How standardized service data transforms pricing, warranty decisions, and fleet strategy across the aftermarket

What if your repair data actually told you what to do next? We sit down with Austin Ledgerwood, National Director of Sales and Insights at Motor Information Systems, to trace how messy service records become clean, standardized, decision-ready intelligence that changes pricing, warranty calls, stocking, and fleet strategy. From a Mountain Dew green Kia lesson at the auction to AI models that harmonize “OC W32” and “oil change,” we dig into the quiet work that makes insights possible—and profitable.

We explore how Motor’s Repair Optics and the Navigator dashboard expose true costs by zip code, break down parts and labor with clarity, and give teams without a data science bench the power to ask sharper questions. Think warranty adjudication grounded in reality, pricing that reflects local markets, and dashboards that surface seasonal and weather-driven shifts in tires, brakes, and routine maintenance. Along the way, we challenge the habit of chasing data that confirms a pet theory and swap it for root cause thinking that actually fixes problems.

Then we look ahead: EV cost of ownership at high mileage, rideshare and car-sharing models, fleet maintenance at scale, and why timely, standardized inputs will decide who gets ahead as autonomy and consolidation reshape the aftermarket. The takeaway is simple but urgent—most companies don’t have too much data; they have too much bad data. Clean the inputs, frame the right question, and let the market speak through the numbers.

Subscribe for more candid, data-forward conversations. If this helped you rethink a KPI or a model, share the episode with a colleague and leave a quick review—it helps others find the show and fuels future deep dives.

To learn more about the Auto Care Association visit autocare.org.

To learn more about our show and suggest future topics and guests, visit autocare.org/podcast

Chapters

• 0:00 Setting the Stage: Data in Aftermarket
• 2:10 Austin’s Path and Data Spark
• 5:05 Lessons from Car Auctions
• 8:40 Motor’s Data Products Overview
• 11:45 Cleaning Service Records with AI
• 16:12 Zip-Code Insights and Navigator
• 20:02 Hurdles: Standardization and Scale
• 22:42 Weather, Seasonality, and Modeling
• 26:52 Root Cause Thinking vs. Confirmation
• 30:47 The Road Ahead: EVs, Fleets, Autonomy
• 35:02 Rideshare, Turo, and Cost of Ownership

Transcript

Mike Chung: 0:07

Welcome to AutoCare on Air, a candid podcast for a curious industry. I'm Mike Chung, Senior Director of Market Intelligence at the Auto Care Association, and this is Indicators, where we identify and explore data that will help you monitor and forecast industry performance. This includes global economic data, industry indicators, and new data sources. Hello and welcome to another episode of Indicators, one of the series on auto care on airs podcast. I am really excited to have Austin Ledgerwood. He's the National Director of Sales and Insights at Motor Information Systems. So Austin, welcome to the program.

Austin Ledgerwood: 0:51

Thank you for having Mike. Very excited. This is cool.

Mike Chung: 0:54

Yeah. So tell me a little bit about what you do at Motor and your role and a little bit about yourself.

Austin Ledgerwood: 1:00

Absolutely. So I joined Motor about four months ago, and it's really been an interesting endeavor. What they're doing at Motor, you know, I of course I'm biased, but we're really doing some unique stuff around the uh aftermarket space, uh, particularly in the data space. Um we have unique data that can drive everything from a part supplier, manufacturer, retailer, warranty companies. It's uh it's really interesting, uh, and it's been a lot of fun. Before that, I've spent a little bit of time at Amazon helping them with their automotive structure. Um, Honeywell, uh, which has nothing to do with automotive. And then for a good chunk of my career, about 15 years, I was at Cox Automotive working for AutoTrader, Mannheim, uh, you name it. I uh I did it while I was there.

Mike Chung: 1:49

That's terrific. It sounds like a broad range of experience, a lot centered around data at, as you mentioned, automated automotive-related companies, but also other uh industries as well, and a company like Honeywell, which certainly touches many industries. So um tell me about the data through line a little bit. Like what sparked the interest in data and insights? Was that something you sort of had a passion for as you were growing up?

Austin Ledgerwood: 2:15

So it goes back to really my first job out of college. I went to work for CarMax as a buyer. And I can't tell you what a um fun experience that is, about 23 years old, open checkbook at the auctions. Uh, I mean, it was dangerously fun. And what was really cool is that we kept setting up how our our buying targets, what we were planning to buy, how we're buying them. And that's when it really started to pique my interest into how automotive data can shift and shape so many different facets of the business. I mean, we would look at what cars sold the fastest, which had the best margins, which would take the most work. And so eventually they did get to an algorithm that started to predict and get them a better analysis on what we should be buying. But it was really that was the kickstart to where I'm like, wow, there really is a lot of great information that can help shape any automotive sector.

Mike Chung: 3:11

That's it, that's so interesting to hear you describe it that way. And thinking about a consumer, an automobile is often an emotional, a very passionate decision. So I'm thinking 2023-year-old Austin at an auction. So a couple of things there, certainly, but you highlighted a couple of data points that you're you and your team were looking at. Um, so tell me tell me a little bit more about that. Maybe some fun stories. Um, oh, maybe I shouldn't have raised my flag.

Austin Ledgerwood: 3:41

Well, uh I'll tell you, the uh everyone remembers their first purchase. And my first purchase was a believe it was a 2001 Kia Rio, Mountain Dew Green. Um thought I had stole it. And um, luckily it sold, living in Miami. Luckily it sold very quickly. But um, I took a lot of uh ridicule on that one, and it was the first one that I got to kind of gut check, feel through, um, kind of like, oh, well, you know, book says it's this, and I didn't take into account the Mountain Dew green color, which um, and it was stick. So I I picked one that made a good margin when it sold, but it was one that I took a lot of heat from. Um, and you never forget your first, like that was my first purchase. Um, but no, that was really what we we were kind of hitting on is that all right, let's start to make more and more smart purchases and less of front shooting from the hip. Um, because again, you know, 23 years old, why wouldn't I buy anything and everything I could? And let me be very clear, we did have restrictions and limits, but um, we got a lot of uh leeway to make some interesting decisions. And that first car was my uh most interesting decision.

Mike Chung: 4:58

That's exciting. Thank you for sharing that. So kind of fast-forwarding today to today, as you're describing your current organization's hands and so many data sources, I can only imagine the possibilities of garnering insights, processing data, diving into really interesting strategic business decision making uh problems, if you will. So tell us a little bit more about the data you're working with, how you're using it, and maybe we can go from there.

Austin Ledgerwood: 5:27

Absolutely. So a core piece of what drives our data, especially from the insights. And, you know, within, I love saying insights. Uh I sell it, but I love saying it. Um so within we have a couple of different products that are really interesting. We have our shop connect, our campaign connect, we're really about the customer experience, really about how shops can manage their stores, collect information around the cars, um, some really cool stuff there. So as we start to dive through our sources, it really became a point of what are some value props that we can offer to the market? And you know, the the first one that really pops out to mind that I thought was really interesting is that we cleanse the data. And it's not from a cleanse standpoint of, you know, hey, we we knock out stuff. It's from uh finding uh common themes for data so it's easier to understand. Um, if you look at an oil change, let's use Jeffy Lu for an example. If I go in there, a technician may input what he writes for an oil change as OC W32 or W30. And then somebody else might write out oil change. So when you start to accumulate that data, it really gets confusing on what was actually done. So we've done a lot of that. And I think you know that ties into the overall market as a whole when it comes to data. Um, all of the recent AI models that are being built, all of the different um endeavors that are around uh really making it more of an automated process from an acquisition, operational uh procurement. It's all about having accurate, timely, clean data. Um, because you you I mean, there's plenty of examples out there where models have been built off of data that was off by $2, and it has sunk companies.

Mike Chung: 7:38

You bring up an interesting point. So you highlighted Jiffy Lube, you talked about repair shops, and thinking about structured data, unstructured data, or even the standpoint of how is an order processed at Jiffy Lube or MITIS or Joe's Garage or a dealer network. I'm just thinking about all the different ways that a something routine like a lube, oil, and filter could be cataloged. And so just tell me a little bit more about how your team has approached that from a sort of, I can see where human intelligence, that industry expertise, as well as kind of feeding that AI model and letting it learn how to do these things with less um human interaction, if you will.

Austin Ledgerwood: 8:26

So we've made a very heavy investment into this. Um, you know, we've built out our own AI model that will start to customize and start to see records and start to recognize trends. Uh, we've created different coding messaging within our data that also helps standardize um repair events, service events. So, you know, for us, it was about getting out that that really structured, clean, easy to understand. And you can see parts, you can see labor, a really, as I keep saying unique, but a very granular, unique way to look at the data. And for us, it comes back to the investment in making sure that it's clean and it's processed. That way it's easy to digest, easy to work through.

Mike Chung: 9:12

So I suppose thinking about what the output might look like, it sounds like you have it to a very say zip code level, the type of work. Are you able to make insights to say these types of vehicles are having these types of services in these particular parts of the country? Is that an example of some of the findings that you and your team may have?

Austin Ledgerwood: 9:34

Absolutely. It's spot on. And what we've actually done is, you know, for a lot of it, it is a very huge file that you can get and you know, it'll show you all of the events. You can break it down by zip. But what we've done recently, and we're going to highlight it uh Apex, which we're excited about, is our navigator. And it's really an easier view of what's going on with data or service events. So instead of you having to look through a full file, let's say that your company doesn't have a full data scientist team, which still exists. Um, you know, there you don't have anybody to go through this huge, you know, Excel file, we'll call it. But with our navigator product, it gives screenshots, dashboards of you being able to customize and look and say, oh, I want to know about brake pads in in Salt Lake City. It'll give you a snapshot of what's happening in Salt Lake City, and it'll also tell you vehicle events. And you brought up a key point. It's really interesting in the warranty space, where we can start to share what's happening, what's the true cost of repairs, what's really happening in service events. And it's not from a tattle or an exposure, but it's to help companies, uh, you know, warranty companies adjudicate claims. It helps them better understand what the market is so they build out their models and their pricing better. So for us, you know, at Motor, we've really done a great job of providing easy information to either digest through an Excel or our fantastic navigator, which we cannot wait to show at Apex.

Mike Chung: 11:16

That sounds exciting. So, what I'm picturing is is like an interactive dashboard that allows you to drill down, pick, uh drill down on variables of interest to kind of zone in on that finding of interest. And like you said, it could be a part, it could be um warranty versus non-warranty, it could be collision, it could be any number of things that somebody is interested in.

Austin Ledgerwood: 11:41

It really is a wide variety. I mean, we can we we do have certain uh precautions around PII, um, like everybody should these days. Uh so we do mask certain things, um, but in general, you can have a much better understanding of what's actually happening in the market looking at our at our data.

Mike Chung: 12:01

Well, thanks for sharing that. And um, if I can ask this question, were there hurdles along the way, challenges that you and your team were kind of like, okay, we really need to get around this particular problem. How do we address this? Are there kind of any um issues that you face, success stories, or even to that point, things that continue to be improved or have opportunity for refinement as we go forward?

Austin Ledgerwood: 12:25

Well, I think with anything, especially in data, um, there's always a new input. There's always a new variable, there's always a new target for what a company might want to see and might want to use the data for. So for us, that's been the biggest adjustment is how do we move on the fly to meet demand? Uh, because we are, you know, it we are branching into other industries, um, which is really very interesting. Private equity, consulting, helping them give a better snapshot to automotive partners that need guidance. So for us, you know, and and I'm and I have to say I'm you know, for four months in, uh, the biggest hurdle was standardization of the data and cleansing it. That was the biggest hurdle, which we have. It's always going to need improvement because there's always going to be another entry point of where, you know, Mike, you enter in an oil change very different from me. So there's always going to be that human effect until that gets standardized at the event level. But I would say that was the biggest hurdle was getting it to where it was easy to understand and looks great.

Mike Chung: 14:09

Right. Oh, thank you for sharing that. You know, one thing that I was thinking about too is we talked about geographical locations. I'm wondering, is there a weather component? And maybe it's a little bit of a two-part question. I can imagine from a dashboard perspective, maybe you're able to get date and and maybe there's some weather, meteorological data in there. And let's just say if there isn't, how would you go about evaluating a new data source, whether it's weather or something else, currency, exchange rates, consumer confidence, any of those types of things?

Austin Ledgerwood: 14:41

So that's that's what's that's a that's a really interesting question. Um, you know, from an overall objective, our you know, focus is to get, again, standardized event data. And when we talk about granular, you mentioned something about weather. You can absolutely start to see trends based on we do the provide the date, the time of when the service occurred. So you can see, you know, hey, we're starting to see tires start to go through. And is that during the summer when there's a lot more wear on them? Or is it during the winter when I need to have my tires upgraded to handle snow, ice, whatever the case may be? So we do have a lot of that in there. And that's where it's really interesting from a company's external company's standpoint, is they can digest the full, you know, the full gambit and start to actually build out those models. So again, ties back to what models are you building? Uh, what is your AI source of truth? Those are all points that we can tell as time progresses. And you can start to see a real correlation of, hey, we're seeing an uptick in this, in these vehicles. Um, so if I were a parts manufacturer supplier, I would love to know, hey, here's what's happening in the market. We're seeing an uptick in you know, oil filters and this, this, this line of, you know, this brand or this model. Um, there's really a you, you know, a a new way to look at this. And to your point, we can absolutely, if you want to build out a model, just start to look at weather, absolutely.

Mike Chung: 16:15

Yeah. Um and it goes back to good, good structured, uh, or well-organized, clean data is was what one of the themes I'm hearing.

Austin Ledgerwood: 16:23

Absolutely. And and as I spoke about Navigator, our dashboard, you know, it really is around what's important to the viewer. What's important, what do they want to see? And what's great is that we are constantly um adapting those to where it, you know, it's more and more, you know, user-centric. Um, you know, it is user-centric now. I hate to say it's not, but it is. But as different industries, different companies, different models look at the data, we can we're absolutely adapting that on the fly.

Mike Chung: 16:56

Well, you bring up a great point because as a market research person, it's what's the research question, right? What is the problem we're trying to solve? And in that consultative role that you and your team play, it that kind of governs how you're shaping, adapting, expanding, and tailoring your platform. So it makes perfect sense to start with. Well, what is it we're trying to figure out here? Because that helps you identify the the source of data and the the model and write down the lines.

Austin Ledgerwood: 17:26

So it's it's you know, I I love, you know, one of my favorite points in my career was working for an insurance tech insure tech, uh, cover genius out of Australia. Fantastic group. Really enjoyed working with them, a lot of fun. They were based out of Australia, just good people. And they had asked me to come on board and start to look at different use cases. So a big part of their business was um when you go to book a trip, like if you went to Delta.com, you'd say, Oh, would you like to protect your trip? Cover Genius powers a lot of that underwriting. So those products are powered by Cover Genius. And they brought me on board to say, hey, we want to really look at the automotive space. Where can we get into? So, you know, I started looking at different points of frustration from a customer aspect. And you touched on this earlier. And so we started looking at what are the drop rates. So you get somebody into a finance office, they love the car, they get into the finance office. What's one of the biggest drop-off rates for when they sit in there? Insurance. If you look at Carvana model, one of the biggest drop-off rates, insurance. So, you know, for me, it was another way to look at data. You know, we like right now, very aftermarket parts focused, which is it really is a very large universe. Um, but as I think back through my career, um, this was an instance to where we looked at from a customer standpoint, what what's affecting the customer? Why are they not buying a car? Why are they dropping? And insurance was a key aspect. It was either too costly or they didn't know who to get, or they would come back later after they got a policy. Um, so for me, I start to look at if I'm an external company like private equity or consulting, what do their customers want to see? Where is the drop-off? Where do they make a dollar miscalculation in their process working with their customers? And that's where I think, you know, as it trickles downhill the data and what the purpose is. That is something that I'm very interested in on how we as motor can start to affect that cycle.

Mike Chung: 19:32

Fascinating. And thank you for sharing that example. And you highlighted time at um Amazon and Cox. Are there other kind of case studies or kind of vignettes that you have that are data related that our audience could benefit from hearing?

Austin Ledgerwood: 19:47

Yeah, I think that, you know, I sometimes we overcomplicate data. And I, you know, this is just for me, my my perspective. I think we overcomplicate data sometimes. I think that we think we know what the problem is and we look for a particular answer within the data to solve the question we think versus the actual problem. So as I hypothesize this is the solution, I'm always going to look for data to prove my solution, not maybe not solve the problem.

Mike Chung: 20:16

Is it like the hammer looking for a nail type of a yeah?

Austin Ledgerwood: 20:19

Yes, very much so. Yes. So while I was at Cox, which great organization, um, I led their finance operations for North America for a couple years. And one of the things they asked me to do is to solve why there were such discrepancies between accounts receivable, collections, etc. And it really was we were we had always looked at the problem the same way. We got to hammer them in, you owe this, you owe this. Instead of actually looking at what caused this, what happened to this, how did we get here? So that's a case where they had said, all right, hammer, where's that nail? And for me, I came in, I said, we're making this too hard. Let's actually look at what the problem is and what the root cause is, and then solve for the root cause. Um, and that, you know, it was a very um, you know, AR is a very masochistic um world. When you're chasing money, it isn't you're not a loved individual, but you become that when you start to solve for the issues and fix everything and standardize it. And that again, I think goes back to what we're trying to do is make it an easy way to understand. And you, whatever your solution is, what your problem is, here's the data, let's fix it.

Mike Chung: 21:42

That's fascinating. Yeah. I I think about just taking that new approach, the root cause analysis can just span so many industries, right? Rather than um, like you said, here's the data set, here's how we've looked at it before. So um thinking about the future of data, I mean, I know you've only been at Motor for four months now, but what do you see as sort of the opportunities in automotive data and problem solving? I know we touched on AI, but what are some of the things that you're looking forward to exploring in the seasons to come?

Austin Ledgerwood: 22:15

You know, I I think that um fleet is gonna be very interesting as we start to get more autonomous cars. Um, I think that as we branch more into EV, you know, there's there's different working parts. So it's going to be that shift of how we looked at the combustion and now how we start to look at the EV. And it's already in place. But I think what's really going to be interesting is how data starts to prove out what's the cost of ownership. Um further proves out how to maintain a fleet. How do I start making my investments from an acquisition standpoint? Um, you know, and again, I think that there'll be, you know, we've seen it with dealerships over the past 10, 15 years, there's great consolidation there. So if I were a private equity firm or a consulting firm, I would want to know anything and everything about a dealership, how it runs. And one of the key pieces, the biggest money maker is the service lane. So, how much are we spending on parts? How much are we spending on labor? Um, and as people start to do call, you know, cars on demand, I think that's where it's going to be really interesting. Of how do I maintain these cars and keep them up to the same standard?

Mike Chung: 23:33

And when you say cars on demand, is that referring to things like rideshare, whether an Uber or Lyft that might be owned by like kind of like a taxi company, for example?

Austin Ledgerwood: 23:43

Absolutely. Use using Uber or Lyft is a great example. You know, I um while I was at Cox, I was part of FlexDrive, which was a fantastic program that enabled drivers to get into new cars. And as long as they met so many different qualifiers, the car was free. So, you know, in that case, it really was looking at, you know, FlexDrive, we supplied the cars. And we had the um software platform in the background that would track and manage the vehicles. And what was really interesting is that we saw several companies like Uber in the past buy a bunch of cars and lose, you know, hundreds, a billion dollars on these vehicles that they were buying and putting into the fleets because there wasn't a lot of background, uh, I don't think a lot of background work was done on what it costs to run these cars, what's the maintenance gonna be, and then how do I securitize the vehicle? So, you know, not to get too far off topic, it's gonna go back to fleet maintenance and it's gonna be the management of fleets. And I think Lyft and Uber with Rideshare are great examples of how do they manage those vehicles? And then as they start to acquire their own vehicles, how do they manage those? You know, I was just in the uh Uber over the weekend, and the driver was talking to me, and he he's in a um a Hertz vehicle that he leases from Hertz every month. And it was a Tesla that had about 180,000 miles on it, and it was it's relatively clean. I mean, I don't, it was a nice car. But I started to wonder, I'm like, God, what's the cost to maintain this at 180,000 miles? Given that an EV is much cheaper in a lot of respects because there's not a lot of wear and tear, um, you know, oil, oil transferring through, not transferring through. But it was really interesting to me to start kind of my brain was spinning on what are they looking at from a data aspect of when they decide that vehicle needs to come off? Is it wear and tear on the vehicle? Is it sudden rise? Is it a new battery? That really I it was kind of funny, but I think that's where a lot of this is going is how do they manage fleets and people become less dependent on owning their own car?

Mike Chung: 26:00

And so just for fleets, I'm thinking of not just Uber or Lyft, it could be the US Postal Service, it could be FedEx, it could be a municipal government, a school district, something like Car2Go or Zipcar. Um Zipcar, yeah. So I'm learning more about fleets as I continue on. And yeah, I can see where the possibilities for smart management of your of your capital expenditures and thinking about well, how long can you drive this car? What's the payoff? Is it worth it to keep this vehicle on the road? That's something where I can see your team offering a lot of insights into.

Austin Ledgerwood: 26:40

Absolutely. And I I love the word insights. I mean, we I sell insights and I say it's insights. Uh I love it. Um, but you know, you look at Turo. Turo is a model to where I could put my car online and somebody else could drive my car. Um, you know, I would love for Turo to be able, and they may have some, it's been a while since I've been on there, but I would as a person who provides their car, I'd love to know what kind of maintenance impacts could I see? How much would it cost if I kept my car on there for a month? What's that end up going to cost me? You know, what what you know, what's the factor of tires if it's driven so far? Um, I think that would be very interesting to see from a from a provider standpoint, because it helps me understand what do I want to price my vehicle for? Do I want to do this? Um I think that there's a really interesting uh play there.

Mike Chung: 27:32

Oh, that's a fascinating and that's a fascinating uh point you bring up because, like you said, from a user perspective, if I want to make that decision, uh right now it's a very big black box in terms of how maybe I can make this much, but how much will it cost me in the end, and having insight into what that might actually look like will certainly inform the decision process.

Austin Ledgerwood: 27:53

And that it goes back to, you know, like a lot of what I'm focused on is a business B2B. But how do how you know I'm I'm starting to build those great stories we have a lot on B2B to C. Now I'm not selling the data to C, but here's how you can better interact and work with C. Uh whether that be repair shops, an actual retail buyer, uh, retail suppliers. Um it's it's really there's a vast audience for what we're doing.

Mike Chung: 28:24

Oh, that's fascinating. So I guess as we're coming up on time here, are there other insights or other things you'd like to share with our audience today? Anything we may have missed?

Austin Ledgerwood: 28:34

You know, I I think, you know, it everyone, you know, I do a lot of generalities, but you know, everyone feels like they have the right data until they don't. Um so, you know, for for us, it's getting out there, sharing what we're doing. Um, you know, we're very excited about Apex. We're very excited about our partnership with AutoCare. Um, I mean, this has been fantastic for us. But really for us, you know, it's it's how do we start to help companies make better decisions, start to see a picture in the aggregate, how are tariffs impacting? Um, you know, for motor, it's all about getting great data, unique and granular data, into the hands so better decisions are made. Um, I'd love to have that as my own tagline, you know, I'll help you make better decisions.

Mike Chung: 29:24

I appreciate that. And you said something that piqued my curiosity in that response. You said everybody thinks their data is good until they realize that it isn't. What are some of the warning signs or perhaps yellow flags, or kind of let's step back, take a 30,000 foot, whatever analogy we like to use? What are some tips you'd recommend for people to kind of assess do they have the right data? Is it because their uncertainty bars are just much wider when they're coming up with an analysis? Is that their R-squared value is not as close to one as they'd like it to be? What are some kind of tips you would give to that, uh, to that aspect of data?

Austin Ledgerwood: 30:03

I I think, you know, one of the things that I've seen so far is successful companies run on great data, but their data is only as good as yesterday. And as the market continues to change, there has to be uh, you know, adaptation to what the market's doing. And you know, I I've I've seen um some companies that are very and this is not a slight on them, but very tunnel visioned. This is the only thing we're focused on. Focused on, and this is what we're focused on, and right now that data set doesn't make sense. I look at as all of us, all companies want to grow, they all want innovation. So it it gets to a point to where you may have great data, do you have enough? You know, from a from a red red flag standpoint, if you start to see you know dollars not doing one for one based on your models and then the actual results, you have a bad data set in there somewhere. Um because a lot of it's you know not performance based by an individual anywhere. It is simply by what's happening in the market, what's what's what's shifting. And if you don't have visibility in that, you're playing off of old, you know, great data, but it's it's not adjusting. Um, and I think some, you know, and some companies have. I've I've been a part of some companies that have done this. It's well, we've always done it this way, or this is what we've always looked at. Or, you know, you don't have enough of uh a you know, enough of a data point around this one specific widget that is the most obscure thing ever. Um, that you you you're sitting on a stockpile of because you can't sell them, uh, but you don't know how to sell them or you don't know what to price them for. So I think, you know, if if if I were uh several different companies within this, I would just open up, uh, probably listen a little more. Um and just just, you know, it it's I've I've never I've I'm rarely hearing of a company saying we have too much data. I don't think I've heard that anymore. You get too much bad data. Um, but I'm not hearing you have too we have too much data. We we we've got our model perfected. I don't know of any company that has that right now.

Mike Chung: 32:21

Yeah, and I think you you touch on two interesting things. One is adaptation, particularly in an economy and a world that develops even more rapidly, perhaps than in years past. And then two, the proliferation of data, both both volume and types of data. And I suppose there's a part three to that with the presumably our capacity to process and make sense of it. So all very important things, particularly now.

Austin Ledgerwood: 32:51

And I I think that's where responsible as a data provider. That's we're also responsible for helping uh companies see things that they may not be looking at. Um I I think we're responsible to do that. Um, you know, like I said, Motor, we have a very unique and granular data set around the aftermarket parts environment that I, you know, I would encourage anybody to send me, you know, two two pain points and if I'll solve one of them. Um but uh you know, that's it's it's really getting back to what data are you using and how open are you to changing your model.

Mike Chung: 33:28

Uh that's really that's really sound advice, and I appreciate that. So um I guess as we wrap up, I just want to ask you a couple of sort of funner questions, and if you will. Um you mentioned Apex, we mentioned Las Vegas. I think by the time this episode releases, we'll be heading, packing our bags and heading to Las Vegas. So tell me a little bit about Las Vegas, things you like to do there, things you might try and avoid there, or maybe new things on your itinerary that you'd like to check out.

Austin Ledgerwood: 33:55

Well, my wallet would always appreciate me not going to the casino. Um, I I really should just go to the ATM, take it out, and just hand it to the dealer and like, all right, I'm good. Uh, but no, well, I'm there. I love to go to like the old Vegas spots. Uh Cattleman's, which is uh, you know, a steakhouse where Frank, Sammy, and Dean used to hang out in. Uh, it's still there. It's such a great experience. Um, but it really is meeting people. Um, I I really love a showroom floor. I've been very fortunate to attend conferences like CES, um, you know, NADA, just some really cool stuff, National Auto Dealers Association, for those that don't know. Uh, but just it's it's really about meeting people and kind of you know pressing the flesh. We don't get to do that a lot anymore. Uh everything's so virtual that it's just I I love anytime I get to go to Vegas, it's meeting new people and you know, trying to figure out what everybody's trying to do.

Mike Chung: 34:51

And I think um you've been around the industry for a little bit. You probably know a lot of people, and just being able to have a reunion of sorts at these events is very meaningful too, I'm sure.

Austin Ledgerwood: 35:01

It's fantastic. I mean, uh, Mike, I'm I'm sure you see it. This uh automotive is very incestuous. Um, you know, it's it's one of those things where you're, you know, a customer that you had 10 years ago is now your boss. Um, you know, a customer or a boss that you had or an employee you had is now a customer. Uh it's great because you get automotive in your blood. You you really don't leave. I left for uh a couple years and I had to get right back in it. Uh so that you're absolutely right. It is a reunion. It's gonna be a it's a lot of fun, and I'm really excited because I have something entirely new to talk to them about.

Mike Chung: 35:36

That's great. Well, I wish you all the best for your adventures in Vegas. And before we close, uh any vacation spots that you have in mind or you have uh on your itinerary for the next year or two?

Austin Ledgerwood: 35:47

Uh I have a uh 16-year-old. So we're doing a lot of college tours. That's gonna be my uh vacation schedule for the next year, I think. Trying to get one picked out. Um, I mean, uh I'm a big fan of the beach. So if he can find a school by the beach that isn't gonna break the bank, I'm all for it. Um, but that's that's probably the the big thing that we've we've got on the horizon.

Mike Chung: 36:09

Do you expect it to be kind of a national search or in one particular part of the country or another?

Austin Ledgerwood: 36:14

You know, I'm I'm I'm a Florida gator. Uh, so you know, I'm I'm trying to keep him in the southeast. Um, you know, I don't want to alienate one, but if he doesn't go to Georgia, uh I'd be great. Uh, but no, he I'm wide open for everyone who wants to go. He's he's he's an outstanding kid.

Mike Chung: 36:29

Well, that sounds like a great um process that you and your family are going through. All the best to your son for a successful college um process, and I hope it's enjoyable for everybody. So, Austin, thanks so much for joining me. I hope you enjoyed it as much as I did. I certainly learned a lot. And we have to just keep being fresh and open to new ideas, ways of looking at data, collecting data, and always being open-minded. So thanks so much for your time, Austin, and to all of our listeners and viewers, um, courtesy of our YouTube channel. Want to thank you for taking time out of your day to catch this episode of Indicators. So have a great day, and we hope to see many of you in Vegas soon. Thanks for having me. Thanks for tuning in to another episode of Auto Care on Air. Make sure to subscribe to our podcast so that you never miss an episode. Don't forget to leave us a rating and review. It helps others discover our show. AutoCare On Air is proud to be a production of the Auto Care Association, dedicated to advancing the autocare industry and supporting professionals like you. To learn more about the association and its initiatives, visit autocare.org.