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

  • Authors:

  • Ruey-Shan Guo

  • Affiliations:

  • National Taiwan University, Business Administration, No. 1, Section 4, Roosevelt Road, Taipei, Taiwan

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

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.