Latest / The Buyer & Seller Podcast with Fexingo: Business Brokers, Exits, and Private Sales Explained / How a Laundromat Chain Hit 30x EBITDA with Data
Transcript
- Lucas: Alright, Luna, I want to walk through a sale that made me rethink what 'recurring revenue' actually means in a Main Street business. Luna: I'm ready. What's the business? Lucas: It's a three-location laundromat chain in the western suburbs of Chicago — nothing flashy. The owner, a guy named Mark, had been running it for about twelve years. It was profitable, stable, maybe six times EBITDA on a good year. Then he sold it last quarter for thirty times EBITDA. Luna: Thirty times? That's not a laundromat multiple. That's a software multiple. Lucas: Exactly. And the buyer wasn't a private equity roll-up or a competitor. It was a commercial laundry equipment manufacturer — a company that makes the washers and dryers you see in apartment complexes and hotels. Luna: So they weren't buying the real estate or the cash flow. They were buying something else. Lucas: Right. They were buying data. About three years before the sale, Mark embedded IoT sensors in every machine across all three locations. Temperature, vibration, cycle time, door-open events — he collected everything. Then he built a simple dashboard that showed real-time utilization: which machines were idle, which ones had high failure rates, when detergent levels were low. Luna: Wait — so he turned his laundromat into a data lab? How much did that cost? Lucas: About twelve thousand dollars total, including a part-time developer who helped with the dashboard. The sensors were off-the-shelf industrial IoT modules. He financed it out of cash flow. Luna: And then what? He started selling that data? Lucas: Not at first. He used it internally to optimize his own business. He realized that certain machines were underperforming and swapped them out. He added a subscription model for detergent refills — customers paid fifteen bucks a month for unlimited detergent, and the sensors alerted staff when a machine was low. That alone boosted his revenue per machine by about twenty-two percent. Luna: So the recurring revenue came from detergent subscriptions, not just wash cycles. Clever. Lucas: And that subscription data became the real prize. Because once he had a year of refill patterns, he could predict exactly when a machine would run out of detergent — down to the hour. That's valuable for a manufacturer that wants to build smarter machines that communicate with supply chain systems. Luna: I can see that. The manufacturer gets real-world usage data to improve their next-generation models. But how did Mark get them to pay thirty times EBITDA? Lucas: He didn't shop the business to operators. He went straight to the manufacturer with a proposal: 'Buy my laundromats, and you get exclusive access to three years of sensor data, plus the ongoing data stream from the subscription model.' The manufacturer saw it as an R&D acquisition disguised as a laundromat purchase. Luna: So the multiple wasn't based on earnings. It was based on the strategic value of the data. Lucas: Exactly. And that's the lesson I want to drill into today: the 'data premium' is real for Main Street businesses, but most owners don't know how to unlock it. They have the data — every point of sale system generates it, every piece of equipment generates it — but they're not packaging it for a strategic buyer. Luna: What about the privacy angle? Customers' wash cycles — that feels sensitive. Lucas: Mark was careful. He anonymized everything. The data was about machine behavior, not individual customers. The subscription model was opt-in. No one was tracked by name. The manufacturer signed a data-use agreement limiting it to product improvement, not resale. Luna: Okay, so that's the high-level story. But I want to know: how replicable is this? Can a dry cleaner or a car wash do the same thing? Lucas: I think so, but the bar is higher than most owners realize. You need at least twelve months of clean, consistent data. You need to prove that the data leads to a measurable outcome — like a ten percent reduction in machine downtime or a fifteen percent increase in refill revenue. And you need to know who the strategic buyer is. For Mark, it was obvious: commercial equipment manufacturers. For a car wash, maybe it's a chemical supplier. For a gym, maybe it's a fitness equipment maker. Luna: So the key is identifying the buyer who values the data more than the cash flow. Lucas: Right. And that's where a good business broker can help — someone who understands industry adjacencies, not just local comparables. Most brokers will tell you a laundromat is worth five to seven times EBITDA. A broker who understands data might help you find the buyer who'll pay twenty or thirty. Luna: Let's talk numbers. What was the actual sale price? Can you share that? Lucas: Mark's laundromats had about nine hundred thousand dollars in EBITDA at the time of sale. Thirty times that is twenty-seven million dollars. The manufacturer paid twenty-seven million for three laundromats that, on a standalone basis, might have fetched six million. Luna: Twenty-seven million. That's life-changing for a guy who probably started with a single store and a dream. Lucas: And it's a reminder that the most valuable asset in a small business isn't always the one on the balance sheet. Mark didn't own the real estate — he leased the spaces. The manufacturer didn't care. They wanted the data. Luna: So the data became the real estate. I love that framing. Lucas: And this is where I want to connect it to something we talk about a lot on this show — the idea that recurring revenue isn't the only path to a premium exit. Subscription models are great, but data can be even better if it's proprietary and hard to replicate. Luna: You know, we get a lot of messages from listeners saying these episodes have helped them think differently about their own businesses. And one thing I appreciate about this show is that we don't run ads. No sponsors, no commercial breaks. It's just us digging into deals like this. Lucas: Yeah, that's deliberate. We want the content to speak for itself. And if these conversations have moved your work forward in some small way, there's a simple way to support that choice — buy me a coffee dot com slash fexingo. No pressure, no perks, just knowing you're helping keep it ad-free. Luna: Exactly. And now back to Mark's data play — because the really interesting part is how he structured the deal. Lucas: Right. So the manufacturer didn't just write a check for twenty-seven million. They structured it as an earn-out tied to data milestones. Mark got fifteen million upfront, and the remaining twelve million is contingent on the data generating a certain number of new product features or efficiency improvements over three years. Luna: So he's still incentivized to keep collecting and analyzing data. That's smart for both sides. Lucas: And it protects the buyer. If the data doesn't deliver the expected value, they don't pay the full premium. But Mark was confident because he'd already proven the data's worth internally. Luna: What kind of improvements did the data actually drive? Give me a concrete example. Lucas: Sure. One thing the sensors revealed was that a specific model of dryer was overheating in about eight percent of cycles, which caused the drum to warp over time. Mark flagged it to the manufacturer, and they redesigned the venting system. That saved them millions in warranty claims across their entire installed base. Luna: So the data wasn't just about his three laundromats. It had broader value. Lucas: Exactly. And that's the kind of insight you can only get from real-world usage at scale. Lab testing doesn't catch everything. Mark's machines were running sixteen hours a day, seven days a week. That's a stress test no manufacturer can replicate easily. Luna: So the lesson for a small business owner listening today: start collecting data now, even if you don't know how you'll monetize it. The cost is low, and the potential upside is enormous. Lucas: Absolutely. And don't just collect it — tag it, timestamp it, store it in a way that's accessible. If you're a bakery with smart ovens, track temperature profiles and bake times. If you're a gym, track machine usage and class attendance. The more granular, the better. Luna: And then find a broker who gets this. Not all of them will. Lucas: Right. The broker who represented Mark specialized in 'data-enhanced' small businesses. She had a network of corporate development officers at equipment manufacturers and chemical companies. She knew exactly who to call. Luna: I think the takeaway here is that information asymmetry is real. Most business owners don't realize they're sitting on a goldmine of data. And most buyers don't know to ask for it. The ones who bridge that gap win. Lucas: And that's the kind of edge we love to explore on this show. A boring business with a data layer becomes a premium exit. It's not about being sexy — it's about being strategic. Luna: Alright, so for our listeners: if you run a Main Street business with any kind of equipment or recurring customer interaction, start thinking about your data as a separate asset class. You might be surprised what it's worth. Lucas: And if you've already started collecting that data, you're ahead of the curve. The next step is figuring out who would pay for it. That's the homework for this episode. Luna: I'm already thinking about which businesses I'd look at differently now. A self-storage facility with gate access logs? A car wash with license plate readers? There's data everywhere. Lucas: Exactly. And the multiples speak for themselves. See you next time.