A multi-category inter-purchase time model based on hierarchical Bayesian theory

Because of recent diversity in consumer demands and the decrease in popularity of mass media, one-to-one database marketing is being increasingly used by companies to increase their competitiveness. Many studies have addressed the issue of inter-purchase time, but few have considered the impact of multiple categories of products on inter-purchase time, which may vary for different products. The aim of the present study was to build a one-to-one multi-category inter-purchase time model using a hierarchical Bayesian model based on a generalized gamma distribution and multiplicative model formulations. Using a hazard rate function, the model was applied to derive a purchase probability for individual customers. To validate the proposed model, field data were collected from a local catalog company and prediction hit rates were compared for different models. The multi-category inter-purchase time model exhibited better prediction hit rates than a basic model. Using the multiplicative model, our multi-category model can estimate the influence of product category on customers’ inter-purchase time.