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Craig Fuller – On The Intersection of Media and Data [Replay] Ep.219

FreightWaves, founded in 2016, is a media and data company focused on trucking, freight, supply chains, and all things logistics.

Episode Description

The intersection of media and data has been the top idea on my mind for quite some time now. And my guest today, Craig Fuller, runs one of the best modern examples of this concept in action. FreightWaves, founded in 2016, is a media and data company focused on trucking, freight, supply chains, and all things logistics. To date, they have raised $91 million, will hit $42 million in revenue this year, split 55/45 between data and media, and have a valuation of between $300 and 400 million today.

Craig’s work building FreightWaves has been an invaluable model for my work at HW Media as Chief of Staff with CEO Clayton Collins and the rest of our team. Conversations like this one have been instrumental in our discussions on the future of HW Media as an all-things housing media business. But we, and especially myself, also believe this model, this intersection of media and data can be a replicable strategy across industries.

The goal of my episode with Craig today was to better understand how media and data can work together to create transformative companies, and I’m grateful for Craig’s willingness to share his experience and expertise. Throughout the episode, we talk about what data sets to focus on first in an industry and how to build around them, developing the product roadmap, the media data flywheel, hiring effective editorial and sales teams, and the investor perception of FreightWaves. Enjoy.

Clips From This Episode

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(00:00:00) – Intro

(00:05:08)  – Why do media companies typically misunderstand ‘data’ and data companies?

(00:07:51) – How did the Media & Data arms of FreightWaves impact each other as you built this business?

(00:13:44) – When you think back about creating high-frequency data, how did you approach building it for FreightWaves?

(00:18:32)  – Where has the bulk of your capital gone in building the company?

(00:28:02) – How do you think about expanding your data sets going forward?

(00:32:50 – Who is involved in your product development process?

(00:38:44) – How did you two sides of your business interact in the early days, and has that changed over time?

(00:50:12) – How do you attract high-quality talent?

(00:54:56) – How do you structure your sales teams? How do you think about pricing?

(01:02:01) – What’s the investor perception on businesses running on Media & Data?

Alex Bridgeman: So, I’ve been excited for this episode because media and data and the combination of the two has been kind of the top idea in my mind for quite a long time now. And I’ve been excited to ask you quite a bit more because I’ve enjoyed all your interviews and podcasts with other folks, and freight is the general topic of discussion generally, but I think there’s so much more to dive into on the FreightWaves business model and how the media and data side interact together. And so, I’ve been excited to take this deeper dive into that model. But one kind of theme that you’ve talked about before and we’ve discussed is how data is often misunderstood by media companies today and the power it has and some of the flywheels you can generate. Can you talk about some of the reasons you think why media companies typically don’t understand data and data companies?

Craig Fuller: I think we have to define what we talk about here first. So, I think the word data gets thrown around a lot in a lot of different circles, but media executives, they’ll talk about data, but they’re talking oftentimes about different things when they refer to data than what we in the data business world really think of. So, what most media executives think about when they think about data is user information. So, this is who are the readers of my content, who is consuming my content, who’s downloaded my podcast, who’s watching my videos. They care about the user, and for them, the world of data is all about the user. And what they’re trying to do is understand who’s consuming the content so they can essentially offer ads up to those individuals or take that information and some monetization strategy offered up to advertisers or sponsors. That’s sort of one definition of data. It’s important if you have an advertising monetization strategy to have a data strategy as well as it relates to users. You have another type of data which would be subscription data, which is who are the subscribers of my content, what’s my daily active user count or monthly active users, what’s my churn, all of the stuff that subscription businesses care about, LTV to CAC, all of that. And that’s all very important to subscription businesses. And that’s a second type of data that’s really important is just user activity, but they really sort of are in the same bucket, which is about how do I monetize the users that are coming to my website. And there’s a third type of data, the data that we traffic in at FreightWaves predominantly, which is market data. Think of it as pricing data, think of it as financial markets data. So, this is the type of data that you would find on CNBC, in the Wall Street Journal, in The Economist, which is basically data about a market that’s trying to understand either the pricing or the economics of that industry. And it’s a completely different type of data, and the users themselves aren’t the origination of the data. It comes from some other third-party source, typically, that you’re sourcing to get information intelligence.

Alex Bridgeman: And within kind of that media data realm, you started with media at FreightWaves and built up a media arm, and that helped enable some of the data piece too. Can you talk about some of the timelines for each side of the business and how one impacted the other in the early years of FreightWaves?

Craig Fuller: Well, so when we really launched FreightWaves, the idea was to be a futures market on trucking. It was the idea of creating a financially sold derivative based on sort of hedging or speculation on trucking spot rates. So ultimately, what a futures contract is is just a financial contract that sold based on some type of third-party price assessment, which means I’m checking on the price and I’m determining what the clearing price of the market is. And ultimately that’s a data in itself. So, think about pricing. We see it in oil with the WTI is the index referred to in the oil industry. You could see it in financial markets with things like the S&P 500 is an index, the Dow Jones Industrial Average is an index. These aren’t necessarily price assessments, they’re indexes. Price assessments are really what we’re talking about when it comes to futures, which is a third-party price of a physical market assessed, and so it’s just a measurement of price on some agreed methodology. And in order to create a futures contract, you have to have that. Well, we realized really quickly that creating a new financial market or a future’s market is a pretty difficult thing to do to a market that didn’t understand it. So, you can’t just start a future’s market and say, hey, let’s go. You have to educate and evangelize people on the market dynamics. And for trucking, there really wasn’t a market data business that provided what we call fundamental data, which is not related to price, but the things that drive the balance of supply and demand in a market. And so there really wasn’t an equivalent of a Bloomberg or a CNBC or any type of sort of commodities news business or market news business for trucking. A lot of the trucking media had stuff that was written a week or a month old, or sometimes quarters old. They would do the interview and then it would get published months later. And you’re like, well, this isn’t really breaking news. And this isn’t really up to the date content. And so, what we set out to do was to fix that because we felt like if we’re going to evangelize futures, we have to sort of take control of our own destiny by writing about the market in a way that nobody else was because it was just sort of unknown, and people didn’t understand it. We never intended to be a media business, but we realized through that process of attempting to sort of talk about futures and talk about freight market as if it was a commodity, that there was a lot of desire for content around the market, that there was this evolution and somewhat revolution taking place around freight. There was a lot of investment dollars going in and a lot of interest in supply chains. And there wasn’t really a media business that had consolidated all that information and could talk about the trucking market or broadly supply chains in a way that sort of the business media would talk about it. And so that’s what we sort of dove into. We hired one reporter, never intended to really make it into a full-time business, but then it became a business because people wanted the content, and then we scaled up from there. And that was really the start of what we call FreightWaves. At the time, it wasn’t even called FreightWaves. In fact, the initial media business, when we launched it, had like two writers, but they didn’t even make the pitch deck to our seed investment because we didn’t consider it core to our business model back in 2017. So, it wasn’t until really 2018 where we realized, okay, this business is- we signed some contracts for advertising, some media contracts, and it really started as the formation of something special. We could tell that people were reading and consuming the content, and it was really the start of something special. And then after that, we ended up launching the data business because we also realized that in order to launch a futures market, you have to have data that can help people make decisions about the market. And there wasn’t a real, a high frequency data business associated with trucking. There was sort of historical price assessments or price assessments that would lag by weeks. And it would be a news article or two that would come out about the market, but it was typically lagging data. And then you had government data, and then you had some third parties that were providing data, but typically they were about a month to a quarter, even a year old by the time they got published. And that just wasn’t the way I sort of think about markets. I think much more dynamically. And so, we started to create high frequency data that was published every single day that really sort of provided that information about supply and demand. That’s when I talk market data businesses, that’s what I’m talking about is the type of data that S&P Platts publishes, Argus, Opus. It’s market data. It’s the kind of stuff you would talk about in the oil markets, the kind of stuff you would talk about in the agricultural markets, the lumber markets, even crypto. Anyone who’s been around the crypto ecosystem, they talk about data all the time, and there are these- this whole data ecosystem that sort of popped up to support the crypto industry. The difference between say crypto and freight is that there is something physical evolved in freight. It’s something that arguably you have to have it. I mean, we could turn off crypto and the world would not end, but if you turn off freight, the world literally comes to an end. Civilization ends. And so, because of that, you have to have it, much like oil. If you turn off the oil and energy, civilization ends as well. And so, it’s really that sort of fundamental to the economy.

Alex Bridgeman: There is a paper that you’ve referenced a few times called The Economics of a Data Business by Abraham Thomas. And in that, he goes through a bunch of the different ways that data companies acquire data, whether it’s through brute force or payment in kind or a couple other methods. When you think back to creating that high frequency data in the early days, how did you go about building that data set?

Craig Fuller: Well, I had the benefit of growing up in trucking, been around it my whole life. And so, literally, my dad and my grandfather were trucking executives and founders, and so they had their own companies. And so, I grew up around it. I learned a lot about business through my father’s eyes, and trucking was the industry which he built his career in, so I learned trucking naturally. And so, I understood really what we needed to have to have data, but I also knew how fragmented. So, if you think about how many trucking companies there are in the United States, about 200,000 individual trucking companies in the United States, it’s the number one employer in 29 states. But there are millions of companies that buy trucking services, whether they’re a small manufacturer, they’re a small retailer, or a big conglomerate like a Walmart or Honeywell or Tesla or whatever. Like every company that’s in the physical economy is either consuming transportation services or it’s selling transportation services. So, it’s all of those individual participants. So, to go individually to sign up every company to get data would be very expensive because it’s very fragmented. If you look at a company like JB Hunt, which is the largest surface freight company in the United States, which means it’s rail and truck, it’s about a $14 billion corporation. But if you look at the total logistics spend in the United States, it’s about 1.8 trillion. And if you take just surface, which would be rail and truck, it’s over a trillion dollars. We’re talking about a business that arguably has about 1% or 1.5% market share. It’s simply not big enough. You can’t take their data, JB Hunt’s data alone, and sort of understand the economy. And if you sort of take the top 10 trucking companies, you’re still less than about 7 to 8% of the surface market. So, it’s a really massively fragmented market. So, one of the sort of reasons that I think no one did what we did was because they didn’t understand how to go get the data, who to talk to, or even the value of it. And so, what we did, we didn’t start with pricing data, which would be the most valuable piece of data in the marketplace. In any type of commodity market, pricing’s the most important. What we said was what drives price since we understood that price is really about supply and demand. And we sort of understood how supply and demand is sort of reflected in the trucking market. So, we went after the software vendors that service the industry and built up an aggregation model of taking in data from software vendors. And when we knocked on their door, they had never sold or monetized the data before. And they were just like- oftentimes they would shut the door on us. We did find there was a couple that had large market share that were willing to provide the data to us. And because they had large market share and they were willing to provide the data, it gave us really a nice opportunity to sort of bring that in. It was sort of funny because back in those days, they said they didn’t even save the data. They didn’t think it had any value, they were deleting it off their systems. And we were able to take that. So, the equivalent, if you think about financial services, so if I want to understand the retail spending and consumer spending, I could- there’s a couple ways to do that. You could go up and knock on Amazon’s door and Walmart’s door and Target’s door, and it would take you years to get all of these retailers to give you the scale. Or you could make a phone call to MasterCard or Visa and basically say, hey, I want to buy your high frequency, anonymized data to understand what consumers are spending, what categories they’re spending. You could also go to some of the banks, and we’ve seen Bank of America and Citi Bank publish these reports where it’s sort of high frequency credit card data. And what they’re trying to do is provide sort of an analysis on a large enough sample size on what consumers are buying. It’s the same idea behind how we built our data business was we went to the software vendors that serviced the industry and basically bought aggregated data. We stripped it so it was anonymized, and nobody knew individual participants in the market. And then we created a methodology which anonymized it and then ended up offering this data for sale. And that was how we got into the business.

Alex Bridgeman: And so, you’ve raised around $91 million to date. And when you’re thinking through building that data set, and then the others that you’ve added over the years, where has the bulk of that capital gone? Has it been the most expensive to build out these data teams, buy the data itself, the media side? Like what has taken up the most resources to build FreightWaves?

Craig Fuller: Yeah, so just to sort of unpack the 91 million, that’s the headline number, but there’s a lot of components that go into that. So, 10 million of that would be debt. So that is not equity financing, but if you look at the cash consumption, there’s a part of that we’ve not even drawn down. We have the option to draw down 20 million, which we’ve not touched. But if you actually looked at the amount of burn, which is the amount of capital we’ve consumed, it’s about $45 million. So sort of look at what we’ve consumed, it’s not 90 million, it’s about 45. We have about 23 million in the balance sheet currently, and then another 20, which is available to us at any moment we want to call on it, which means we have a very rich balance sheet. And I like to say we have an infinite balance sheet, which means because we’re not burning capital, we’re really sort of capital neutral or cash flow neutral, which means we’re not burning, but we’re not increasing our cash position. And so, because of that, it means that the company, because we have very small amount of burn, can basically live on forever without every having to raise money. And that’s a nice place for a founder to be. But having said that, data businesses are very expensive and they’re very slow to sort of catch on. One of the advantages I had early on at FreightWaves is that I was, because we built the media business, I was a known commodity. We were talking about the freight market a year before we actually launched our data business. I think we were 17 or 18 months into our media business before we actually launched the data business. And so, we were well on our way. And at that point, we had probably burned $6 to 7 million. It wasn’t a huge amount of burn at that point. But once you implement the data business, which the platform cost us a million, million and a half to launch, the data wasn’t very expensive in those days when we first launched. We took a lot of open data sources or government data that we were able to scrape websites, and sort of take APIs from Fred, put it together with this sort of really finite data. And one of the things that people don’t realize about Bloomberg’s business model is, we look at Bloomberg today and we’re like, oh man, they have all of the data that you would want about the economy or the financial markets. The reality is Bloomberg actually started with a very small, very finite set of data that had never been seen before, but it was very valuable. And that was the same thing we had done was we started with data that was very valuable, had never been seen before. It was unique. And that was really where our first efforts went into. But these businesses are- it takes a while because you have a substantial upfront cost and investment to build the business. Our media business was probably cash negative for about a year. Actually, after we launched, so probably about two and half years, it was cash negative, but I bet we total- sort of looked at the total amount of capital that the media business consumed up until the point we got to cash break even, it was probably the order of about 3 to 4 million. It wasn’t a lot of money. The bulk of the capital went into building the data platform, the SaaS business, the UI, the UX, and the initial data science, and then just the go to market. Go to market can eat you alive in a data business because these things take a long time to become credible. And the thing is when people are buying data businesses, it’s very different than buying what I call a workflow business. So, think about a workflow business, everything from sort of Microsoft Word to PowerPoint to- all of these businesses are sort of workflow, which means they are helping you do your job more efficiently by cutting out effort to do something. And so, essentially, a workflow business, Slack is a workflow business in some ways, email is a workflow business. An accounting software is a workflow business. QuickBooks is a workflow business. All these businesses are workflow and effectively what they’re trying to do is make your job easier, faster, quicker, and potentially cheaper. Those are workflow businesses. Data businesses are very, very different. What the data businesses is that I have data, and I’m trying to help you understand something about the market. Now, if we talk data in the media context, it’s about how do I- who are the leads and who am I trying to reach? I’m not talking about that. I’m talking about fundamental market data businesses are trying to help you understand the economic cycles of your business. And so, it’s pricing, it’s market intelligence, it’s supply and demand effectively. And those things take a long time because if you’re a buyer of that type of data, let’s say that you’re the head of financial planning at a big box retailer, you’ve been using data for- your company’s probably been using it for decades. Just take somebody at Walmart who’s sitting in the inventory planning and financial planning part of Walmart. Well, they’ve been buying transportation services since the business was born, and they’ve had models that they’ve used, whether it’s economic models or pricing models, they’ve used their own data and they probably supplement it with some forecasting data. And so, you’re trying to break in there with this entirely new data set that’s never been seen before. And you’ve got to prove that your data is better than what they’re currently using and it’s worth them buying. That’s a pretty significant challenge to do that. And so, it’s a slow go in the early days. The good news is we were a known commodity because of our media business. And because we were a known commodity because of our media business, it gave us credibility with the companies to at least have the conversation. And so, what happened is we launched the product in May. We didn’t actually start charging customers until August. And we said, look, you can have access to the data for three to four months and you can back test it. You can play with it. And then if it has value, you can buy it. And that was essentially how it started. That’s how we got our first customer. So, the first three to four months, nobody spent a dime with us. And it wasn’t until really that sort of four or five months in before we started to generate any kind of revenue. And it was very slow. I mean, if you go back into 2018 and look at our revenue from data, it was a couple hundred thousand dollars in total. And we had probably at that point spent $10 to 12 million in capital just to invest in the front-end system. So, this is sort of a- this is a venture style bet because the reality is while we believe our data is really, really good and it’s important and it will do what it says it will do, you never really know that until you get it in the market and get it in the hands of someone’s actually doing it every single day. And they’re back testing it, and they’re saying, okay, this is more reliable than the current data that I have. Because it can’t be just as good. It has to be steps better. It has to be levels better. It can’t be just as good or even slightly better. It has to be leaps better for you actually to monetize it. Because you’re not only replacing the way they do business today, but you’re replacing an incumbent. And if incumbent is trusted as a source, the challenge is you’re trying to prove that your data has more relevance than they do. That’s why these data businesses rarely, rarely are successful. And I think that’s the- and talk about market data businesses are rarely successful, simply because it’s not like you can just take the same data. The other problem that data businesses have, and the reason that they’re typically unsuccessful, is they start with very small amounts of data in the early goes of it, and they try to go after a proven market. So, let’s say, take Flying is an example, another business that I own, the data set that you would sort of default to would be airplane pricing data. So, like what is the used airplane pricing, or if you’re in the automotive sector, what a car is, what’s the used car market go for? Think about that for a second. There are already data sets that provide airplane pricing or used car pricing that have been trusted and well regarded, and every auto dealer and everybody buying or selling a used car is looking at those data sets to reference to say, hey, how is this? And all of a sudden, you come up with a new pricing model and you’re like, hey, the new- a used Model 3 Tesla is $50,000 in your data set, but this one says it’s $42,000, and you’re trying to convince somebody to use the new data to reflect it. It’s very difficult to do that. So, I think one of the mistakes that a lot of data businesses do is they try to go out and serve an existing market with the same sets of data. We didn’t try that. We tried to provide data that wasn’t readily available that we thought provided more intelligence about the market, but wasn’t in a red ocean. We went after a blue ocean strategy versus a red ocean strategy because I think sort of the innovator’s dilemma, it was a market that the incumbents didn’t see nor want nor even think there was value. And so, they sort of ignored it.

Alex Bridgeman: So, when you think of that first data set that is hard to get, no one’s seen before, it takes a while to build up the infrastructure to get that data and sell it to customers, and you’ve now like planted your flag in this data set, how do you think about adding those second, third, fourth, and fifth data sets? Like where do you expand from there? How do you think through that part of the strategy?

Craig Fuller: I think every market would be different and ultimately your customers. So, I think in the early phases of business, and this is true for any technology business, really true for any business, but technology businesses are sort of special in the sense that unlike sort of like almost any other business, it’s easy to get started. Like I can open up a shop today, whether it’s online or physical, and I’ll get pretty good market feedback. I may have to buy some initial inventory, but chances are I’ll know quickly, before I burn a lot of capital, whether my business is going to work. Technology businesses don’t work that way. And data businesses are even more challenging because oftentimes in the very earliest days, if you’re a data business, customers have not- they don’t trust it. If I run a workflow business, let’s say that I can make your podcast better by making the audio better or I can make it easier to edit the podcast, I can distribute the podcast through all these platforms, I can just make your life easier, the first time you use that product from a workflow standpoint, you’re going to know it’s better. And I think Slack is a great sort of modern example of that. The first time we use Slack, it sounds pretty ubiquitous. Like I have email, I’ve used other messaging solutions, but all of a sudden, you use Slack, and you’re like, oh, this is better than what I’ve been using, and you sort of migrate to Slack. Or the first time you got into an Uber, you’re like this is sort of weird, I’m getting into some dude’s car and now I’m in the car. Like, why wouldn’t I just order a taxi? You think this before you get in the car, and then you’re in the car, you’re like, oh, that was easy. The dude showed up and he delivered me and I didn’t have to take out my wallet to pay, and I didn’t have this creepy dude yelling at me wanting a tip. Like it’s an experience in itself. And I think you know, okay, this is a step up where you’re going to use that product. Data’s very different. Because what you have to have with data businesses is you have to have the user that’s going to potentially- your customer that’s going to potentially buy it, actually use it in their business, and you don’t know whether they’re actually going to do that. So, it’s a completely different type of experience than say workflow. And I think for us, it was one of these challenges of sort of instinctually understanding what they needed in the market and understanding how the market’s built, building that, and then adding a bunch of data that we instinctually also thought they would want. So sort of as you continue to build it, you start adding these data, this data point. And I think we were wrong. Our batting average on 70, 80% of it was wrong. I mean, at one point, three years ago, three and a half years ago, we had been in this for about a year and 85% of the data that was being consumed, the consumption, was the same data sets we started with. So, all this effort and energy and money and capital went to buy all these secondary data sets, which at one point we had 150 different types of data suppliers, and yet, it was like three data sets that made up 99% of my revenue. And yet we were spending all this money inefficiently that didn’t matter. And so, we pressed reset. The company, so in 2019, we’ve been doing it for a year and a half. We learned really quickly what data that people wanted, what they didn’t want, what they were willing to pay for, what they weren’t willing to pay for. And I think, we then sort of got rid of a lot of the data that wasn’t valuable and focused on what we found was valuable, and really took about a year. So, it was during COVID, we had gone through this massive growth before COVID of adding a bunch of data, a lot of it not being used. And frankly, the experience wasn’t better. Better is not- in data businesses, having more is not necessarily better just because it actually makes the user’s experience worse because you have all these libraries of data sets that actually don’t get used. The system’s slower, you’re having to maintain data sets that aren’t real valuable. And we went through a process of sort of cleaning up data that wasn’t valuable at all and getting rid of it. And one of the things you have to do in data businesses is sort of look at consumption. Like what are people actually using? Not only can you do that by just sitting down with customers, but there’s a lot of products like you can use to sort of monitor API activity or monitor on screen activity to look at how it’s being used and what’s the consumption of the data. And then that should be your answer on how to drive intelligence out of it.

Alex Bridgeman: So, who becomes involved in that product development process? Does your sales team who’s either selling sponsorships for media or the data product, do they tell you, hey, these customers want this data set, we should create it? Does your editorial team who’s writing and using the data, do they say, hey, we really want this data set, do they give input? Who’s on that team of determining which data set to go after next or which feature or product development piece happens next?

Craig Fuller: In your earliest days, it’s the founder. For me, it was me, the founder of the company. And if you don’t understand the market, I think one of the things is, well, let’s just make up an industry. We’re going to go out and attack it. And I think you looked at that livestock business at one point. And so, we’re going to go out and create- but there’s a media business based on livestock, on cattle. Okay. You’re not a farmer, I’m not a farmer. And so, we don’t know the farming business very well. We don’t know what- I eat beef, but people also buy trucking services, but it is massive stretch to go from, hey, I’m eating beef and I consume cows, and I sort of understand how the livestock business works to somebody who has very deep knowledge of actually how cows get sold and the supply chain that goes into that. And you have to have someone like that on your side if you’re going to start a market data business. Because the moment you go to market, and I’ve seen this in transportation, where we were a part of an accelerator in FreightWaves’ very early life, and I’m big- I come from big trucking – mega trucking is the term that truck drivers love to use. And what that means is I grew up and as part of a- was sort of one of the godfather families of big trucking, like big- I’ve been around the industry my whole life, big dominant family in the industry, still fragmented, a rounding error. If our family exited the industry, nobody would notice because it’s such an insignificant part in the overall scheme of things, but it’s still a big family. So, I knew the ins and outs of the industry, and I brought that knowledge to the market. So, I was pretty instrumental. And I had a product manager who had worked in software who built one of the first pricing services. He passed away. He was effectively my co-founder, even though he was so humble that he didn’t want the title of co-founder. He was sort of the original guy with me that started it, and our combined understanding of how the market worked, he understood the software side of the world, and I understood the mechanics of the industry. We worked together to define those first data sets. And again, it’s important that you have deep knowledge. So, if we’re going to go start a cattle- a livestock business for data, we would have to understand not just the pricing of the cows, but what are all the data sets that the farmers, the ranchers, and the butchers and the meat suppliers, what do they need to make their business work? And I don’t think you can do that as an outsider because I think if you set down sort of like the Model T thing, that if you ask somebody what they want, they want a faster horse. Like if- and so if Henry Ford had asked, what do you want, you want a faster horse. No, you really want something that’s better than a horse. And I think that’s the same thing in data businesses. If you go and ask people what they want, if we had asked them two years ago or say four years ago when we started it, what they wanted, they would’ve said they wanted better pricing data, but they would’ve never paid for it because they wouldn’t have believed it. We realized what actually they wanted was things that drove price, that drove supply and demand, that actually predated the price, that could predict price. And we focused on that first, and that was something that’s pretty important. So, in the earliest days, I think you have to have a founder who understands in the industry. And this could be somebody who’s worked in the industry deeply, who’s been in the workflow that says, hey, this is a problem, and I’ve tried to solve it. But they have to have a solution. They have to understand how that solution can be applied in their business I think before they can get anyone else to buy into it. Now what’s happened since then is for the first say two years of our business, everything was about these initial data sets that I was involved in. And then, at some point, a founder becomes his own worst enemy because what you actually want to do with your team and the engineering team, I think this is true in a lot of businesses, is you are wanting your vision to sort of play out in the business versus sort of the customer’s feedback of what they actually want. So, there’s an instinct for me to be like, oh, this would be really cool if we could have this data set or could do this with that. The reality is that customers also now have started to use the product, start to evangelize the product, they start to understand it. They then have their own sets of needs. And I think you have to have like your team. So, we have a customer success team that works with our customers on a daily basis. We have our sales team that’s talking to, pitching new customers. We have our BDR teams. And then we have an entire product team. I think we have about a dozen people that are in product, and all they do is think about new product iteration. Our data science team is also building data science models, almost think of it like an R&D lab of sort of a drug research business. Let’s say if I was going to run a pharmaceutical business and I’m in the development side of it, I probably have a lot of scientists that are just running all sorts of experiments constantly. And we have a data science team that does that. And then we have a sourcing team that goes out to the market to work with potential data vendors and partners. And oftentimes what we’re doing is we’re experimenting. So, we go out and do a temporary, say, six-month deal with them that doesn’t cost us a lot of money. And then it goes into R&D, and the R&D team tries to basically create new data experiences.

Alex Bridgeman: We’ve talked about it a few times, the fact that your media business evangelizes your data product over time, and that a lot of that early credibility came because you started to build an audience through the media business, how do the two sides of that business, how did they interact in their early days, and how do they interact today? Because there’s a ton of flywheels between those two businesses that I would love to dive into.

Craig Fuller: Yeah. So, a media business’s success is all about evangelizing something or about educating context. And so really, if you think about building a media brand, it’s about speaking to an audience with an audience. And so, what I mean by that is if you’re talking about an industry, you’re talking about the industry in a way that people who are practitioners in that industry understand. I mean, I think it’s really important to our model at FreightWaves is our media business is successful because we’re talking at the level of the people that are active. We are active practitioners in the industry. And when we’re talking about it, we’re talking about it with authority because we’ve been there, done that, and we can sort of explain how and why something is the way it is with a degree of authority. And what we’re able to do is tell stories or anecdotes or provide context because it’s firsthand knowledge. But then we’re using the data that we have in our internal systems, our own data sets, to articulate why something is the way it is. It’s not just, hey, Craig said the freight market is going to go into a recession. Well, that’s one thing. And I’m now at a point, I have enough credibility where people sort of believe that, but I also bring data to back up my arguments. And in the early days, people will be very cynical. And look, it’s not that everyone sort of said, okay, yes, you have data, and yes, you’re Craig, and what you say is God and let’s worship it. That’s not the way it works. I think you have to sort of make enough assessments of the market and make enough calls that are- to really be credible, you have to, in some ways, be contrarian in some of your calls, because if you’re not contrarian enough, then people are just like, well, you believe what everybody else believed. And so, I think part of the value that we’ve proven with our data sets is that our data is so high frequency and so in tune with what’s happening in the market and we’re bold enough and call it brave enough or ballsy enough to go out and say, hey, we think this is happening based on these data sets, and this is some other additional anecdotes to back it up that when those things are contrarian and they become true, the people really start to develop a level of trust to those data points that they wouldn’t otherwise have. And I think for us, it’s in the earliest days when we built the business, it was the first adopters were sort of in the network or a part of the community, were reading and consuming the content, believed that we had some interesting perspectives on the market. But there was a finite number of those, maybe 150 to 200 companies in the earliest days that sort of believed in what we were saying, part of this ecosystem that we were putting out content. But growth slowed pretty aggressively in late 2019 because we had sort of reached a finite market. But something really interesting happened to us in 2020. And one of the big issues that we had in the earliest part of our business was we were going up against competitors that had data that had been around for decades, and they would say, this is new data. Like, do you really need high frequency data? We have all this history, and this history has been proven after cycle, after cycle. It’s decades long. It’s much better than this data. Yeah, it may be high frequency. It may be fresh. But what’s the value of it if it doesn’t have any history? That was the argument being used against us. In fact, it was used against us in February of 2020; they we’re still hammering that point. And we were watching COVID. This is one of the values of a media business, and particularly what we do, which is sort of global supply chains, is we’re watching what’s happening in China. We’re watching what’s happening in Europe. So, China would’ve been January of 2020, February would’ve been Europe. And then, it hadn’t really hit the United States. It hadn’t come to the state of Washington at this point, COVID hadn’t. But we were talking about things actively and talking about the freight market and what was happening. So, we’re in June with what was about to happen, and all of a sudden, COVID hit. And if you think about supply chains, supply chains, and I think this is the true for a lot of industries, COVID broke everything. So, like any history that existed in the pre COVID era, it’s sort of like World War II, any history that existed prior to the war or prior to the end of the war is sort of not very valuable in economic or market models because everything’s rewritten. And it’s the same with COVID. COVID basically pressed reset. It’s a once in a lifetime opportunity to press reset on the economy, for better or worse, and probably we’ll look back in five years and say that might have been worse. But it did press reset. And when it pressed reset, it gave us an enormous opportunity to define the need for high frequency data as being the most reliable sets of data. And then what we did was we made some pretty aggressive calls that were, at the time, people didn’t believe them. So, we were sort of calling the mini cycle in March 2020, what was happening in the economy. It was taking a while for people to catch up. People were like, oh, this isn’t going to have an impact to the economy. This would’ve been like March 5th, 2020. Our largest competitor was like, oh, COVID is just going to flash over. It’s not going to be a big deal. And we’re like, no, like we’re seeing this play out in China with data we have, we’re seeing it play out in Europe data we have. You are nuts if you don’t think this is going to be a massive impact. And then, in April of 2020, I wrote an article about the aggressive V shape in the economy. Bloomberg TV called me the most aggressive person on TV they had all month because I was like, it’s coming, we’re seeing the economy’s going to have an aggressive V-shape. And it was a very like unpopular- I was getting hate mail, people were so angry because they’re like, don’t you read the news and all these layoffs, and I’ve been laid off and I’m worried about my job, and you’re talking about this- like, stop putting out nonsense is what people were accusing me of. Like, you’re just trying to manipulate the market because you want people to buy your data. And I’m like, no, it’s coming, it’s coming. I’m telling you, the economy’s going to have a massive rebound. And I made all these arguments with our data that we could see. And it was very contrarian. And it wasn’t until September where people started to- that contrarian call was no longer contrarian. And people were like, okay, that was a credible call, but he got lucky. That’s what they said. And then this past, just two and a half months ago- But it did, it created credibility. Like there was a lot of sort of new credibility created. And then two months ago, at the end of March, March 31st, 2022, we saw the same developments in the data, but the opposite direction, that there was this massive cooling. So, we saw this massive heating in the economy in April of 2020 in supply chains, which are way upstream to consumer consumption, by the way. Like it takes 8 to 12 weeks before, and maybe even 3 quarters before it hits the consumer market. Like, the supply chain chaos you saw last summer and last fall we were seeing in January. We were predicting it’s going to happen, and we were putting out articles, it’s coming, it’s coming, it’s coming. But those were not what I call massively contrarian calls. But when we called the latest recession, it was quite contrarian back in March, early April of this year. People were like, man, the economy’s great, employment’s great. You’re nuts; there is no way. But what was interesting was the stock market had an aggressive reaction. The transport sold off 15% the day that article came out. Like the next day, the entire transport crashed, and it was our call. And like it was sort of mindboggling that we had crashed the market. The industry, which we operate in, the stocks had crashed. It’s like, oh my gosh. Like I didn’t- sort of like that domino thing where you touch it and then everything falls over, it’s exactly what happened. And then we saw two weeks or three weeks later, Walmart and Target both came out on two separate days about excess inventories. It was the data we were seeing that was telling us that that was going to happen. And now we’re seeing, at this very moment, an even more aggressive slowdown. We’ve seen it in housing. We were able to predict that the housing market was going to slow down pretty aggressively because the data, think about it, you’ve got to move lumber. And if you’re not building new homes, you’re not moving lumber. And if lumber slows down, it means that people- new housing starts are going to slow down. We’ve seen it in retail sales. We’ve seen it in- all of this development you actually see in the freight data because it’s really upstream. So we’re able to now talk about the market, but that credibility is completely different. And we did one, too, a couple weeks ago where we talked about the global slowdown in container movement, where basically there was a- everybody assumed when China opened up, there was going to be this massive tsunami of containers coming from China with all these products that had been backlogged. And we said that’s not happening at all. We’re not seeing any of that. In fact, we’re seeing the opposite. As China comes online, the economy’s actually slowing down. The amount of containers moved out of China is actually going down and sharply contracting, not increasing. And so, it means that there is a much broader recession. And I think now- and when we posted that, there was a sell off, I think, 18 global shipping stocks. This is, if you remember, there was a period in June where the stock market had this sort of like surge, like everybody’s oh, the bolt- the bear market is over with and everybody sort of declared the end of it. And then we reported this article on a Tuesday morning – it was actually on a Monday evening. And then Target came out that morning and talked about they were going to have to revise their earnings that came out or their forecast that was like three weeks old and said, oh, it’s even worse than we thought. Well, that was published that day. So, it sent this massive sell off in global shipping stocks around the world, and then everything else has fallen apart since. So, I won’t take credit that we crashed the market, certainly not. But I think what’s happening is this high frequency data is now moving the market in ways that- sort of mindboggling how it is. But that credibility really is important in how you build these businesses. And you got to make some contrarian calls. Like if you’re telling things that everybody else thinks, you’re never going to become a standard.

Alex Bridgeman: Yeah. And a big part of that is having writers and an editorial team that really deeply understand trucking. And to get those folks, it’s very difficult. You have to pay better. You have to offer a great environment where they can really like use all their skills and interest and passion for trucking and freight and logistics. How do you build a company that attracts those types of people? Because people have options, how do you make your option better than the other ones that are on the table for them?

Craig Fuller: I think mission’s really important. So, people who are- so I think if you look at the sort of nature of our staff, a lot of them came from the industry. And I sort of use the ESPN analogy. Think of like the people you watch on college football, like I’m a big college football fan. It’s Kirk Herbstreit, or now RG3, because Baylor, like these are former players, former quarterbacks, maybe they’re former coaches, maybe Philip Folmar is on TV or Mac Brown is on TV. He’s a former coach. Like they make incredibly compelling journalists, but sportscasters because they’ve actually been on the field. And I think if you look at ESPN, they’ve done a great job of filling out the ranks of not only sports fanatics, but they can articulate and be journalists, but they’re also, at the end of day, they are, they were there in the game. And I think for us, it’s a lot like that, that I think we have gone out and hired people from the industry who lived it and breathed it. And look, the thing about freight, I think the thing about sports, the thing about any of these industries that are sort of high stress industries and experiences is you get burned out really quickly. Like if you’re in freight, you’re having to deal with service failure. It’s a thankless job for the most part. So, you’re making money and maybe you’re making six figures, but you’re getting yelled at every single night, and you’re on your phone at 2:00 AM because a shipment didn’t show up or a factory’s been shut down or you’re supposed to have some type of- you’re transporting organs and the heart gets misrouted or something. Like this is the way freight works, constantly dealing with service failures and you’re getting yelled at, and it’s an easy way to burn out. You can make great money, but it’s an industry that burns them out. And I think what we give the opportunity to do is attract those very people who have had deep experience and offer them a chance to be in the market but not on the field. So, think of the ESPN analogy as like RG3, who was the Heisman at Baylor, like he loves football, like that’s his life, but now he decided between being a backup quarterback, which is not really in the game, like he may get one play a year, makes good money or- and so like, you don’t really get to be in the game, you’re in the game but you’re not. Or sitting on the sidelines and vicariously living in the game every single day, every single minute. And you’re cheering for the teams and you’re judging the teams and you’re judging the plays, and that’s what a sportscaster does. And I think that’s the thing that is really attractive to someone who’s been in the industry is that they get to do that from afar. It’s like I always say, it’s like freight brokerage without service failures because you have the adrenaline, you have the like energy of being in the market and it’s something you know really well, but you don’t have to deal with the nonsense when something goes wrong. Like you get to talk about other people’s failures. You don’t have to talk about your own because you’re not responsible. So, I think that’s been really attractive. And we’re credible. I think to build any type of business, if you’re going to get people who are really smart, who are articulate, who have opinions about stuff because that’s what media’s about, you’ve got to be pretty secure as a leader to bring them- I don’t think, going back to sort of the cattle example, I would not be a credible leader of a bunch of ranchers or people writing about the cattle market because I know jack about the cattle market. And I think credibility’s really important to media businesses and particularly data businesses because you are making the calls and you’re deciding whether you’re confident enough to make the calls. And one thing about FreightWaves is I often make the contrarian calls. Like our team is publishing content, but it’s when these massive sort of changes happen, a lot of time that I have the biggest microphone. And I think the team has to- I have to be, A, confident enough to make them, but also the team has to respect my analysis. Because if they don’t, then they’re not going to stand behind it.

Alex Bridgeman: Yeah, that’s a good point too. I want to hear a little bit more about sales structuring because there’s kind with two sides of the business, you’re selling sponsorships, ads, events, newsletters, digital. And then on the other hand, you’re selling data subscriptions. Do those operate as separate sales teams? And then, how do you structure each of those teams with within the two sides of the business?

Craig Fuller: They do, they have entirely separate go to market motions. So, your media business is all focused on advertising, and effectively advertising is nothing more than someone that- a business that’s trying to really do two things. One is they’re trying to build a brand and create a brand that’s associated with some part of that ecosystem that you cover. And so brand building is sort of one exercise, or it’s top of funnel for customers. They want customer actions to sort of create outcomes. And that takes a person that understands how to do that in a media business that can sell that. Selling data is very different. Selling data is if a media- I can take someone off the street who’s never sold freight who has zero knowledge of the freight industry, and they can sell media all day long. And I’m not trying to dismiss media, but they can sell that because that’s just about audience segmentation and hey, I want to reach truck drivers. Okay, well, that’s easy, we can help you reach truck drivers. The sales process for selling media actually is much more down the line, matter of fact. It’s more competitive, but it’s much more down the down the line. Teaching someone to sell data of a market, when you’re talking to people about the data, the person on the other end of the phone or the other end of the screen is likely as knowledgeable about the market as anybody you’ll talk to in the market. And what I mean by that is if you’re not- if you don’t understand how the data can make their life easier or make them more money or save them more money or mitigate risk or whatever action this data’s supposed to do, they’re not going to buy from you. And so oftentimes, when you’re buying- when you’re selling data businesses, and it’s been difficult even for our executive management team, our CFO in particular who did not come from freight and he came from SaaS, always wanted to create a SaaS sales team. He wanted to hire traditional SaaS people. And it was something that we never found success doing; when we did it, they never really worked out. And part of it was because the people that were selling the product that did not come from the industry were not relatable to the other person on the phone. The person that was buying the data knew that person didn’t know what they were talking about and couldn’t articulate how the data could impact their life. And so, really, we have a sales team that comes to the industry that can talk and articulate it and basically relate with the other person on the phone. We have a BDR group. We have market segmentation, think about data businesses, particularly pricing businesses, is a two sided market, buyers and sellers of a service. And so, you’ll have different segments that you’re selling to. So, if I’m selling into a sort of consumer product packaging company, like Nestle, or I’m selling into a PepsiCo, it is sort of one type of salesperson. If I’m selling into a transportation provider, it’s another type of salesperson. If I’m selling into a hedge fund, it’s an entirely different type of person. So, you’ve got to have different segments for your sales organization.

Alex Bridgeman: Yeah. And building on that concept of different segments and sales, how do you price sonar, and then do you have different pricing plans or tiers for different types of customers? How do you think through pricing for your various sonar segments?

Craig Fuller: Yeah, so pricing’s an interesting one because I think a lot of companies, it’s interesting because it’s the most impactful thing to a business but it’s the thing that most companies spend very little time thinking about. We spend an enormous amount of energy thinking about it, and we probably aren’t right. Like we’ll make changes to it on a periodic basis and realize, hey, we left a lot of money on the table, or hey, that didn’t work. But effectively, if you think about a data business, if I have one truck and I’m Jim Bob Trucker and we have one truck, the maximum amount of value that I can get from the product is very finite to the impact, the economic impact I can make to that one truck. Let’s say that we can drive 17% more revenue on one truck and a truck will do $200,000 a year. So, the maximum impact that I can add to that truck is $34,000. That’s the maximum theoretical impact I can have. But if you’re talking to Amazon or take Walmart as an example or take JB Hunt. So, JB Hunt has say 22,000 trucks at their disposal. A 17% impact times 22,000, or $17,000 times 22,000 is a lot of money. We’re talking a lot of zeros. It’s like $3.4 billion or something or 3- Anyway, I can’t do the math in my head, but it’s a lot of money. Whereas a company like Amazon, so Amazon will spend $70 billion on freight transportation services in a year. You think about a, say, 5% impact to Amazon’s business. 5% impact times $70 billion is a $3.5 billion impact. It’s just this stuff is massive. And so, the way we price it is we segment it and say if Amazon’s buying it or a big box retailer’s buying it or a big trucking company’s buying it, then they’re in a category, and the maximum return they can get is X number of dollars. You’re not going to charge an Amazon 3 billion for the product or even one tenth of 3 billion. Now you may charge- So think about a small trucking company, the maximum he can get is $34,000 in additional utilization. You’re probably going to charge him about 20% of that. Like $7,000 is what he’s going to end up paying. But you’re not going to go to Amazon and try to charge them $700 million. Like even though you can demonstrate a $3.5 billion financial impact, you still can’t get 20%. So, there’s a finite ceiling when you go up from that. But they shouldn’t also be paying $7,000 because they’re going to put it across their whole network. So, we have a matrix that’s based on the industry they’re in and the size they’re in. And then we try to look at the returns on what they’ll get, knowing that there’s a finite ceiling. But the goal really of any data business is to make it like heroin, where basically they’re so addicted to it that they literally can’t move off the drug. They have to have it in their operations, and if they don’t have it, then it becomes very painful for them. And that’s exactly the way we think of it is you can start very low with data businesses in price, and you can continue to scale that up as you sort of go.

Alex Bridgeman: And speaking of scaling up, what’s been the- from your conversations with investors or strategics or other much larger data companies, what’s the investor perception of the media data combination within one business?

Craig Fuller: Media gets no value. So, I mean, reality is that in some ways, the media business detracts from the value of the multiple. So, the way investors sort of come at this is you’re never going to get a blended multiple. I’ve tried to pitch blended multiples. It won’t work. And what I mean by a blended multiple, let’s say the business is- so we’ll do 42 million this year. Like of that, just to keep things as simple as possible, say 50/50, even though it’s not exactly 50/50 anymore. So, 20 million is coming from say media and 22 million’s coming from SaaS, and it’s like 26, but it doesn’t matter. Anyway, to keep the math simple, it’s half and half. So, a $40 million business, its half and half. And basically, the way to think of it is you would want a blended multiple, which means I want the business to trade at say 15 times $40 million. Well, it doesn’t work that way. Because what investors will do is they’ll say, Craig, we will give you a- these are 2021 multiples. We’ll give you a 20X multiple on your data business and we’ll give you a 3 to 4 multiple on your media business. And so, now you’ve ended up with this very different- I wanted a $600 million business, but now I’ve ended up with a 20 times $20 million business, which makes it 400 million, plus my media business is going to trade at say four times, I’m going to end up like at 4/80. So, it’s a pretty significant delta between two. Or they say, hey, we’re going to give you a premium number, and a 20X multiple in today’s market would be a premium number for any business. We’ll give you a premium number on your SaaS business or your data business. So, we’ll give you the 20X, and we’ll give you zero on the media business. And that would be basically what you did. So your counter argument would be, well, I should sell off the media business or spin it off. The problem is that the chances are the SaaS business would not grow fast if it didn’t have the media. So, I think you have to sort of decide if you’re going to do this, that you’re okay with the fact- look, and you don’t want a media multiple. Like you certainly don’t want to trade your SaaS business and your subscription business for a 4X, let’s say. I mean, come on, like $40 million business trading at 4X is 160 million, who wants that? I would much rather just say, okay, I’ll take a 20X multiple in my data business, and you can- the media business just become scraps. It’s fine. Now on exit, it may be different. I don’t know. I mean, we’ve certainly had offers to sell the business. The challenge of doing that is that most buyers don’t want media. Most people that are going to buy your SaaS business just don’t want a media business. And the reason they don’t want it is that the types of companies that are looking to buy our data business typically are not- they don’t have a media DNA to them. And one of the challenges that you end up with is if you’re writing content that is polarizing, maybe you’re covering a scandal, or maybe you’re talking about a recession, that recession is likely bad for the person’s core business. Like if there’s a recession, that is not going to be good for their business, and they’re telling their shareholders, hey, our business is going to be fine even if there’s a recession or even if it’s a downturn but there’s no recession. When you’re talking about those things, that becomes difficult for a CEO to underwrite. They’re not going to be comfortable upsetting some of their potential customers or current customers. And so, it’s difficult for someone to get comfortable with that type of call. So it means that really the way I think of the business is that our exit strategy probably means that we’re a standalone business for many, many years, unless we found a market data business that came from another part of the world, like think of names like Bloomberg comes up, S&P comes up, a Moody’s comes up, an Equifax comes up, a data business that understands the sensitivities of media. Those would be the likely buyers, but it’s unlikely to be a workflow company because it’s just what we do is so different than what they do. Unless they just wanted the data business and wanted to just shut down the media, which would be quite sad, I think, because a lot of our success is driven by the media business.

Alex Bridgeman: Yeah, certainly. Thank you so much for coming on the podcast. I really appreciate it. I love chatting media and data. It’s always fun to get to chat with you more. So, thank you for sharing some time. I appreciate it.

Craig Fuller: I always enjoy it. Happy to dive into the intersection of these two worlds.

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