Customer Appointment Analysis in Automobile Dealerships

Abstract

Appointment scheduling prior to in-person visit to vehicle service centers is a well-known activity in our daily lives; we often do that to save our- selves time during our visits. Prior knowledge of customer arrivals help the service store manage- ment navigate through their daily demand and estimate a revenue goal. However in real-world, several people end up missing their appointments. To avoid business losses, dealers often practise en- gaging with customers beyond their capacity, but it only leads to operational inefficiency. In our work, we extrapolate several vehicle dealer stores that have high as well as low missed appointment rates and show interesting customer visit patterns post-scheduling. Additionally, we propose a Ma- chine Learning based solution to empower deal- ers with optimal allocation of appointment slots among their customers and generate maximum revenue from the arrangement. Our motivation in the paper is to enhance the daily service demand process in the dealerships with excellent customer care.