An empirical comparison of new product trial forecasting models

While numerous researchers have proposed diAerent models to forecast trial sales for new products, there is little systematic understanding about which of these models works best, and under what circumstances these findings change. In this paper, we provide a comprehensive investigation of eight leading published models and three diAerent parameter estimation methods. Across 19 diAerent datasets encompassing a variety of consumer packaged goods, we observe several systematic patterns that link diAerences in model specification and estimation to forecasting accuracy. Major findings include the following observations: (1) when dealing with consumer packaged goods, simple models that allow for relatively limited flexibility (e.g. no S-shaped curves) in the calibration period provide significantly better forecasts than more complex specifications; (2) models that explicitly accommodate heterogeneity in purchasing rates across consumers tend to oAer better forecasts than those that do not; and (3) maximum likelihood estimation appears to oAer more accurate and stable forecasts than nonlinear least squares. We elaborate on these and other findings, and oAer suggested directions for future research in this area. #1998 John Wiley & Sons, Ltd. Almost every textbook discussion of the new product development process includes a call to conduct some form of market test before actually launching the new product. Such an exercise serves several objectives, including the desire to produce an accurate forecast of the new product’s sales performance over time. These forecasts can help lead to a final go/no-go decision and can also assist in the marketing and production planning activities associated with the product launch. In the case of consumer packaged goods, conducting a market test historically saw the company’s sales force selling the product into retail distribution in one or more markets for one to two years, after which a decision of whether or not to go national with the new product was