Mike Chung: 0:08

Welcome to AutoCare OnAir, a candid podcast for a curious industry. I'm Mike Chung, Senior Director of Market Intelligence at the AutoCare 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 that will help you monitor and forecast industry performance. This includes global economic data, industry indicators and new data sources. Happy to welcome Nathan Shipley, Executive Director at Circana. So, Nathan, welcome to the show. Good to see you, Mike. Thanks for having me on the show. Glad to be here, Absolutely.

Nathan Shipley: 0:51

So tell us a little bit about what you do for Cercana. Sure thing, mike, I've worked with you and the association for a long time and in my role at Cercana my job is it's an industry analyst role and so what our company does very high level. I will probably get into that a little more detail, but we track a variety of different retail industries, and the automotive aftermarket is one of those, and so my job is to work with retailers, manufacturers, associations such as the Auto Care Association, on just kind of what we're seeing through the eyes of the consumer in terms of what we track and all that in terms of what we're seeing through the eyes of the consumer in terms of what we track and all that in terms of what we're seeing what's selling and some of the whys behind it. My job is to kind of communicate that out to the folks that we work with.

Mike Chung: 1:33

Oh well, thanks for that introduction, and tell us a little bit about how you got to your role at Cercana.

Nathan Shipley: 1:40

You know, mike, you gave me this as a kind of a pre-question. I thought a lot about it, but I will give you a quick history lesson. The gentleman that used to run the automotive business at what was the NPD Group, named Larry Moore, was a neighbor of mine growing up in the Houston Texas area.

Nathan Shipley: 1:56

Junior year of college I was washing my car among all things Very fitting and said, hey, would you want an internship? And I had no clue what he did. But I said sure, and so I started working for this company that was at the time called the MPD Group and I was making a lot of copies and shipping out a lot of paper reports at the time, and it was an internship that turned into a full-time gig a couple of years later and I've been here for most of my career. I started out doing some client management work. I actually left the company and worked to work for Nielsen on a CPG brand, which gave me a really interesting perspective on consumer behavior and what type of information is available in a category or industry that is very fast moving, whereas, you know, obviously automotive categories aren't quite as fast moving. But ended up coming back a couple of years later, and again. I've been here most of my career and have been in this role now for about seven years. The industry analyst role.

Mike Chung: 3:02

Oh, thanks for sharing that overview. If you don't mind me asking, what did you study in college? Did you have visions of working in this line of business?

Nathan Shipley: 3:16

No, I was probably the guy. I've got a business degree. I focused on marketing, but didn't have a specific plan in mind as to what I was going to do. I thought I'd go into a sales role of some sort, but this company, which again was NPD and then we'll probably talk about it, but has turned into Sarkana, through a variety of steps, what we do is it's pretty interesting. I find consumer behavior to be fascinating. We're all different in terms of how we approach things, and so being able to work in a kind of a consultative type role where we get to go in and work with companies and help them understand their customers whether that be the end consumer, or help them understand their customers which for a lot of manufacturers, is the retailers that are selling the products understanding why people are doing what they're doing, what they're buying, what they're not buying it's really interesting. So, no, I didn't see myself going into a role like this, but I've really enjoyed doing what I do and I'm a car guy, so that helps too.

Mike Chung: 4:18

Yeah, you mentioned there are different categories that your company covers. I think it's really fascinating how, as you shared, you're washing your car, you gravitate towards the automotive, you have an interest in it and you're covering that area. So just tell me a little bit about what type of consumer data you're collecting and viewing in your role.

Nathan Shipley: 4:40

Sure, yeah, and I guess this is probably a good time to introduce the idea of Sarkana. So my career was primarily spent at a company called the NPD Group, and the NPD Group tracked behavior and purchasing patterns in what we call general merchandise industries. So the automotive aftermarket is one of those industries, but you could be talking about consumer electronics or cosmetics or apparel, anything that's not food, like groceries, or consumer packaged goods like toilet paper, paper, towels, shampoo, etc. So NPD was focused on the general merchandise side of a store or of categories or industries. There's a company out there that's been around forever, it's called IRI. There's a company out there that's been around forever. It's called IRI. Iri tracked and does track or did track grocery and consumer packaged goods and didn't really focus on the general merchandise industries that NPD tracked. And so over the last two plus years or so, through a series of private equity moves in terms of NPD and IRI being purchased by private equity, those two firms were merged and the combined firm has a new name which, as of a little over a year ago, was Circana. And so Circana is tracking the complete consumer. We are tracking everything that a consumer could buy, whether it's general merchandise categories or food or CPG, and so I am still focused on the automotive aftermarket. But what's really cool about my role is that I have the ability to see spending or purchase behavior across everything that a consumer might buy, and how what's happening at, maybe, a grocery store is impacting what's happening in the automotive aftermarket, as an example. And so you know.

Nathan Shipley: 6:30

In terms of the data sources you know there's there's two primary sources. You know most industries not all, but most. We have partnerships with retailers, and those retailers send us their sales information. For most, all the stores in the aftermarket, it's every store that's under their umbrella, every store, every item, every week, and there's some caveats to that in terms of what categories, et cetera. So we're able to see, you know comparatively, what is selling and what's not, and so there's a lot of data security in terms of us not exposing individual retailers. So we're not able to see individual retailers, but combined, we're able to say, hey, this is what's happening.

Nathan Shipley: 7:15

The other side of what we source information from is receipts, and so we work directly with consumers, and those consumers provide us their actual receipts from stores or online, if they shop online, and so we know, through the panels that we have, who those people are where they went, what they bought, what they paid, where else they went. You know, it's really fascinating to watch a consumer maybe hop around between different auto parts stores in the same month. It's like, well, if you're going to one, why are you going to this other? And was it an out-of-stock issue? Was there a price issue? There's some fascinating learning behind that type of information. So, again, two main sources of data that's coming in point of sale, and then receipts, and then in terms of what we can actually do with it, it's a whole other conversation because there's a lot of analytics that can be layered on top of that to do some pretty powerful stuff.

Mike Chung: 8:11

That's really fascinating. Thanks for that overview. So I'm going to play a little bit of that back and dive into it, because there are two things that kind of made me think of some follow-up questions here. So, just as an example, the grocery stores, right. So if your organization has partnerships with, let's just say, safeway, giant, let's just even say Walmart, right, you're able to. They send you their sales data and you can kind of bucket it, categorize it at the store level, then roll it up to a regional level. You can look at it for fruits and vegetables or I don't know, like cereal, various categories, and you can slice and dice the data in different ways. And it's through those partnerships that you're able to get comparatives, I guess, like you say, between those grocery stores.

Mike Chung: 9:05

And like you said, there are some data sharing agreements in terms of the proprietary nature of how that data can be shared out. Am I getting that correct, Nathan?

Nathan Shipley: 9:14

That's right. Yeah, I mean in terms of point of sale data, like what you're talking about, retailer privacy is paramount. Data security is paramount, meaning we can't in a business model like ours, we cannot expose individual retailers. That doesn't work. So how does the model work? We aggregate this data together. So if we're talking about the food industry as an example, let's talk about the automotive aftermarket we're not reporting. When we report out what's happening, we don't report about individual retailers. It's the aggregate of those retailers that we're reporting, with the goal not, again, we can't expose retailers. Uh, that wouldn't work.

Nathan Shipley: 9:53

Um, and so you know a lot of folks. Well, why would retailers do that? Or, you know, in any industry, why would? What's the what's the benefit to them? And the benefit to those retailers is that we report back to those retailers. You mentioned Walmart as an example. Walmart's able to see their performance, walmart's performance relative to the aggregate of every other retailer that we're tracking, and so it's performance.

Nathan Shipley: 10:19

But it's what's happening in individual markets, as you mentioned, certain parts of the country, certain cities in the country, and you can kind of go down from there, you can drill in deeper. You know, are they performing well in certain categories in a store but not in other categories in a store, and if there's opportunities to then dive in to say, all right, why aren't we performing well in this category that we're talking about this? You know whatever category this we're talking about. So that's the nature of the model, is it's insights into what's happening in the categories in which they play.

Nathan Shipley: 10:55

And the most basic explanation I have and it seems to resonate pretty well is you know, let's say I'm a retailer, you know, I personally live in Houston, texas, and my goal this year, internally, is to grow 5%. And you know what? In the Houston Texas market, I'm growing 5%. I'm right in line with my personal goal of growing 5%. But then I look at data from a company like Cercana that's tracking every retailer in the Houston market and that data tells me that the Houston market, on average, is growing 9%. And so, yeah, I'm hitting my personal goals of five. But this information tells me I'm losing market share and my competitors are growing faster than me for some reason, and I need to figure out why that is. What am I doing wrong? What are they doing better? What's going on? And that's where it starts at a very high level, and then we start drilling in a pretty intricate level of detail to figure out what's going on and how to fix it.

Mike Chung: 11:55

So benchmarking is certainly one of the advantages that anybody who is subscribing could glean from this and monitor their performance versus the rest of the markets. That makes a lot of sense?

Nathan Shipley: 12:09

Yeah, that's definitely it. But you start getting into heavy analytics around promotion strategy what's working, what's not? Pricing strategies by markets what price points are working? Are you overpriced, underpriced when you promote? Are you promoting too high, not enough? Are you promoting too much? Are you giving product away? Are you not promoting enough? The analytics that can be done. It's very, very powerful analytics on what's happening at a very granular level. When you start digging into it, it's pretty neat at a very granular level.

Mike Chung: 12:44

When you start digging into it, it's pretty neat, sure, and two things that I was thinking about. One is in terms of the meta trends. When you aggregate all this data, sometimes I get this question how are private labels doing? That's something that you would be able to see across the market for any category, whether it's cereal or motor oil or filters or what have you Indeed?

Nathan Shipley: 13:05

That's right, yeah, and so you know, again, talking about retailer confidentiality, you know private label. You know we're not going to be talking about an individual retailer's private label brand, but an aggregate. Yes, we're aggregating. You know, it's motor oil. We're going to put all the private labels together and call it private label and we can see this private label taking share from brands, yes or no? And that is a question that's coming up a lot these days, it seems.

Mike Chung: 13:30

Mike by the way, and to that end, when you talk about the analytics that are possible, I can see merging of that data with things like inflation consumer confidence. To say what? Inflation consumer confidence? To say what? Like a driver's analysis right To say if private label is increasing, sales of private label products are increasing. Perhaps. What can we attribute that to? Is that fair to say?

Nathan Shipley: 13:55

It's very fair to say. And so I mean, among other things, we do a lot of forecasting work and so the models they take a lot of those macro factors, like you just mentioned. You know inflation, what's happening with inflation, what's happening with food prices, what's? You know there's all these, there's all these inputs that go in, and then the model can look back and say, all right, what we can see, sales, you know demand, and then we can have all these macro inputs and like what is most correlated with demand from a macro standpoint and like what is most correlated with demand from a macro standpoint, what's not correlated with demand. And so, as an example, one of the demand drivers. So let me back up, we can see, through our point of sale data, what's happening with food prices at the grocery store. You hear about it in the media, you feel it personally when you go to the grocery store. We all feel it personally. And you go to the grocery store, we all feel it. And so we can see how food prices correlate with demand in certain categories and, as an example, the tires category. It's one that we track. The highest correlated macro factor that we're seeing right now is food prices and that we can go back over time and it kind of makes it's like okay, okay, that's, that's. That's not a big shocker, but not all categories are like that. Not all categories have a high correlation with food prices, but tires in particular do um, and let's see.

Nathan Shipley: 15:20

No, I'm not going to go down that rabbit hole per se, but yes, you can start diving into macro factors and macro economic factors and kind of their correlation.

Nathan Shipley: 15:38

But even outside of the macro economic, within the four walls of Sarkana, increasing food prices again as an example, right, wages have gone up X percent but food prices are going up Y, which means that has taken spending power away from a lot of consumers because they're spending more of their income on food as a percentage of their total wallet. Where is that money coming from? Where are they cutting back if they're cutting back? And so it's interesting to look at it that way too. And so, kind of tying it back to my role, my job is kind of look at the macro, like what's happening big picture with consumers in our space and some of the whys behind it. If you were to ask me what's happening with so-and-so item and so-and-so category, I probably wouldn't be able to answer the question off the cuff, I need to go port-a-port or something. Yeah, the information internally. It's fascinating the amount of information that that Sarkana has compiled.

Mike Chung: 16:46

Yeah, so I'm going to revisit some of those questions. But if we back out a little bit, what general consumer trends are you seeing? You mentioned inflation and feel free to talk about just overall general consumer behavior. Is it perhaps spending less because of inflation? Is it going from a premium brand to a more mainline brand or economy brand? Is it delaying of purchase? Can you tell me a little bit about that? And then, what's happening in the automotive aftermarket?

Nathan Shipley: 17:22

Sure, yeah, it's the last. It's been four or five years now since COVID became a thing, but observing consumer behavior and kind of the macro economy over the last several years has been fascinating. Not all for good reasons, obviously, but present day, yes, inflation is hurting the consumer. We are seeing credit card debt per household at relatively high levels. Savings rates have declined. Savings amounts have declined per household. Delinquencies are up a little bit. Housing-related expenses are really chipping away at discretionary spending power. Are really chipping away at discretionary spending power.

Nathan Shipley: 18:13

So I guess big theme one is this concept of destruction of discretionary spending power. You go back three or four years ago or five years ago and the economy looked different. Interest rates were a lot lower, spending on a lot of things stopped because we all were locked down, so we weren't spending money on travel and sports for our kids and whatever else. Oh and, by the way, there was stimulus money being pumped into the economy and so all of a sudden there was very high levels of discretionary spending power at that time and so that's changed. You know, back then we saw credit card debt coming down and savings rates going up. Back then we saw credit card debt coming down and saving treats going up and from a big picture, at least financially, not so much from a public health standpoint housing and food as two examples. Utilities those make up a fairly significant amount of their weekly or monthly budget, and so when their income hasn't kept up with the rate of increase of those big items, their discretionary spending power is affected by it.

Nathan Shipley: 19:28

So, from a pricing standpoint, we are seeing actually all this talk about prices and higher prices and everything's more expensive, and now what you're starting to hear, what you are hearing, is prices are. Things are calming down with price, and the rate of change definitely has slowed, and in some industries it's turned negative. We're seeing price deflation in some categories, but food is an example, while the rate of change isn't what it was meaning. The rate of change has slowed down. Prices are still way higher than they were two, three, four years ago, and so that's still eating into discretionary spending power of the consumer. So we are seeing trade down in some regards. You know, I think you need to think about trade down both. From a, I'm standing at the shelf in a store that I always shop at and am I trading down from brand A to brand B or brand B, a private label as an example, or am I trading down in terms of where I shop? Am I typically shopping in a specialty store and now I'm going to a mass retailer? Or I'm typically shopping at a mass retailer and now I'm going to a dollar store? So we're seeing some of that as well A little bit bigger picture than all of that.

Nathan Shipley: 20:40

There's some things that happened during and because of COVID that we're talking about and watching, things like where people are living right now. There are certain states that have seen a reduction in their population and certain states that have seen an increase in their population, and this whole work from home economy, work from home market, has really enabled a lot of that. As I always use the example of I was working a technology job and I was required to be in San Francisco for that job and I moved there for the job and now all of a sudden, I can work from anywhere and that's not my home. Why would I continue to live in a city or state where the cost of living is so high? I'm going to move somewhere else, and so I'm going to start trying to tie some of this to the automotive aftermarket, because that example I just used, maybe that person living in a tech-focused city didn't need a vehicle because those cities have mass transit etc very good mass transit and now that person has moved to a city where maybe mass transit's not quite as available and so they had to go buy a car, right, and so there's a little tie to the automotive aftermarket.

Nathan Shipley: 21:50

I'm sure we'll talk about that in more detail. But there's macro. I think what you're hearing with retail in general is a cautious outlook. There's just general concerns from a macro standpoint about the ability of the consumer to continue to spend with where prices are, interest rates are, et cetera, and I'll pause there.

Mike Chung: 22:15

Yeah, I'll make one comment and then circle back to another question. The real estate is a great topic. I don't think we'll dive into it now, but it will be interesting to see if that movement of residences the example you just gave California to another city perhaps that will slow down as interest rates remain high, as prices of homes remain high and as homeowners with low mortgage rates have a little bit of a disincentive to sell their home and move right.

Nathan Shipley: 22:52

I'm one of those that happened to buy a home when interest rates were two and a half percent, right, you know. And so, like the running joke is like, that's your retirement home, nathan, because you're never going to walk away from that two and a half percent you know, you really have to sweeten the pot to make it attractive for you.

Nathan Shipley: 23:10

Generalities, of course. But yeah, and then on the flip side, folks that would like to get into a home. Rates are where rates are today compared to where they were several years ago. It just makes it much harder to get into a home for that same person. So, yeah, that's not helping, and obviously with inflation I mean, I live in Texas with property taxes and all that you know if you own investment homes and you have renters, well, your costs of those homes are going up, so you're driving rent prices up too, you know. And so it's just this conundrum for consumers in terms of what they can afford because of what's happening with the economy today.

DTP: 23:51

Consumers in terms of what they can afford because of what's happening with the economy today. This is DTP IT Director and Sustainability Committee Staff Liaison at Auto Care Association. Are you passionate about shaping your industry's future? Join an Auto Care Association Advisory Committee and make a real impact as a volunteer. You will drive innovation, tackle key challenges and collaborate on cutting-edge solutions for the entire supply chain. Don't miss out. Join us at our upcoming Leadership Days event to start making a difference. Learn more at AutoCareorg slash Leadership Days and find information on current committees at AutoCareorg slash committees.

Mike Chung: 24:38

One other note about inflation is I know auto insurance rates have gone up, maybe up to 40% in some cases year over year, and that's just another component of any consumer's budget. You mentioned shelter, housing. If you have a car payment, certainly that can be a substantial portion of somebody's income payments. Certainly that can be a substantial portion of somebody's income, but another monthly expense right In terms of if your car insurance is higher, I can see how that would have an impact on the family budget. To perhaps, as you mentioned, shop for, change brands or change locales. And tying to that, earlier you said something interesting in terms of looking at the consumer data that you have and being able to say that, oh, this consumer bought at store X and also went to store Y, or they visited different competitors. So I'd like to dig into that a little bit more. So I was thinking about you're able to see consumers purchase patterns, but are you also able to see if they go to a store and not purchase something there?

Nathan Shipley: 25:38

That's through traditional data collection methodology that we have. The answer to that question is no. We track what people buy period. So if they walked into a store and didn't buy anything, we're not going to know that.

Mike Chung: 25:53

So that's the answer to that question yeah, Okay, that's helpful because I think when you think about the future, right, what do you see down the road no pun intended in terms of companies like yours who are able to amplify that database? Right, Because you have point of sale data, you have consumer data. Is there going to be another layer of data to perhaps change those forecasts?

Nathan Shipley: 26:24

Ooh, so are you talking at kind of using the example of that consumer that walked into a store and didn't buy and walked back out? Exactly? You know I, in terms of what we're doing. You know, mike, I don't know if I'll go down that path deep. I mean, we are very focused on consumer behavior and will we ever be able to talk to? I mean, we do a lot of in-store work, depending on the industry. I think, just kind of backing up and thinking about the question.

Nathan Shipley: 26:58

Think about the power that an e-commerce retailer has, because you use the example of a consumer physically walking into a store and not buying anything, right, and they walk back out, okay, so what do you do with that? Think about an e-commerce retailer that can track what I'm searching for. It's the same idea, right? I went to let's use Amazon, right? I went to amazoncom and I typed in tennis shoes. I didn't make a purchase, but they know I'm looking and so the fascinating. And what do you do with that data? So this guy came to our site and he typed in tennis shoes and he poked around a little bit and he left. So which path do you go down? He's in the market for tennis shoes, so we should start lobbing ads at him on his phone. Or do you now know that I'm Amazon?

Nathan Shipley: 27:52

That, okay, let's look at consumer search behavior for this category, and 80% of consumers come to our site and they don't actually type in a brand. They just type in tennis shoes. They're not worried about the brand. Or is it the other way around? 80% are coming to the site and when they're searching for shoes, they're typing in Nike, and so, wow, nike has a pretty strong following the amount of information that can be gleaned off of search behavior, and that's not even purchase behavior.

Nathan Shipley: 28:27

This is just searches. Again, this is not suggesting at all that we're going down that path. I'm talking in general. This has nothing to do with Sarkana. But what retailers do with this, you know, because most, most retailers have obviously e-commerce side of their business too, and so there's this whole being able to know what's selling, et cetera, but it's also what's not selling, and so tracking people going in and out of stores is one thing, but also tracking what's happening online, I think, is another fascinating input. In terms of talking about the data lake and how much information there's out, there is just another one to throw at you.

Mike Chung: 29:04

I think that's a great example and you could go in a lot of different directions with this right, because it might be somebody getting some comparison shopping right. I look online, get a feel for what the prices are for certain products, certain models, size, availability, but then I go to a brick and mortar store to purchase it right. So there could be those use cases and I think putting together that complete picture right Is that possible with point of sale data? Perhaps there's the online survey type of data, the in-depth interview, the focus group. But you raise a great question or topic in terms of I'm looking for my phone, but all the data that's on my phone, in terms of tracking behavior or in person, if I'm carrying my phone, how can that location data perhaps be used to make a composite image of Mike Chung's purchase, shopping, et cetera, behavior?

Nathan Shipley: 30:04

Well, and I think, because there's company that doesn't do that, I think we're probably both thinking of one in particular, right that does just that. Right, they track movement data with phones, and I think there's a couple of general best practice and not to start preaching, but there's so much information coming at all of us from all different areas.

Nathan Shipley: 30:30

One is like the cell phone movement data, the search data I just talked about information like what Circona has, which is actual purchase behavior, from receipts or from point of sale. There's so much information, a best practice that I try to take what are we trying to understand? What is the actual question we're trying to answer? And then let's back into it and figure out which sources will help with that. So if I'm a retailer, as an example, and one of my questions is I'm trying to understand if out of stock you know I'm a brick and mortar retailer Are out of stocks affecting me, yes or no? Mortar retailer are out of stocks affecting me, yes or no?

Nathan Shipley: 31:14

Well, one way we could figure that out is we could that example you gave a minute ago with actual purchase behavior. We could go talk to a group of consumers that shopped at three different auto parts retailers in the same week and we could follow up with them and say why did? Did you go from A to B and B to C? Right, because we're trying to figure out, was an out of stock the issue? And maybe they'll tell us that. They tell us that we're trying to answer a specific question, so we're kind of curating our research to just that.

Nathan Shipley: 31:44

If we had the ability to go back and talk to people that walked into stores and walked back out, why did they do that Right? And so, if we're trying to answer the question, was there an out-of-stock issue? Maybe then that group of people that we know walked into that store. We could go back and ask them that question. You know, but it's, there's so much information available from so many different sources. I think a lot of us are like what do we do with all this information resources? I think a lot of us are like what, what do we do with all this information? There's so much. And so, trying to start with the end in mind, not to sound cliche, but there are very relevant and specific business questions and issues that can be addressed by these things.

Mike Chung: 32:24

you have to know what you're trying to figure out first that's really helpful advice, because I can see where some of the challenge is. As you say, what is the question we're trying to solve? Are we looking at the right data sources? So can you tell me some of the challenges that you've either seen or do see with regard to, if you're working with a group of analysts, they have a set of data they're trying to solve, a advice that you might give to those groups or if it's from a technological perspective right, because I think now we live in an age where there's much more computing horsepower than there was perhaps when you and I were in college, right? So is that a concern that you and your team have?

Nathan Shipley: 33:13

Don't try to age me. I just graduated college, like three years ago, Mike.

Mike Chung: 33:18

Don't try to age me. I described when you accomplished, like three years ago Mike, come on, you're looking really good for 29,. Nathan, let me see.

Nathan Shipley: 33:22

Yeah, I know, I don't know if it's the gray, I don't know if it's going to be going to be a live video or just the audio. But you know a couple of thoughts you know. One is yeah, I kind of hit on that point what is the end goal? What are you trying to answer? If you're just diving into a data set with no specific question that you're trying to answer, it's going to be very hard to do something with it. Two is question your data source. Make sure you understand that it's representative of whatever audience you're trying to analyze.

Nathan Shipley: 34:01

I think very important is don't assume this is the beauty of data. Don't assume that how things are in your own world are how things are in the world. Meaning I live in Houston Texas, right, meaning I live in Houston Texas, right. A lot of people drive full-size trucks and full-size SUVs like Chevrolet Suburbans and F-250s and that's just the market where I live. And what you don't see is a lot of Subarus as an example. And so if I just looked at kind of my own world, I'd say, ah, subaru has no market share. You don't see it right. But then you go to colorado as an example of what you don't see a lot of full-size trucks. You don't see a lot. You know. What you do see is a lot of mid-size trucks and a lot of jeeps and a lot of four-wheelers and a lot of subarus, right.

Nathan Shipley: 34:47

And so in that world, like, oh, subaru is like they're crushing it, so you can't look at your own world and think it's what's really happening, and so you have to look at information that's representative of whatever it is you're trying to analyze, and so don't be surprised when some trend pops up that in your mind you're like that can't be true. Well, because that's just your own perspective of the world. And that's where you get information. You start talking to consumers and looking at point of sale data. I'm like, wow, people actually buy this world. And that's where you get information. You start talking to consumers and looking at point of sale data. I'm like, wow, people actually buy this stuff and it's a thing so obviously your source. Don't put blinders on to the world and then think about what the end goal was. I think those three things can really help focus any kind of work you're trying to do in terms of looking at information.

Mike Chung: 35:37

Really helpful advice. And I want to go back to one of your earlier examples. So you mentioned that tire sales. There's some correlation to food. Sure, and probably oversimplifying, and I just want to not necessarily get into that specific example. But when you make a revelation like that, some of the things that come to mind, for me at least, are we've talked about monthly expenses, right, tires is typically not a monthly expense, but how do you reconcile, say, a regularly occurring spend, whether rent, versus something that is not a regularly occurring event or an emergency type of repair? And then the second part of that is I always think about modeling and what pool of data you're looking at. What are the? Is it the dependent variables? Are you including the right dependent variables, because perhaps there's something that we as an analyst team may have not considered and are not including in that. Can you tell us a little bit about your thoughts on those matters?

Nathan Shipley: 36:49

Yeah. So a couple of thoughts. You hit the nail on the head with timing. A set of tires, I mean absolutely. You got to flat to replace something whatever.

Nathan Shipley: 36:59

A set of tires for a typical consumer is what three years, four years, maybe five, you know to buy a set of tires? Um, and so if I find a consumer that walks into a tire store today to buy the same set of tires that I bought four or five years ago, on average I'm looking at a price point that's 40% to 50% higher. That's based on point-of-sale data that we have. And so there is absolute sticker shock, and we have all rode this wave of food prices. We go to the grocery store every week, and it's been a little bit every week. And here we are a few years later and it's like whoa, they're 30%, 40% higher than they were, but something like tires or an automotive battery or something like that.

Nathan Shipley: 37:42

If you are a major repair, you just don't do it that often. You walk in and oh, by the way, you're already feeling squeezed by something we talked about higher food prices, higher utilities, higher insurance, whatever it is and then you walk in and that set of tires that was eight800 last time you bought it. Now it's compared to quick math, that was a thousand or 1100. There's a shock there when that price point gets hit. And so, in an economy like this, what we do see is okay, there's trade down taking place right. It's this instant decision because you're already feeling so squeezed, and every other part of your financial life, I just can't do this. I'm going to trade down. So I don't know if that gets at the answer to your question directly.

Mike Chung: 38:30

Oh, it's very helpful and I think it aligns with the things that we've been talking about the trade down, contending with inflation, higher cost of living and I don't have the stats at my fingertips, but if I remember correctly, there are four tiers of tires and how the lower tiers have been selling more, partly because of the things you've talked about and partly because the quality has gone up over the years.

Nathan Shipley: 38:55

So that's a good start. Yeah, and that's. I mean I don't want to talk too deep on the tires industry in particular, but yeah, that is a great example of one where we are seeing very clear data that there's trade now taking place and so that concept if you look at other major purchase categories, it's happening. And one thing and we're probably getting close to time, but one thing we haven't really talked about if we talk about the industry as a whole, is this higher income consumer that, like me, I don't know about you, I know Jackie working from home, and I didn't work from home prior to COVID, but I do now. So my driving patterns have changed, my ability to work from anywhere has changed, and so driving patterns have changed. And this higher income group has, oddly enough, become more engaged with the aftermarket.

Nathan Shipley: 39:52

And you start trying to dig in some of the whys behind that. Well, you can start looking at what's happened with vehicle prices over the last several years, and that's a moving target today. But what happened with new car availability and all these things? And so it kind of pushed the average age older, and so now average of cars, and so now that's pushed that consumer more into our sweet spot and they have some more time on their hands, maybe now they're doing some more DIY instead of having work done for them, and so that's benefiting the industry. And so, kind of tying it back to data analysis, you can't just look at a category and say, wow, the category is growing, that's fantastic. And when you start digging in and you start breaking that apart, it's like, well, wait a second.

Nathan Shipley: 40:35

The core consumer of this category, which traditionally is a lower income consumer and they typically drive much older cars and they're DIYers they're actually suffering, they're actually deferring maintenance and they're trading down and there's a lot going on here in this bucket.

Nathan Shipley: 40:52

But over here in this high income bucket, that maybe isn't traditionally thought of as a core DIY consumer and not someone we're thinking about marketing to. But now, all of a sudden, they've decided to keep their car longer and they're doing more DIY because they have the time, because they're working from home five days a week. They're driving the sales of brand and items in certain categories and so when you group it all together it's like, ah, category is doing pretty good, brands are doing okay, you know, but in reality there's two very different stories going on with two different consumer groups, and you start to see retailers that recognize that or brands that recognize that, and they start changing strategies around. We need to market and try to sell to these groups differently, because their needs and their issues right now are very, very different. But if you look at it just in aggregate, things are doing okay. It's not quite the story that's really going on.

Mike Chung: 41:44

That's a great example and it makes me think of, as you've talked about before, what's the story within the story, right? So I guess, like two questions that I have as we go to close here, one, looking into the future, five years, 15 years, what are your thoughts in terms of the challenges that lie ahead for data analysts in the stories that we're trying to extract from the data? And I'll just harken back to something you talked about earlier e-commerce. Right, that was a game changer throughout the 90s, into the 2000s. And how do you, as a data company for lack of a better word factor in new kind of channels of purchase or new data sources? So, thinking about the future, what challenges, what opportunities do you see with regard to new data sources, the volume of data, perhaps, the technical aspects, the infrastructure of managing that data, the accuracy of that data?

Nathan Shipley: 42:49

Yeah, I mean the new channels. That's something. A company like e-commerce is a great example. We've evolved, you know, and that's like all right, that's a channel we need to be tracking, and so that's what we do, right, we work with pure play e-commerce retailers like Amazon, as an example, or it's Walmart that we've worked with for a long time the dot-com side of their business, right, and then you can get into a first party versus third party, and there's all these different things.

Nathan Shipley: 43:10

So when we see something where because, again, what our company is focused on is what are people buying and so when a channel like e-commerce becomes what it has become, if we're not tracking that, that's a problem, that's a blind spot to what's happening. So we do track it. As you look down the road and think about challenges, a variety come to mind. One is consumer privacy. That's a big one. It has been and will continue to be a big one, not to get down to the whole, data privacy, what we're dealing with with the Auto Care Association in terms of who owns the vehicle data, all these big topics that are happening in our industry. But it's no different than the data being pulled from a phone, right? They're tracking my location or what I'm buying as a consumer and how that's being tracked. It's all the same. It's consumer data, and so does data privacy. What happens with that? You know, how does that evolve? That's one thing I'll be thinking about. And, as other sources continue to come online, filtering out the noise, that's a big one. Don't make it too complicated, because, at the end of the day, we're just simple people that are just out buying stuff and so just how do we track that? So where does it go from here down the road?

Nathan Shipley: 44:31

I think, big picture, with retail, it's going to be fascinating to watch. I think when e-commerce really started to be taking people are taking notice of it, it's like man. Five or six years ago we were literally in a presentation, let's say, at Apex. I would be talking about the number of retail doors that closed in the last 12 months. I was like 12,000 stores in the US across all of it just closed.

Nathan Shipley: 44:56

And it's because this whole e-com story that's not something we're talking about today, and the reason why is because brick and mortar retailers have gotten very competitive and where are they strong and so very competitive against pure-fly e-comm guys. And so what's going to happen with online? What's going to happen with brick and mortar and how does that evolve? I think it'll be fascinating to watch. From a data collection standpoint, obviously, the faster we can get information out the better. So data speed is something that I know we're always focused on. But yeah, I mean as an analyst, I think those big things, some of the things we've already talked about in terms of just filtering out the noise will continue to be important.

Mike Chung: 45:42

Sure. So I think you may have already answered this last question that I have for you, but if you're talking to a senior in college, maybe a master's student, who's getting ready to graduate and enter the so-called real world, what advice might you have for people as they think about careers? How data is collected, how data is analyzed and how it's used.

Nathan Shipley: 46:03

Yeah, I think this is one I've used for a while. But if you're coming into a new role, don't assume that whatever company you're coming to work for, whatever role you're getting into, let's say that company you're working for they don't have it all figured out. In general, whether it's data and whatever it is, they don't have it all figured out. If everything was figured out, a lot of us wouldn't be working right.

Nathan Shipley: 46:27

There's a reason why companies have as many people as they do because every single day, there's a new challenge in terms of what, how they're selling what, the products they're making, the information coming in their way, and so, again, don't assume it's all figured out and look at whatever role you're in or you're going into as tremendous opportunity to make a difference in whatever it is that you're doing and that can be done through information and something. Even on our side we'll have new analysts that will start and they'll start digging into a category that maybe I've kind of personally written off. I new analysts that will start and they'll start digging into a category that maybe I've kind of personally written off. I'm like, ah, I don't want to look at that. And I'm like, hey, did you know this is going on in this category? And I'm like what? I had no idea, right? So it's, I think, be curious. Don't assume all the answers are out there because they're not. Have fun with it.

Mike Chung: 47:14

And I think you know. Just a quick corollary to that is you shared that from your perspectives. What advice would you give to executives across industries when they think about data? We didn't go into AI, but certainly that's. That could be another topic for another day but as executives think about the proliferation, availability, the ability to process and, you know, use generative AI. What might you say to the executive crowd?

Nathan Shipley: 47:41

Yeah, I think same thing. I've kind of already hit on. Ai is to your point, mike, that's something we didn't touch on, but you think about the future. I mean, it's here, right, it's coming hot and heavy, but it's no different than anything else filtering out the noise. And I have to imagine that if you're an executive at one of these major retailers, you have sources of information coming at you daily, some of which you know what they are and some of which you have no clue, and you might have two sources that tell you the exact opposite thing in terms of what's happening in the market, and so coming up with trusted advisors, information you know trust because of sound methodology and kind of staying focused on that. But also it goes back to what are you trying to answer, what are you trying to know about your business that maybe that information can help with, or maybe it can't, and you need to look somewhere else because there's just so much information swirling around. It's important to filter out the noise.

Mike Chung: 48:38

Well, really appreciate your insights and taking the time to talk with us here. 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. Auto Care On Air is proud to be a production of the AutoCare Association, dedicated to advancing the auto care industry and supporting professionals like you. To learn more about the association and its initiatives, visit autocareorg.