Evaluating regression techniques for service advisor performance analysis in automotive dealerships

Abstract

Service advisors are a crucial contributor to the profit and loss of an automotive dealership. It is thus crucial that their performances are continuously evaluated to make decisions that ensure continued profitability. Artificial intelligence (AI) algorithms like the finite mixture of regression (FMR) have been previously explored as a tool for benchmarking the performance of dealerships. However, it is essential that the AI algorithm is trustworthy and that its predictions can be explained. This study aims to develop a performance analytics platform that is reliable and explainable. It develops and evaluates variants of FMR based on different regression techniques, focusing on both reliability and explainability. The results show that the implemented FMR variants not only provide accurate performance assessments but also offer transparent insights into the factors influencing dealership performance.