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.

This paper describes an appropriate category discovery method that simultaneously involves a customer's lifestyle category and item category for the sustainable management of retail services, designated as ``category mining''. Category mining is realized using a large-scale ID-POS data and customer's questionnaire responses with respect to their lifestyle. For the heterogeneous data fusion, we propose a probabilistic double-latent semantic indexing (PdLSI) model that is an extension of PLSI model. In the PdLSI model, customers and items are classified probabilistically into some latent lifestyle categories and latent item category. Then, understanding of relation between the latent categories and various purchased situations is realized using Bayesian network modeling. This method provides useful knowledge based on a large-scale data for efficient customer relationship management and category management, and can be applicable for other service industries.