Latest / The Buyer & Seller Podcast with Fexingo: Business Brokers, Exits, and Private Sales Explained / How a Niche Software Firm Sold at 30x EBITDA with Zero Recurring Revenue
Transcript
- Lucas: So we've done nearly a hundred episodes now where the punchline is almost always the same: recurring revenue equals a higher multiple. Subscription contracts, service agreements, take or pay — the more predictable the cash flow, the richer the exit. Luna: Right. And today you're telling me we're doing the exact opposite? Lucas: Exactly the opposite. I want to talk about a deal that closed about eighteen months ago — a niche construction software company based out of Atlanta. The business sold for thirty times EBITDA, and it had essentially zero recurring revenue. Luna: Zero. How is that even possible? I mean, we've seen route-based businesses hit sixty, seventy times because of repeat service contracts. But a software company with no subscriptions? Lucas: That's exactly the puzzle. The company made a piece of project-management software specifically for mid-sized commercial construction firms — think general contractors doing fifty to two hundred million in annual revenue. Their license model was perpetual, one-time fee, plus an optional annual maintenance contract that maybe thirty percent of customers renewed. Luna: So thirty percent renewal isn't nothing, but it's hardly a SaaS retention curve. Lucas: Not even close. A typical SaaS business at scale churns maybe five to seven percent annually. This thing had seventy percent annual churn on maintenance. So the buyer — a larger construction tech roll-up out of Texas — was effectively paying thirty times for a stream of one-off license sales. Luna: What were they seeing that a spreadsheet wouldn't capture? Lucas: The key metric was something the deal team called 'domain stickiness.' The software was deeply integrated into how estimators and project managers bid on jobs. It connected to specific cost databases, union wage tables, and material pricing feeds that were regional to the Southeast. Switching costs were enormous. Luna: So even though customers weren't on a subscription, they kept buying new licenses every few years because tearing out the system would break their estimating workflow. Lucas: Exactly. The average customer bought a new license every thirty-two months. That's not quite recurring revenue, but it's repeat purchase behavior driven by real dependency. The buyer calculated a lifetime value per customer that was actually higher than many SaaS companies in commercial construction — because the average deal size was larger and the gross margin on licenses was above ninety percent. Luna: Okay, but thirty times EBITDA still seems rich for a non-recurring model. How did the valuation hold up in due diligence? Lucas: This is where the structure gets interesting. The base purchase price was sixteen times EBITDA — already a solid number for a non-recurring software business. The rest of that thirty times came from an earn-out tied to customer retention over three years. Luna: So the seller only gets the full multiple if the repeat purchase behavior actually continues post-acquisition. Lucas: Right. And the earn-out was measured not on revenue, but on a metric called 'active license base' — the number of customers that had purchased a license within the prior twenty-four months. The seller had to maintain or grow that base by at least five percent annually. If they did, the earn-out paid an additional fourteen times. Luna: So the buyer de-risked the multiple by making it contingent on the very behavior that justified the premium in the first place. That's clever. Lucas: It's become more common over the past few years, especially in vertical software deals where the revenue model isn't subscription but the customer economics are still attractive. We're seeing more earn-outs tied to non gaap metrics like active users or license base rather than just revenue or EBITDA. Luna: What happened in the end? Did the seller hit the earn-out targets? Lucas: The earn-out period ended in Q1 of this year, and I've heard through the grapevine that the seller exceeded the five percent growth target — they grew the active license base by about eleven percent over three years. So the full thirty times multiple effectively paid out. Luna: That's a great example of how buyers and sellers can bridge a gap with creative structure. By the way, this kind of deep-dive deal analysis is exactly the sort of thing that helps listeners think about their own businesses differently. Lucas: Glad you brought that up, because it ties to something we've been thinking about. This show exists because we love talking about the mechanics of real business sales — and we keep it ad-free so we can focus on the substance. If these conversations have moved your work forward in some small way, listener support is what keeps it going. You can find us at buy me a coffee dot com slash fexingo. Luna: Yeah, it's a small gesture that makes a big difference for us. And it means we never have to interrupt a deal story with a commercial break. Lucas: Right. Back to the construction software deal — one thing I didn't mention is how the seller positioned the business for the earn-out. Before the sale, they invested heavily in customer onboarding and training, which drove that repeat purchase behavior. They essentially created a dependency on their ecosystem. Luna: So the seller wasn't just lucky — they built the business with this exit in mind. Lucas: Exactly. They had a data room showing customer lifetime cycles, not just annual recurring revenue. They mapped out the average time between purchases, the reasons for repurchasing — often a new project type or expansion to a new division — and they demonstrated that churn on the maintenance contract wasn't actually correlated with losing the customer entirely. Most customers who didn't renew maintenance still bought a new license within eighteen months. Luna: That's a powerful narrative for a buyer who's used to seeing SaaS metrics. You're essentially saying: don't look at the subscription renewal rate, look at the re-license rate. Lucas: And the buyer had the sophistication to understand that. The acquiring company was a roll-up that already owned three other construction tech platforms, so they knew the industry. They weren't a financial buyer trying to apply a generic SaaS playbook. Luna: So part of the multiple premium was also the strategic fit. The buyer could cross-sell this software to their existing customer base, which made the earn-out more achievable. Lucas: Huge factor. The buyer estimated they could push the software to their own installed base of about two thousand contractors, which gave them a path to double the active license base without the seller doing any additional work. That made the earn-out look less risky from the buyer's side. Luna: I'm curious — do you see more deals like this emerging in 2026? Where buyers are willing to pay up for non-recurring models if the switching costs are high enough? Lucas: I think we'll see more, especially in verticals where the software is mission-critical but the market hasn't moved to subscriptions yet. Think about practice management software for law firms, or dispatch systems for trucking companies. A lot of those businesses still sell perpetual licenses because their customers prefer a one-time cost. But the switching costs are massive. Luna: So the key takeaway for a business owner listening might be: don't feel like you have to force a recurring revenue model just to get a good multiple. If you have real customer dependency, you can still command a premium — you just need to prove it with the right data. Lucas: Exactly. The construction software seller didn't try to pretend they were a SaaS company. They leaned into what they actually were: a high-switch-cost, high-margin, repeat-purchase business. And they found a buyer who understood that story. Luna: Alright, next episode I want to circle back to something we touched on — the earn-out mechanics. How to negotiate them so you don't leave money on the table. Lucas: Good idea. There's a whole art to defining the metrics and the timeline. We'll dig into that next time.