Identifying target green 3C customers in Taiwan using multiattribute utility theory
Issues of environmental protection have become more and more important. Consumers are increasingly concerned about how their behaviors impact the earth. Therefore, with the emergence of a customer-oriented market, identifying consumers behaviors has become an important issue for businesses. Determining how to identify target customers who are satisfied and willing to pay more, is an important issue. In this study, we applied data mining techniques to cope with this problem; a list of questionnaires was used to determine the preferences of customers with respect to a green 3C product in Taiwan. In order to tell the differences between the heterogeneous customers, clustering analysis is needed. Behavior variables, psychological variables, geographic variables, demographic variables, environmental knowledge, attitudes toward environmental protection and non-purchasing environmental behaviors were used to profile customers. Step-wise regression and ANOVA analysis were used to obtain the suitable segmentation variables. After clustering analysis, customers were segmented into different groups. The promoter or passive one in each cluster, as indicated by the net promoter score technology, is the satisfied customer in the corresponding cluster. A bi-objective nonlinear mixed integer problem is constructed with multiattribute utility theory; optimal price and promotion strategies can be provided as the foundation for marketing for satisfying both supplier and customer with a winwin concept. Then, business can really transmit the selling information to the users and improve the satisfaction of detractors to increase sales and profit.
cluster analysis green product nps maut nonlinear mixed integer programming