TY - GEN
T1 - Modeling customers' loyalty using ten years' automobile repair and maintenance data
T2 - 2nd International Conference on Data Science and Information Technology, DSIT 2019
AU - Zhang, Sheng
AU - Tan, Xueliang
AU - Wang, Jiawen
AU - Chen, Jianghang
AU - Lai, Xinjun
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/19
Y1 - 2019/7/19
N2 - Automotive after market (e.g. repair and maintenance) is one of the most lucrative business in the entire automotive industry chain. However, with the fierce competition recently, many service providers (also known as 4S shops) are facing a problem of customer churn. It would be most valuable to investigate the determinants of customers' loyalty. Ten years' data of a 4S shop is analyzed where customers' social-demographics, maintenance patterns and habits, car characteristics, repair types, fees and discounts, etc. are available, and machine learning approaches are employed. This paper investigates the causes for the customer churn in 4S shops, and proposes several solutions to improve the management and operation of 4S shops. Results from this analysis shed light on customers' loyalty behaviors and automotive industry customer relationship management.
AB - Automotive after market (e.g. repair and maintenance) is one of the most lucrative business in the entire automotive industry chain. However, with the fierce competition recently, many service providers (also known as 4S shops) are facing a problem of customer churn. It would be most valuable to investigate the determinants of customers' loyalty. Ten years' data of a 4S shop is analyzed where customers' social-demographics, maintenance patterns and habits, car characteristics, repair types, fees and discounts, etc. are available, and machine learning approaches are employed. This paper investigates the causes for the customer churn in 4S shops, and proposes several solutions to improve the management and operation of 4S shops. Results from this analysis shed light on customers' loyalty behaviors and automotive industry customer relationship management.
KW - Automobile 4S shop
KW - Behavioral prediction
KW - Customer churn
KW - Customer relationship management
KW - Data mining
UR - http://www.scopus.com/inward/record.url?scp=85072804151&partnerID=8YFLogxK
U2 - 10.1145/3352411.3352449
DO - 10.1145/3352411.3352449
M3 - Conference Proceeding
AN - SCOPUS:85072804151
T3 - ACM International Conference Proceeding Series
SP - 242
EP - 248
BT - Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, DSIT 2019
PB - Association for Computing Machinery
Y2 - 19 July 2019 through 21 July 2019
ER -