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Research on Model of Retail Customer Loyalty and Cluster Mining

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Tutor: TianHong
School: Dalian Jiaotong University
Course: Applied Computer Technology
Keywords: Customer loyalty,RFM,Data Mining,Clustering K-means
CLC: TP311.13
Type: Master's thesis
Year:  2008
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It is difficult to win only relied on the commodity itself in the increasing fierce market competition and the competition is indeed to win customers in today market. In this case, it is very import topic that customer loyalty model can be build. There are vital role how to better identify the potential and enhance customer loyalty, as well as to prevent the loss of customers.It is import that the thesis studies the analysis behavior characteristic to domestic retail trade customer consumer in order to evaluate indicator system for retail trade customer loyalty. Proposed that suits model to customer loyalty in the domestic retail and the data mining method. Comparative analysis with RFM model shows that the model adopted retail and verify that its effectiveness and feasibility.After the establishment of loyalty model,selected the appropriate method for mining data. The NK-means cluster algorithm used in this paper based on analyzing the advantages and disadvantages in K-means algorithm, experiments show that this algorithm is effectively and accurately in customer loyalty assessment. In the data analysis process, the system of customer analyzes that used VC development carried on the data analysis. It demonstrates the data mining process about the application of the customer loyalty assessment. Meanwhile,with the data mining results it proposes the Marketing advices of every customer group in different customer loyalty. This application gives out a practical way to improve the marketing strategy of retail companies and to advance the management idea of retail companies. The last part of this thesis does a summary of the thesis¡¯s study and addresses some ideas about technology and application prospects of customer loyalty research in retail industry based on data mining.
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