| This paper is trying to do some systematic exploration on the application of data mining in commercial banking customer relationship management. For this purpose, the paper will further its discussion from the following aspects based on related document summary:Firstly, research about some basic concepts and methods of customer relationship management and data mining will be carried out, and about the internal relation between data mining and customer relationship management of commercial banks.Secondly, we will do some analysis about basic demographic characters of credit card customers in the branch of some commercial bank which will be set models. By analysis, we can see there are still some potential information in the customer data which need mining. Therefore, data mining technology can be put into practice to find out the potential characters. The classic data mining clarifying methods include neural network and decision tree, because decision tree has been highly valuated on several crucial characters of data mining, and according to the task of data mining, namely to forecast whether to apply for loan or not in the near future based on present customer information, we decide to adopt decision tree to set modes.Thirdly, we will to set model of decision based on the customer data of branch of the commercial bank, put forward clarifying regulations, and then forecast new customers whether to apply for loan or not, meanwhile, carry out verification about the accuracy and elasticity of forecast by using mathematical statistics.Lastly, customers of commercial banks will be divided into five categories according to the analysis of decision-tree model, and then analyze behavioral characters and its contribution to commercial banks of each kind to work out different marketing plans and customer channel management strategies, at the same time, research and develop new products and services in the light of different customer requirements. |
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