Latest / The Buyer & Seller Podcast with Fexingo: Business Brokers, Exits, and Private Sales Explained / How a Local Pharmacy Hit 15x EBITDA with Adherence Data
Transcript
- Lucas: A single-location independent pharmacy in suburban Ohio just sold for 15 times EBITDA. Not a chain, not a compound pharmacy with specialty infusions — a neighborhood pharmacy with a drive-through and a drop-off window. Luna: Fifteen times? That's well above the typical eight-to-ten multiple for an independent pharmacy. What was the secret sauce? Lucas: It wasn't prescription volume. It was data. The owner — a former hospital pharmacist named Dr. Ellen Cross — built a system that tracked medication adherence for her patients. She shared anonymized, aggregated reports with two local accountable care organizations, or ACOs. Luna: So the ACOs paid her for the data, not for the pills. Lucas: Exactly. Under value-based contracts, ACOs get penalized when patients don't take their medications. Readmission rates spike, outcomes worsen. Dr. Cross's pharmacy had 1,200 patients enrolled in her adherence tracking program, with an 87 percent adherence rate. She charged roughly forty dollars per patient per month for that data feed. Luna: That's about fifty-seven thousand dollars a month in pure data revenue. With basically zero marginal cost once the system was built. Lucas: Right. And that revenue stream was under long-term contracts — three-year agreements with auto-renewal clauses. The buyer, a regional healthcare services firm, essentially acquired a data pipeline that also dispenses drugs. Luna: Let me push back on something. How replicable is this? Most independent pharmacies don't have the EMR integration to pull adherence data in real time. Lucas: That's the moat. Dr. Cross spent about eighteen months and roughly sixty thousand dollars integrating with the two major EMR platforms used by local physicians. She hired a part-time data analyst to clean and structure the reports. Most pharmacy owners don't have that skill set or the patience for regulatory compliance around HIPAA. Luna: So the barrier is both technical and operational. That also explains the multiple — the buyer is paying for a capability that would take years to replicate. Lucas: And that's the lesson for small-business owners listening. The highest multiples aren't always going to the businesses with the most revenue. They're going to businesses that own a defensible data asset or a recurring contract stream that creates switching costs. Luna: It reminds me of the janitorial firm that sold at 12 times EBITDA with no turnover. In that case, the defensible asset was the workforce stability. Here it's the data. Lucas: And the financial profile is similar. The pharmacy's core prescription business — filling about two hundred scripts a day — was only growing at two to three percent annually. But the data side was growing at over thirty percent year-over-year. When a buyer runs the numbers, they see a high-growth annuity attached to a stable base. Luna: What did the actual sale process look like? Was it a competitive auction or a negotiated deal? Lucas: It was a limited auction — three interested parties, all healthcare services firms looking to expand into value-based care. The winning bidder was a company that manages chronic disease programs for Medicare Advantage plans. They saw the pharmacy as a way to test adherence interventions in a real-world setting. Luna: That makes sense. They weren't buying a pharmacy — they were buying a laboratory. Lucas: Exactly. And the seller structured part of the earnout around adherence metrics staying above 85 percent for two years post-close. That aligned incentives and justified the upfront multiple. Luna: I want to go back to something you said earlier — the data revenue was under long-term contracts. How important was that for the multiple? Lucas: Hugely important. The buyer's valuation model discounted those cash flows at a much lower risk premium because the contracts had auto-renewal and termination penalties. Without those contracts, the data revenue would have been treated as speculative, and the multiple probably drops to ten or eleven times. Luna: So the lesson for any small business with a data angle: lock in multi-year agreements before you go to market. Lucas: Absolutely. And it's not just healthcare. We've seen similar plays in logistics companies that own route optimization data, or marketing agencies that own proprietary audience panels. The principle is the same — data that creates switching costs for the buyer. Luna: One thing I'm curious about: did Dr. Cross have a broker or did she self-manage the sale? Lucas: She hired a boutique healthcare M&A firm that specializes in pharmacy transactions. They helped her package the data business as a separate operating segment with its own P&L. That transparency was critical — buyers could see exactly how much EBITDA came from drugs versus data. Luna: It's almost like she created two businesses inside one legal entity. The low-margin drug business and the high-margin data business. Lucas: That's exactly how the buyer's investment memo described it. And they paid a blended multiple that weighted the data segment at roughly eighteen times and the pharmacy segment at eight. The fifteen times overall is just the weighted average. Luna: For a business owner listening who thinks they might have a data asset they're not monetizing, what's the first step? Lucas: Start by asking: what information do I generate as a byproduct of my daily operations that someone else would pay for? Then test the willingness to pay — approach one potential buyer with a pilot. That's what Dr. Cross did. She offered a free six-month trial to one ACO, proved the adherence improvement, then negotiated the forty-dollar-per-patient fee. Luna: So the data has to measurably improve someone else's outcome. It's not enough to just have numbers. Lucas: Right. In this case, the ACO's readmission rate for diabetic patients dropped by 12 percent in the first year. That's a concrete, attributable result that justified the ongoing payment. Luna: It's fascinating. And it shows that even a traditional, main-street business can build a modern data revenue stream. That's something we really try to highlight on this show — the practical playbooks that actually work. Lucas: And you know, a big reason we can keep digging into these cases without any ads or paywalls is that some listeners choose to support the show. If these conversations have moved your work forward in some small way, a couple of dollars a month is genuinely what keeps these going. Luna: Yeah, it's that simple. Buy me a coffee dot com slash fexingo — all lowercase, no pressure. It makes a real difference for a show like this. Lucas: Alright — back to the transaction. One other detail I want to highlight: the earnout structure. Dr. Cross stayed on as a part-time data consultant for twelve months, earning a salary plus a bonus if adherence rates stayed above 85 percent. Luna: That's smart. It gives the buyer continuity and the seller a financial incentive to ensure a smooth transition. Lucas: And it protected the multiple. If adherence had dropped to 70 percent in year one, the buyer could have clawed back some of the purchase price. But it held steady at 86 percent, so she hit the full payout. Luna: I think the takeaway for our listeners is clear: look at your business through the lens of an acquirer. What asset do you own that they can't easily build themselves? If it's data, start packaging it today. Lucas: And if you need inspiration, just remember: a pharmacy with a drive-through in Ohio figured it out. Your business probably has something similar waiting to be discovered. Luna: Alright, that's our episode. We'll be back next week with another real-world deal breakdown. Lucas: See you then.