Research of computational customer modeling based on large-scale datasets and various situations in retail service

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This research proposed 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 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. This method is applicable for marketing support, service modeling, and decision making in various business fields, including retail services.