| Summary: | 碩士 === 長榮大學 === 資訊管理學系碩士班 === 101 === In the era of customer orientation, enterprises need to do customer relationship management in order to improve customer service quality and enterprise competitiveness. And to do the customer relationship management, we must first understand customer purchasing behavior. Because the customer purchasing behavior has interactive, dynamic and many other features, to understand it by personal experience cannot meet the management needs. At present, large number of customer transaction data has been gathered and stored in databases, how to analyze the customer purchasing behavior information from large customer databases becomes one of the important research issues.
The aim of this research is to investigate the customer purchasing behavior by multivariate approach. First of all, we run k-medoids clustering methods with the combination of association rules and RFM scores data and interpret the meaning of the outcome clusters. Then, the customer groups identified through the k-mean clustering method and traditional RFM scoring method are compared in order to understand the difference between these two methods. Finally, we conduct decision tree analysis to analyze what is the major variable determine the assignment of customer group in previous k-mean clustering analysis. The results of our research show that the combination of association rules and numerical data analysis can provide a new insight of customer purchasing behavior; and compared two different customer values identification methods provides a new perspective of customer value; and through the decision tree analysis, we understand the purchase amount (M) in the RFM model is a more important variable. By understanding the customer purchasing behavior and identify customer value, we will be able to provide enterprise to make appropriate marketing strategies for effective customer relationship management and enhance the competitiveness of enterprises.
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