Latest / The Buyer & Seller Podcast with Fexingo: Business Brokers, Exits, and Private Sales Explained / How a Local Dry Cleaner Sold at 30x EBITDA with Route Data
Transcript
- Lucas: Luna, I want to talk about a deal that closed three months ago in St. Louis. A small dry cleaning chain — four locations, no central plant, basically a mom and pop operation — sold for 30 times EBITDA. Luna: Thirty times? For dry cleaning? I thought the standard multiple for that sector was around 8 to 12. Lucas: It is. The industry average for a dry cleaner is roughly 10x. This one did three times that. And it wasn't because the revenue was growing fast or because they had some proprietary chemical process. It was route data. Luna: Route data — like pickup and delivery routes? Lucas: Exactly. The owner, a guy named Frank, had been running the business for 22 years. About a decade ago, he started keeping a handwritten log of every customer's pickup day, drop-off time, and any pattern he noticed — which customers always came on Tuesday, which ones were seasonal, which ones referred friends. Luna: So basically a CRM in a notebook. Lucas: Right. But then about five years ago, he digitized it. He paid a college student two thousand dollars to build a simple database in Microsoft Access. Nothing fancy. It tracked every transaction per customer, the frequency, and the average ticket size. Luna: And that data was what the buyer wanted? Lucas: That's what moved the needle. The buyer was a regional service company that already owned a pest control firm and a lawn care business. They understood route-based economics. When they saw that Frank's dry cleaner had a 73 percent annual customer retention rate — and could prove it with five years of transaction-level data — they realized they could fold those customers into their existing routes and drive incremental margin. Luna: So they weren't buying the dry cleaning equipment or the storefronts. They were buying the customer list and the behavior patterns. Lucas: Exactly. The hard assets — the presses, the steamers, the racks — they valued those at maybe 200 grand. The real estate was leased. The entire enterprise value of 4.2 million dollars was almost entirely goodwill backed by data. Luna: Four point two million on what EBITDA? Lucas: Frank's trailing twelve-month EBITDA was 140,000. So that 30x multiple. The buyer structured it as 2 million cash at close, a 1.2 million earn-out tied to retention over two years, and a 1 million promissory note. Luna: So the earn-out effectively forced Frank to make sure the data actually worked post-sale. Lucas: Right. He stayed on for six months as a consultant, basically showing the new team how to interpret the data. Things like: customers who miss two weeks in a row have a 40 percent chance of churning within a month unless you send a reminder. Or that Tuesday is the highest-value day because corporate accounts drop off their suits. Luna: That level of granularity is rare in a small service business. Lucas: Incredibly rare. Most dry cleaners don't even track customer names. They just hand you a ticket and you pay cash. Frank's database had 6,200 unique customer profiles. And he knew that 1,800 of those were active in any given quarter. Luna: So the buyer saw a chance to cross-sell pest control or lawn care to those 1,800 households. Lucas: Exactly. The buyer's CFO told me they estimated that converting just 10 percent of those dry cleaning customers to their pest control service would add 90,000 in annual EBITDA with almost zero acquisition cost. That alone justified the premium. Luna: So the multiple wasn't really about dry cleaning at all. It was about a data asset that happened to sit inside a dry cleaning business. Lucas: That's the whole story. And it's a pattern we're seeing more and more in the lower-middle market. Service businesses that own sticky, route-based customer relationships can trade at multiples that look like software companies. Luna: But Frank didn't set out to build a data company. He just wanted to remember his customers' preferences. Lucas: That's what makes it replicable. You don't need a data science team. You need a discipline of writing things down and keeping the record clean. Frank told me he spent about 15 minutes a day updating his log. That's it. Luna: Fifteen minutes a day for five years turned into a 4.2 million dollar exit. Lucas: Versus if he had sold to a traditional buyer — another dry cleaner — he probably gets 1.2 to 1.5 million. The data created three million dollars of additional value. Luna: I think a lot of business owners hear 'data' and assume they need to buy expensive software or hire a consultant. Frank shows that a shoestring approach can work. Lucas: He used Access. A defunct Microsoft product that a college student could navigate. The point is not the tool. It's the habit. Luna: So if I'm listening and I run a landscaping company or a pool service or even a coffee shop, what's the one thing I should start tracking today? Lucas: Customer visit frequency and average spend per visit. That's it. Two columns. Track it for six months, and you'll have a dataset that makes your business look more predictable to a buyer. Luna: And predictability is what drives multiples up. Lucas: Right. Because a buyer can underwrite the revenue more confidently. They can model retention, they can plan cross-sell, they can finance the deal with less equity. Every basis point of predictability adds to the multiple. Luna: I have to say, I love this story because it's so concrete. It's not about some tech startup with a billion-dollar valuation. It's about a dry cleaner in St. Louis who paid attention. Lucas: And that's the whole thesis of this show, really. The most interesting multiples are often hiding in the most mundane businesses. Luna: If today's episode gave you one of those small but useful insights — maybe you're thinking about what data you could start tracking in your own business — I hope it lands. Lucas: Yeah. The reason this show stays ad-free is that listeners who find value in it sometimes choose to support it directly. If that's you, it's as simple as buy me a coffee dot com slash fexingo. Luna: No pressure, no pitch. Just a way to keep these conversations going without commercial interruptions. Lucas: And speaking of concrete data stories — I want to zoom out for a second. Frank's dry cleaner is one example. But there's a broader market dynamic at play. Luna: What do you mean? Lucas: Private equity firms and family offices are rotating capital into service businesses with defensible customer data. They're calling it 'data-enabled services.' The thesis is that these businesses combine the stability of recurring service revenue with the margins of a software company over time. Luna: So a dry cleaner with route data trades more like a SaaS company than a manufacturer. Lucas: Exactly. And the multiples are still below true SaaS multiples — SaaS companies trade at 50x or more. So buyers see it as an arbitrage: pay 30x for a business that behaves like a software company but hasn't been repriced yet. Luna: How long do you think that window stays open? Lucas: Probably another three to five years. As more owners hear stories like Frank's, more will invest in data collection. That will compress the advantage. But right now, the number of small service businesses with clean, auditable customer data is tiny. Luna: So there's a first-mover advantage for owners who start today. Lucas: Absolutely. And the cost of entry is almost zero. A notebook and a pen. Or a free Google Sheet. The hardest part is consistency. Luna: Frank did it with 15 minutes a day. That's less time than most people spend scrolling social media. Lucas: And he turned that 15 minutes into a 4.2 million dollar exit. I don't know many investments with that kind of return. Luna: I'm curious — did the buyer ever use the data to cross-sell pest control? Do we have any numbers? Lucas: I checked in with the buyer's operations director about a month ago. They had already converted 120 households in six months. That's about 6.5 percent of the active base. They're on track to hit 10 percent by the end of the year. Luna: So the earn-out is probably going to pay out in full. Lucas: Looks that way. Frank gets his full 1.2 million, plus the note gets paid off early because the cash flow from the cross-sell is covering it. Luna: That's a happy ending for everyone. The buyer got a deal, Frank got his number, and the data is still generating value. Lucas: That's the ideal exit. When the asset you built keeps working after you leave. Luna: Alright. Next week we're looking at a very different kind of data story — what happens when a business has no customer data and tries to sell anyway. Lucas: That should be a good reality check. See you then.