At the heart of a new product sales forecasting model for consumer packaged goods is a multiple-event timing process. Even after controlling for the effects of time-varying marketing mix covariates, this timing process is not a stationary one, which means the standard interpurchase time models developed within the marketing literature are not suitable for new products.

In this paper, we develop a dynamic changepoint model that captures the underlying evolution of the buying behavior associated with the new product. This extends the basic changepoint framework, as used by a number of statisticians, by allowing the changepoint process itself to evolve over time. Additionally, this model nests a number of the standard multiple-event timing models considered in the marketing literature.

In our empirical analysis, we show that the dynamic changepoint model accurately tracks (and forecasts) the total sales curve as well as its trial and repeat components and other managerial diagnostics (e.g., percent triers repeating).