Latest / The Buyer & Seller Podcast with Fexingo: Business Brokers, Exits, and Private Sales Explained / Why a Courier Business Sold at 40x EBITDA with Density Data
Transcript
- Lucas: There's a courier business in Austin, Texas, that sold earlier this year for forty times EBITDA. On paper, it looked like a pretty standard last-mile operation—a dozen vans, some part-time dispatchers, no proprietary software. The kind of business most people assume sells for five or six times cash flow at best. Luna: I've seen those numbers before. A lot of owner-operators get excited about the revenue but don't realize the buyer is mostly buying the trucks, which depreciate. Forty times is wild—so what was different? Luna: So instead of selling a truck fleet, he was selling a map of predictable demand. That makes sense—if you're a regional logistics company looking to expand into Austin, that data saves you a year of route testing. Lucas: Exactly. The acquirer was a mid-sized logistics firm that already ran routes in Dallas and San Antonio. They knew the Austin market was under-penetrated, but they didn't have the granular street-level data to know where to put their first depot. This seller's data effectively answered that question—and it came with a live customer base that was already ordering. Lucas: If these conversations have moved your work forward in some small way—maybe helped you think about your own business differently—we keep this show free of ads by design. No sponsors, no pre-rolls, no interruptions. If you want to support that choice, there's a link in the show notes: buy me a coffee dot com slash fexingo. Luna: Yeah, that's exactly why I love doing this show. No one's trying to sell you anything between the actual ideas. It's just us and the topic. Lucas: And the data piece—because that's really the core here. The seller didn't just hand over a spreadsheet. He structured it so the buyer could simulate a route merge. He showed them: if you combine our Austin routes with your Dallas routes, here's exactly how many miles you cut, how much driver time you free up, and how many more daily stops each truck can handle. Luna: That's the kind of projection that justifies a forty multiple. You're not just buying existing cash flow—you're buying a proven path to more cash flow. How did he even start collecting that data? Most small courier owners are too busy driving to build a dataset. Lucas: He used a off-the-shelf routing app that logged every stop automatically. I think it was Routific, but honestly any gps tracked dispatch system does the same thing. The key wasn't the tool—it was that he never deleted the logs. He kept eighteen months of history, and when he decided to sell, he exported it as a geocoded CSV. Luna: Which is probably a ten-minute export. But most owners don't think about that until they're in the middle of due diligence, and by then it's too late to show a clean historical trend. Lucas: Right. The buyer's analyst told the seller that the data alone saved them about six months of route optimization. That's the kind of time value that drives a premium. The analyst also ran a density score: what percentage of deliveries fell within a half-mile of another delivery on the same day? For this seller, it was over seventy percent. That's extremely high for a single-city operation. Luna: Seventy percent density means most of his route was essentially one tight cluster. That's the dream for a last-mile operator—short distances between stops, low fuel cost per package, high driver utilization. The buyer could basically double the volume on those routes without adding a single truck. Lucas: Exactly. And that's the math behind the forty-times multiple. Let's say the business was doing two hundred thousand in EBITDA. A standard multiple in logistics is maybe five to seven times. That gets you one to one-point-four million. The seller got closer to eight million because the buyer saw that same EBITDA could become three hundred and fifty thousand within twelve months just by layering in more volume from existing customers and a few new accounts in the same zip codes. Luna: So the buyer was effectively paying for future EBITDA at a lower risk profile. They had proof the routes could handle more. That's a very different conversation from 'we have a great team and a nice culture.' Lucas: Totally. And I think that's the lesson for any route-based business owner listening—whether you're in couriers, vending machines, mobile repair, even home services. The asset that matters most isn't your truck or your van. It's the map of where your revenue comes from and how repeatable it is. Luna: I made a mistake once on a similar valuation. I was looking at a courier business in Phoenix, and I calculated the density score wrong—I used total stops divided by total square miles, which gave me a great number. But I didn't account for the fact that twenty percent of their deliveries were to a single industrial park that was about to be redeveloped. The buyer's analyst caught it. Lucas: What was the actual density once you removed that park? Luna: It dropped from sixty-eight percent to about thirty-five. The deal fell apart because the owner couldn't prove the remaining stops had any clustering. That was a five times ebitda deal, not forty. The data told the real story. Lucas: That's a great cautionary tale. The Austin seller was smart because his data was clean and he'd already done that analysis himself. He presented the density score in his offering memo. That's rare. Most owners think buyers care about revenue growth or gross margin—and they do, but the data moat is what gets you a premium. Luna: What kind of buyer was it? Private equity, strategic, or an individual? Lucas: Strategic—a regional logistics company that already operated in Texas but wanted the Austin density. They paid mostly cash, some earnout tied to retention of the existing customer base. The seller had a two-year non-compete and stayed on as a route consultant for six months. Luna: So the earnout likely protected the buyer if the customers churned. But if the density data was as good as you say, the churn risk was probably low. Lucas: Low risk, but not zero. Even with great density, if the customers are price-sensitive or the contracts are month to month, you can lose them fast. This seller had about sixty percent of his revenue on weekly recurring orders from small medical offices and law firms. Those are sticky—they need daily document delivery. But the rest was ad-hoc, on-demand work, which is much more volatile. Luna: So the multiple wasn't just about density—it was about the mix of recurring versus spot business. The recurring part justified the high end, and the ad-hoc part probably capped it. Lucas: Exactly. The buyer baked that into their model. They assumed the recurring accounts would stay and they'd convert some of the ad-hoc customers to subscription plans over the first year. The data helped them identify which ad-hoc addresses ordered frequently enough to make a subscription profitable. Luna: That's a pretty sophisticated analysis for a twelve-van operation. Most owners I meet don't even segment their customers beyond 'regular' and 'occasional.' Lucas: Right, and that's the gap the Austin seller exploited. He didn't have an MBA or a data science background. He just had a spreadsheet habit. Every Sunday night, he'd pull the week's delivery log, tag each address with the number of orders that week, and color-code them. Green for five-plus, yellow for two to four, red for one. That simple color-coding became the backbone of his data room. Luna: I love that. It's not about expensive tools—it's about consistency. If you start doing that today, in eighteen months you have the same kind of asset. Lucas: Exactly. And that's the core takeaway for anyone who runs a route-based business. Start logging every stop. Keep the history. When you decide to sell, that dataset might be worth more than all your physical assets combined. Luna: But I want to push on one thing: not every route business can achieve that seventy percent density score. If you're doing rural deliveries, your stops might be miles apart. Does that mean you'll never get a premium? Lucas: Not necessarily. There are other data moats—like customer lifetime value, repeat purchase rate, or even seasonal demand patterns. A rural courier might have lower density but extremely high customer loyalty because there's no competitor within fifty miles. That loyalty is valuable. But the valuation playbook is different: you'd sell to a larger rural operator who can spread fixed costs across a wider area, not to a density-seeking urban consolidator. Luna: So you need to know which buyer values your specific data signature. The Austin seller was positioning for a strategic that cared about density. If he'd pitched to a private equity firm that just wanted cash flow, he might have gotten ten times at best. Lucas: That's the real art of the exit—knowing who wants what you have. The seller hired a business broker who specialized in logistics and had relationships with regional carriers. That broker knew exactly which data points mattered to those buyers. That's worth the commission. Luna: So the broker was the one who said 'you need to build that density map.' Lucas: Actually, the seller had built it himself before he ever talked to a broker. He just had the instinct that more data would mean more leverage. The broker helped him package it and find the right buyer. But the data was already there. Luna: That's the ideal scenario—the owner creates the moat, the broker finds the match. I'm curious: did the seller end up happy with the outcome? Lucas: He told the broker he felt 'overpaid'—which is the best thing a seller can say. He walked away with about eight million after taxes and earnout. He's now consulting for other courier owners on how to build their data rooms. So the episode isn't over—he's helping others replicate his exit. Luna: That's a great full-circle story. So for anyone listening: start logging. Even if you're a one-van operation. You might not sell for forty times tomorrow, but in a year or two, you'll have a very different conversation with a buyer. Lucas: And that's the bottom line. Thanks for joining us on this episode of The Buyer & Seller Podcast. We'll be back next time with another story of what actually drives a premium exit.