Why traditional launch forecasts fail in dynamic markets

“Forecasting has always been central to strategic decision-making. Nowhere is this more apparent than in healthcare. From launch planning and pricing, to access strategy and pipeline investment, forecasts underpin some of the industry’s most consequential decisions.

Yet many forecasting approaches remain rooted in the past. Historical sales data can tell us where the market has been, but not necessarily where it is going. In markets being reshaped by innovation, policy changes, procurement decisions, and AI-driven disruption, understanding human behaviour and decision-making is just as important as understanding historical trends.”

– Matteo Fabiani, Associate Director | SKIM

The challenge: forecasting launch adoption in a dynamic market

To prepare for launch, our client needed to understand not only the uptake potential of a novel contrast agent, but also how future market changes could influence adoption over time. While the asset offered clear differentiation, there was significant uncertainty around how healthcare stakeholders would respond in an increasingly competitive and price sensitive market.

The forecast needed to account for multiple decision makers, tender agreements, portfolio contracts and growing pressure from both branded and generic competitors. The client also wanted to understand how future events could reshape demand in the years ahead (even those they could not yet define).

Traditional forecasting approaches could address parts of the challenge by relying on historical trends, but not the full picture. The question was: how to generate a forecast that remained robust in a market that would not stand still.

Our solution: The Goldilocks problem

To address this challenge, SKIM developed an integrated forecasting framework that combined the strengths of historical market data with the predictive power of choice modelling.

At the heart of the approach was what we call the “Goldilocks problem”. Historical sales data can provide a realistic view of market dynamics, but it is inherently backward-looking and struggles to account for events that have yet to occur. Choice modelling, on the other hand, can capture how stakeholders respond to different product profiles, pricing strategies, and competitive scenarios, but may not fully reflect the real-world constraints influencing adoption.

“Too often, forecasting teams are forced to choose between realism and flexibility. Historical sales explain yesterday, while choice modelling explores tomorrow. By combining the strengths of both, we created a forecasting approach that remained grounded in real-world market dynamics while adapting to future changes.”

– Matteo Fabiani, Associate Director | SKIM

Rather than relying on a single methodology, we integrated both. Historical sales data was used to ground forecasts in observed market behaviour, while advanced choice modelling quantified how different healthcare stakeholders would respond to future trade-offs and market scenarios.

To further enhance realism, the model incorporated the influence of real-world events like tendering, contracting agreements, and multi-stakeholder decision-making. Future events, such as competitor/generic launches and pricing changes, were then layered into the forecast to simulate how adoption and market share could evolve over time.

The result was a dynamic forecasting tool that helped the client explore uncertainty, test strategic assumptions, and make more confident launch decisions, even in unprecedented scenarios.

The outcome: A fit-for-purpose model to guide strategy

The outcome was more than a forecast. The client gained a shared understanding of the factors most likely to influence launch success, the scenarios that could reshape adoption over time, and the strategic levers available to respond. This allowed teams to evaluate launch, pricing, and access decisions with greater confidence, even in the face of significant uncertainty.

A key element of the project was ensuring that the right stakeholders were involved throughout the research process. Forecasting, market intelligence, commercial, and market access teams helped shape the assumptions, scenarios, and business questions underpinning the model. This not only strengthened the quality of the outputs, but also fostered early alignment around a shared view of the market and the challenges ahead.

The final deliverables were designed with end users in mind. Rather than providing a static forecast, SKIM developed an intuitive simulation tool and reporting framework that enabled teams to independently explore scenarios, evaluate strategic options, and update assumptions as market conditions evolved. By equipping stakeholders with a common language and decision-making framework, the research continued to support strategic planning long after the initial launch forecast was delivered.

What this means for forecasting in the healthcare industry

Our work highlighted a simple but important truth: better forecasting is not about more data. It is about generating smarter insights, enabling better decisions, and creating alignment around a shared view of the future. Three lessons stand out:

Dynamic modelling beats static assumptions. By combining historical sales with conjoint preferences, we built a forecast that is both credible and flexible, ready for multiple future scenarios.

The smartest forecasts combine multiple perspectives. Historical data provides credibility, while behavioural insights reveal how markets may respond to change. Together, they create a more robust view of future demand.

Shared understanding drives better decisions. When commercial, forecasting, and market access teams align on the same assumptions, planning becomes faster, more confident, and more coordinated.

Here’s the bottom line: when you integrate behavioural insight with real-world constraints, you move from predicting the future to truly shaping it.

Building confidence in launch and access decisions

If you need to understand the opportunity for a new asset in a competitive healthcare market, SKIM combines forecasting and preference research to bring greater confidence and evidence to strategic planning.

Better forecasting is not just about predicting demand. It’s about understanding how markets may evolve and what that means for launch, pricing, access, and portfolio strategy. The strongest decisions are not driven by more data alone, but by a deeper understanding of how stakeholders are likely to respond as market conditions change.

As healthcare markets become increasingly shaped by competitive, pricing, and access dynamics, forecasting can no longer rely solely on what happened yesterday. Organisations need tools that help them understand how stakeholders may respond to what happens next.

Get in touch to discuss your next strategic forecasting challenge.