Episode 107: Benefits of a Fleet Data Analyst

This is the Fleet Success Show. This is the only show designed for fleets where we won't pitch you on products, but rather we're gonna pitch you on ideas. Ideas around the four pillars of fleet success: stakeholder satisfaction, intentional culture, resource efficiency, and risk management. Ideas from business and technology to Fleet and leadership. These are ideas for tomorrow from the lessons of yesterday and today. This is the only show designed to help you raise the lid on your fleet because your fleet can only be as great as you are. We see that greatness in you. This show is for you, and we're glad you're here. We hope you're ready. Now let's get into the show.

All right, welcome back for another episode of the Fleet Success Show. I'm your host Josh Turley, joined again by Jeff Jenkins.

Hey, hey.

And joining us today, a little guest speaker from a land far, far away, Mark Cannon.

Hey, very nice to be here. Thanks for having me.

Yeah.

Hey, so today we wanted to talk about a, you know, it's something I know you've given presentations on this before. Specifically talking about telling your story. We wanted to talk about the benefits of having a full-time resource or a fleet management analyst in your shop operation. It's one that a lot of fleets don't have the luxury of having. They don't have the ability to go hire one. They don't know what to look for. Some of them have a hard time justifying getting a fleet management analyst. And if you're lucky to get one, I've seen a lot of shops that struggle to keep them because it's a very competitive field finding good fleet management analyst talent. So I wanted to kind of lead off and kind of get your guys' thoughts on why, if people don't have an FMA or haven't ever entertained the idea of having a fleet management analyst, why should they? And then if they know they want one, but maybe they just don't know what to look for, or do I hire somebody with a data science degree or fleet background, who can become a data analyst, that kind of thing. What are the qualifications that we look for? And, you know, how can they interview and find this type of talent? So I kind of lead off, why should, if they don't have one already, or maybe they lost one and they're thinking about, maybe I don't need one. What are the benefits that a fleet manager can get by having a fleet management analyst at their disposal?

Take it away, Mark. Give it to us.

Well, I mean, there's many reasons. I mean, so first and foremost, we're all under pressure to be as efficient with resources as we can be. And so in order to do that, right, you have to measure performance and you have to, you know, analyze the working circumstances so that you can drive that efficiency. So one of the things we talk often about, I think, is, is, you know, telling your story with data, right? So in the in the limited battle for resources that are out there, the better that we can translate the work that we're doing in operations to leaders, right, especially those in finances. We can do that with numbers and money. You know, the better chance we're going to have to have access to the resources. And frankly, the way we do that, you know, is to be able to show the quality of the work we're doing in an objective way, right? So if you can get an FMA on board, you have now dedicated resources to do— excuse me— a dedicated resource to do precisely that, right? To spend their time looking at the data, measuring performance, looking to gain business intelligence through various analyses, and to really set the foundation for doing data-driven decision-making, right? Which is so key, right? It's objective versus subjective. Instead of, this is what I think, or this is what we've always done, or this is what my experience tells me, which that's important too, but being able to give insight into that experience by looking at objective measures and objective factors really is key. So, you know, I think that's—

Would you say, Mark, would you, as you're, you know, playing devil's advocate here a little bit, right? If you, you know, you're you're the boss of the fleet manager and the fleet manager comes to you and says, you know, I really think I need an FMA, right? Would you know, how often would you see this? Or, you know, like, could you imagine this scenario where the boss says, well, wait a minute, isn't that your job as the fleet manager, right? Is to look at the data and make decisions and, right, come up with the recommendations to give to us. Like, why do you need an FMA to do that for you? That feels like, you know, your entire job.

Yeah, that's it.

I say that tongue in cheek, right? Like, because I know that fleet managers are busy and I don't want anybody to like, you know, throw rocks at me or anything. Devil's advocate, right? Because we all know that we've got bosses like this that are like, hey, you know, you need to get more juice out of that lemon kind of a thing, right?

Right. Well, I think, I think a couple of things. Number one, look, let's face the reality of it. There are some folks out there who are running small enough fleets that it may be tough to justify the additional resources. Generally speaking, in my experience, once you get— once you approach about 1,000 VEUs, you're going to be able to justify the need for that analyst. And that's a bigger conversation of how you get there. But, you know, as you know, Josh, and I don't have to explain this to you or Jeff or to our listeners, but yes, that's true. You know, interpreting and helping to make decisions based on the data analysis is part of the fleet manager's job. But fleet managers wear a whole heck of a lot of hats, right? They're also the primary liaison to leadership of the operation departments. You know, they're making decisions every day. They're making sure the maintenance and repair team are doing their job, the technicians are performing, that, you know, that vendors are performing or being held accountable, that parts are in and supporting the parts. So they're responsible for supporting the entire operation. And they— and it's just not realistic of fleets of a certain size and larger that they're going to be able to put in the focused, dedicated time into, you know, number one, making sure we got good data, right? That we're executing our data management plan because we all know garbage in, garbage out. And then number two, essentially searching for insights to be gained from that data, whether it's, you know, something that's going on on a daily basis, like what's the availability rate of the fleet to you know, larger, more strategic things like fleet rightsizing. You know, do we have the right count and the appropriate, you know, for what we need? It's difficult to identify holes, you know, when you're in the midst of it. For example, you know, a good data analyst is going to shine a light both on the problem areas, but also on the areas that you excel at. And that'll let you to kind of dig deeper and find the root causes for those issues. And where you're good at it, to maybe exploit what you're good at. So I think it's a matter mostly of, of what I started with though. It's, it's, it's the hats. It's the amount of hats. There's no way that a fleet manager of a certain fleet size and larger is going to, is just going to be able to handle all of those aspects, you know, and, and still gain legitimate good insights and really do the job of what a good fleet data analyst would do.

So, you know, this isn't a skill either that's like most people have because you're not taught really data analytics, let's just say, through your primary education, right up through high school, when you graduate high school. It's not. You know, I— so for me, starting out in trucking and growing up, you know, I actually did not understand data and really more financial data than anything else until I was already a vice president of operations and customer service because nobody stressed the importance about analyzing data. It was a lot of emotion-driven decisions that happened, right?

Go with your gut.

Yeah, right. It's like, you know, You're dispatching trucks. Okay, why don't you hit this amount of revenue? Why haven't you hit this amount of miles? Why are your trucks— like, there's all these decisions you're making and it's like, well, it just feels right. So I'm just going to go ahead and do this as opposed to something else. And it was— I know there's people above me, right, that had those skills and that did that, but it never trickled down and it never got taught, which made it a lot more difficult. So I spent my first— oh my gosh, 14 years in trucking. Never really analyzing data, understanding data, because it was never pushed on me to do that.

Or the impact that your decisions were having on the data, correct? Or like maybe even the metrics that, you know, your higher-ups were looking at and hoping to drive and improve. I was like, well, I don't, you know, I don't know that. And so I don't understand my actions, how they tie back to the overall whole health of the organization, right?

Because, because I had my small little— like, as a dispatcher, you have your small little, you know, 30 to 50 trucks, whatever it ends up being. And that's my whole focus.

Well, I would say then in that case, actually, you were doing data analyst. You just didn't realize it, right? Your data might have been on a whiteboard, right? Or it was on a spreadsheet or something like that because you were looking at that and here's your 50 trucks and you knew what your objective was and you knew how to, you know, best make decisions to, you know, I will say operationally efficient, you know, efficient size. That's a terrible word, but optimize your operations so that it works. And, you know, that you were meeting your personal goals, right?

Right.

So you knew the data you needed to make those decisions.

I did, but it was a very small scope. Yes, extremely small scope. You know, when we're running 2,000 trucks and Jeff's got 50 of them, right? And, you know, my decisions, they did have an effect around other people and I just didn't realize it. And I mean, I didn't care, I guess, either.

Right. So that's the, that's the bigger pattern, right? That is, Mark, as you talk about, as the fleet gets bigger, right? The, the pattern has these ripples, right? Like the, the ripple pattern grows. And so you've got to be able to identify patterns and trends, not just, you know, on a case-by-case basis. We talk about this a lot here at RTA, right? Uh, is that you can't just build something for the individual case. Like, you have to look at the whole system and look at the, you know, how this one system affects other systems.

Exactly.

And look at an overall trend, right? And I think that's the key that, that a lot of us don't get in, you know, in our fleet management experiences, we're not taught to look for trends or what trends to look for. And sometimes, honestly, and I think this is really where data science and, you know, we talk about AI, machine learning, this is where that discipline really comes in handy, is identifying the hidden trend, you know, the trend that's harder to see because there's so much data. Yeah. And this is a big word, but it's offuscate, right? That it's that it kind of like makes it like a, like a hazy glass. It's hard to see through. All of that data, right? And you can't find the one piece that's going to give you that almost magical insight, like, oh, that's the piece of data that I couldn't figure out and I couldn't see it because the, you know, it was hazy. And that is honestly like, if I just change this one thing in a process or in a purchasing program, or if I change just this one piece of like when I'm ordering vehicles, it has this huge ripple effect. And so identifying those singular variables in the data that really drive huge results. That's not a skill that we can really teach, or, you know, like, that's something you really have to learn through statistics, through, you know, if you're really good at Excel, you might be able to find some of this stuff. But I bet you, as much as we use Excel in Fleet, yeah, right? And I hear this about Word and all these other things, you know how much things you can do in Excel that nobody's ever touched before? A lot. Like, I don't know if Mark—

absolutely—

is the, like, the Solver, the Solver add-in in Excel.

Oh yeah, it's a neat feature. I've used it like maybe once, right?

Right. And you're a fleet analyst and you've used it once, right? I bet you, you know, half the people in fleet, you're like, I don't even know what you're talking about right now, right? Um, you know, I mean, like, there's a ton of these things that will do built-in analysis. You can actually get regression analysis out of Excel, right?

Yeah, that I use.

That's how powerful That tool is super powerful, but we use it for, you know, like just putting in data and then we never get the data out. Right. And an analyst is going to come in and they— and I think the difference between like a really good analyst and just a, you know, like a fleet manager, what we talk about the benefits of, it's knowing the tools, right? Like a really good mechanic and tech knows when do I need to use this tool versus that tool. Right.

Yeah.

I can't use, you know, the same wrench on every job. There's a time when I need an impact and there's a time when I need an air ratchet and there's a time where I just need to break out the cheater bar. Right. But knowing when that moment is, you know, like that comes with experience with the tools. And I think that's, you know, it's something as we talk about why a fleet manager might need a fleet management analyst. Is because they just haven't spent the time and the tools that an FMA would have.

Yeah, I think you've hit the nail on the head there, right? Hopefully a good fleet manager is going to be, you know, comfortable and familiar enough with analytics and with data that they're going to be able to leverage it and use it to drive forward the organization. But to your point, they need to know what can be done and what needs and what they need out of the analysis. Right. The FMA is going to know exactly how to go do it. And to your point, exactly. So, and you know, you made another earlier point where I can think of examples. There's a county very near to where you guys are sitting, for example, where they had skyrocketing M&R costs. And you talked about finding that singular variable. Well, they found out that 80% of all of their— of all of the increased costs lived within 2 VMRS codes. So what was going on? And they were able to do— take two large steps. One was a driver behavior issue, had to do with the suspension, and the other was getting training to the mechanics on that particular piece. And they were able to do it more efficiently, more quickly, and so forth and so on, stock more parts in that area. So they were able to find that sort of individual variable, as I think the word you used. I think you hit the nail on the head.

Yeah, I think And it was probably hidden from them. It was like, oh, like, you know, we know we've got this major skyrocketing cost and we don't understand exactly what to do with it. But if we take that and, um, you know, just by using the right tools, we can find that one piece of data, like you said, you know. And I'm— you glossed over this really quickly, but you said driver issue and suspension, right? Like, you know, without getting into specifics, but that sounds like somebody's jumping a truck in the desert, right?

You hit the nail in the head. Yeah, yeah.

I was like, what other driver issue would— I mean, unless they put hydros on it and they're outside, you know, doing, uh, doing hydro rallies or something.

Yeah, you hit the nail on their heads.

That's funny. Um, and so as we look at, you know, obviously you get an analyst in-house like this, and one of the big things they can really drive on, you've already mentioned it, which is resource efficiency. Um, I mean, they're going to have obviously other pillars that they can add into. They can help you with risk management, identifying PM compliance issues, that kind of thing, making your PM process better. Uh, they can help you with, um, you know, definitely in stakeholder satisfaction, right, and analyzing some of the data you get back from your stakeholders and customers. Uh, even I would say like they're going to help you with intentional culture, right, the last of the 4 pillars there, right? You know, they can look for things and look for trends that, you know, this this tech might be struggling a little bit, or their times have dropped off, or, you know, give you some HR insights, you know, because they can look at several pieces of data and be able to put together a pattern.

Right. Yeah. I mean, to that point, you know, we did some work with one of our customers and we were able to correlate as she was able to improve the engagement with her mechanics. Sure enough, their productivity metrics went way up. You know, how many hours they were posting, their efficiency on those work orders. So, and they correlated perfectly and beautifully. So, it certainly gives you an opportunity to justify, you know, decisions that were made, resources that were put into various initiatives and that sort of a thing. And has a direct impact on intentional culture from that perspective, in my opinion.

Well, and I think you just mentioned something too, which is critical is, justifying, you know, like almost providing an ROI on intentional culture, right? You know, like you talk a lot about it and like the benefits that you can get from it, right? But, but having an analyst like, hey, this is kind of a before and after, this is where we were at with these key metrics, and then we can single-handedly, again, finding that variable that might be hidden, might be, you know, a little bit opaque. How do you, you know, yeah, intentional culture was the thing. They can find those variables and say, well, we can only tie this back to, you know, like the hiring of this one person or the implementation of these things because it happened on this date kind of a thing. And that's where we see the trends start ticking up. So, um, so as, you know, fleet managers are listening to us and, and, you know, listening to this podcast episode, I think one of the natural questions is, all right, like, we definitely could use one. I don't have time for this. I don't have the skill set looking at data. You know, like, I know I want to make better decisions, 'But the thought of learning how to use crazy add-ins in Excel scares me,' right? Or I'm going to name drop some like really common analyst tools right now, and if you don't know what they are, this should also tell you that there are people out there who do and can use them, right? So like when you hear of things like Python, R, you know, we look at SQL Server and using SQL, you know, scripting, um, you know, doing regression analysis, whether that's in Excel or in R or Python. you know, using tools like Tableau and Power BI, IBM's Cognos. I mean, there's a dime a dozen of these things that are out there. And if any of those tools sound totally familiar to you, that might be a sign, right? Like a little Jeff Foxworthy, you know, you might be— you might— if it sounds familiar, you might be an analyst, right? If it doesn't, then maybe that's just not your jam, or it's not something you're familiar with yet. And, you know, you've got some work cut out for you if you want to try to do this on your own. If you're in that boat and you're saying, okay, but I would just rather let's go hire it, let's go bring somebody in that knows what they're doing. You know, like Margaret, what would you look for in an ideal fleet management analyst?

Yeah. So obviously you want someone with, with strong analytic skills, right? Ideally you have someone with a degree in just that. You know, there are plenty of universities that offer degrees in data analysis, data engineering, data science. There are some other degrees though that you find work well because they're used to having to work with data and large datasets. So economics majors and masters, finance folks, obviously folks in mathematics, someone who has those hard skills. And the reason the degree is important is You know, they're still going to be on-the-job learning, but they'll have, to your point earlier, they'll have strong familiarity with the tools. They're not getting through those programs without having used the tools and kind of having a really good sense of their power, what they can do for you. And of course, they obviously have familiarity working with large datasets and trying to find, you know, those diamonds in the rough with regards to those unique variables, to your point. So I would say that's definitely, you know, step one there. And then, you know, in a perfect world, you'd have someone who has fleet experience. And that's what gets tougher because, you know, to be frank, I think that because of the push in industry today, you know, everyone's heard of big data, everyone's heard of the Internet of Things. And, you know, and so using data has really become in most of the professional world an expectation. For decision-making and that sort of a thing. And so there's a lot of that out there to some degree, but finding someone who's strong analytically, but also has the ability to bring context to that data in the setting that you're working in, in this case, obviously we're talking about fleet. So that's kind of tough to do because on the one hand, you know, I think you can make a fair argument that data analytics is a lot harder, let's say academically in your mind than fleet. Uh, but it's, it's, um, it's something you can go out, you can get a degree in, you can get a certificate in, readily available, right? Exactly. Whereas fleet experience is something, you know, certainly there, you know, you can get a CAFM, you can, and those are great, but I think most in the industry would agree there's a time element to that, right? You just need experience working in it and it's a lot more on-the-job, uh, training. So If you can put those two together, boy, you're in really good shape.

And, you know, and then I guess the third piece is kind of like a little bit of like a unicorn.

Yes.

Yes. You're looking for somebody that's got like, hey, they've got a 4-year degree or at least 4 years of experience doing an analyst and, and then another, you know, 3 to 4 years running in fleet circles, either doing analysis for fleet or maybe the vice versa where they spent 4 years being a fleet manager. And then decided, you know, I'm going to go to school and get, get my, you know, a data science degree or something.

Right.

That's probably more rare, I would say. But I think there's an element of— I think we've talked to enough fleet managers and what's the common theme we always hear about? How did you get in a fleet? Accident. By accident, right? Like I kind of fell into it, you know, and then it just stuck. And I think a lot of us are kind of that way. It's like, yeah, we'll try this, you know? And so I think you pair that up is that nobody really goes into fleet You look at the, you know, if I were to go survey here at ASU the number of kids coming out of that data science program, there's probably maybe 130. Like, if I were to throw a brick at a wall, it might be a little bit more than that now. But, uh, of those 130, how many are going to go into a fleet application versus, uh, exactly pharmaceutical or, you know, a warehousing or logistics or something like that where it's totally operational? Or, uh, you know, there's probably a little bit more money involved. Than what we find in fleet typically. So I think it is, you know, it's rare to find that combination of talent. But you said there was a third thing that you would probably even look at.

Yeah, I, you know, I think this is underappreciated because when you think of, you know, data scientists, data analytics, you know, some of us probably have some preconceptions with that. And I really think strong communication skills is very important. The ability to to write, the ability to speak, to present, you know, putting together information because the data analyst is going to have, you know, tons of outputs that they'll see as valuable, but they have to be able to discern and to distill that into the chunks that can be, you know, that can be swallowed by their audience, whether it's, you know, leadership or other stakeholders. And so the whole point is to tell your story with data. And so strong communication skills is really, I find, often gets overlooked when folks are hiring for this kind of position.

And I think that's like an interpreter.

Yeah, exactly right.

You know, like somebody, a fleet manager would have to be the one that kind of the face of the presentation or, and they'd have to understand the numbers just as well to be able to get in front of other people and justify, well, how did you come to that conclusion? You know, and if the answer is, well, this is just what my analyst told me. That's going to be hard to— it's hard to sell that, right? And so then you have to know it. Otherwise you get the analyst out in front and they can justify themselves. But if they present well and they communicate well and they can write a really nice report that's easy to digest, because I think that's honestly, you know, executives are looking for that is like, give me the summary version, right? Cut to the chase. Don't sit here and, you know, write a whole bunch of stuff that doesn't matter to me. Like put the, you know, what are the biggest takeaways and biggest insights right up front? And being able to present that in a way that's compelling, that drives action. And Mark, as you've said in your presentations, right, that tells your story with data, because honestly, like, the light has never been brighter in fleet. You know, we are in the spotlight. These are our 15 minutes of fame. And you need to be able to tell your story with data. And if you can't do that, you need to get somebody who can and help you with that. And if you just go out and get a data scientist off the shelf, you know, go grab a kid from college, you might get a third of what you're looking for, but then you still got to teach them fleet. And hopefully— and I will tell you, going through college does help you with this. So, you know, I mentioned the 4-year degree, it does help you a lot. You write a lot of freaking reports in college, right? You do a lot of— you do a lot of presentations in college, right? So when you get thrown through the grinder on, you know, putting together good stuff and that kind of thing, at the same time, I've, you know, I've seen some of the work that college kids put out it's not the best, right? Yeah, it's— it needs a lot of refinement. Yeah, I put in some of those papers myself, right? And I remember getting shredded a few times, uh, you know, by upper, uh, what do you call that, graduate students, uh, you know, doing their internships and TAs or whatnot. Um, but for the most part, just the exposure to having to write, you know, I mean, like when I was in a program, like we wrote a 50-page, uh, project, right? And that was something I'd never done before and I wouldn't have ever done had I not gone through that. You know, until, uh, until I got through college. So it definitely teaches you a lot about written communication skills, and they force you— usually as part of your undergrad, you got to go through Communication 100, you got to go through, you know, I think there was another one like Business Communication or something like that. Um, you know, so there's definitely some things that, that they teach you as part of that process. But it's for anybody who's thinking like, oh, I want to get into data analyst, or I want to be a fleet management analyst, you know, even if you haven't gone to college, there are a lot of programs available for you that you can make that flip if you want to do it, right? You can flip into and go from managing a fleet to, you know, really being like a super-powered fleet manager.

Absolutely.

Or fleet management analyst. And the pay is honestly, uh, everything I've seen is the pay is pretty equal between the two. If not, the analyst might actually get paid a little bit more. Because of how unique and in-demand that skill set is.

Yeah, that's a good point. And actually, Josh, that's a— you bring up something that I've seen as a mistake organizations make, is they equivocate a fleet management analyst to just whatever their general business analyst is, and they shoot themselves in the foot. You know, they'll hire for a position and, you know, maybe they didn't offer the appropriate compensation And so they, they'll settle, right? They'll settle for someone who has a piece of the background we're talking about. And in the end, it's like money spent and you're not really getting what you need out of it anyway. So that's also—

I usually think of like business analysts as more like I can ask questions and then regurgitate it into a report, right? Or into a, you know, hey, this is the current state of affairs, right? Like I'm analyzing the current business, you know, and then I would feed that to a consultant who would then make recommendations from it.

Right.

Right. But as a business analyst, I wouldn't necessarily make those recommendations. FMAA, I would actually say, goes the next step, which is not only am I going to look at it, but I'm going to tell you what's causing it and why that's bad. Right. And from that you can infer like, well, then we should change it, right? Or, you know, and I think that's the difference between a really good FMA is they're not just a business analyst. That's like one, you know, one arrow in their quiver.

Right.

But they have a lot of other things that they're bringing to the table. right? Uh, they're not bringing knives to gunfights. They're, they're coming fully armed for bear.

So is there a, uh, is there a fleet size that a dedicated fleet analyst resource would be needed?

Yeah. So it, it, in my mind and in, in my experience, um, and I know several, you know, of my colleagues kind of share this approach, but it's at somewhere at a right around 1,000 VEU. Now that gets challenging because you know, what's a VEU? I know you guys have talked about that on other shows before, right? But for those who—

such thing as a standard VEU, right?

Uh, I know, you know, I guess, but there's not— there should be, right? You know, so a vehicle equivalent unit, uh, is right, is, is a way of normalizing, uh, vehicles of various class and size and makes and builds into one standard vehicle, typically thought of as a sedan. And so depending on the makeup of your fleet, right, so 1,000 sedans would in theory be 1,000 VEUs for most fleets anyway. There are some exceptions to that. And so inasmuch as you have a mix of, you know, 1-ton trucks and 5-ton trucks and, you know, Class 8s and construction equipment and agricultural equipment, et cetera, et cetera. And basically that VEU is determined around what is the level of maintenance and repair effort that's necessary You know, what's the level of effort necessary to maintain and repair this asset and keep it in good, you know, order for my fleet? And so generally, that will have some impact on the entire staff size, right? How many mechanics do you need? And then how many mechanics you need will typically, you know, be an indicator for how many parts staff you need, and so forth and so on. And what we typically find when we look at, you know, the nature of the work being done, the the frequency at which ad hoc reports are being done, the opportunity to simply create enough data points that you want someone dedicated, focused on that data, that for, you know, for whatever reasons that when you approach that 1,000 VEU mark, you start to see, yeah, this is more than even a fleet manager who's got strong data analytics skills can really handle by themselves, right? Certainly even in a, if there's no analytic skills on the team and even in a smaller fleet, you could argue that an FMA would be very valuable, but, but at some point it just becomes bigger, uh, than for, you know, because of all the hats the fleet manager is wearing, it just becomes too big. Uh, there's just too many data points. There's too many opportunities there.

So, yeah. So the, the smaller fleets, right, that may not be able to afford or, you know, can't justify that full-time position, do they have options for a fleet data analyst, or is there something that they can do to help their fleets that is really is not in the scope because they don't have the money for it?

Yeah, I mean, so obviously, you know, as Josh just pointed out, getting some training and some, you know, and some insight in that is always a good idea. But, you know, frankly, there are folks you can call, right? Analytics as a service is a real thing.

And for that matter, even the larger fleets, you know, like we call it like fractional FMAs, you know, right, right. It's definitely a real thing that's out there. Yeah.

Right. And, uh, you know, Jeff, even the larger fleets though, because of the point that Josh made earlier about this sort of unicorn aspect of finding someone who really, uh, is really truly can do the FMA because they have, you know, all of those three areas that we talked about. So it's not even uncommon, I think, for larger fleets to either outsource their FMA work or even, um, or supplement it depending on how large they are, right? Um, so, um, so yeah, so I would say that's, that's the answer as far as I'm concerned. It can be a fractional FMA, like a part, you know, it could be a part-time FTE, it could be an off-site FTE. It could also be, um, there's a series of reports or, you know, you know, let's say it might be your your quarterlies and your annuals, as well as, you know, certain things that you do with somewhat of a regularity or even an ad hoc report. You know, you can, you can certainly— there are people out there who are more than happy to dig into your data and do have that experience and go after it for you.

Yeah, you know, it's something that, you know, like that, that a lot of people don't realize, you know, that even some of your vendors have that capability built in. I know we've designed reports in the past And while it's been one thing to design the report, it's another to take it to the next level and actually have an analyst available that can say, yeah, here's the data from that report, but here's why it matters, or here's what you should be paying attention to when you're looking at that report. Not, you know, like, you've got this whole subtotal, but that's not even the best piece of data on this report. You need to be looking at this, right? And here's the variable that really is— you should be paying attention to. Um, and so I think sometimes just, you know, ask some of your providers, you know, like, uh, I'll tell you is that if any of our clients were to ask if we had that as an FMIS, yes. Right. Ask your FMIS provider if this is something they have in-house, right, that they can, you know, lend a resource to you. You know, if you're an FMC, your FMCs had better have this in-house because pretty much their entire job is managing your fleet. So ask them about it. Right. Make sure that they're doing a good job with it. Otherwise, you may as well just be doing it yourself. You mentioned one thing earlier, which was— oh shoot, now I'm trying to remember it. I just blanked on it. Um, but we talk about the, you know, you need this communication skill, you need this presentation skill. Um, you know, we, we need these. It's like this unicorn almost, right? Um, where would you even, you know, like, I'm trying to think, like, the you know, where do you even post for a job like that, right? Do you try to tap into the colleges and just like grunt through it and, hey, let's go ahead and recruit some people out of, you know, at ASU or something like that? You know, just I guess throwing it up on the, on the NAFAB boards might be a good option too. Yeah, yeah, that's probably like as far as I'd say on that, but Yeah.

I mean, that's, that's a tough one because again, you're looking for someone who has experience and who can give the data context. And so hiring for this straight out of college is challenging, especially if, um, from a resource perspective, you're talking about having one person on board or two people. If you're a larger organization where you have a team and you have, you know, two or three or more, or maybe they're teamed up with a fleet, with a system administrator, right. Um, where you can kind of develop those skills then. Yeah. Then I think that's exactly the way to go. Right. You can go straight to the colleges. and, and find someone who have the aptitude, uh, to learn it and the right attitude. And kind of this could be their experience in fleet, so to speak, right?

Um, you know, like, and we went through this process, right? And, you know, and we've had— I want to say we had probably like 100 applicants that, uh, that applied for that job. Um, you know, so like it was a very competitive pool, but there was really only a handful, you know, that had all 3 of these skills in spades. right? A lot of them, though, probably the majority were heavy data science, right? Um, you know, the other smaller majority were heavy fleet background but no data science. And then we had, you know, like a good probably handful that were, hey, they got— they had all three, right? So it's, you know, very interesting. Like, it's, you know, if you are going to go out and do this, just know, uh, you know, there's a lot of people getting into this field and there's a lot to pick from, uh, but it's going to be a narrow field. It might be like 5% of your total applicant pool. And if you struggle getting job applicants, even for like technician roles, uh, thankfully I'll tell you is I actually think there's probably more data scientists out there now, uh, than there are technicians. But, uh, it's just crazy how that's, that's flipped on its head. I do remember what I was going to ask you. So we were talking about VUs, um, right? And you said 1,000 VUs. And so I want to kind of break this down for listeners in case they don't understand how the VU thing works. You mentioned that a VU if we wanted to, we could basically equate it to a sedan. You know, so if you were a fleet and you were running nothing but sedans, it would be about 1,000 sedans, right? So you're a taxi fleet, if you're a limo fleet, something like that. Now, a lot of, you know, like listeners and customers, they might be government, so it's going to be a mix, right? But maybe they're the light-duty side of government. Maybe they don't deal with fire apparatus or sanitation. You know, it's just pretty much light-duty pickups and sedans. Guessing they'd probably be, you know, for 1,000 BEUs, they're probably looking at about, I don't know, what do you say, 700, 750 maybe, depending on the mix, maybe a little lower.

It does depend on the mix. And to your point, it depends on what they're doing. My experience from, frankly, I'm doing a lot of studies with local governments is if they're, they generally range at a ratio for between 2.2 and 2.4 VEU. So in other words, if you took their fleet size, you know, so 500, yeah, exactly, right around somewhere between 450, 500 there. If you're up in the north, yeah, you know, the further north you go, so if you're, if you're a local government, let's say in northern United States and in Canada, that's going to be, you could even crack that 2.4, we'll see 2.45, right? And as you go south it generally gets a little lower, very, very rarely below 2.2. And that number actually applies as well to most universities, at least if it's like a state university, because if you think about it, those are like small towns of their own, essentially. And I usually find that they're also ranging in, you know, they might— their bell curve might be slightly lower, so they might bottom out more on the 2.1 side. But, um, uh, yeah, but then again, I know certain universities that have—

they've got like the transit agency and things like that. So, you know, that would add— that would be exactly there on the VU side.

Yep.

Flipping that to like, okay, that's city and government. Universities in a trucking environment are like 1,000 VUs. That's going to be very small, right?

That's very small.

200, 250.

Yeah, right, exactly.

That stage. So yeah, thinking in your head, well, I'm, you know, I'm a small trucking operation. I'm, you know, and when, if you're at 250, you're actually a fairly large trucking operation, right? Uh, and we're comparing you to a, you know, like a county that's got 10,000 assets, right? These are just not the same. That's why we use BEUs and we talk about that. Um, but yeah, 200, 250 is a trucking agency, like that's the point in time. And a lot of this, because it comes down to, and why the BEU is a nice comparison, is it comes down to cost. How much budget and money are you managing going out the door? Um, you know, and honestly, like, you know, you look at what you pay a a fleet analyst, uh, and that's anywhere from, you know, like I would say $80,000 to $100,000, $110,000.

Right.

Uh, and honestly, that depends on where they are because I've seen fleet scientists, like data scientists, those guys, they can go get a job at Amazon for $150,000, $200,000. Exactly. Right. And so it's, you have to be, you know, like, and if we're really honest about the unicorn status of the job, um, you know, like that's going to command a little bit higher pay for somebody who's really good.

Right.

But you don't necessarily need perfect and you don't need somebody who's just a genius, you know, Steve Jobs type. You need somebody that can make sense of the data and who likes Fleet. Like, you need somebody who's a little bit passionate about the industry, which is something we like, you know, we talk about in culture. I just find people that are— that really like your purpose and what you're trying to do.

Right.

So, well, any last thoughts there, Mark?

Yeah, if I could, a couple of sort of words of caution. You know, you mentioned how your vendors are out there and they can do this, and that's certainly the case. We also mentioned how it's a really good idea for fleet managers and other folks to at least have some familiarity and comfortableness, let's say, with working with analytics. And I think that's really important because, I mean, I don't think I have to tell anybody listening, but, you know, folks' jobs often is to sell you something, right? And so you have to be able to sniff that out, right? Are the results I'm getting objective and solid and as a service to me, or is it to get me to lease or buy another vehicle or something? I know, and I don't mean that accusatory, but it's just the reality of the nature of business, right? You're always trying to get the sale, so you want, you want to be able to, to understand that. And so knowing your source, knowing the type of vendor, what's in it for them, I think that's important, right? Um, different, you know, different depending on the type of vendor you're leveraging, they're going to have different objectives and different motives, and you have to be able to understand that. Yeah. And I'd say, and the last piece is, you know, going back to your point about how hard it is to find folks is it's important to understand that, you know, transportation isn't fleet. It's very close to fleet. It's related to fleet. And that there's an area where you will find a higher concentration of folks who have been doing analytics. Certainly it's better than no experience at all because there's a fleet element to that. but it's another thing, you know, you'll see, you'll find someone who's been in logistics for quite some time, comes over to the fleet management side and, you know, struggles because of that, that of context. So these are just some, some missteps or some sidesteps I've seen folks, you know, take here and there that I thought I'd point out.

So you definitely have to manage expectations in that regard, right? Like seeing somebody come over from operations or dispatch, just like don't assume that they're going to be like a magic pill, right? You're going to have to train them almost as much as you would. Somebody fresh out of college, but hopefully a little bit less, especially within the same vertical, right? Like if you're hiring as a trucking agency, hiring somebody for fleet management, fleet maintenance versus operations, the dispatch, at least the two they've played in the same sandbox, right?

Exactly.

The data might be different. You know, maybe they're dealing with a different type of material, but, you know, just don't expect them to make the jump and be like, oh, well, you were in fleet and you were an analyst, so therefore you know how these two connect.

Exactly.

You know, that's, that's not quite the same thing. I think that's a great word of caution. So anything, Jeff?

Uh, you know, I think the only thing I would add, because this can seem very overwhelming, you know, we talk about a shortage, we talk about unicorn status, we talk— I mean, just anytime you talk about data in general, I think people get a little skitterish, right? So just, you know, we're always here to help out as resources. So, um, you know, if you don't know where to start or you don't know where to look, Give us, give us a holler, send us an email, give us a call. We'd be more than happy to point you in the right direction.

I mean, we'll even give you a job description if you want to. You want us, you know, like we've got one we can— we just posted, uh, happy to share that with you guys if you want to, you know, use it and plagiarize it as much as possible.

Yeah, I mean, our goal for this podcast is to help you guys get better at running your fleets and to be more successful. So if there's anything we can do to help, just like I said, reach out.

All right, well, I think that's going to wrap it up for us on this episode. I appreciate you guys listening. If you have any feedback or if you want to get any of the resources we talked about on this podcast, you can email us at podcast@rtafleet.com. If you want to ask Mark a question directly, you can always email him and I'll publish his email here, which was mcanton@rtafleet.com. He's more than happy to if you want to reach out and ask him a question about an analyst or, you know, like if there's a key metric you should be looking at. Uh, you know, Mark, you're a guy who definitely loves data.

Yeah.

Loves telling your story with data. Uh, and so I know, uh, you wouldn't, you wouldn't hesitate to help anybody if they had a question on that.

Not at all.

Otherwise, we will sign off here and we'll see you next time.

See ya.

Great. Thanks, guys.

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