Customer-item category based knowledge discovery support system and its application to department store service

In the framework of personalization or micromarketing of services, an effective strategy is to examine customers or items of a specific category. This paper describes an actual service support system using discovery of category-based customer behavior knowledge. The method is realized by modeling a customers' purchase behavior with some purchase situations or conditions using massive point of sales data with a customer ID (ID-POS data) in a department store chain. We automatically generate categories of customers and items based on a purchase patterns identified in ID-POS data using probabilistic latent semantics indexing. We produce a Bayesian network model including the customer and item categories, situations and conditions of purchases, and the properties and demographic information of customers. Based on that network structure, we can systematically identify useful knowledge for use in furthering business intelligence or sustainable services. This method is applicable for marketing support, service modeling, and decision making in various business fields, including retail services.