Identifying and Clustering of Target Customers of Green Products

Identifying and Clustering of Target Customers of Green Products

Miao-Ling Wang
DOI: 10.4018/978-1-60566-114-8.ch008
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The number of consumers concerned about the environment is growing. Although the promotion of green products is recognized as a basic method for solving the waste crisis and improving the environment, resources for producing or serving green products are relatively limited, causing inconveniences and elevated prices for the consumer. Therefore, it becomes significant that those customers who are willing to sacrifice convenience in order to purchase higher priced green products be identified. Through the affirmation of target customers in an effective marketing system, enterprises can recycle used products efficiently, increase profits and successfully transmit advertising information to consumers who are disposed to buy green products. In this chapter, we apply data mining techniques to cope with this problem. After clustering the customers, a bi-objective nonlinear problem is constructed with multiple attribute utility theory; the target customers form the foundation of marketing.
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Literature Review

The rapid development of information technology and the Internet has changed traditional business environments. Companies are realizing that they can use the Web to effectively communicate with customers, making their business easier. The author had proposed a Web mining system that incorporates both online efficiency and off-line effectiveness to provide the “right” information, based on users’ preferences (Wang & Wang, 2005). To efficiently construct a Web site that will provide information about green products, we first need to identify customers according to their preferences, so that Web customers’ behavior can be characterized.

Based on the above ideas we will first introduce the different demands of green marketing and in order to cope with these different demands, the existing research about the customer features will be investigated. Facing the customer heterogeneity and for a successful marketing, the suitable variables for profiling the green customers are discussed. Furthermore, MAUT and related techniques are introduced to construct a compromised model of the consumers between alternatives with conflicting objectives. Finally, the solution procedures for the proposed model are presented.

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