Authors: Masaki Tanaka; Setsuya Kurahashi
Addresses: Graduate School of Business Science, University of Tsukuba, 3-29-1 Otsuka, Bunkyo, Tokyo 112-0012, Japan ' Graduate School of Business Science, University of Tsukuba, 3-29-1 Otsuka, Bunkyo, Tokyo 112-0012, Japan
Abstract: To analyse after-sales services for passenger cars, the traditional research methods only considered the lifelong value of customers when the all services were viewed, while aiming to improve customer retention rates. In this paper, a framework is proposed for time series data mining focused on the time order of a purchase history. The purpose of this study is to acquire the primary reason for a long-term relationship between an automobile dealer and a customer using analyses of relationships between automobile maintenance records and customer retention rates. We attempt to clarify the characteristics of the high retention customers and improve the accuracy of the customer retention predictions as a result of the clustered customer obtained by machine learning. This research is able to show the usefulness of the service science through the application of the engineering methods in the service industry.
Keywords: automobile industry; vehicle maintenance; after-sales service; customer retention rate; time series data mining; passenger cars; automotive services; car dealers; machine learning; clustering; service science; services.
International Journal of Computer Applications in Technology, 2015 Vol.52 No.2/3, pp.160 - 167
Published online: 26 Sep 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article