| Summary: | 碩士 === 國立臺北科技大學 === 商業自動化與管理研究所 === 91 === As the dramatic changes of the marketing environment, the customers whom enterprises treat are not homogenous but heterogonous. Thus, how to identify the heterogeneity of the customers and apply clustering method to identify the difference between the customers has become critical point of successful marketing activities. Practitioners always use RFM method to evaluate customer value and segment the customers. But RFM method can’t explain the difference between different groups and evaluate the result. As a result, we define interpurchase time as the heterogeneity of the customers and estimate it by using hierarchical Bayesian method in order to overcome the disadvantage of the RFM method. After the verifying of the method, we found that RFM method can be applied successfully in 3C logistic industry. In addition, the result estimated by the hierarchical Bayesian method can help us to evaluate the result of the clustering from RFM method and to describe the different behavior of transaction of each person. Moreover, practitioners in 3C logistic industry can build up the appropriate mechanism of marketing segmentation based on our proposed approach as soon as they conduct customer relationship management. Finally, we have applied the similar approach in manufacture industry to identify the products heterogeneity. Based on the results, we found that heterogeneity analysis method can be applied as well. After modeling the failure time of the objectives, we can predict the reliability of the objectives, which is contributed to the practitioners in manufacture industry.
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