Abstract:
Customer classification is one of the major tasks in customer relationship management.
Customers have both static characteristics and dynamic behavioral features. To apply
both kinds of data to conduct comprehensive analysis can enhance the reasonability of
customer classification. In this paper, customer dynamic data is clustered using a hybrid
genetic algorithm and then is combined with customer static data to give reasonable
customer segmentation by using neural network technique. A novel classification method
which considers both the static and dynamic data of customers is proposed. Applying die
proposed method in a bank's datasets can obviously improve the accuracy of customer
classification comparing with traditional methods where only static data is used.
