| Owing to intense competence in domestic and international customer market，traditional domestic customer market business model has gradually become theadvanced one based on customers，data and information. Customer segmentation isthe premise and foundation of the great change. At present，China’s banks dividedcustomers basing on experience or simple statistical methods，and it does not meet therequirement of complex business analysis．Along with the depth spread of data miningamong the management of customer，it is becoming important and urgent to focusedcustomer segmentation by adopting various advanced data mining methods．To solve the banking personal customer segmentation problem, this paper usesdata mining technology and establishes the commercial bank customer segmentationmodel based on the existing theory of customer segmentation. In ord er to obtain thebetter customer segmentation results, the optimization method is put forward to adjustthe above model. Firstly, the research background and significance are put forward,the research status at home and abroad is described briefly, and res earch ideas andgoals are determined. Secondly, the theory of customer segmentation and technologyof the date mining are analyzed; then according to the present situation of customersegmentation for commercial bank credit card, the data mining models are built basedon the K-means clustering algorithm and the decision tree method; finally, use datamining technology and obtain the results of the mining models according to customercredit card data of a bank.This paper establishes the customer segmentation models based on data miningtechnology, and the validity of the models are verified by the credit card data of abank customer. The segmentation results can be used to guide the bank product designand the implementation of the marketing strategy to obtain greater profits. Theresearch has an great effect on improving the level of our country commercial bank’scustomer segmentation. |
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