WHAT WE DO?

Sales Forecasting: Prescription-based Models

Pharmaceutical and biopharmaceutical markets are typically characterized by uncertainty about the future. While this may be true of many industries it is particularly the case for pharmaceutical markets. A change in reimbursement scenario or new product indication can mean millions in incremental brand revenue. Likewise a new competitor or loss of patent protection can cost millions. Further complicating these matters is the fact that the exact timing of these positive and negative events is unknown.

Our prescription-based models are designed with the purpose of taking the mystery out of the future.

Our approach to prescription-based forecasting is best described as "part-art, part-science". We use state-of-the art forecasting methodologies and software to establish forecasting baselines. We then layer on the impact of market events through careful selection of appropriate analogues. This process involves identifying a situation from the past (either in your current market or outside your market) which is as close as possible to the market event you are facing. In some cases we allow the client the option of adding analogues to the model as he/she sees fit.

The end result is a customized client-owned sales forecast model which puts the control for forecasting at your fingertips. We design our models to address all the possible forecasting questions that your executives or brand team may pose and allow you with insights to make the best marketing decisions.

Patient-based models allow for specific insights into the dynamics of a pharmaceutical market that are not provided by prescription-based models. (See diagram below)

Our patient-based forecasting models are tailored to the client’s particular market dynamics. Since medications are often ‘sold’ in numerous therapeutic markets, we often segment our models into specific disease areas. This allows the client to identify the market sales and opportunities offered in distinct markets.

The diagram is a modified version of one used in a recent patient-forecast model.  It illustrates not only the overall approach to patient modeling (top-down method) but also highlights our focus on 'pictures' over 'numbers' to convey the forecasting message.