Knowledge extraction by probabilistic cognitive structure modeling using a Bayesian network for use by a retail service

By understanding the behavior, satisfaction level, or values of the customer, the productivity and level of customer satisfaction of a service industry can be improved. Such customer-based considerations are estimated from questionnaire data in a general manner. The useful estimation of such considerations requires effective methods for modeling the cognitive structures of customers based on such data. However, it is difficult to model the behavior or decision making process of the customer, which involves nonlinear or non-Gaussian variables, using conventional statistical modeling techniques, which assume linear or Gaussian models. The present paper describes a method of constructing a probabilistic model of the cognitive structure of the customer, which clarifies the satisfaction level and decision making process of the customer of a retail service through statistical graphical modeling. The proposed method constructs a probabilistic cognitive structure model by integrating questionnaire data and a Bayesian network, which can handle nonlinear and non-Gaussian variables as conditional probabilities. The model structure can be constructed automatically based on information criteria and can embed some of the experiences of the model designer and/or physical or social rules in advance. The proposed method is applied to an analysis of the requested function from customers regarding the continued use of an item of interest. We obtained useful knowledge for function design and marketing from the model constructed by a simulation and sensitivity analysis. The proposed method can be applied to various services that use a variety of data.