Latest / The Buyer & Seller Podcast with Fexingo: Business Brokers, Exits, and Private Sales Explained / Why a Party Rental Business Sold at 60x EBITDA with Peak Season Data
Transcript
- Lucas: So today I want to talk about a party rental business that sold for 60 times EBITDA. And before you think that sounds crazy, the buyer wasn't paying for the past earnings — they were paying for the data. Luna: Data from a seasonal rental business? Like how many bounce houses they rented out each summer? Lucas: Exactly, but way more granular. This was a company in the Midwest — call them 'Celebration Rentals' — that did tents, tables, chairs, linens, dance floors, the whole thing. About 70 percent of revenue came between May and September. The rest of the year was basically break-even. Luna: That's a tough profile for a traditional buyer. Banks don't love seasonality, and a multiple on trailing twelve months cash flow looks thin. Lucas: Right. But the seller had kept meticulous records for five years — not just revenue totals, but booking lead time by event type, zip code density, even the day of the week that weddings were booked. They could show that Memorial Day weekend bookings had a 90 percent probability of being placed at least 60 days out, and that the average order value was $4,200. Luna: So the buyer could predict inventory needs almost to the unit. That's huge for a seasonal business where you're stuck with equipment that sits idle eight months a year. Lucas: Exactly. The buyer was a regional event company that already ran venues and catering. They wanted to add rentals, but they didn't want to guess at how many tents to buy. The data let them model a 22 percent improvement in inventory utilization in year one. That alone made the deal work at 60 times trailing EBITDA. Luna: What was the actual EBITDA number? Because 60 times a tiny number is still a small deal. Lucas: EBITDA was around $340,000. So the purchase price was about $20 million. But the buyer wasn't buying $340,000 in profit — they were buying a playbook to deploy across their existing customer base. The seller's data showed that 40 percent of wedding bookings came from two zip codes. The buyer already had a venue in one of those zip codes. They could cross-sell immediately. Luna: That's the kind of synergy that makes multiples make sense. It's not that the business was worth 60 times its own earnings — it's that it was worth 60 times what the buyer could earn with it. Lucas: And that's the lesson for any small business owner listening. If you're thinking about selling, the most valuable thing you can build isn't revenue — it's a dataset that proves repeatability. This seller didn't have a fancy CRM. They had a spreadsheet and a habit of entering every booking detail for five years. Luna: So the buyer's due diligence was basically running their own models against the seller's history. Did they find any surprises? Lucas: One big one. The seller had always assumed corporate picnics were their most profitable segment because they were high-ticket. But the data showed that corporate bookings canceled at three times the rate of weddings and had a shorter lead time. Weddings had a 95 percent fulfillment rate and averaged 14 percent higher add-on sales. That insight alone shifted the buyer's go to market strategy. Luna: So the data actually challenged the owner's intuition. That's the kind of discovery that justifies a premium — it shows the buyer they're buying a real understanding of the market, not just a revenue stream. Lucas: Exactly. And the seller was smart about how they presented it. They didn't hand over the raw spreadsheet. They built a simple dashboard showing booking velocity, seasonality curves, and customer lifetime value by segment. The buyer's CFO later said that dashboard saved them three months of analysis. Luna: That's the part I want to underline. You don't need a data team. A well-organized Excel file with consistent columns and five years of history is worth real money. Lucas: Yeah. And the multiple — 60 times — it sounds extreme, but the buyer's internal rate of return projection was still over 25 percent. Because they weren't buying a company, they were buying a data asset that made their existing business more efficient. Luna: So if someone runs a seasonal business — landscaping, pool cleaning, holiday decorating — what's the one data point they should start tracking today? Lucas: Lead time. How many days between first contact and booking. That's the single best predictor of demand certainty. If you can show a buyer that 80 percent of your peak-season bookings come in with at least 30 days notice, they can staff and inventory with confidence. That reduces risk. And risk reduction is what drives multiples up. Luna: And it's free to track. Just a column in a spreadsheet. Lucas: Exactly. Now, the other thing that made this deal work was the seller agreed to stay on for six months to train the buyer's team on the data system. That's common in these data-driven deals — the buyer is buying the owner's intuition as much as the numbers. Luna: Did the earn-out tie to performance? Lucas: No earn-out. Upfront cash for the business, and a separate consulting agreement for the transition. The seller wanted a clean break and the buyer wanted full access to the data methodology. They agreed on a flat fee for six months of part-time work. Luna: Clean deal. So if I'm a small business owner listening, what's the takeaway? Start tracking something today that proves your business is predictable. Lucas: That's it. Pick one metric — lead time, repeat customer rate, or seasonal booking density — and record it consistently for two years. When you go to sell, that data might be worth more than your equipment or your brand. Luna: And it doesn't cost a thing to collect. Lucas: Yeah. And honestly, if today's episode gave you one idea that's worth a coffee to you, that's the link — buy me a coffee dot com slash fexingo. Listener support is what keeps this show ad-free and focused on real deals. Luna: It's a small gesture that makes a big difference for us. Lucas: So back to the data point — lead time. One of the most interesting findings from this deal was that the seller's average lead time actually decreased over five years. They got faster at converting inquiries. That trend told the buyer that the business was becoming more efficient, not just bigger. Luna: So the direction of the metric mattered more than the absolute number. Lucas: Exactly. A buyer looks for improvement trajectory. Flat data is interesting. Improving data is valuable. And that's why this seller got 60 times — they didn't just have history, they had a story of getting better. Luna: Alright, so next time you're at a backyard party, look at the tent rental truck and think about the spreadsheet behind it. Lucas: Exactly. For The Buyer & Seller Podcast, I'm Lucas. Luna: And I'm Luna. See you next time.