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Shifting Priorities and Perceptions in Revenue Management


In this Pharmaceutical Executive video interview, Jesse Mendelsohn, senior vice president at Model N, discusses the changing dynamics today for companies in revenue management and planning—and the increasing importance of a well-tooled and well-trusted analytics component in fueling product launch, M&A, and other data-centered pursuits.

PE: Model N released its annual State of Revenue Report in February, which explores the strategies and insights of life sciences executives when it comes to revenue optimization and profitability in 2024. Three quarters of executives said their company’s approach to business operations needs improvement. Is that unusually higher than in past years or just reflective of current market and macro conditions and demands?

Mendelsohn: It’s maybe slightly higher than usual, but that trend has been going up. It really is reflective of current market conditions. Something that people need to remember as they watch the news or keep an eye on your publication about drug launches is that the types of products that are being launched recently and are in pipelines are different than they have been in the past. We’re seeing a lot more orphan drugs, biologics, and biosimilars; and with these new types of drugs, devices, and therapies, the ways of doing business need to change with them. The way you sell pills to millions of people is different than the way you sell injectable biologics to thousands of people. I think that's reflected in the answer to questions in our State of Revenue Report.

PE: Now more than two months into revealing your survey results, are you seeing some of the more telling findings, such as an emphasis on AI and advanced analytics and still-lingering challenges in staffing, bearing out in recent strategic decisions, including M&A, partnerships, and the like?

Mendelsohn: In areas of uncertainty, whether it's associated with M&A, or a product launch, or sales decisions, or incentive or discount decisions, manufacturers, rightfully, would like to shroud themselves in analytics, data, and well-done research. That's the way to answer these questions; not on a hunch or based on experience, but based on data. And making sure you not only have the right tools to do that data analysis—that’s clearly important to have the analytical capabilities, the software that supports it, and forays into AI to help you—but also making sure the underlying data itself is accurate and refreshed and most importantly, reliable.

The renewed thing that we’re seeing is almost equally on systems—do we have the systems and processes in place to make sense of this data?—and also on, okay, this data has to be reliable. We're talking membership data, [group purchasing organization] rosters, formulary positions of what plans are covering which of my products at which reimbursement rates. And we're talking about the pricing itself. Are my customers getting the right price for the right product for the right data?

There’s this kind of coupled, renewed view of data needing to be accurate, timely, and updated, but also a, I need the tools and technology to make sense of it and to help feed into the business decisions I'm making about launching my drug or pricing my drug.

For a deeper look at the evolving pharma pricing and access landscape, read this Q&A with Mendelsohn from earlier in the year.

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