Latest / The Buyer & Seller Podcast with Fexingo: Business Brokers, Exits, and Private Sales Explained / How a Commercial Janitorial Business Sold at 400x EBITDA
Transcript
- Lucas: So I was talking to a broker friend of mine last week, and he told me about a commercial janitorial company that just sold for 400 times EBITDA. Luna: Wait, four hundred times? Not forty? Lucas: Four hundred. I double-checked the number. The business was cleaning office buildings in three midwestern cities. About 80 contracts, mostly recurring monthly. The owner was pulling in around $2 million a year in revenue, and the EBITDA was roughly $150,000. He sold for $60 million. Luna: Okay, that sounds like either the buyer made a huge mistake, or there's something I'm missing about janitorial services. Lucas: The buyer was a mid-sized private equity firm that specializes in facility services. And they didn't buy a cleaning company. They bought a data platform that happened to clean buildings. Luna: I'm going to need you to explain that. Lucas: The owner had spent three years building a proprietary system that tracked every single metric you could imagine. Labor hours per square foot, chemical usage per contract, customer renewal probability based on recent service history, even the optimal route for cleaning crews to minimize travel time. He had dashboards that showed, in real time, the profitability of each contract down to the penny. Luna: So he turned cleaning into a software business? Lucas: Exactly. The private equity firm wasn't buying the trucks and the mops. They were buying a system that could be replicated across dozens of other cities. They looked at the data and saw that the churn rate was under 3% annually, and the average contract had been in place for seven years. That's recurring revenue with a reliability that rivals a SaaS company. Luna: And that justifies a massive multiple, because the buyer can plug their existing cleaning acquisitions into that system and instantly improve margins. Lucas: That's exactly the play. The firm has already bought five other janitorial companies in the past two years. They plan to roll them all onto this platform and, according to their projections, increase EBITDA by 40% across the combined entity within eighteen months. Luna: What were the specific metrics that made the buyer so confident? Lucas: The big one was labor utilization. Most cleaning companies run at around 65% to 70% billable hours. This company was at 92%. Because the system optimized routes and schedules, crews spent almost all their time actually cleaning, not driving or waiting. That directly boosted margins. Luna: And the buyer can roll that out across their other acquisitions. Lucas: Right. The second metric was chemical cost per square foot. The owner had negotiated bulk pricing with suppliers and built a system that tracked usage against baseline. If a crew used 10% more than expected, a manager got an alert. They reduced waste by 25% industry-wide. Luna: So it's not just about having data, it's about having the operational discipline to act on it. Lucas: That's the part most owners miss. They collect data but don't create a feedback loop. This owner built alerts and accountability. If a contract's profitability dipped below a threshold, the system flagged it and suggested a price adjustment or a service reduction. Luna: You know, this reminds me of a conversation we had a few weeks ago about pest control businesses. Those also sold for big multiples because of recurring contracts. Lucas: Same principle. But janitorial is even more interesting because the contracts tend to be larger per customer. An office building might spend $5,000 a month on cleaning. If you have 80 of those, you're looking at nearly $400,000 in monthly recurring revenue. Luna: And with churn under 3%, that revenue is essentially an annuity. Lucas: Let's talk about the numbers in a bit more detail. The business was earning $150,000 in EBITDA on $2 million in revenue. That's a 7.5% EBITDA margin, which is actually low for the industry. Most cleaning companies run 10% to 15%. Luna: So the multiple wasn't based on current profitability. It was based on the system's potential. Lucas: Exactly. The buyer modeled that by applying the data platform to the existing revenue, they could push margins to 18% within two years. That would turn $150,000 EBITDA into $360,000. At that point, a 400x multiple on current EBITDA looks more like 167x forward EBITDA, which is still high but more defensible. Luna: And if the buyer rolls out the system to other acquisitions, the combined entity could have $10 million in revenue with 20% margins. That's $2 million in EBITDA. They probably paid $60 million for the platform, but the overall deal economics might work. Lucas: That's the thesis. The seller didn't just sell a business. They sold a playbook. And the buyer was willing to pay a premium for speed — they didn't want to build the system themselves, because that would take years. Luna: I wonder how replicable this is for other service businesses. Like, could a landscaper or a pressure washer do something similar? Lucas: Absolutely. And we've seen it happen. But the key is that the data has to be granular and actionable. Not just 'we cleaned building A'. It has to be 'building A took 4.2 labor hours, used 1.3 gallons of chemical, and the contract is up for renewal in 60 days with a 94% probability of renewal based on recent satisfaction scores.' Luna: That level of detail requires an investment in software and training. Most small business owners don't have the time or the technical skills. Lucas: This owner didn't either. He started with a spreadsheet and a part-time data entry person. Then he moved to a simple CRM and a scheduling app. Over three years, he gradually built it up. He told the broker that the total cost was maybe $30,000 and a lot of weekends. Luna: So the barrier to entry isn't that high. It's more about the discipline of tracking and acting on the data. Lucas: Exactly. Look, if these conversations have helped you think differently about your own business or a business you're looking to buy, we'd love it if you'd support the show. We deliberately don't run ads on these episodes, and that's a choice we make to keep the content focused on what matters. If you want to support that choice, the link is buy me a coffee dot com slash fexingo. Luna: Yeah, it's a small way to make sure we can keep doing deep dives like this without any outside pressure. Really appreciate it. Lucas: So back to the janitorial story. One detail the broker shared that really stuck with me: the owner's system also tracked customer satisfaction through a simple post-service survey that went out automatically. If a score came in below 4 out of 5, the owner called the client personally within an hour. Luna: That's incredible. It builds loyalty and gives real-time feedback. Lucas: And it fed into the renewal probability model. The data showed that customers who scored 4.5 or higher had a 98% renewal rate. Those who scored below 3.5 had a 60% renewal rate. So the owner prioritized fixing low-score accounts before they even considered leaving. Luna: That's a textbook example of using data to reduce churn. And it directly impacts the valuation multiple. Lucas: Exactly. The buyer looked at that and saw a system that could reduce churn across their portfolio from 10% to 4%. That alone is worth millions in retained revenue. Luna: What about the competitive advantage? Could a competitor copy this system? Lucas: In theory, yes. But the owner had a three-year head start and a culture that was built around the data. The buyer told the broker that even if a competitor started today, it would take them at least two years to catch up, and by then the combined entity would have expanded to 15 cities. Luna: So the data moat is real. Lucas: It's a classic first-mover advantage in a fragmented industry. Most cleaning companies are still run on paper schedules and manual billing. There's a huge opportunity for any owner who's willing to invest in a basic data infrastructure. Luna: What about the seller? What did he walk away with? Lucas: After taxes and broker fees, roughly $40 million. He was 52 years old and had started the company 18 years ago with a single van and a mop. He told the broker he's now working as a consultant for the buyer for two years, helping integrate the system, and then he plans to sail around the world. Luna: Good for him. That's the dream exit. Lucas: It really is. And it's a reminder that in today's market, operational data can be worth more than the actual operations. If you're running a service business and thinking about an exit in the next few years, building that data layer could be the single highest roi project you take on. Luna: I think a lot of our listeners probably run businesses where they have more data than they realize. They just haven't organized it in a way that tells a story to a buyer. Lucas: That's exactly the lesson from this episode. The cleaning business was not special. The system was special. And that system is something any owner can start building today. Luna: Any final thoughts? Lucas: Just that we'll be tracking this buyer's rollout over the next couple of years. If they can replicate that 92% labor utilization across their other businesses, this deal will go down as one of the smartest acquisitions in the facility services space. Luna: I'll be watching too. Thanks, Lucas. Lucas: Thanks, Luna.