Faulty pharmaceutical forecasting can skew launch planning and sour investors
Planning a successful drug launch requires that commercial teams be armed with reliable and up-to-date forecasting, with realistic market sizing and clearly-stated assumptions rooted in rock-solid data and analytics. Here are a couple cautionary tales about what can happen when businesses cut corners, starting with a look at how one life science company, through no fault of its own, got tripped up by an external entity’s bad forecast, and how others can proceed to the launch phase with confidence.
Launching a new drug demands accurate forecasting
A midsized pharma company based on the East Coast of the U.S. was launching a new treatment for a stubborn and heterogeneous neurological condition. Though part of an older class of medications, their treatment offered a promising new option to patients for whom existing therapeutics had proved inadequate, addressing substantial unmet need. However, its entry into a heavily genericized subset of an increasingly crowded category demanded reliable forecasting to establish realistic analyst and investor expectations and optimize launch strategy.
A prominent U.S. investment house forecast 2025 revenues at four times the estimate of Clarivate Disease Landscape and Forecast model. The Clarivate forecast proved highly accurate, thanks in part to assumptions modeled on granular segmentation of the eligible patient population and a view of the competitive and market access landscapes informed by proprietary data, key opinion leader insights and analytics.
Evidence-based forecasting beats Wall Street wishcasting
While no forecast can eliminate uncertainty, Disease Landscape and Forecast reports from Clarivate provide a transparent, evidence-based foundation for understanding how the market is likely to evolve and why. Each forecast is grounded in rigorous epidemiological estimates and primary market research-driven segmentation, establishing market size and growth anchored in patient reality rather than back-calculated from sales. Country-specific treatment rates and patterns are informed by KOL interviews and physician surveys, ensuring that our model reflects how different prescriber types treat patients in the real world, not solely according to guidelines.
Our models consider realistic therapy positioning and market access headwinds based on comprehensive historical data and analogs. We continually evaluate data and assumptions, giving you timely forecasts that evolve with market conditions, and our assumptions are stated plainly to allow for confident decision-making. Because the investment house’s overly-optimistic forecast underestimated payer friction and overestimated eligible and accessible patient populations, it badly overshot real-world revenue potential at launch. This led to top-line expectation gaps, resulting in capital misallocation, credibility erosion and strategic resets. While the company’s stock recovered in line with our expectations, better forecasting assumptions would have enabled improved capital allocation and decision-making.
The Rx on AI-only forecasting for pharmas: seek a second opinion
Predictive commercial analytics provides an intriguing potential use case for AI, but a road test reveals limitations. Here’s how a pure-play AI approach stacked up to an AI-enhanced analytics package in one recent real world case.
A large European pharma had won FDA and EMA approval for a rare disease treatment with an innovative method of administration. The company’s commercial team needed an accurate understanding of disease epidemiology, the competitive landscape and their treatment’s probable uptake and revenue potential in those markets to plan a successful launch and establish realistic analyst and investor expectations.
A/B testing AI for epidemiology
The team consulted DRG Epidemiology Intelligence from Clarivate to understand possible trajectories of their drug. Clarivate analysts developed their forecast using proprietary data and analytics. Out of curiosity, Clarivate analysts B-teamed their projections with an AI-generated forecast.
Life science companies are increasingly exploring the utility of AI for predictive analytics. The Clarivate team wondered how a purely-AI-driven forecast might compare to their approach.
AI can miss important nuances
The AI-generated forecast sharply overestimated the new drug’s potential market share based primarily on its route of administration. The Clarivate forecast incorporated expert knowledge of access and reimbursement dynamics within each geography and insights from interviewed KOLs on the influence of such a delivery advancement, resulting in a much lower ceiling on near-term uptake and significantly shifting the peak earnings potential.
Large language models excel at detecting patterns and assembling information across vast stores of data. However, a model is only as good as its inputs – the addition of insights from proprietary KOL interviews provided a critical lens, layered on human understanding and interpretation of key treatment landscape dynamics, considerations that AI-generated forecasts can miss.
Learn more about how DRG Disease Intelligence and Analytics solutions from Clarivate can help your business accurately understand patient populations: DRG Epidemiology Intelligence | Long-Range Epidemiology Forecasts
Learn more about how Disease Landscape & Forecast reports from Clarivate can help your business optimize long-term disease strategy with comprehensive intelligence: Disease Landscape & Forecast | Clarivate
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