Latest / The Buyer & Seller Podcast with Fexingo: Business Brokers, Exits, and Private Sales Explained / How a Car Wash Sold at 1200x EBITDA with Membership Data
Transcript
- Lucas: So there's a single-location express car wash in Phoenix that just sold for a multiple that would make a software founder blush. Luna: How high are we talking? Lucas: Twelve hundred times monthly EBITDA. The business was doing about seven thousand five hundred dollars a month in EBITDA, and the final sale price was just over nine million dollars. Luna: Wait — is that a typo? Twelve hundred times monthly? That's like a hundred times annual EBITDA. Lucas: Exactly. For context, a typical independent car wash sells for maybe two to four times annual EBITDA. This one did a hundred times annual. The buyer didn't care about the equipment or the real estate. He cared about the database. Luna: Okay, so tell me about this database. Lucas: The car wash had eight thousand four hundred active unlimited-plan subscribers. Ninety-four percent monthly retention. Five years of clean payment-history data — credit card numbers, vehicle make and model, frequency of visits, average ticket. The buyer was a regional operator with twenty other locations, and he knew that if he could retain even half those members, the acquisition would pay for itself inside three years. Luna: So the multiple wasn't really about the car wash itself. It was about the recurring revenue stream and the data. Lucas: Right. The physical car wash was almost a liability — the building needed a new roof, the equipment was six years old. But the membership contracts were essentially a subscription business attached to a service business. And the buyer had a playbook: plug those members into his existing loyalty app, cross-sell his other locations, and use the data to optimize pricing. Luna: What did the data room look like? Was it some elaborate ai driven analytics platform? Lucas: No — it was a spreadsheet. Three tabs. Tab one: monthly member counts and churn by cohort going back five years. Tab two: average revenue per member per month, which was about thirty-two dollars. Tab three: a simple churn curve showing that after month six, retention flatlined at ninety-four percent. The broker told me the buyer spent forty-five minutes with that spreadsheet and made the offer. Luna: That's fascinating. So the seller had been tracking this data all along, or did they clean it up for the sale? Lucas: The seller had been tracking it from day one because he used a third-party point of sale system that generated those reports automatically. He didn't realize it was valuable until the broker said, 'Stop talking about your new conveyor belt. Show them the churn curve.' Luna: So the real asset wasn't the physical plant — it was the member relationships. How did the seller originally acquire those members? Lucas: He launched the unlimited plan four years ago at thirty dollars a month. First month free. He spent about fifteen thousand dollars on Facebook ads targeting zip codes within a three-mile radius. Within six months he had two thousand members. The key was that he never discounted the monthly price — he added value instead, like free tire shine and a monthly interior wipe-down. So the perceived value went up, the churn went down. Luna: That's a classic SaaS playbook applied to a car wash. And the buyer saw that. So what does this tell us about the future of service-business M&A? Lucas: I think we're going to see a bifurcation. Businesses with clean recurring revenue and membership data will trade at multiples that look like software — ten to twenty times annual EBITDA. Businesses without that will still trade at the traditional two to four times. The gap will widen because buyers are getting smarter about data. Luna: And the sellers who invest in building that data infrastructure early — even if it's just a good POS system and a spreadsheet — will be the ones who exit at life-changing numbers. Lucas: Exactly. This car wash owner is in his late fifties. He started the business fifteen years ago as a manual wash bay. He built the membership program almost as an afterthought. And it turned his nine-hundred-thousand-dollar business into a nine-million-dollar exit. Luna: Let me ask a practical question. If I'm a small business owner listening to this — say I run a laundromat or a dog grooming place — what's the one takeaway? Lucas: Start a membership program today. Even if it's ugly. Even if you only get ten members in the first month. The data you generate over the next three years will be worth more than any piece of equipment you own. And when you go to sell, that data is what commands the premium. Luna: And don't hide it in a shoebox. Track it in a spreadsheet from day one. Lucas: Right. The broker on this deal said the only reason the multiple was that high was because the seller could prove retention. Without the data, it's just a story. With the data, it's a valuation. Luna: I've been thinking about this a lot lately, actually. Like, how many of these micro-businesses are sitting on gold mines they don't even see? Lucas: It's a huge theme. And honestly, a handful of listeners have mentioned that these shows have helped them see their own businesses differently. That's the kind of feedback that keeps us doing this. Luna: Yeah, and a small group of people even chip in to support the show directly through buy me a coffee dot com slash fexingo. That's what keeps it ad-free and lets us dig into deals like this one. Lucas: So if you've gotten something out of these conversations, that's a simple way to make sure they keep coming. Luna: Anyway, back to the deal — the buyer actually flew out to Phoenix to meet the seller in person. He wanted to see the look in his eyes when he talked about the members. Lucas: That's the part that doesn't show up in the spreadsheet. The buyer said he knew it was a good deal when the seller could name ten members by their first name and describe their cars. That human connection combined with the data — that's the sweet spot. Luna: So the data gets you in the door, but the story seals it. Lucas: Exactly. And that's why I think this deal is a blueprint. It's not about having the fanciest equipment or the biggest location. It's about building a recurring relationship and having the receipts to prove it. Luna: Alright, next week we're looking at a case where the seller had the data but the buyer misinterpreted it. That's a cautionary tale. Lucas: Looking forward to it. Thanks for listening, everyone.