Using Radial-basis Function Network for CLV  

作　　者：李纯青 郑建国 

出　　处：《成组技术与生产现代化》2002年第3期53-56,共4页Group Technology & Production Modernization

基　　金：ThisresearchsupportedbyNSFC (70 12 10 0 1)andtheEducationDep artmentofShaanxiProvince (0 2JK0 0 9) .

摘　　要：Analysis and comparing with three existing and popularly used forcasting customer lifetime value (CLV) methods, which are the Dwyer method, customer event\|method and fitting method, and using performances of the existent artificial neural network, we apply the Radial basis Function(RBF) network to forecast the CLV, the RBF network can approach the objective function partially. To every input/output pairs, it only needs adjust the weight a little and learn quickly which is very important to the forecast precision. Simulation and experimental results on the customers’ data of a company in Shaanxi Province reveal the effectiveness and feasibility of the RBF network.\;Analysis and comparing with three existing and popularly used forcasting customer lifetime value (CLV) methods, which are the Dwyer method, customer event\|method and fitting method, and using performances of the existent artificial neural network, we apply the Radial basis Function(RBF) network to forecast the CLV, the RBF network can approach the objective function partially. To every input/output pairs, it only needs adjust the weight a little and learn quickly which is very important to the forecast precision. Simulation and experimental results on the customers' data of a company in Shaanxi Province reveal the effectiveness and feasibility of the RBF network.\;

关 键 词：BP网络 BRF网络 CLV 客户价值 

分 类 号：F713.5[经济管理—市场营销] TP183[经济管理—产业经济]