A predictive analytics approach to improve the dealers-manufacturer relationship in the after-sales service network; case study in the automotive industry

ABSTRACT

The dealers are responsible for connecting customers to manufacturers in the automotive industry. An effective relationship between the dealers and the manufacturers can lead to customer satisfaction and loyalty. This research provides an approach to evaluate factors affecting the dealers-manufacturer relationship and develop a solution for predicting how manufacturers can continue or terminate the cooperation with their dealers in the after-sales service network, which is less discussed in the literature. We used predictive analytics tools and classification methods to predict dealers’ cooperation as a dependent variable in two classes based on a 2-year real-life collected data. Our results revealed that dealer performance violations, changing dealer principles, and the services revenue have the most effect on the cooperation of dealers in the automotive after-sales service network. Also, this research proposed a dealer management solution for manufacturers to show them the correct decisions for the dealers’ cooperation at the right time. This solution can be used in real-life cases in after-sales service networks to prevent using redundant resources, improve the weak performance of dealers, avoid unnecessary termination of cooperation, avoid the costs of attracting new dealers, and decrease customer dissatisfaction.

KEYWORDS:

• Dealers-manufacturer relationship
• after-sales service
• classification methods
• predictive analytics

Disclosure statement

No potential conflict of interest was reported by the author(s).