#8 Why Most Brokers Get Freight Pricing Wrong (And How to Fix It) — Chadd Olesen, AVRL

Auto-transcribed via Deepgram nova-3. Speaker labels are approximate; light cleanup applied.

[00:00]

Jesse Buckingham: People need to really dive deeper into the data. One of the things that I've said constantly, like for forever, is that freight analysts aren't actually analysts. And a lot of them don't know SQL. A lot of them don't know R. A lot of them don't know Python. My analysts do a lot of reasons why we might not be executing freight appropriately for organization. But it ultimately impacts how we price freight. And so I think some brokers look at a rate and they're like, that was a bad rate. It might have been the right rate at the right time. We just didn't execute it correctly. We believe that if you're gonna automate transactional spot bidding, you should have your pricing team own it, or someone who is familiar with pricing own it. From the standpoint of they're better at analyzing data. Welcome to the Freight Show podcast.

Today, we're joined by Chad Olison, founder and CEO of ABRL, to break down how technology is transforming freight pricing and why most brokers still get it wrong. Chad shares how his journey from working with top three PLs to building ABRL shaped his understanding of dynamic pricing and why averages don't tell the real story. He explains how data driven strategies, automation, and composition scoring can help brokers price smarter, win more bids, and finally, make automation profitable. We also explore the future of freight pricing, how speed, precision, and real market intelligence are changing the game, and why brokers who fail to adapt will be left behind. Alright, let's dive in. This episode is brought to you by Voomah, the back office automation platform for freight brokerages and 3PLs. From AI powered document handling to streamlined workflows,

[02:03]

Voomah helps logistics teams scale smarter. Learn more at Voomah dot com. Alright. Welcome everyone to another episode of the Fray Show. I've got my man Chad here on with us. Chad, it's great to see you.

Chadd Olesen: It's good to see you, man. How are you? Doing very well.

Jesse Buckingham: Awesome. I'm excited for our conversation. Always enjoy them. You've always got compelling and interesting takes on things and I love kinda hearing your story on things. Maybe to kinda kick it off, you know, you've been at the forefront of brokerage pricing and the evolution of brokerage pricing over the last few years and things that know, it's probably one of the areas of brokerage where there, you know, really has been some of the most, like, meaningful and significant change just in the way that, like, rates have thought about and and delivered. But maybe, like, you could kinda take us through your sort of perspective on on like where the industry has sort of come from over the last like ten years and maybe bring us up to the present and and we can start there.

Chadd Olesen: My evolution is like really interesting. That's like a really hard question because a lot of people know like my very first customers were like top 10 3PLs and some of them had seen me speak at an event for Walmart and then they asked me to come into their office and do some stuff with them. And my first customers had large data science teams, you know big analytics teams, like I've worked with Scott Friesen forever you know and like I've taken a lot of my learnings from people like that. As AVRL started to scale down the top you know 5,100, we realized that a lot of freight brokers didn't have the right tooling to be able to automate transactional spot bidding at scale. They thought that they did, they thought that they could do use averages or blending strategies

[04:05]

but the market rates are too dynamic. So what ends up happening is people are like, oh you know, DAT rates suck or green screens rates suck and in reality they just don't know how to use them. So what we've been working on at AVRL for, I don't even know probably like the past two years is how do we get customers to be successful with the rating rates that they bring to the table? And for us, it's been a transformation and like a learning journey as well primarily because we thought that the brokers knew more about pricing freight than we did. I think that they know a lot but I think that they don't know why they believe a truckload costs a certain amount and to like deconstruct that has been really an interesting path that you've been on. You know, a lot of people, was talking to you about this before the show, is like a lot of people think about us as an automation company, really, or pricing company that does deploy our own automation in some areas. But, like, just like you and I have worked together, we have mutual customers that use you guys as the automation and we are the engine behind it. And so for us, like in our future state and where we're kinda headed is like it doesn't matter who your automation provider is. I need to buy well in the market and understand where I don't buy well and deploy complex strategies. That's really what our technology does.

Jesse Buckingham: Yeah. And it is I mean, we are like adjacent to it, you know, and we've obviously partnered together and like, it's such a you peel the onion on it and you realize, like, to do that well is really it's complex. What have you learned? Like, tell me more about, like, what you've learned about how to how to price freight. And you mentioned the the challenges of sort of, overly relying on some of the, like, the benchmarks. How did the, like how do you start to pass through that? How how should folks be thinking about it? I'm gonna use like a really easy example.

[06:08]

Chadd Olesen: This is probably like the like a super common example that everyone could use. So if you take like a rate from Elizabeth, New Jersey to Chicago, this is a core lane with strong supply and so companies are going to buy significantly below average in this lane. And what will end up happening is you will have incumbents on that lane that bring the rates higher. And so your average is an inflated average. The same exact time, you take the exact same market area, let's say Freehold, New Jersey to Chicago, your rate is identical from an average perspective from Elizabeth to Chicago, but you don't buy at the average, you probably buy somewhere between low and average and the reason why is it's still strong supply but now you're non core, you have a 40 mile deadhead on it. So when we look at a structure like that, we believe that companies traditionally don't really predict their cost well enough. Think a lot of companies who started to go after dynamic pricing, convoy, Uber Freight, etcetera, they were looking at how do I price freight to win from shipper? Necessarily what is my cost to higher capacity in the market? Interesting. And then if I can't win at my cost plus margin, maybe I actually don't want that freight at all. And this happens, know, everywhere all across the country. Reason why methodologies like that are important is that when you, you know, we look at automating quoting for a 3PL, we're talking about hundreds of thousands of shipments a day. And so you have to be more accurate where your costs are. Those costs obviously fluctuate too, if I won three shipments from

[08:09]

Elizabeth to Chicago, my fourth rate probably can't be the same rate that it was on the first several, you know? And so for us, really going in into like complex like composition scoring, like where do we buy relative to the market from, you know, market region, not really lane. I don't think that people who price by lane are very successful. And so, my team's coming in with complex strategies like that. It's like, statistically, this is where you actually buy in the market. This is where you don't buy. Like, we actually wanna focus on areas where we buy more competitively than other people.

Jesse Buckingham: Can I like I wanna make sure I'm tracking, say say the first example, and maybe I can play it back to you where you on that lane, you most brokers can buy below market because the market average is being pulled up because there's a lot of larger carriers on that and that what their pricing just tends to be a little bit more premium? And so if you're actually going out and trying to secure the sort of like smaller carriers, you could probably probably buy below. That might not be true on a, you know, a different market where you're gonna buy higher because everyone like, there are no major carriers on that. And so everyone's kind of trying to compete for the the same capacity or there's, more deadhead that is kind of wrapped up in that. And so a lot more of that capacity is gonna like, there's gonna be more variance because some of it's gonna have to be coming into that area.

Chadd Olesen: Correct. Am I sort of thinking about that in the right way? Yeah. Take that example that I have. Like, Freehold and Elizabeth very close together. However, we now have to convince a carrier to drive 40 miles to pick that load and then come back to Chicago. When you look at that structure, you have, like, two things. Right? Like, a lot of people look at, like, cost to hire capacity. You also have cost execute freight. And it's gonna cost us more to execute freight out of that picks in Freehold than picks in Elizabeth.

[10:12]

And that is because of the deadhead primarily? That's correct. Yeah. So like the rating structure, it's not like it's not like you wanna go and add, you know, two dollars a mile for the 40 mile deadhead. That's not really how that works, but it is gonna cost us more to serve that lane definitely.

Jesse Buckingham: Yeah. Yeah. So maybe take me through it, like, how should so what would how should you solve those problems? You know, those sorts of examples, like, what would what would it look like to implement a pricing strategy that I properly adjusted for for some of those things?

Chadd Olesen: I think when you look at like implementing the strategy, it has to be bay like each company's strategy would be independent of one another based off of where they buy relative to the market. Right? So for us, like we actually do go through historic data and looking at, hey, statistically, we believe that you have an 87% of covering at or below this amount based off of where you historically buy that freight. And so, you look at like major regions, markets, etcetera. You take like, you know, the 13 bellwether states, 40% of the volume comes out of those states. Like, we do wanna target strategy outbound those regions or hey, you know 15% of all the freight moves on the top 50 lanes like we do want strategies there. Yeah. You basically wanna build bucketing strategies to essentially accommodate for hey, if there's outliers in my strategy, you know, like, let's say I traditionally buy from the like Southeast, the Northeast a specific way, there will be still markets inside the Southeast and the Northeast that are opposite of my traditional buying pattern. People need to really dive deeper into the data. Of the things that I've said constantly like for forever is that freight analysts aren't actually analysts and a lot of them don't know SQL, a lot of them don't know R, a lot of them don't know Python.

[12:14]

My analysts do and we're coming in with analysts to really help companies analyze their data and understand where are we buying well, where we not buying well and trying to find trends and patterns in why are we fumbling freight in a specific region or in a specific facility. And a lot of times it has to do with care, like you know, carrier sales fumbling it or we can't get an appointment and so we didn't get on the board fast enough. There are a lot of reasons why we might not be executing freight appropriately for an organization but it ultimately impacts how we price freight. And so, I think some brokers look at a rate and they're like, that was a bad rate. And it might have been the right rate at the right time. We just didn't execute it correctly. So, there's a lot of things that go into the data analysis piece that we're doing with companies and helping them understand like, your rates were actually accurate. That pick was, know, at 4PM on a Friday and it dropped at 8AM on a Monday. Like, of course you couldn't execute it at the rate.

Jesse Buckingham: Yeah. There's this interesting I've been thinking about this recently where a lot of it's there's some challenges in thinking in in sort of passing through the data. Right? Because you you also see you know, what I see and I've chatted to various, like, operations leaders around, which is you sort of you set your customer's sell rate based on what you think you're gonna move the freight up, but they're they're actually like not independent variables either. You know what I mean? Like what you think you're gonna pay is correlated with what you're actually gonna pay, you know, in the sense that, you know, like a lot of carrier teams will kind of try to work to hit the number, you know, as well. And so there's, there's some sort of like interesting dynamic there, but then to your point, it could go the other way as well. Right. Which is like the rate was right, but you know, you didn't execute it well.

[14:15]

And and that balloon cost and and how do you actually, like, sort of tease out what was like a a bad prediction versus how do you actually, like, sift through and sort of understand? I mean, that seems like you've gotta kind of actually get get into the data at, a pretty deep level to sort of to figure out the, like, signal there.

Chadd Olesen: We don't look at cost and margin as one. We look at them as independent layers. And so we say more about that. Yeah. We splice the data. Broker submits a rate, it's $2,000. We don't look at that rate as $2,000. I look at it as my projected cost was $1,700 and my margin was $300. And so when you go and you actually do an analysis, you could actually look at costs separately from margin. It's the only way to do it.

Jesse Buckingham: That's interesting. And so when you when you work with folks, take me through well, maybe I'm like, I'm curious to understand, what do you think the, like, best practice is here? Like, I I'd love to, like, understand, like, what do you what do you think the, like, ideal sort of setup is for a broker that wants to be extremely good at pricing and especially, like, pricing at scale? What do you think like, what what are their sort of strategies in it, like, actually look like? But then also, what is the setup internally? Like, maybe culturally from a leadership perspective, from a capability perspective, what's the, like, sort of the DNA of folks that are involved in actually making that happen well?

Chadd Olesen: I won't mention this customer, the name of the customer, but I have this customer that I was just talking about with you before the show and their leadership knows that they know how to make money in automated pricing. And so they allow the account rep to control margin. However, if the account rep cannot control margin,

[16:18]

they operate in a split model and they'll give it to their enterprise team to control the logic because they do know how to make money with their automation. And I think that when we look at ideal setup, it obviously stems from leadership. Like we either go all in or we don't go all in. I think you could probably agree with me on that with automation in general. It's like, hey, if you wanna just like test it, don't because you're not gonna have buy in and that will fail and you're gonna blame the technology provider. We also require our customers to have a leader to drive our program. And I'm sure a lot of people have seen me post like pricing analyst roles and pricing director roles because those people are hiring someone to come in and run our program. We personally believe something differently than a lot of three PLs. Believe that if you're gonna automate transactional spot bidding, you should have your pricing team own it or someone who is familiar with pricing own it from the standpoint of they're better at analyzing data, they're better at understanding, you know, what was our MAPE or our margin of error for those people who don't know what MAPE is. They're better at understanding complex strategies like hey, we can support eight shipments outbound at planet x rate, after that we cannot support it, you know. And so for us, we're really helping companies find analysts and I've met some really strong analysts over like the past year who I'm actually helping them try and find new roles but it's because they understand unique blending strategies, they understand what goes into a cost prediction and how to separate cost versus margin. My team specifically, have our own market intelligence team where we're coming in and teaching people how to do this. And I think that for us, we really believe that

[18:19]

an organization really does need to want to automate it if they wanna if they wanna make money. It's kinda funny because I've had companies tell me like, oh, your tooling's like more complex than other people's. And my response is always like, well, do you care more about easy to use or do you care more about making money? And from leadership perspective, they care more about making money. Maybe some end users are like, I want it easier to use. Our theory is maybe they're not the right person to run the technology if that's the case. And I think for us, we've always been on this warpath of we need to educate the brokers to level up on technology, not dumb our tech down to meet the broker. Not saying that the brokers are dumb, but very intelligent, but they're just not as familiar with complex technology. I think one of the things that my organization's really trying to do is level our customers up. I think they're gonna have to, especially as we continue to see margin compression.

Jesse Buckingham: And it sounds like you've made some, like, sort of investments in your team and sort of thinking about your own capability set and what you bring to the table, I'm curious where this sort of concept of the market intelligence team look like. I'm like, what what is it like engagement model actually look like with with customers?

Chadd Olesen: It's interesting. Had originally started with this team called IBG and it stands for incoming bot group where they would sit down with the broker and go through logic and understand, you know, like, what how do you buy, like, etcetera. And I think that model was okay. However, they didn't know more than the broker. Right. We were extremely lucky to hire someone who had been at CH I think for seventeen years. They worked for another really big brokerage in Knoxville for you know, four years leading their analytics team. I've worked with him for probably five years. He was a free agent and we jumped on the opportunity to build our team around him. He's building out a team of analysts

[20:20]

and some other areas in data science to really help us understand how we deploy the best tooling for our customers. We think that as the market like continues to grow, you have different probably data aggregators or data providers in this space. We really need to be the most familiar with what are the comp like, are the strategies that we could be to deploy to be successful with the green screens, to be successful with the DAT, to be successful with someone's homegrown rating engine that needs to probably be rebuilt because it's too slow, you know, things. But like that team is super critical to my future. It's really where we shift from a technology provider to a consulting company, I think is what you're gonna see from us.

Jesse Buckingham: Yeah. And it sounds like, Chad, you're sort of on this journey where maybe in the early days of the business, you were sort of, like, starting with some of the automation and then, like, you've developed and accrued a lot of this understanding of the, like, brains and the intelligence sort of layer. And maybe sort of, like, take take me through a little bit of that that journey and then you started to go there. Like, what's the what's the sort of future for for the business that you're that you're building?

Chadd Olesen: It's interesting. I don't know what's what's our future? I do think that we're gonna be a dominant player in transactional pricing. I think that we probably will be the dominant player.

Jesse Buckingham: Well, and even even this like transition of sort of starting to think of yourself more as a consulting company, which is a slightly different identity I think in in some ways.

Chadd Olesen: Yeah. I think, you know, our automation is incredible. Most people probably don't know this. We own our own browser. So we don't do RPA like a lot of people think that we do. We do full HTML manipulation and we insert code into the shipper system that doesn't exist and so it gives our customers the ability to apply custom strategies that other people wouldn't.

[22:21]

And I think that we still retain like that entire side where we want to deploy our own automation. We know it's best in class. When we look at where companies have failed with our model is that they didn't know how to price freight accurately at scale. And so for us in this transition into how do we help a customer come in and really not just implement automation, implement automation that scales with their business and helps them win valuable freight. Like that's where we wanna be as an organization. And I'd rather partner with companies like yours to go after the operational side so that we can trim fat and create margin expansion as we become more competitive from bidding.

Jesse Buckingham: Can I so one of the things that you said was that in order to implement spot quoting at scale like this, you really need to like, to automate it? I'm curious about like, what's the give me the bull case for like, why should you want to do this? You know? Like, what is the what is the upside when this goes well? Like, what what's the sort of impact? Why do you think that, like, this is the right direction for folks to move?

Chadd Olesen: Yeah. I think that there's several reasons. I think that I'm gonna talk about what is not important first. I think that originally the companies who tried to come in and automate transactional spot bidding were like, hey, you don't want your brokers spending time doing this. And I think that that is like the absolute wrong way to to look at this. I think that the benefits of automating transactional spot freight at scale are

[24:26]

undeniable. I know every single hole in my shippers routing guide and I can call Kroger tomorrow and tell them every single hole that they have and that I already move active capacity on that lane and I also buy below market. So when we look at automating transactional spot freight, I wanna lead with data and analytics now I literally have insight into everything that's happening in my shippers routing guide. That's super important first of all. As I think that as you look at the space becoming more and more competitive, there are three PLs who are consulting their shipper on where they have gaps or where they have a routing golf guide falling apart or where they have a consistently failing, hey, eight weeks in a row we saw this same freight, there's something that's happened inside your your network. I think that that's like the first layer is the data play is I think is like invaluable.

Jesse Buckingham: And so yeah. And then so basically like, because you are so deeply embedded in seeing and understanding all of the opportunities you are building sort of like profiles of the contours of that freight that help you to understand where routing guides might be failing in ways that maybe they're not even sort of fully noticing, or maybe they are, but like, this is data that allows you to then have a more compelling conversation about like, let me, let me solve this problem for you. Yeah. And, and I just so happen to also have trucks in that area. Right? And and I could support you.

Chadd Olesen: We it was actually announced at oh my gosh. What conference? There was a conference like a couple weeks ago. I wasn't at it. But Matt Leo at CH, he texted me and he was like, hey, you know, Sam Anderson from Bay and Bay was on stage today and he was like, AVRL helped us win a $6,000,000 award with a shipper we had never engaged with before. And it was because of

[26:28]

that exact strategy that I just like described where they already they knew where they had gaps in their routing guide. And so they literally, when they went to bid, they also had a discussion with the shipper about that specific lane. I think that it gives companies a massive leg up than than the broker who bids on, you know, spot freight when he has time. I think at scale you couldn't, like it's almost hilarious not to automate bidding, right? Like I have a 100% participation rate, right? Like let's say I'm a broker and I work with USPS. You have to have what? 80% participation? Regardless of bidding or not, you still have to do it. What happens when they drop 1,900 loads on a Tuesday? Well, that means you have four people who are now actively managing that forward so that you don't lose that business. Like automation is key there. I think also when you look at trying to identify opportunities where you're really good at buying and where you're not good at buying, a lot of brokers are guessing at what they think the rate should be. Then they don't have the ability to analyze it later on are we actually as competitive on that lane as we think that we are. And then I think like volume, you know, have a carrier in Birmingham or if they're broker in Birmingham, they're averaging 700 loads a month positive, know, and these net new shipments, like that's a lot of freight. And so I like kind of laugh sometimes when people are like, oh, the market's like super rough. And yes, it is. But I also am not sure if everyone understands that there are some brokers who are making money and making a lot of money right now.

Jesse Buckingham: What are they doing differently? Like, that is enabling that? Is it some of this stuff that you're describing, which is like, you know, like a a tighter kind of understanding of

Chadd Olesen: where they can buy and so they're able to bid on and win a lot more business? Yeah. I mean, get rid of the nukes. Right? I mean, let's say you won 10 shipments and you lost $400 on four of them, you probably went negative.

[28:35]

If I won six shipments that were all positive, like, I'm probably in a much better place. And so I think that a lot of companies don't use complex strategies in bidding because it becomes too complex and they're like, oh, it's too hard to manage. It is until you're making money. Right? And then all of a sudden your leadership's like, we're gonna double down on this initiative completely.

Jesse Buckingham: Yeah. It's interesting. Like, if you can figure it out, the upside's huge. But there is also the opportunity to blow yourself up as right? Yep. And so and maybe this kinda comes back to your first point, which is like, you don't this isn't you don't like dip your toes in the water here in in some respects. Right? Because it's a capability that needs to be developed internally. Less about like, hey. Do we get adoption? But more about are we gonna commit to making this work because we believe the upside potential is really large.

Chadd Olesen: Yeah. And I think also on that note, like, you're competing against some brokers who are taking it extremely seriously. And I I'm not sure, like, I've seen a couple of posts lately where someone would be like, you know, I talked to a shipper and they're like completely against automation and I'm sitting here and I'm like, man, Kimberly Clark, Best Buy, LG, Kraft, like they won't even work with you if you can't just automate your bidding. Like they don't even have a load board anymore you know, like I'm just sitting here and I'm like laughing because I'm like I don't know if a lot of brokers realize that at some point the shipper is going to require it. And I wouldn't wanna be the broker holding the bag and hadn't ever started. That's a scary place.

Jesse Buckingham: Yeah. I think there is a little some some folks like love to hear those stories because it helps them to sort of validate maybe a decision that like, well, we don't we don't really have this capability yet. Yeah. But it does seem that the trend is kind of undeniable

[30:37]

in some respects. Right? Where like and I and I kind of, you know, if I was sitting in a shipper's shoe, why would I not want to be able to instantly get prices that, you know, and I and I can totally imagine that, like, from a risk perspective, if you have trading partners that are bidding on stuff and are not able to honor it, well, that's probably not gonna be a relationship that sticks around for a long time because you know, what will the the providers that will end up winning are the ones that can bid yeah. In this way and will execute and can execute because they're doing that bidding accurately.

Chadd Olesen: Yeah. I think that's like the same thing with with some of these posts that I've seen recently about RFPs, right? Like the shippers aren't wanting you to price all 40,000 lanes. Like, we wanna see the ones that you're the most competitive on. And there was I did this case study article with Transportation Topics and Dylan at Axle. And what he like, there's this like huge write up about it and Dylan was like, you know, what makes Axle so successful at this is that we don't participate in everything. We only bid on what we're really good at moving. And I think that that's like a big difference between some brokers who really kind of fail at automating transactional freight and then the ones that succeed is I don't wanna take money. I don't wanna take loads that I'm gonna lose money on or I'm gonna have to reprice or I'm gonna have to give back. Like we shouldn't just participate in them. Like, we should literally participate in what we're good at, and we should talk to the shipper about it.

Jesse Buckingham: Yeah. You mentioned though that in some instances, shippers are requiring like 70 to 80% participant. Is that like that they that you are putting a bid in on 70 to 80% of spot loads that are getting

Chadd Olesen: No. That's like, I want a participation either you're bidding on it or you're not bidding on it. Like, I wanna know, are we gonna participate and at what rate or am I not participating at all in that? So a decision, essentially. Yeah. Exactly.

[32:47]

Jesse Buckingham: So so it's the ability to kind of say yes or no. And then if you're saying yes, to be able to execute on what you say you you can do.

Chadd Olesen: Yeah. And I think, like, also, like, let's not undervalue the fact of, like, why I'm not participating. Right? Like, I wanna sit down with the shipper and walk them through a data table and be like, hey. I didn't participate in these loads because of x, y, and z. And I think that I think that there's value in evaluating also what we're not participating in holistically from an organizational level like, hey, I didn't bid on any same day loads. You go back and you look, that's 35% of my business that you're not even participating. Are there areas where I could participate in same day freight? Maybe not four hour but lead you know, if it is posted at 8AM and it picks at 3PM a Tuesday like, could I support that? Probably.

Jesse Buckingham: Yeah. Interesting. One of the other objections that I hear from folks is that this is like that this practice is a race to the bottom. Yeah. I'm curious what you say to that.

Chadd Olesen: I mean, it's laughable. I think that I think that there are some shippers that are looking for the lowest priced freight always. Okay? And I'll use like some water companies as an example, okay? Gonna use Niagara, Nestle USA and Danone, okay? Niagara, yeah, they want like the cheapest provider, right? Nestle will pay a premium on their product and Dannon is a little bit more neutral. Like even though I'm moving the exact same commodity, I shouldn't price all three the same, like I could have the same lane, right? Chicago to Memphis. All three of those shippers should be priced differently based off of

[34:47]

of coverage plus my margin. And so, I actually think like, yeah, there are shippers for sure that want that race to the bottom. But I think that there are really a lot of, like a lot of shippers that are looking for what's the rate, what's my relationship with this carrier and am I willing to pay a premium on it? My, you know, one of my market intelligence team strategy is like, Ray, it doesn't matter if you don't have a relationship. We have to have a relationship with the shipper. We should be talking to shipper daily, weekly, you know, about what's happening in the market. The people who are racing to the bottom, they don't have a relationship with that shipper. I think that's like a failure as at the organizational level completely.

Jesse Buckingham: On the shipper side, are they do they what are the what are their systems around this? So, obviously, they're sort of putting their loads out, receiving bids. What is what does it look like from their perspective? How often is there a system or algorithm or, like, version of, like, something that is deciding who to tend to the freight versus, like, a human actually making that decision and how is that changing or evolving at all?

Chadd Olesen: I think that the human making a decision about the freight is, like, statistically how it's gonna happen. I think there's probably only a couple of shippers that are using, like, automated systems. Okay. However, I'm gonna use, an example of of a an API framework. So let's say you're Dollar General, you require responses back from your carriers in three seconds or it times out. Okay? So you look at like a rating engine. Right? A rating engine takes, you know, a second but what they don't tell you is that Blue Yonder is gonna take a half second on this side and a half second on this side of process. So you really only have, you know, two seconds to, like read the load, price load, return a rate.

[36:49]

We're working with a lot of our customers on speed of transaction. My system processes data in about twenty five milliseconds. Those of you who aren't as technical, it takes you like four hundred milliseconds to blink. We we are trying to rework a lot of our larger customers pricing engines to get them under a second so that I can get them in first because if you're, let's say, a 100 brokers or a 100 carriers who are submitting rates, they are getting ranked first, second, third, fourth by speed. And then you're dealing with the planner's time and attention, right? The planner's probably not going to look at all of them. They're probably going to eyeball it. Hey, I like that person. Select.

Jesse Buckingham: So is that lit but so that it's literally that fast. So like yes. That fast. Yeah. A planner clicks the button and then and so the three seconds is actually just driven by them not wanting their team to, like, wait five seconds to get. So the three seconds is driven by the fact that Blue Yonder will time out after three seconds. So you won't even receive the rate if it takes longer than the shipper, like, requiring three seconds, but the system is needs oh, that's interesting. Yeah. So you've gotta get you've gotta get back in. Yeah.

Chadd Olesen: I wish I could tell everyone the name of this carrier. It's a four p l that we work with, and we work with their brokerage side. They brought us a bunch of data because they weren't winning freight from their own managed trans division. And we went and looked and it was because, you know, the rating engine would take on upwards of eight seconds to process data because they would pull data from, you know, x system and this system and this system and this system and then it would aggregate it and process it, right? So a lot of three PL's like, while you have a tech team, like, lot of them don't know about parallel processing. A lot of them don't understand that you can do calculations and parallel process it at the same exact time. We've been

[38:50]

going in and working with our customers on rebuilding their rating engines to get them in under a second. I think it's really important.

Jesse Buckingham: Yeah. That's very really interesting. Yeah. One thing that I think about often is what is the optimal way to price freight? Or, like, how how should this whole sort of thing work? Because one of the things that that's interesting is that, you know, the market over the years has gotten a lot more competitive. And so there's been this increasing pressure. Like if you go back thirty years, I was not brokering freight and I wasn't in the industry. But as I understand it, a lot more of this stuff was you had time. Right? So you would, you know, get an opportunity. You might call some carriers and you're actually kind of not bidding off a prediction of the market, but something that like resembled or like, oh, I'm definitely gonna be able to move this freight and then you would sort of mark it up. But now, you know, obviously, like the speed, everything kinda pushes you in this direction of needing to to make predictions. So there's sort of one thought that I have is is there a way actually to get back to like with technology to some version where bidding is happening based on real time carrier bids as opposed to predictions? So that's like one thought. The other is that when I see a lot of and maybe this is a lot of what you tackle is that you can kind of have like a, you know, a regression model essentially to sort of predict rates. But but the way that you actually price it to your point is it's actually very it's not quite that. Right? It's like, hey, if it's if it's my fourth, you know, first three loads, it goes like this, but then it's actually gonna jump a lot. And if there's weather, it's gonna look like this. And in this scenario, I'm actually gonna. And it's not quite this, like, you throw all of those like variables in a regression model, you get some sort of blended average that is definitely wrong, but like technically correct. And and I'm curious sort of what you think about like yeah. What do you think that like, how how should this work in a perfect world if you could kind of like change anything about how the industry was sort of structured or maybe that there was like technology that existed everywhere?

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And what do you think, yeah. What do you think about that sort of other piece where it's you know, the way that you actually kinda need to do this well is is that you're, like, you're accounting for all of these variables that don't sort of move in this smooth way?

Chadd Olesen: My theory is that we probably don't wanna use like machine learning models to to price freight. Yeah. I think that it could be I think that the market is so dynamic that people who try and use machine learning probably end up overfitting their models. I think that I have this customer tell me one time that rates don't matter. And and I kind of almost like believe that, that rates don't matter because it really does still bait it is still really based off of relationship. And so a shipper might be willing to, you know, sell you freight for more than me. And and that that's, you know, that's kinda like the reality of the world, and so we look at, like, what is, like, my cost to buy capacity? You and I don't have the same cost to buy capacity because you don't have a strong, like, I don't have a strong carrier sales team as you do, maybe I have a carrier rep who sells a load on the first call and you call 15 carriers and actually negotiate it. And so I think, like, for us, we don't have this, perfect, like, this is what it is and this is the strategy that you need to deploy. When we go in and we look at data, build them like what we think the strategy is based off of how they traditionally buy. And then we've obviously model it and change it but we form our hypothesis based off of that

[43:02]

and it's not a black box. I wanna look at everything, every reason why increased rate or decreased rate or why I'm adding margin here or is this a cost here? We think that the companies who have are going to fail and who have failed, they generate a rate from a black box and then we don't have a way to dissect it. That's a dangerous place.

Jesse Buckingham: Yeah. Yeah. I think I think that's right. I'm curious. One of the things that you've spoken a lot about is how every company buys differently. And I'm and I imagine as you get in and sort of look at the data, you can start to sort of reveal these things, but it's not like a static thing. Right? Like, you might be able to say, hey. We are actually, like, seeing a shitload of business on this lane and we're not buying well. I'm curious, like, how that data ends up getting used to to, like, build better capabilities on the carrier side. Like, you know, maybe like building lane, you know, lane density or, how that data actually sort of translates into like, how do you move your capability and your buying, you know, expertise?

Chadd Olesen: Yeah. I'll give like an example. Let's say that you buy at average from DAT, like as an example, you moved a 100 loads and on you buy average because on 50 of them, you bought 5% above and on 50 of them, you bought 5% below. That doesn't necessarily mean that you buy average, that means that you buy 5% away from DAT every single time. And on some lanes your 5% could lose you a $100 and on some lanes a 5% could lose you $200, you know, and so we don't go in, like when we go in and do the analysis, like we look at a lot of companies actually buy, you know, 20% away from DAT on every load. So like the average is the aggregated average of the industry. But yeah. We look at are there incumbents on this lane where like where like, you know, what's capacity look like on this lane statistically? And so we wanna go in and do an analysis on that. Like, that's what we wanna go in and look at. But yeah. Companies buy I think that companies, like, traditionally probably talk shit about DAT and they're like, ah, DATs rates suck. And I just think that they don't know how to use them, and they don't know how to they I just don't think that they know how to use them.

Jesse Buckingham: Yeah. That's interesting. What do you how how do you think a broker should be benchmarking the performance of their carrier team. Because, you know, one way that folks do it is to sort of look at, you know, the DAT and and how you're sort of buying relative to that. But sometimes that's like sandbagging and sometimes it's not, you know, like, what have you sort of seen there?

Chadd Olesen: It's hard because there's a lot of reasons why we could fumble freight. We could fumble freight for like a number of reasons. I use like an an example of one that I see like constantly. It's like we look at lead time as, you know, same day next day. Right? Like that's how a lot of people look at it. Well, Crown and Cork, 40% of their freight trades over fifteen days out. Which means that if today I'm bidding on Crown and Cork's freight, 40% of it doesn't pick for fifteen days, which means I'm not even gonna take that into carrier market for eleven days. Right? But I'm gonna get awarded it today. I don't know what's gonna happen in the next eleven days, one. And then what does the market look like then? And so we, I think that a lot of people, like when you're manually bidding, right? People look and they evaluate extremities like that and they're just like I don't wanna bid on that or I don't want this or like I don't want this or like, this is good or whatever.

[47:07]

Look at it and we're like, okay, I now actually have to project my cost eleven days from now when I take it into the carrier market. Not, so like what, you know, like I'm gonna project my cost based off of pick, not based off of why I'm bidding right now in real time. And there's a lot of factors that go into to that. Especially that's very yeah. Especially because the market will catch up at some point. Right?

Jesse Buckingham: Yeah. Interesting. Yeah. Chad, I love going deep on this. You know a ton about this stuff, and I've learned a lot from from this conversation. Really appreciate it and enjoyed enjoyed chatting.

Chadd Olesen: I appreciate you. Thanks for having me on.

Jesse Buckingham: Thanks, man.