Navigating the Complexities of Automotive Data

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. This is Mike Chung, and I am really delighted to have Daniel Zenko, director of Data Innovation at AutoCare Association, joined me as a guest today. So welcome, daniel. Thank you for having me. So, daniel, tell us a little bit about what you do at AutoCare.

Daniel Zenko: 0:54

Hi, my title is Director of Data Innovation, which really means that I'm dealing with kind of new ways to disperse and disseminate data for our users and our members. That's pretty much. Our goal is to kind of improve the way that data is consumed by our users.

Mike Chung: 1:14

So why is it called data innovation?

Daniel Zenko: 1:17

Well, we try to find innovation, but mostly in a way the data is delivered. There's not much innovation about the way the data is collected or what data we are using. It's pretty much what industry was using all along. It's just a matter of efficiency and urgency, of the ability to consume the data in a proper, timely manner. That was kind of missing before. So we are trying to improve on that and, of course, to find new data sources that might be useful for our members and community.

Mike Chung: 1:52

Sure, so we'll dive into some of those subtopics as we go on here, but before we do that, tell me a little bit about your journey to AutoCare. What brought you here and how did you get here?

Daniel Zenko: 2:02

Yeah, I'm not an auto care lifer at all. I spend my career in different industries, but all of my work was related to the market intelligence sales intelligence to find useful data to be used by different industry insiders to be able to sell their products better. So in that context, my previous experience was actually pretty relevant to what we're doing here, and, of course, each industry has its own quirks and idiosyncrasies, but overall it's pretty similar, I would say. But overall it's pretty similar, I would say.

Mike Chung: 2:45

Sure.

Daniel Zenko: 2:57

So, when we think about the automotive aftermarket and recognizing that not all of our listeners may necessarily be in our industry, what kind of data are you referring to help understand the market trends, to put their own performance in a context, to be able to understand better what's really going on with their sales? Because your sales might be up 10% last month but without having a context, knowing that overall industry is maybe up 50% or maybe just 5%, it's really hard to gauge how well you're really doing and should be happy with your sales efforts, or maybe just 5%.

Daniel Zenko: 3:24

it's really hard to gauge how well you're really doing and should be, happy with your sales efforts, or maybe you should be worried about what's going on.

Mike Chung: 3:32

So some of what you're talking about now sounds like benchmarking data, so sales performance data of a company relative to the rest of the industry, for example.

Daniel Zenko: 3:40

Correct, correct and the industry for a while struggled to have a useful benchmark that will really be indicative of what's really going on.

Mike Chung: 3:49

So, before you joined AutoCare, what were some of those benchmarks that people in the industry were monitoring?

Daniel Zenko: 3:57

Well, there wasn't much when it comes to that like a cumulative industry effort.

Daniel Zenko: 4:03

There are some isolated data feeds from separate retailers or program groups that our members would be able to access, but that wasn't really much, and also there was some of the government data that can be used, but all in all it was mostly on a yearly basis or just in a specific slice of the market that was covered, especially when it comes to the hard parts data, meaning data for the parts that are sold behind the counter. For the front of the counter data, there was pretty significant data out there that users could use.

Mike Chung: 4:46

Sure. So one of the things you mentioned was hard parts data. So if I'm not in the automotive industry and I'm a consumer, I have my own car. Are you referring to, say I go and get a part changed, say I get my brakes done at a? Would that be considered behind the counter or hard parts?

Daniel Zenko: 5:07

Well, it depends. An easier way to explain this is when you go to the AutoZone store, let's say, and you want to buy some part, if that part is displayed out there, you can just pick it up and bring it to the counter that's front of the counter item like wipers or a refresh or something like that. If you have to talk with a, with a person there and he goes back into the warehouse to bring you actual part, that's behind the, behind the counterpart, and there'll be, like you know, chassis parts or like brake pads or stuff, like you know, stuff of the most significant, like volume and weight, and that that usually goes into the integral part of the car and that's would certainly be very model make specific.

Daniel Zenko: 5:50

It sounds like that uh, yeah, I mean, depends, you know, some, some parts are very specific to make a model and some are relatively general. It's really, you know, depends on the type of part that makes sense.

Mike Chung: 6:02

And then something else you mentioned before that may be a new term for some of our listeners program group. Can you just briefly tell us what a program group is?

Daniel Zenko: 6:10

A program group is basically association or some kind of joint effort by the wholesale distributors, which are the companies that provide parts to our shops.

Daniel Zenko: 6:27

For example, shops don't keep all of their necessary inventory at the shop. So when you come there with a problem, very often they don't have that part available right there to put it in your car. So they call those distributors to bring this part to them from their warehouses they have all over the country and timeliness is essential here, so that part often makes it to the shop in like half an hour or less, which is really amazing. And so these distributors, they keep all the parts in their warehouses and have the disposal to the shops as soon as needed. And to gain pricing power and to streamline some of the operations these wholesalers organize into these program groups where they join forces. And for several reasons that's a good approach to give them more leverage in the market.

Mike Chung: 7:23

That makes a lot of sense. Thanks for giving us that background. One of the other things you touched on was government data and thinking about the automotive aftermarket. My understanding is that it is not necessarily a US census defined entity, right? So I think that adds to the challenge of getting that indicator data that can be useful for people across the industry. Is that kind of in line with what you were talking about?

Daniel Zenko: 7:49

Yeah, yeah, I mean there is a category called, like, aftermarket retail stores, which is relatively easily defined and understandable, but there is a slew of different other providers of the parts and services in the industry that are not necessarily easily defined and also there's a good portion of our sales in our industry is happening through the street orders like Walmart, who have a bay that you can bring your car to do some basic repairs and change parts and stuff like that. So it's really hard to use the government data to get a wholesale idea of what's really going on overall in the industry.

Mike Chung: 8:39

So what I'm hearing is the government data can be very rich and informative. However, since the automotive aftermarket is so spread out across so many retail outlets, it could be a grocery store, it could be a big box store like Costco, where you could have service done you can get front of the counter parts, for instance.

Daniel Zenko: 9:01

But the stat that you highlighted is a specific, specific NAICS code for auto parts, accessories, entire store sales and therefore doesn't capture everything right, and there's also another complication with the online retailers like like Amazon or eBay that have a pretty healthy you know car parts business there and some of it it's their own sales. So it's like just like a market. They provide marketplace to the independent salesmen and those are really not classified anywhere as such. So it gets kind of murky out there when you want to kind of grasp what's the really full market out there for parts.

Mike Chung: 9:42

Sure. So pretty broad, open-ended question. But what data sources should people in the industry be following?

Daniel Zenko: 9:52

I mean still the government is the best source out there for this general data. There's also a significant amount of data that comes out of the companies like Visa or MasterCard, when you can monitor the swipes of the cards at the retail places and get some kind of information from there. Also, the Autograph Association provides a lot of information that's useful for industry, as well as some other industry specialist companies that specialize in providing data in the industry, like IMR and MPD and some others.

Mike Chung: 10:35

So going back to the government data first. What makes the US government data such a rich resource? What are some of the advantages of the US government data?

Daniel Zenko: 10:46

Well it's. You know, it's a well organized in and, again, it's limited in its core but what's available is well organized and will maintain and there's like a lot of restatements when it's necessary. So it's quite dependable and you know, and there's no really I should put it fudge in the data that will be created by a special interest of somebody who's providing the data. So it's objective. That's really really good.

Mike Chung: 11:21

So what I'm hearing is regular process, objective, quote-unquote, third party if you will, and they do seasonal adjustments and other statistical processing to make sure it's representative. Is that fair to say?

Daniel Zenko: 11:35

Yeah, yeah, they're quite good at that. And also it comes with a regular schedule. So if some indicator is published, usually like second Tuesday in a month, you can count it. It's going to happen that way. So there's regular cadence of the data. That's again it's a basis for any kind of serious analysis, but still not probably enough to really satisfy all the needs that you have to monitor what's going on in the industry.

Mike Chung: 12:00

The second one you mentioned was MasterCard, visa. Can you explain a little bit more about what you meant by that?

Daniel Zenko: 12:07

Yeah, I mean when you swipe your card at a grocery store or any kind of store, mastercard, visa retains that information and resell it. You know they are not allowed to resell who actually swiped it, your identity or anything, but they have liberty to resell. You know what was bought when you know, and so that data is anonymized and you know and sold, you know, in some kind of packages to the specific industry players. You know that might be interested in this particular type of data. So that gives us a pretty good insight about you know, dynamics of the sales and you know what kind of items people buy, let's say together in kind of the same basket and stuff like that. So you know it's pretty useful. It's very wide, you know there's almost too much of it, you know but you know, with the proper analysis you know there's some insight that can be taken from there.

Mike Chung: 13:06

So that's what I wanted to clarify. When you said MasterCard and Visa, I thought about data that's available for purchase and then that could require further analysis to drill down, categorize the data, and I would expect that to be similar in in concept to, say, some of the commercial providers you highlighted towards the end of that answer. You mentioned npd groups or kana, other other data sources where a subscriber could purchase data and then use it for analytical purposes, to understand the consumer, understand consumer behavior better.

Daniel Zenko: 13:44

Yeah, and also there are specialist players who, let's say, focus on exactly what's happening with Amazon so which parts sold well in Amazon, which companies sold well there and trends there Monitor, internet searches and stuff like that there's a lot of information out there that can be used, but it's best used as triangulation. It compares you with something else. That gives you kind of more broad understanding of what's going on.

Mike Chung: 14:12

Sure, and to that point. I think that's not to make this sound like a commercial for the data platforms that AutoCare necessarily provides, but one of the things that you architected for the TrendLens platform was economic and industry indicators. I think there are nearly 50 data points that people can look at.

Daniel Zenko: 14:33

Yeah, that product was originally brainchild of the Market Intelligence Committee and Association, but we actually put it in a digital form association but we actually put it in a digital form. Basically, we we've selected maybe like around 50 economic industry indicators that we feel the most important for our industry participants to monitor and follow and and we put them together in in this package that we publish on our website for free for all our users, and that package is updated as the new data comes along which is not published on the, about sales of the cars, sales of gasoline, unemployment rates, inflation, all kinds of stuff that are useful to understand better in a context of the industry.

Mike Chung: 15:37

And some of those other indicators are disposable income, consumer debt, inflation, unemployment, prime interest rate and when we think about participants in the industry, our quote-unquote audience, it's wide as far as well as broad, and what I mean by that is type of company, from parts manufacturers to distributors, to retailers, to service providers, and then roles within those companies, from sales and marketing category management, executive, c, c-suite there's a whole range of individuals that are monitoring different touch points, if you will, to keep a pulse on the aftermarket yeah, I.

Daniel Zenko: 16:23

I mean, we provide a lot of information about that and I'm sure that all our industry participants can figure out which broad indicators are kind of related to their own sales process, either be like a weather or a disposable income or maybe price of gasoline, and you know. So they're always already tracking them. But we provide this technology tool to follow them all at the same time and to get some kind of insight of interaction of these different indicators to give them better understanding of what's the big picture, how the market is really behaving overall.

Mike Chung: 17:09

And when you put together this platform, how did you decide on data sources? Tell us a little bit about what you were looking for in data sources.

Daniel Zenko: 17:21

Well, number one requirement was the data is published monthly, because all these indicators are refreshed every month, so we have to have a data point that actually is published monthly so we can use it. Also, we're trying to find indicators that have national level data but also regional level if possible, so we can get some more insight. And again, some of them are fairly obvious, like you know, miles driven, prices of gasoline and stuff like that.

Daniel Zenko: 17:56

But some others are maybe not that obvious but definitely are important to understand well the overall health of US economy, like housing starts and disposable income, and you know the consumer confidence, for example. Those are very important ones to really understand what's going on the level of indebtedness of the consumers.

Daniel Zenko: 18:20

There's several ones unemployment rate. Those are all very important factors that have a significant impact, not just our industry but all industries in the United States. But definitely important to understand what's going on with ours too is the United States, but definitely important to understand what's going on with ours too.

Mike Chung: 18:39

So another aspect of, or another part of, the TrendLens platform is, of course, demand index, which is the anonymized aggregated point-of-sale data. When you're building consensus to create the demand index, can you tell us about that process? Was there anything that made it more?

Daniel Zenko: 18:57

challenging, for example. Well, our industry, specifically category management committee in our association and its members, you know, recognize the lack of adequate data to monitor what's going on with sales, especially hard parts in our industry. So they joined forces and decided to share their sales data in a way that can be useful for industries. So while constructing this particular data product, we consulted with both retailers and problem groups who produce this data and the manufacturers who consume this data on other side, in order to find the level of detail that's useful but also not overwhelming, in the sense that it's really hard to actually produce the data of necessary quality. This is one of the situations that good enough, the perfect is enemy if enemy of good enough. You try not to make it too complicated as a product in the first place, so the data delivery may be delayed or not possible. On the other hand, you have to make it detailed enough to be useful for users.

Daniel Zenko: 20:11

It was a gradual process. We started with 15 different categories or segments to follow. Now we are up to 130-something, and it's constantly growing, growing not just in width, but also in depth. We are getting more and more in detail, which makes the data more actionable and more useful for different kinds of users.

Mike Chung: 20:32

Well, that's fascinating More than eight-fold growth from 15 to more than 130 categories, the consensus building and, like you said, the depth there. Are there other aspects you mentioned.

Daniel Zenko: 20:43

You talked about national to regional for in dollars, sales in units and all other kind of potential data and we are constantly expanding it based on the interest of users. What part of the market is Datafill? It's served regarding the data coverage, so we're always trying to get better and deeper coverage, but not too much strain to the data contributors who actually help us with sending the data for these particular categories.

Mike Chung: 21:29

And to circle back on a topic you touched on earlier, with regard to the fidelity and usefulness of that data, there's also that element of confidentiality and making sure that, uh, somebody can't reverse, engineer and figure out, say, a competitor's market share, for instance. Yeah, I'm balancing it with that aspect.

Daniel Zenko: 21:53

We have a big number of data contributors and it's hard to say exactly, but we probably cover like 80% of the full market in any category. Transparency is not an issue that much. Every other data is well hidden in a mass, so it's hard to really decipher what's going on. Plus, we don't go in that much of a detail. You know we focus on like nine geographic regions and United States. That's as deep as it goes, so I don't see that as a problem. But the fact is that we do have the biggest coverage of any kind of other providers of this kind of information, and I think it's a pretty big distance between us and the second largest provider of such data.

Mike Chung: 22:47

Sure, and now that this subscription product has been available for several years, it has momentum. Do you feel like it's easier to have new participants in terms of adding more to that 80 coverage, if you will? Yeah absolutely.

Daniel Zenko: 23:07

I mean we are working with several other retailers and and program groups to join the panel. Although again, we have coverage where it's very commanding, like 80%, we can still do better and try to get as close as we can to the 100%. I mean this product, unless we have 100% of the market which we'll never have, obviously will always be approximation you have to take a lot for the adjustment for the part that's missing and try to figure out okay which part is actually missing for a particular part. But again, it's still by far the best and most comprehensive indicator of that sort out there.

Mike Chung: 23:52

And to circle back on something you mentioned, the US government will do data restatements. So if you were to add more contributors, then certainly that would necessitate a kind of a restatement, recalibration of sorts. Is that fair to say?

Daniel Zenko: 24:16

our representation. Even though it's not 100% of the market, it's still relatively representative, meaning that these new contributors we might sign on will not make big difference in overall indices. You know, which is very unlikely, that will happen anyway because, again, we cover already all the major retailers and program groups. So you know, you know there will be some, you know, on margins, some small changes, but we don't anticipate any kind of big change anytime soon.

Mike Chung: 24:45

When you were constructing the demand index, I asked you about challenges. How about things that were perhaps easier and glided through a little more easily than you may have expected? Was there anything that was remarkable that way?

Daniel Zenko: 25:00

well, it's kind of interesting with this kind of type of data is that um and the higher some of the up is up in a in a company you know, leadership, that we discuss this. It's easier for them to understand what you're trying to do. This because this is not a product that focuses on individual SKUs or anything like this. We just cover, let's say, breakpads that's what we do which is pretty wide and for a lot of category managers maybe not detailed enough to really spark their interest. But if you hire executives of these companies to understand what's going on overall with the brake pads, that's quite useful information.

Daniel Zenko: 25:41

So to get a bind from the upper management of the industry was relatively simple because they understood the strategic importance of having this information. But again, when you talk with the people whose job is to just cover ceramic breakpads, having a number for overall breakpads is not necessarily that useful. So on that side there's a little challenge to get to have information detailed enough to be useful for even ranking file or category managers In some categories. It might be a little tricky but we're getting there. A lot of categories that we cover are now split into relatively small chunks, small pieces.

Daniel Zenko: 26:22

So, we're definitely going in the right direction.

Ted Hughes: 26:25

Hi, I'm Ted Hughes, executive Director of AWDA and Senior Director of Community Engagement for the Auto Care Association. We provide our members with numerous avenues for connection and collaboration through our diverse range of committees and communities. Whether you're interested in advancing your career through the Women in Auto Care program or our vibrant Under 40 group, or simply wish to network and glean insights from fellow distributors, shops and manufacturers, we have dedicated committees and communities eager to connect with you. Learn more at autocareorg slash communities.

Mike Chung: 27:00

If you were to give advice to somebody who's building a data platform in any industry, whether it's a free resource or a subscription resource, what pieces of advice might you give to that person?

Daniel Zenko: 27:13

Well, first, I would say, focus on on selling the vision, meaning you know, getting buy in regarding what goal you're trying to achieve and worry about details later. You know, because if you start let's say, in our case, if you started with three or 15 product lines or categories, it's not necessarily a big difference, you know, it's not necessarily a big difference. It's important to get a buy-in from all important players in the industry and to have a consensus about what we're trying to do with this so we can continue developing the product in the right direction. Last thing you want to do is to build something and then in the middle, realize, oh, wait a minute, this is not what. You know what's needed. You know this is more like, uh, you know, solution is a short problem, you know, instead of other way around. So, you know, make sure that you start from the, from the, from the correct starting point. You know that. You know that you're not, uh, barking wrong tree.

Mike Chung: 28:16

I would say so, to replace some of that, what I'm hearing is communicating the concept to your audience, making sure that they understand it and you're aligned on that, rather than worrying about some of the details. You gave the example of three versus 15 product categories, so I can see there where the process is going to be very similar whether it's three, 15, or 150, but really getting buy-in on the concept. And then, to harken back to something you said earlier, don't let perfect be the enemy of good enough.

Daniel Zenko: 28:50

Correct, you know, because with data there's always, you know, there's always something going on, some kind of restatement going on. Something was misclassified, so there are going to be changes, those little restatement changes, but those changes are on the margin usually, so you don't need to worry too much about that, because that's just the way it goes.

Mike Chung: 29:13

The process will take care of those things. Yeah, yeah, yeah.

Daniel Zenko: 29:16

But if you have everybody aligned in what we're trying to achieve with that everything's fine.

Mike Chung: 29:22

So, thinking about, say, demand index, what does the future hold?

Daniel Zenko: 29:29

Well, we can go even deeper and in more detail with the stuff we have, but there's a limited leeway, but there's a limited playing field that we can still expand to.

Daniel Zenko: 29:42

So we are looking into different areas. We're looking into indexes that will be more specific to particular types of sales, let's say e-commerce, maybe to have indicators that will cover exactly what's going on with the sales over the internet. Then there's an interesting concept about maybe expanding this to the HD field, like heavy-duty vehicles, but just for the heavy-duty parts. We're looking into it and eventually we might get to that. Also, we are working with some service providers, companies that provide car repair services and maintenance services to capture data on that level, like brake jobs, all changes, changes of batteries to provide information on that level. Because right now of batteries to provide information on that level, because right now what we do is provide information on the level of point of sales when part is sold, but we don't really cover how many. All changes were happening in the United States in the last month which we can and definitely we'll give some kind of insight to our users.

Daniel Zenko: 30:57

That would be interested. And stuff like that. There are other parts of our industry that are lacking the necessary data and we are trying our best to figure out. Is it feasible to actually collect the data and disseminate it in a proper way?

Mike Chung: 31:21

So what I'm hearing is you've highlighted about 80% of market coverage with our current demand index and that's going to be US light duty or, I guess, automotive parts, and so now we're looking further afield to different quote-unquote markets and platforms or channels, shall we say whether it's e-commerce or heavy duty.

Daniel Zenko: 31:38

Or maybe like a tool and equipment sales. There's another aftermarket, adjacent industries that are heavy players in our industry but they're not necessarily covered. Or maybe something about tires, specifically that kind of stuff. So yeah there's definitely room to expand, you know, in the right direction, and unfortunately it seems that our industry is lacking like necessary data in a lot of aspects. So there's plenty of ground to cover. It's just a matter of finding something that's feasible and can be pulled off in a relatively quick manner.

Mike Chung: 32:15

This is not my field of expertise, but I think about all the computing horsepower, all the storage. Can you touch on any of those infrastructure concerns or is that another department?

Daniel Zenko: 32:26

Well, so far it's not particularly bad regarding that, because, yes, we're talking about millions of lines of data, but in the grand scheme of things, that's not huge. We can definitely handle it Even further. The concerns are mainly on the performance of the website. Because more data is loaded on it, it takes slower to actually render the data, which makes worse consumer user experience. So that's something we need to particularly worry about to make sure that we don't upload so much data in a system that it can't handle it with the proper speed. That's our primary concern at this point to make sure that the user experience is adequate.

Mike Chung: 33:23

Makes a lot of sense, and thinking about data platforms in general, what is the future of data platforms? We've seen a lot of data visualization over the past decade and a lot of ways to customize your view and do analysis from there. But looking into your crystal ball five years, 10 years into the future, what could a data platform like Demand Index or otherwise look like in the future? What might we see technologically develop?

Daniel Zenko: 33:53

Well, it's more about customization. Really, in the future, what I think is going to happen is that people will be able to choose their individual experience of using the data, because right now, it's like a buffet table Saturday in a restaurant Everything is out there and for a lot of users, a lot of that data is immaterial because it doesn't really touch what they do. So maybe we can organize it in a way that you get better experience of using it by removing everything that's not really in your you know, in your ballpark and something that you want to use and also provide the you know ability to render the data on your cell phones and other platforms.

Daniel Zenko: 34:46

Right now it's pretty much only on a computer, but you know we will definitely look into, you know, expanding it on other data platforms look into expanding other data platforms and thinking not just about demand index, but not just about demand index, but data platforms in general.

Mike Chung: 35:03

We hear about AI right, so are we getting to a point where we can use AI in a data platform to answer insightful research questions?

Daniel Zenko: 35:18

Well, yes and no, as this artificial intelligence tools are improving, there's still a lot of questions about their ability to critically assess what's right data what's not. But if you provide this tool let's say these five charts and ask them to analyze those charts and create some kind of insight out of it, I believe I can do a pretty good job with it because the scope is defined. They don't need to search the internet for different potential data sources that may not be properly vetted. You know there's no confusion in their artificial mind, in you know what's the data that they should focus on. They have this, you know, limited set of data inputs that they have to put in some kind of context and relationship.

Daniel Zenko: 36:16

I believe they can help with that, you know, to basically to create some kind of commentary or write ups or you know stuff like some basic analysis, you know. So, yeah, that that I can see on that narrative side. Definitely they can. They can help on you know number crunching side. It's just, you know, you basically have to check the math all the time, which then kills the efficiency. You have to spend more time verifying that everything that was included was supposed to be included, you know or not. So I'm a little reserved about that. But definitely on the output side, on the kind of presentation of the data side, artificial intelligence can help.

Mike Chung: 37:06

So it sounds like to get some words on a blank sheet of paper to get a narrative started. That seems facilitable. As for the numerical analysis, the statistical, the forecasting analysis, that will certainly require a bit of human intervention, if you will, to make sure that the analysis is appropriate.

Daniel Zenko: 37:29

Yeah, and at this point there's not much problem regarding number crunching. There are plenty of tools that are very good at you know playing with numbers, you know. So there's really no need for another layer of you know technology to help with that. The problem starts really when, okay, you have all this data, and data is verified and it's correct, and now how to create a story around it. So I'm not saying that artificial intelligence will be able to give you a perfect pitch, perfect story about you know what's going on based on these particular inputs, but it will give you some proper guidelines that you can kind of okay, so this is how it looks like and then you can you can maybe a bit more deeper into it, or maybe, you know, adjust a little bit what output is from artificial intelligence and go with that you know.

Daniel Zenko: 38:24

So that interpretation of the data is a little tricky, you know, because you know glass half full, half empty. It's really, you know, depends on your personality really, how you interpret it's really, you know, depends on your personality really, how you interpret certain numbers you know. So maybe we can count on AI to be kind of impartial about, you know, especially if you have some kind of political views. You know you can always interpret numbers that convey your narrative that this particular administration is bad for economy or is particularly good for economy. Something like AI, again, if you give them limited sources to play with, can probably give you an objective story without any kind of bias.

Mike Chung: 39:11

Oh, that's helpful and I think the context that somebody like you or I might have being in the industry, and we can add that truthing element to the narrative that an AI, a generative AI tool, may provide.

Daniel Zenko: 39:29

Yeah, yeah, absolutely no, there's a, there's a, there's a. You know, there's no way I think that these tools can replace human input. But they can provide some help, definitely again, but it has to be, has to be done in a way that it's, you know, controlled and uh, and reasonable, you know so yeah definitely there's. There's something there, but maybe not as much as some people hope for and also not as much as some people are afraid of.

Mike Chung: 40:02

Yeah, it's certainly an interesting development that we'll keep watching.

Mike Chung: 40:07

Just in our last few minutes, I want to touch on a couple of things that you and I have talked about from an economics perspective and for our audience. Daniel has a lot of background in economics and we've had some good, healthy discussions on some of these topics and perhaps we could pursue these in another conversation on this podcast. But one of the things that we've talked about is US debt. Okay, so concerns of the United States being over leveraged. We're at about $36 trillion of debt right now. Nominal GDP last year was about $29 trillion. Us debt was recently downgraded to AA plus, I believe, and we're in an era of sustained interest rates being high, so is this a big deal? What's your interpretation of these circumstances?

Daniel Zenko: 41:03

Well, when thinking about death, I think the best way to refer to it is to understand how much of the burden for the government is servicing the debt. Some governments have relatively small debt but nobody wants to borrow their money, so they have to pay huge interest rates to get those funds, and even small debt for those countries is a really big deal. Countries like the US have a lot of leeway there because everybody wants to lend them money and give them cheap interest rates for that now has to pay around 3% of the national GDP every year for servicing the debt, which is slightly higher than in the last decade or so, but significantly still lower than, let's say, during the Reagan years.

Daniel Zenko: 42:08

So, yeah, it definitely is going up. The price of the servicing, the debt, is definitely going up, but compared to the long-term average it's not that bad. And also compared to the other developed countries like Japan or the.

Daniel Zenko: 42:27

United Kingdom or something it's still much, much better. It's still much, much better. So, yeah, doesn't seem story doesn't really check out as the debt is like a big problem all of a sudden that you know creditors will decide, oh no, us is not country that we can, you know, let money to. You know we have to be careful with that. It's just not not something at this point that that seems of serious concern, especially in the context of other economic indicators. It's that inflation rate is, let's say, average. If you look at the nocturnal term it's falling and the US economy doesn't show signs of going into either depression or overheating at this point. So there's really no concern at this point of international creditors or domestic creditors to really worry about the creditworthiness of the United States government. So the overall story seems like a non-story at this point. You know, again, things can change, but nothing really there, you know, to worry about as much as some people would like to.

Mike Chung: 43:45

I appreciate that perspective and you touched on. Another topic and I think we'll close with this is inflation. We've had high inflation. It was a little above 9% in 2022. It's currently at about 3%. We hear about the goal being about 2%. What are your thoughts on a 2% goal? Is it appropriate, thinking about our industry, consumers in our industry? What are your thoughts about 2% inflation goal?

Daniel Zenko: 44:17

Well, that kind of that goal is a little bit moving of the goalpost. You know, for the long, long time of federal you know, federal US Fed had a goal to have an inflation band between 2% and 4%. That was an acceptable range and somehow all of a sudden that 2% lower band became a goal, which is not necessarily true, because cutting interest rate is pretty much the only tool that Ferdinand Garland has to prop the economy. The only tool that Ferdinand Garland has to prop the economy. If interest rate is already very low and inflation is very low, then simply there's not much lever there. They can't do much to further prop up the economy.

Daniel Zenko: 45:10

In the case of an emergency, 2% is okay as maybe a goal, but it's not really the goal.

Daniel Zenko: 45:15

The goal is to keep it somewhere between 2% and 4% and long-term average, I think, is something like 3.4% in the United States. So current inflation rate of 3% is well beneath that level. So I don't believe that federal agencies would mind that it stays just like it is, providing the economic growth is solid. Now there are some talks that there will be need relatively soon to cut interest rates, which will again heat up the economy and bring up maybe some inflation you know together, but still you know there's a plenty of room there at this point to move. You know inflation a little bit up if necessary or down if that's the goal, you know. So we are in a good spot in this point. And uh, again two percent as a goal. I think it's better to understand that there's a lower band, lower band of the bandwidth that's acceptable for the inflation. I know for some people it's maybe hard to accept that zero inflation is not a good idea, but it's really not. Having a small 2-3% inflation is perfectly fine and actually desirable.

Mike Chung: 46:30

That's helpful to clarify that band, as you talked about, and perhaps one of the things that's been hard for consumers is this is following a period of sustained high inflation, so it's like a little bit of salt into an open wound and perhaps over time things will equilibrate in terms of our adaptation to sustain interest rates.

Daniel Zenko: 46:51

It's a recent bias because current generation of of grownups.

Daniel Zenko: 46:56

They grew up in an era of very, very low inflation Since the last 20 years. It was exceptionally low inflation rate for the United States that pretty much never before existed, or maybe rarely, so they were kind of conditioned to expect inflation rates and interest rates to be really, really low. But those low interest rates were just a product of the government trying to prop US economy after the dot-com boom and bust, after the real estate boom and bust and also mortgage crisis. So those low interest rates were not normal. It was just a specific target to actually help boost the US economy, which helped, but it's not something that is good long-term for a way to do it. So I would say current interest rates and current inflation rates are pretty much average and pretty much what you want. So again, in specific situations you want to adjust it a little bit, but unless good reasons come to actually do that, I think we are in the proper band. Regarding both inflation rate and unemployment rate and interest rate, it's all about around the long-term average right now.

Mike Chung: 48:15

Oh, that's helpful. I love the recency bias and it made me think about if you grew up in Boston before the Red Sox won the World Series in 2004 and the Patriots did really well for a long time. Kids who grew up in Boston since that era are used to the Boston teams winning all the time, but we know that it was 86 years between uh before they won it again in 2004. So I think that's a that's that's.

Daniel Zenko: 48:41

No matter how hard you have to try to explain that to somebody, they will never understand because that's not their experience. They weren't there. That's what then? That's all they know.

Mike Chung: 48:49

Well, as we close up, Daniel, has been really a great pleasure to talk with you on this edition of Indicators. Is there any last thoughts that you had as we were talking that you might want to share with our audience?

Daniel Zenko: 48:59

Yeah, yeah. One of the indicators that we follow is the survey that we do among the executives in our industry about the expectations about economy in general and our industry.

Mike Chung: 49:12

Our business confidence index and economic confidence index, and unfortunately, the results of these surveys are pretty discouraging.

Daniel Zenko: 49:20

I mean our executives are not really good at this point to forecast what's going to happen.

Daniel Zenko: 49:32

Tell me what you mean by that I mean that it would really be good idea to for them to to to ask somebody on their team to spend more time looking at specifically our indicators in our fact book, to kind of spend more time with the data to get a better insight and hopefully better predict what's going to happen in the future, because there's really no other way to do it but to actually dig deep in the data and get a better grasp of really what's going on so what I'm hearing is, when our executives answer this survey, they might be thinking things are going well, but in fact they're not.

Mike Chung: 50:05

Am I hearing you correct?

Daniel Zenko: 50:06

all the way around. Again, it's a little bit maybe recency bias and this and that you know, but clearly when you look at the, the chart of their expectations and chart was what happened it just doesn't match.

Daniel Zenko: 50:16

They don't align doesn't align and you know, I know they're busy, but I'm sure they can find somebody, that team, to kind of give them like briefings here and there what was really going on, to kind of just get a more realistic perspective, you know, of the of the additional benefit. You know them and their companies, you know interesting. So unless we have something, it is a relatively small sample size, we maybe get perspective of the benefit them and their companies Interesting.

Mike Chung: 50:34

So unless we have something it is a relatively small sample size we maybe get 50 responses. So perhaps we're with the shining stars whose organizations are just outperforming. But you heard the challenge everybody out there who takes this survey Engage your team, study the fact books, study trend lines and let's watch those predictions and indicators get even stronger. So thank you, daniel, for joining us today. Really, really illuminating conversation. 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 Auto Care Association, dedicated to advancing the auto care industry and supporting professionals like you. To learn more about the association and its initiatives, visit autocareorg.