Dynamic changepoints revisited: An evolving process model of new product sales

This paper posits a new framework to model the trial of and repeat purchasing of a new product. While much research has examined underlying shifts in consumer purchasing patterns, the typical assumption has been that the underlying purchasing process remains the same although the purchasing rate may change over time. Motivated by Fader, Hardie, and Huang's development of a dynamic changepoint model [Fader, P. S., Hardie, B. G. S., & Huang, C. -Y. (2004). A Dynamic Changepoint Model for New Product Sales Forecasting. Marketing Science, 23 (1), 50–65], we consider an evolving process as consumers gain more experience with a new product.Our framework assumes that consumers progress through two purchasing states, becoming more regular in their inter-purchase times as they gain experience with the product through repeat purchases. More specifically, they move from an initial state of exponential purchasing to a “steady state” that is characterized by a more regular Erlang-2 timing distribution. The proposed model is very flexible and nests a number of existing models, enabling it to explain a wide range of observed behavioral patterns. We apply our evolving process model to the same datasets used by Fader, Hardie, and Huang [Fader, P. S., Hardie, B. G. S., & Huang, C. -Y. (2004). A Dynamic Changepoint Model for New Product Sales Forecasting. Marketing Science, 23 (1), 50–65] and compare our results to a number of competing models. We find empirical evidence to support the use of a two-state model, since it yields relevant insights as well as improved empirical performance. We discuss the implications as they relate to forecasting new product sales.