New models from old: Forecasting product adoption by Hierarchical Bayes procedures

A method for obtaining early forecasts for the sales of new durable products based on Hierarchical Bayes procedures is presented. The Bass model is implemented within this framework by using a nonlinear regression approach. The linear regression model has been shown to have numerous shortcomings. Two stages of prior distributions use sales data from a variety of dissimilar new products. The first prior distribution describes the variation among the parameters of the products, and the second prior distribution expresses the uncertainty about the hyperparameters of the first prior. Before observing sales data for a new product launch, the forecasts are the expectation of the first stage prior distribution. As sales data become available, the forecasts adapt to the unique features of the product. Early forecasting and the adaptive capability are the two major payoffs from using Hierarchical Bayes procedures. This contrasts with other estimation approaches which either use a linear model or provide reasonable forecasts only after the inflection point of the time series of the sales. The paper also indicates how the Hierarchical Bayes procedure can be extended to include exogenous variables.