Identifying Customer Characteristics by Using Rough Set Theory with a New Algorithm and Posterior Probabilities

Analyzing Customer Characteristics is an important issue in marketing. Recently, studies about Customer Characteristics focus on two main directions: Customer Identification and Making Decision. Many data mining theory are applied successfully to identify and classify customer, especially Rough Set Theory (Ali Ahmady 2009), (James J.H. Liou, Gwo-Hshiung Tzeng 2010), (Saiful Hafizah Jaaman 2009). But a key problem when using Rough Set Theory to identify important customer characteristics is time-consuming. Because of this reason, it is difficult to integrate Rough Set Theory into solving Customer Identification problem. Besides that, Making Decision is a necessary mission of Analyzing Customer Characteristics. Expected Opportunity Loss index is often used to make decisions under risk and uncertain situation (K. Khalili Damghani et al. 2009). However, it is too simple and does not reflect the experience values. This paper introduces a new model of Customer Characteristics which applies our proposed algorithm to identify Customer Characteristics and presents a Posterior Expected Opportunity Loss index to make decision.