Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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Manufacturing corporations aim to sell their goods while they try to keep a sound customer relationship by providing high quality after-sales service (A/S). This is because while such services have always been important in marketing and sales industries, they are currently gaining importance in the manufacturing industry as well. Therefore it is important to identify the needs of different customer groups and to provide respective A/S for each group accordingly. In this study, we propose a framework that consists of fuzzy clustering and an association rule to identify customer groups and their needs. We first carried out fuzzy clustering of customers in terms of indicators of CSI (Customer Satisfaction Index). Next, the association rule is used to grasp the kind of A/S that customers consider important. Our results identified three groups of customers and their needs: Group 1 represents those who have a high degree of satisfaction, loyalty and high number of complaints. This group considers the home visiting service most important. Despite the fact that the Group 1 has high degree of complaint occurrence, they show a high degree of loyalty. Group 2 has very high degree of satisfaction and loyalty with a low level of complaints. This group considers important the A/S factors in all of service sections, including at the call center, the home visiting service, and claim handling. Group 3 has average satisfaction, number of complaints, and loyalty. Group 3 customers put weight on A/S factors dealing with the call center and the home visiting service. We expect that manufacturing firms can strengthen CRM (Customer Relationship Management) by offering tailored A/S for each group accordingly.