again for joining. It was a great conversation and we'll talk soon.

A.J: Thanks very much.

Greg: Well, there's a really fun conversation with AJ McGowan, president of AutoVision. The latest Reynolds and Reynolds acquisition. We're really excited to have A.J. and his team on board and be able to build this product into the Retail Management System from Reynolds and Reynolds. Before we hop off here. Don't forget, you can watch or listen to all episodes of Connected on YouTube, Apple or Spotify podcasts, and make sure to subscribe to be notified every other week when new episodes are released. Thanks so much. We'll see you in two weeks.