Building a Data-Driven Future in Automotive with Allen Levenson

In this episode, Allen Levenson, Director of Data and Analytics at AutoMobility Advisors, shares his perspective on what it takes to build a successful data and analytics function in the automotive industry. With experience spanning OEMs, vendors, and dealers—including roles at Asbury Automotive Group, Prospect Vision, and General Motors—Allen breaks down how companies can overcome legacy system challenges, adopt AI and advanced analytics, and build a strong data culture that drives ROI.

From identifying foundational investments and organizational structures to balancing centralized and decentralized models, Allen outlines the six key areas that determine success. He also discusses connected vehicle data, privacy concerns, and the evolving expectations around consent and personalization. Whether you're exploring internal data monetization or grappling with data governance, this episode offers valuable insights for automotive leaders looking to harness data to stay competitive in a rapidly transforming industry.

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Transcript

Angela Simoes:

Welcome everyone to another episode of the Mile Marker podcast. My name is Angela Simons, your host, and I'm here today with Alan Levinson, director of Data and Analytics for automobility Advisors. Welcome, Alan.

Allen Levenson:

Great. Hey, thank you for having me today.

Angela Simoes:

Yes, thanks for your time. So before we jump into the topic, why don't you give our listeners a little bit of your background and then also what does AUTOMOBILITY Advisors do?

Allen Levenson:

Sure. So let's see. I had really three phases to my career. Started out as a consultant, worked with Bain and McKinsey. I then came up the sales and marketing route, and for the last 25 plus years I've been in the automotive space, have a sort of varied background in our industry as one of the very few people to have worked on the OEM side, vendor side and the dealer side.

Angela Simoes:

Oh wow, okay.

Allen Levenson:

I spent about eight years as the chief sales and marketing officer with Asbury Automotive Group, one of the six publicly traded dealer groups. I then ran for about nine years, a smaller company called Prospect Vision that helps car dealers mine their customer data and do very targeted marketing, one of the early quote equity mining plays. And then I joined up with General Motors as they were standing up a centralized data and analytics group called the Chief Data and Analytics Office, and left there in 2023. And since then I've been pursuing more of a portfolio career. I'm on the boards of a couple of companies and then doing some advisory and consulting work. Spend about 80% of my time now with Auto Mobility advisors. What we do is we help companies build their business within the world of automotive and mobility. We're kind of what you would call a boutique consultancy.

All folks like myself who have worked with various companies, Nissan, Honda, Ford, as well as some of the technology providers. And so we help companies figure out their go-to-market strategy, how to go to market, and then of course some pretty hardcore business development, both existing companies that might be looking to move into and build an automotive vertical as well as newer companies that are a little bit newer in their life cycle. And also quite a bit companies that might be based outside the United States that are looking for help to build their business in automotive and mobility here within the United States.

Angela Simoes:

Great. And we are here at the CAR Conference in San Diego, and you'll be giving a presentation tomorrow. So give us a summary of what you'll be talking about tomorrow.

Allen Levenson:

Sure. We'll be talking about data and analytics and specifically how to build a successful data and analytics function and culture and kind of what that takes. I'll be looking at it sort of at a higher level, how literally it starts at the top, literally the board of directors to be successful. As you can imagine, data has become a very important part of many companies. It's really the foundation for so much of the future of our industry. If you think about digital transformation, connected vehicles, autonomous vehicles, et cetera, data's the foundation. So being able to have access to good, clean, well-governed data is very important and there's a lot of investments and a lot of failures. So I think we'll talk about sort of how you can see to it that you're on the success side as opposed to the failure side.

Angela Simoes:

So yeah, data has been a topic for a while. How do you utilize it and really, is it really just getting the data or then there was the whole do we monetize data? How do you get not only access, but then what do you do with it? And so maybe that's something that we can kind of dig into a little bit, especially here at the car conference, talking to auction houses, dealers, folks in the auto remarketing space. What's going to be important for that group as far as how do they implement a data analytics platform?

Allen Levenson:

Sure. Yeah, no, good question. Again, it's foundational to so many of the newer things that are happening in this space. There's a lot of focus on trying to figure out what the right value of a vehicle is, and particularly on a used vehicle, how to do that. There's a lot of AI and technology now that's being used that can help you pinpoint at a much higher level. There's a lot of different use cases. These all require proper data. What's interesting is we call it data and analytics. I find 80 to 90% of the effort and focus is data, and maybe 10 or 20% of the time do you actually get to use it and analyze it and derive insights. Interesting. Just so much effort trying to get access to good clean data, combining data sets governing it, it's often you don't know what's necessarily in some of the data. I came out of General Motors, which is arguably one of the more challenging companies in the world from a data perspective. It's a company that has thousands and thousands of data sets or databases that the company insourced and outsourced it a dozen times over. Its a hundred years.

So it was a little bit of a dog's breakfast. You don't have a unique identifier on a customer. You might have in the service department. We know you as this and in the sales department, we know you as this and there's a vin, but there isn't a unique customer id. So trying to combine these data sets and do what's called master data management, basically know who you are and know all of your interactions that you've had with us is an enormous challenge.

Angela Simoes:

I mean, does it make more sense to just try and say, okay, from this point forward we are going to have those unique identifiers for a customer and we're going to structure it so that we can understand it versus trying to go back and look at, okay, here's the thousands of data sets we have. How do you integrate it or do you focus on the moving forward?

Allen Levenson:

Good question. It's a little bit of both. Obviously you can do better going forward, but if you're going to be successful, you've got millions and millions of customers, you want to know a little something about them and leverage that today. And so no, it is a lot of focus to try and work with what you have and make the best of it and make it a good actionable database. One of the quotes, it's been around for a couple years now out of the Economist, is that data, not gold is the world's most precious resource. And so no, you obviously can do better going forward. Truthfully, if you think of in our industry, the legacy manufacturers are at a disadvantage versus folks like Tesla and the newer companies and rivian who are building on a much more modern database and a series of tech stack and such, so It's a lot easier to do it right now versus all of the technical debt and legacy issues that are out there. You got to do it.

Angela Simoes:

And so for companies that have said, okay, yes, we know we need a better data and analytics platform on how to analyze, I mean, what are some of the first steps that they do? There's this with lots of things with software-defined vehicles, with your autonomous driving systems, do you try to do it all or so do you hire a data and analytics team in house or are there platforms out there that listen, it's you get more bang for your buck if you just hire that, but then you got to hire somebody or someone internally has to manage that, right? So what are the first steps to move forward?

Allen Levenson:

Yeah, and that's a lot about what we're going to talk about tomorrow. I have kind of six key areas for folks to be successful, and the first one is you do need to make a fairly sizable investment if you're going to go at this and do it right. And that's both an investment in people as well as an investment in technology. Generally speaking, this is a little bit newer area, and while we are starting to train more and more people in decision sciences and all that, there still aren't that many really good data engineers and architects and scientists, so they're expensive. So you need to be willing to spend the money if you're going to do this. There's things, obviously a lot of focus on upskilling existing people.

You might want to spend money on the tools and the technology to allow people to do that. You don't want have to focus just on data scientists, but you want to give the tool so that your people who've been in the company can leverage and do some of the kinds of things they need to do. So one is you absolutely do need to make a pretty big investment. Second gets into sort of the organizational issues. A lot of companies have built sort of centralized data and analytics groups, chief data offices or chief data and analytics offices. One is if you are going to do it, people have struggled with, where do you put it sort of marketing. You kind of know the CFO where that person reports in data and analytics. Does it report into the IT department? Does it report into the marketing department? It gets a little sticky and people try different things. What's most important is that the person, that group has pretty high visibility and is within a level or two at most from the CEO. The other important part there is what do you kind of centralize versus decentralize? My thoughts are, and having studied this quite a bit, is you want sort of a centralized for certain things, but then decentralized your data. It just doesn't make sense to have each group. Imagine Chevrolet and Buick and GMC each managing that separately. It's too complicated. All of the newer important analytics, advanced analytics and ai, there just aren't that many people that are good at that. So it's something that you need to build that muscle in one place and let the others leverage it. That said, you absolutely need people at the more region level or at the department level who are experts and know that piece of business. So

Angela Simoes:

The marketing team is going to use the marketing data and finance will use that kind of thing.

Allen Levenson:

And if you have built, in the case of gm, a centralized data and analytics function, you want to have within that teams that you've brought in, people who know the actual business. They're not just data scientists, they're business people and understand how to sell cars, how to sell accessories, how to sell OnStar and connected services, but they have an analytic viewpoint. So that's an important piece. You mentioned a little bit about the privacy, all that, that's an enormous piece.

Angela Simoes:

Yeah, big one.

Allen Levenson:

So you have to put a lot of focus on not only things like cybersecurity, and certainly we saw what happened with CDK, and we are an industry by the way, that is very much at risk for those kinds of attacks because the hackers, they look for industries that are heavily SaaS oriented that will actually have a major disruption, and automotive is a good example, but then there's this whole customer privacy given a lot of what this data is all about is customer focused, and there's been some issues in our industry with that not being done properly. I guess a fourth area, I probably should have brought it up earlier, is building a data culture. I was at a conference, there was hours and hours on just that one topic, but that's kind of walking the walk and talking the talk, and that's having your, again, at the board level, really requiring your people to be data-driven so that they have scorecards that they're using and requiring of their people. Well,

Angela Simoes:

I imagine you need buy-in at all levels because if you're going to make this significant investment and it becomes a major part of your business, if people haven't bought in and aren't using the data or aren't maintaining the data, then it's a wasted effort, right?

Allen Levenson:

Absolutely. That is just very well said. And again, since it's something new, if you don't have that, it won't be successful. Every company has a chief financial officer and needs to do finance. This is newer and you need to get that culture. And there was a pretty well-known McKinsey study that showed the companies that are truly data-driven and have this culture, they acquire customers at a higher rate, they retain them, and consequently they have much, much higher profits

Angela Simoes:

Because it is such a significant investment in not only finance actual funds, but people effort changing the mindset, like you mentioned culture. How do you recommend that companies set out, okay, well, if I'm going to make this investment the ROI need to achieve, is it the specific, I guess types of ROI? Is it within a specific timeframe because it's the famous ERP installations that take years and years and years, and then by the time they're done, it's a completely outdated system. So I guess it would depend on the size of the company, the size of the database that they're building, that sort of thing. But kind of generally, what's your advice as far as, listen, if you're going to make this investment, you need to experience ROI within a year, three years, that kind of thing?

Allen Levenson:

Yeah, no, a very good question. It's a tough one in that some of what you're doing is foundational and building tools that will help you in the future, and it might take longer. Now what happens, and certainly at GM and other companies, there's always pressure to show results. So you sometimes, quite frankly, will focus more on the short term just to show that this is successful versus building the foundation all the way. On the other hand, if you can't show that you're on your way to success, you might not get the continued funding.

So there's always that dynamic. I do believe though that a strong data and analytics function is one that has a commercial mindset. Quite frankly, at gm, when we got there and built out this new group there had data and analytics happening. It was kind of driven by the IT people and they weren't as much business people and they were kind of doing things because they thought it was interesting. In our case, it's all coming from the business, and we very much track and measure how much business value we created. If we developed a tool that helped with managing inventory, you would do the analytics to show it. Based on that, we moved more vehicles more quickly at higher margin, and it had this kind of ROI and we set out to always have a return of six to 10 times the cost of our group. So there was a lot of measurement, but it's not always easy. You are building foundational tools that if done right will be helpful for hopefully years to come.

Angela Simoes:

Do you find, as you're talking to clients that are embarking on this journey, are some people still trying to figure out how to monetize the data and falling into that trap? And I think most of us have heard of the failures of Autonomo and Wei, Joe, right? I think it was Wei, Joe, and that utilizing the data to again, predict pricing or predict the types of cars that are going to be needed, predict maintenance, all those kinds of things have its own ROI, right? But then I think it's still going to be an attractive active thought that, well, let's also monetize this data. So what's your conversation with somebody when they bring that up? If it still comes up, maybe it's not a thing anymore.

Allen Levenson:

Well, there's two ways to look at monetization, and by the way, it's a very relevant issue. There's what I call sort of external monetization selling data, and then internal monetization using that data to help your company

Angela Simoes:

Make more money, somehow

Allen Levenson:

Make more money, new products and services, better customer experiences. Interestingly, it is worth a second to go back. In the case of General Motors, the whole reason this group that I joined was put together was because of this McKinsey project in 2016 study that said the external monetization opportunities selling all this data through Wii, Joe, and all these kind was going to be some ridiculously absurd number,

Angela Simoes:

Like billions of dollars,

Allen Levenson:

Kind like hundreds of billions.

Angela Simoes:

Billions, okay.

Allen Levenson:

And

Angela Simoes:

That was wrong.

Allen Levenson:

I was employee number 30 in the group. Employees one through 29 were all focused on external monetization. I was the first person brought in to start saying, all right, how do we start using this internally and helping the company be more successful today? If the group's 300 people, I think there's about 299 focused on internal, there might be a person still focused on external selling the data. That said, there is a lot of focus on connected vehicle data, and that's what at Auto Mobility Advisors, we spend a lot of our time thinking about and helping companies that are trying to bring to market tools to do preventative maintenance, to do the parking, to be help you order the hamburger without having to pull over and look at your app, but talk to the vehicle. And when you show up, your charging happens quickly and immediately and you don't have to worry about the handshake with the charger. All these hundreds and hundreds of different services, conveniences and services that are really true and helpful, good things that will make your life better, that's monetization. And again, it's been a challenge. The connected vehicle data is among the larger data sets in the world. You're pulling down on millions of cars, thousands of pieces of data, often every three seconds, and a lot of times it isn't being used up until somebody comes up with something. So they've been pulling it, not really looking at it. And so you go to use it and they say, oh, it's went down and we haven't been collecting it for six months, and you don't realize, and then by the way, every OEM has a different system and then their 2022 through 2020 fours have this system and then the 20 fives have this one. And so all these companies, and a lot of our clients are trying to work with this data, and the integrations with the OEMs is just very difficult because each one is different and the data is pretty challenging. But there are ways to hopefully work through that and without selling a customer's data, without their knowledge, which is not what we're trying to do, but work with them and allow them to give consent for things that they want to order their hamburgers and park their cars and do all those things will make for a much better driving experience.

Angela Simoes:

Well, and in those cases, you're utilizing I would say a limited amount of data as far as just GPS. Where is the vehicle in relation to, let's say they're using their MAP application and they've said, I'm going to arrive at this destination and on my way to this destination, it's going to be six. So it's dinner time and there's a burger place, and hey, how about having a burger and you need a break? I mean, that's not real personal data. That's just where is the vehicle in the journey? Is that simplifying it too much or

Allen Levenson:

Is it you are right, and there's a lot that you can do without getting too personal. That said, I'll give you an example. If you think about connectivity today, the most utilized tool today is remote start. If you live in the Midwest or the Northeast on a cold day, you want to start

Angela Simoes:

That part. You love your remote, warm it up, love start,

Allen Levenson:

And if you're in a hot area, you want to start it. That's the number one reason people use it. So one of the products we worked on and was an interesting one is to try and add value to that, to help predict when someone is likely to go on a trip and send them a reminder, I dunno about you, but I hate when I go out in that three degree weather and I forgot to start the vehicle. 

Angela Simoes:

Yes.

Allen Levenson:

So to use ai, you can look and see that Alan leaves Mondays at seven o'clock most of the time, and so therefore we could send them a reminder, Hey, don't forget to start your vehicle. Great. You then might wonder if you're really good, look and see that Alan usually drives from A to B and usually takes 20 minutes, but today there's an accident. So maybe

Angela Simoes:

Notify, Hey, you got to leave early

Allen Levenson:

10 minutes earlier, or if we see that Alan needs gas or whatever, or there's snow, great. Well, you might be doing that based on Alan going from A to B, maybe B is my office. Maybe B is where my mistress lives, and all of a sudden my

Angela Simoes:

Wife

Allen Levenson:

Is driving the car that day or my partner.

Angela Simoes:

It's a scandalous example, Alan. It's a

Allen Levenson:

Scandalous example,

Angela Simoes:

But a real, I mean, but

Allen Levenson:

Here you went about doing something strictly for good, a convenience to make life better, use the

Angela Simoes:

Data,

Allen Levenson:

And you have to be careful because there are always those outliers and you have to manage for that.

Angela Simoes:

Well, and I mentioned insurance might also be another one that really requires a lot more personal data to monitor your driving so that you get a discount if you're, I forget what they call it, user based, usage based insurance, usage based insurance usage. If you get in an accident, maybe the sensors go off and it's automatically starting up a claim on your app or something like that. So yes, there are those examples where it's much more, I guess intrusive would be the word when it comes to pulling data. Yeah, no,

Allen Levenson:

I have obviously some strong thoughts on that particular one, and I'll actually be talking about it tomorrow. There was a fairly significant issue in our industry with in fact, general Motors that is still playing out in the courts where apparently they supposedly, I no longer with the company, it wasn't my group, but apparently creating driving scores on customers without their knowledge and then selling that to companies that were to insurance companies through a third party. So people were supposedly, it was a big article in the New York Times and a couple follow-ups, the Texas ags office, there's a lot of people involved, but apparently people were seeing price increases in their insurance when they had never had an accident or never had a speeding ticket, and it was based on this supposed driving score. That should never happen.

You should always, always know if your data is being shared, and there are times where you will agree to do that, and you do it knowingly, and hopefully you get value for it and you're getting more targeted ads. Sometimes you might get to share if the company is making money on it, they share that with you. Again, in the case of insurance, that's one of the few major success stories in that it is a better way to price insurance based on how somebody actually drives as opposed to my credit score or my age and gender. And quite frankly though, you want people to know that gets half the value. If people know their vehicle is being tracked,

Angela Simoes:

They're going to behave. They drive better

Allen Levenson:

Absolutely. Occasionally my partner drives my car and the other day he was driving like a lunatic, and I'm like, slow down. This could hurt me at some point.

Angela Simoes:

If my premium goes up, you're paying the difference.

Allen Levenson:

But anyhow, the whole knowledge that you are being tracked and you've chosen to opt in

Angela Simoes:

And

Allen Levenson:

Give consent, you hopefully will drive better and get better premiums.

Angela Simoes:

Yeah, I think consent is, that's a key word, right? It's a key part of it. And to your point, yeah, if I know I'm being rated every time I get behind the wheel, I'm going to drive a little more responsibly. Not that I'm a daredevil, but it certainly will think twice about rolling through the stop sign and actually stop or things like that.

Allen Levenson:

Yeah. I don't know about you, but I absolutely hate when the little red light goes off showing you that you need to stop, you're going to hit something. It's like, no, there's a vehicle there somebody coming this direction, I'm going to go around. But you feel like God damnit.

Angela Simoes:

Well, so funny. Good point. Right? So when that little alarm goes off, does that register in the system and does it send back to the insurance like, well, their little alarm system went off three times this week and 10 times last week. So I mean, those are the kinds of,

Allen Levenson:

I believe it does. I mean, I know certainly a lot of the data sets that are used are things like hard stops, hard turns, how quickly accelerating, where you drive, when you drive late at night on weekends, Saturdays, these are all things that go into a driving score based on the data which show people who drive it these times do have a higher likelihood of an accident.

Angela Simoes:

Going back to establishing a data analytics practice, utilizing new technologies, that sort of thing. It's kind of a loaded question, but because it is such a significant investment, if companies can't afford to do it or just decide, I don't think it's worth it, they're just, I mean, would you go as far to say that companies that don't find a way to utilize data to analyze their business or analyze how to better help their customers, do they survive? Do they eventually go away or do they just kind of stay stagnant? Like

Allen Levenson:

I would say for the most part, the companies that are data-driven, making the investments, building the culture, will absolutely be far more successful in most industries, and that certainly includes in the automotive space and the remarketing space. Sure, there are certain ones where you can get by better longer, but at the end of the day, there's many examples of companies that have disappeared over time. They haven't made the investments in the latest technology. We haven't talked at all today about ai. That is,

Angela Simoes:

How could we have not touched on that?

Allen Levenson:

That's one of the six things I'll talk about tomorrow. It is a very real trend and very real technology, and that will have enormous impact on the future of a lot of our work. And so to not make those investments, again, there'll be a lot of failures early on, just like there was when the internet came about. But can you imagine people today

Angela Simoes:

Without the internet not leveraging, oh my God, no. It's almost a God-given right at this point in our lives to have internet

Allen Levenson:

And ai, particularly gen ai, which is the newer one of a couple years ago, that's the one that's transformative. And so I do believe companies are going to have to make this investment. They need to be smart. They might need to look to bring in people in India and some of these lower cost areas where you can find some of the talent at often a third, the cost. So there are ways to do it more cost effectively. There are certainly a lot of companies coming to market that can help you. I mean, at the conference here, there's plenty of companies, several companies that are talking about how to use data to value a vehicle as opposed to needing a human being to go do that and take the pictures and automatically and

Angela Simoes:

Such. Do you see AI as helping the, I guess, getting started process or maybe, I guess a better way to rephrase the question is, I'm a company, I know I have to implement data and analytics the way it was done the last few years, big investment. Some people, it's going to be kind of difficult, but now there's ai. Does that shorten the timeline? The technology's a little more sophisticated, so there could be a faster implementation. It's easier to understand. Do you see that kind of helping?

Allen Levenson:

So the way I think about it is there is a progression of analytics. There's your more basic descriptive is the term. You'll hear analytics, and that's more having dashboards and things to help you measure results and derive some insights. In the past that probably would've been done through things like Excel these days. Companies might be using Tableau or Power BI or Looker and some of these BI tools that help you do that. Then you get into what's called advanced analytics, predictive prescriptive ai. The first piece, quite frankly, is getting that core foundational stuff. Get your data together so that you can analyze it. Excel probably should start to go away and you should be using some of these a little bit more sophisticated, but these days very readily available and inexpensive tools to do good descriptive analytics and again, know what's selling, what's not selling, what's working, and then start to then do your more advanced analytics and listen, with AI right now, there's a lot of hype and you don't want to do AI for the purpose of it. You do want to figure out what are some of the use cases that really do have that

Angela Simoes:

Makes sense,

Allen Levenson:

The ability to be transformative and focused there. If you look at, again, there's every company out there now, you go to NADA, every company that there says, we're doing ai. But pulling that aside, there are some very significant use cases. One developing software program that is just a much better way, let it take the first pass, and then the person comes in and refines it up and refines it. Developing advertising and marketing, doing the creative, the writing, I'm sure so many

Angela Simoes:

People are doing that now. The yes,

Allen Levenson:

And adding the photos and all the things on demand so much more quick, all the call center, all of the chatbots

Speaker 3:

And

Allen Levenson:

All that much better. So there are some very clear areas where the value is very real, and I would pick a couple and get some use cases, but again, data is the foundation. You have to have that

Angela Simoes:

Data

Allen Levenson:

To be able to analyze it, to then start finding these trends and do all these things that you want to do.

Angela Simoes:

Right. Well, I mean the space itself is exciting because you can clearly see where it could go and the possibilities. But for you, what's the most exciting thing for you when you look out a year or two, five years from now?

Allen Levenson:

That's a good question. I guess to me it's when we do have good clean data. We've been making these investments, we are using the connected vehicle data coming off the vehicles, and we're managing that well, and we are utilizing ai. And so we can truly do customized what's called next best action, figuring out what's right for you, different for me, personalized and I think you'll see companies that do that well will be able to acquire customers.

Angela Simoes:

It's along the lines of AgTech ai, so kind of having these agents that serve almost like a concierge, they get to know you and your habits so well that they can. Alan really loves sushi on Tuesdays, so

Allen Levenson:

Yes. Now obviously, again, there's a lot of focus on ag agentic of late, but yes, it's being automated within the whole world of analytics. Really the future is AI making it so much easier. Whereas today, even what the example I used, bring on a tool and get all the data in there, then you need someone to sort of go look at it and say, oh, wow, Cadillac sales have been declining in this region, and the AI does that for you. It does the anomaly detection, it finds the trends, and that's where the agentic really is a major step forward from what we have today. And by the way, it allows you to say, just talk and say, please show me what are the key trends impacting Cadillac. And it's much easier to pull it out for more of a lay person.

Angela Simoes:

Right? Gosh, I can't even imagine trying to do that without ai, without a prompt. You know what, just being able to ask the question, you having to look at, I get the vision of my brain of the TPS reports from office space, which is paper-based, but still that kind of, holy cow, you have all this data. How do I even begin to figure out what the trend is? Right. Well, this has been really great. I mean exciting because just so much to be done, and I mean, things are changing so quickly. I would love to have you back and see where things are by the end of the year. So yeah, thank you so much for your time.

Allen Levenson:

You bet. My pleasure. It's our whole automotive mobility space. There's more happening now than probably anytime in our a hundred plus year history and data is the foundation, so happy to do that.

Angela Simoes:

Awesome. Thank you so much.

Allen Levenson:

You're welcome.