Modeling customers' loyalty using ten years' automobile repair and maintenance data: Machine learning approaches

Sheng Zhang, Xueliang Tan, Jiawen Wang, Jianghang Chen, Xinjun Lai*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2019 2nd International Conference on Data Science and Information Technology, DSIT 2019
PublisherAssociation for Computing Machinery
Pages242-248
Number of pages7
ISBN (Electronic)9781450371414
DOIs
Publication statusPublished - 19 Jul 2019
Externally publishedYes
Event2nd International Conference on Data Science and Information Technology, DSIT 2019 - Seoul, Korea, Republic of
Duration: 19 Jul 201921 Jul 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2nd International Conference on Data Science and Information Technology, DSIT 2019
Country/TerritoryKorea, Republic of
CitySeoul
Period19/07/1921/07/19

Keywords

  • Automobile 4S shop
  • Behavioral prediction
  • Customer churn
  • Customer relationship management
  • Data mining

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