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The Art and Science of Forecasting Drug Launches

Overcoming Launch Challenges and Maximizing Success in a Complex Landscape

In the pharmaceutical industry, appropriately allocating resources and planning for a drug launch has never been more crucial. There are new obstacles and challenges with supply chain management and the application of multi-tier pricing. Moreover, as payer influence plays a more significant role and new drugs target smaller patient populations with unique and complex needs, developing and executing a winning launch plan becomes even more challenging. With the increasing competition and growing costs of developing new drugs, forecasting demand and revenue has become an endeavor that is equal parts art and science. In this blog post, we explore why accurate forecasts are essential and why they are complicated and challenging to develop.

Forecasts play a crucial role in the decision-making process for pharmaceutical companies. The output of your forecast impacts revenue and budget planning, manufacturing and supply chain planning, resource allocation, marketing campaign planning, and compensation plans.

As essential as forecasts are, they are equally complicated and challenging to develop. One study found that 60% of 1700 analysts’ forecasts are off by more than 40% over or under.1 Much of this is due to a few factors that include:

  • Desire to “make” a forecast hit an arbitrary target
  • Poor data quality or failure to factor in the uncertainty within the data
  • Limited understanding of product and market nuances
  • A rigid approach to modeling that doesn’t ensure alignment is bias-free
  • Assumptions too reliant on the past, not considering the potential of the future

Having launched 100+ products, we understand firsthand how critical an accurate forecast is to the launch success. Our collaborative approach marries top-down epidemiological data and market analogous to sophisticated bottoms-up models built on market insights. We de-risk forecasting by developing case scenarios to understand key drivers and influencing factors.

We embrace a more dynamic, flexible, and data-driven approach to overcome some of the challenges associated with forecasting. Here are some strategies that we found to be helpful:

  1. Start with high-quality data: A reliable forecast requires accurate and comprehensive data. Collect data from multiple sources, including internal and external sources, and validate the data to ensure accuracy.
  2. Use advanced analytics: Traditional forecasting methods may not be enough to handle the complexity of the pharmaceutical industry. Advanced analytics, such as machine learning and artificial intelligence, can provide a more accurate and granular forecast.
  3. Collaborate with cross-functional teams: Forecasting should not be siloed in one department. Collaborate with cross-functional teams to get a more comprehensive market view, including regulatory, clinical, and commercial perspectives.
  4. Embrace scenario planning: The pharmaceutical industry is unpredictable, and forecasting is never foolproof. Scenario planning allows companies to prepare for various outcomes and make more informed decisions.
  5. Continuously evaluate and adjust: development pipelines, competitive landscapes, regulatory and policy guidelines constantly evolve, and forecasts must be continuously evaluated and modified based on new data and insights.

Having a credible forecast is critical to drug launch success. The challenges are many, but by embracing a dynamic, flexible, and data-driven approach, pharmaceutical companies can overcome these challenges and maximize success in a complex landscape.


Ali Soroush Herspiegel Consultant

Ali Soroush

Senior Consultant

Eric Babiash

Eric Babiash


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Herspiegel is a leading professional services firm helping biotech start-ups and the world’s largest pharmaceutical companies navigate the path from science to brand performance and change patients’ lives. We are committed to using strategic insights from medical to commercial to market access to solve critical problems.