| Summary: | 碩士 === 東吳大學 === 資訊管理學系 === 106 === The measurement of customer lifetime value is important because it is used as a metric in evaluating decisions in the context of customer relationship management. The main goal in this research is bank customer’s segmentation by discovering the transactional relationship between them in order to deliver some specified solutions in benefit of some policy about customers cross-selling in financial industry. In this paper, we use a novel application using Recency, Frequency, and Monetary (RFM)-based clustering and Customer Lifetime Value (CLV) analysis for customer segmentation. Customer segmentation based on customer lifetime value is one of the new approaches regarding customers’ segmentation and is one of the efficient methods in segmentation of customers. Hence, the relative importance (weight) of each variable of CLV model is determined by using Analytic Hierarchy Process (AHP) method and a survey of experts. In this research, the data come from Kaggle and UCI web sites for data science and machine learning platform. In addition, the market segmentation can help banks identify their opportunities and threats. It is clear that customer segmentation influence marketing strategies for banks. Therefore, beyond simply understanding customer value in each cluster, the bank would gain opportunities to establish better customer relationship management strategies, improve customer loyalty and revenue and find opportunities for cross selling.
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