Using Decision Tree and Regression Tree to Segment Container Shipping Market for Customer Retention

• Main Authors: LIN, LE HUI, 林樂輝
• Other Authors: Chen, Kee-Kuo
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/dh3p57

博士 === 國立臺灣海洋大學 === 航運管理學系 === 106 === This study employs classification and regression tree (CART) to segment customer retention for container shipping carriers to divide their customer groups. From the integrated marketing perspective, both methods of decision tree and beta regression tree are used to segment this market by exploring the relationships between the shipping service attributes and the Likelihood of a customer not churning to a new service provider (LNC) which is used as a proxy of customer retention. After using the above two methods to analyze the empirical data, some results are discovered as the following: (1) service quality, as a partitioned variable, separates the overall market into five groups with different functional relationships between LNC and various marketing activities; (2) the average LNC of the highest customer retention group is about 60% which reflects that the customer retention and then customer loyalty are low in this industry; (3) the attributes of price and discount, personal selling and customer relationship have significant impact on likelihood of customer retention in descending order; (4) satisfactory price and discounts are the most important attribute to support the likelihood of customer retention; and (5) attributes of word of mouth, advertising, and switching cost have little importance for increasing LNC. The results provide container shipping managers the clues to understand the different reasons for types of relationship with the five customer groups. This information is very important to determine which relationships prove the most valuable, and in turn, to form an adequate customer portfolio. By managing different portfolios for different segments of the customers, sales representatives who have high relational intelligent ability in container shipping companies could then convert data into profit for themselves.