Goodbye Static Forecasts: Dynamic Sales Modeling for Smarter Pharma Decisions

Sales forecasts typically rely on static time series created based on a fixed launch date, efficacy, and market conditions.

But reality is rarely that simple. If a project is delayed or a competitor enters the market sooner than expected, static forecasts quickly become outdated, requiring constant manual updates and rework.

But what if sales forecasts could adapt dynamically to changes in development timelines, competitive landscapes, and efficacy profiles?

We now have a pretty cool feature at Captario that does exactly that. Instead of relying on fixed sales curves, we can transform any time series into a dynamic, parameterized sales model that uses ramp-up, peak sales, LOE, and Ramp-down.

This means:

🛠️ Sales forecasts automatically adjust based on launch timing, competitive activity, and efficacy shifts

📊 Portfolio analysts can explore more scenarios, faster, without rebuilding forecasts from scratch

⚡ Decision-making improves because we’re looking at a range of possible outcomes, not just a single best guess

For example, we can now easily test statements like:

👉 “If we launch after our competitor in July 2028, peak sales decrease by 30%”

👉 “For every six-month delay, reduce peak sales by 5%”

This approach moves beyond traditional "best-base-worst" case modeling. Instead of defining one sales curve, we create a flexible model that represents hundreds of scenario combinations—a holistic view of a project’s value.

With dynamic sales curves directly linked to development models, we gain a more realistic, data-driven foundation for investment decisions. Analyzing more scenarios leads to better strategic choices.

I am interested to hear your thoughts about this new possibility. Let’s talk in the comments! 👇