Latest / The Buyer & Seller Podcast with Fexingo: Business Brokers, Exits, and Private Sales Explained / How a Small Insurance Agency Sold for 27x EBITDA with Renewal Data
Transcript
- Lucas: We talk a lot on this show about recurring revenue and how it drives multiples higher. SaaS, cleaning contracts, gym memberships — we've seen 15, 18, even 22 times EBITDA. But I came across a case recently that beats just about anything we've covered. Luna: Okay, I'm intrigued. What was it — a software company with some crazy net retention? Lucas: Not software at all. It was a three-person insurance agency in Boise, Idaho. Sold earlier this year for 27 times EBITDA. Luna: 27 times? That's insurtech valuation territory. Was it some kind of tech-forward brokerage? Lucas: Minimal tech. What they had was a decade of meticulously tracked policy renewal data. Every client, every policy type, every renewal date and premium, all in a clean spreadsheet that showed a 92 percent annual renewal rate across their book. The acquirer — a regional insurtech firm — basically said they were buying the data as much as the policies. Luna: So the data was the asset. That's a different paradigm from most small business sales where you're buying customer relationships and maybe some goodwill. Lucas: Exactly. The acquirer could model future cash flows with near-certainty. They knew that if they kept service quality steady, 92 percent of those policies would renew. And on top of that, they could cross-sell their own products — things like cyber liability or business interruption coverage — to that same base. The renewal data gave them a proven, predictable pipeline. Luna: So the multiple isn't just about the recurring revenue itself. It's about the predictability that data offers. That's a subtle but important difference. Lucas: Right. Two agencies could have identical revenue and identical renewal rates on paper. But if one has three years of granular data showing exactly when renewals happen, what drives lapses, and what the average premium increase has been — that agency will command a significantly higher multiple. The buyer is paying for reduced uncertainty. Luna: I think that's a really valuable takeaway for any business owner listening. It's not enough to have recurring revenue. You need to prove it's recurring with data. Lucas: And the agency's owner didn't set out to build a data company. He was just meticulous about record-keeping. He kept a detailed log of every renewal conversation, every rate change, every cancellation reason. By year five, he had a dataset that any acquirer would drool over. Luna: It reminds me of something we discussed way back about laundromats and their coin data. But in insurance, the data is even more valuable because the policy terms are longer and the revenue stream is more predictable. Lucas: And insurance has this structural advantage over something like a cleaning contract. A cleaning contract might be month to month. An insurance policy typically renews annually, and there's a real cost to switching — paperwork, underwriting, potential coverage gaps. That sticky behavior builds over time and shows up in the data. Luna: So what was the purchase price? You mentioned 27 times EBITDA. What did that work out to in dollars? Lucas: The agency had about 1.2 million in revenue and roughly 400,000 in EBITDA after paying the owner's salary. So the deal was around 10.8 million. For a three-person shop in Boise. That's life-changing money. Luna: And the acquirer — the insurtech firm — they're betting they can layer on their own digital distribution and maybe even use the data to train underwriting models. Lucas: That's exactly the play. They've got a machine learning team that ingests renewal data from every agency they buy. Over time, they can predict lapse probability at the individual policy level. That lets them price retention offers more precisely and even flag policies that are underpriced. The data gets more valuable as the dataset grows. Luna: So the small agency owner was sitting on a gold mine without fully realizing it. He just thought he was running a solid local business. Lucas: And that's the part I love about this story. He didn't have a fancy CRM or a data scientist. He had a spreadsheet he updated every Friday afternoon. But because he did it consistently, he built something that a data-hungry buyer would pay a premium for. Luna: It's a great reminder that the boring operational habits — the record-keeping, the follow-up logs — can be the most valuable assets when it's time to sell. Lucas: If these conversations have moved your work forward in some small way, we'd be grateful if you'd consider supporting the show. It's what keeps us ad-free and focused on stories like this. You can find us at buy me a coffee dot com slash fexingo. Luna: And every bit of support really does help us keep digging up these case studies. Now, you mentioned the acquirer was an insurtech firm. Were there other bidders? Lucas: There were three. One was a traditional regional agency that wanted to expand into Idaho. Another was a private equity roll-up platform. And the insurtech firm won because they valued the data at a higher multiple than the others. They were essentially buying a dataset with a distribution channel attached. Luna: That's a fascinating dynamic. The traditional buyers were valuing the business based on cash flow. The insurtech was valuing it based on data potential. And the data premium was huge — probably 10 to 12 turns of EBITDA more. Lucas: Exactly. And it's not just insurance. We're seeing similar dynamics in other industries where data can be aggregated and used for predictive models. Think about veterinary practices with patient records, or dental clinics with treatment histories. Any business with repeat transactions and detailed records could be a candidate for this kind of premium. Luna: It raises an interesting question for owners: should you start thinking about your data as a separate asset class within your business? Maybe even tracking it separately in your financials? Lucas: I think that's smart. If you can quantify the predictive power of your data — for example, if you can show that 90 percent of customers who buy Product A will buy Product B within six months — that's a data asset that has value beyond your current operations. A strategic buyer might pay handsomely for that insight. Luna: So for a business owner listening, what's the one action they should take this week based on this story? Lucas: Audit your records. If you have customer transaction histories, renewal dates, churn reasons, or any behavioral data that goes back more than two years, start organizing it. The cleaner and more complete it is, the more a buyer will pay. And if you don't have three years of data, start now. In five years, you'll have the asset that commands a 27 times multiple. Luna: It's a long game, but the payoff can be enormous. And it requires no special software — just consistency. Lucas: Exactly. The Boise agency owner used Excel. I'm not joking. He had one workbook with a tab for each year. And that workbook was the centerpiece of the deal room. Luna: That's a great image. The humble spreadsheet as the most valuable document in a ten million dollar transaction. Lucas: It really underlines the point: the value isn't in the tool, it's in the discipline of tracking what matters. And for most businesses, what matters is customer behavior over time. Luna: We should probably mention that not every business will fetch 27 times EBITDA. There were specific market conditions — low interest rates at the time, high demand for predictable cash flows, and a hungry insurtech sector. But the principle holds across many industries. Lucas: Right. The multiple was exceptional, but the underlying logic is universal. Recurring revenue plus data equals a premium. And that premium is only going to grow as more acquirers become data-driven. Luna: I wonder if we'll start seeing more small business owners proactively packaging their data for sale, almost like a data room before they even list the business. Lucas: I think we will. In fact, some brokers are already advising clients to prepare a 'data appendix' alongside the traditional financial statements. It's a way to differentiate your listing in a crowded market. Luna: So the takeaway is clear: track your data, keep it clean, and when you're ready to sell, lead with it. Great episode, Lucas. Lucas: Thanks, Luna. And for our listeners, if you have a story about how data played a role in your business sale, we'd love to hear it. You can reach us through the show notes. Until next time.