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Here’s How I Helped a Client Forecast Sales of Her New Product! (302996)

Keywords: proprietary, big,data, forecasting, new, product

A pharmaceutical company developed a cough syrup lasting 12 hours. Product management needed data to price the product and do forecasts. A national discrete choice survey of 40 pediatricians and 60 primary care physicians was completed. Objectives were 1) determine demand, 2) estimate the relative value of an 8-hour versus a 12-hour product, 3) determine possible effects of retail price on market share for children and adults, and 4) estimate the cannibalization rate of two existing products. A discrete choice approach was used to quantitatively model the market share value of the new product that relieved coughs for either 8 or 12 hours. These two products were then combined with three price points for adults and children. A demand forecast and net revenue model was developed using the discrete choice model data combined with Big Data from the IMS National Prescription Audit for this product category. A price, share, and net revenue trade-off analysis was performed. It indicated a 12-hour product priced at the mid-range values of $3.50 for adults and $1.75 for children yielded peak net revenue with minimal cannibalization of existing proprietary products.