Churn Detection and Prediction in Automotive Supply Industry

Abstract

Companies have both large certified enterprises and small unauthorized service providers as their competitors in the automotive supply industry. As technology related industries undergo more intensive competition, churn detection and prediction become essential to be precautious about leaving customers. The literature for churn detection offers numerous statistical and intelligent methods. In this study, Artificial Neural Networks and Decision Trees are applied to detect the churn in and analyze the validity of these methods for the automotive supply industry. The problem involves both categorical and continuous numerical decision inputs which cannot simultaneously fed into Decision Trees. In this case, continuous inputs should be divided into binary categorical ones by splitting into various intervals which are called buckets. Particle Swarm Optimization algorithm is implemented for finding optimal buckets for the churn problem data. Results indicate that while both algorithms are promising, the bucket tuning for Decision Trees complicate the churn detection process