Big Data Model of Customer Satisfaction and Service Experience (CSSE) for Automotive Aftersales Services

Abstract

Customer expectations on the quality services provided by an automotive company always change over time and require to measure from time to time. Previously, such problems cannot reflect real-time for immediate action based on original data received from customers through questionnaires. The objective of this paper is to develop a model and identify the Customer Satisfaction and Service Experience (CSSE) score using a big data platform. The overall methodology is organized by establishing the entire idea and development of CSSE modelling through data compilation, modelling process and model credential. An evaluation system based on the quadrifid graph model was employed. A model of CSSE is proposed using big data platform analysis. Next, establish the process and method of data cleaning. Continue by promoting the model mechanism and model architecture and finally model computation. The model computation is cultivated by the application of Service Sensitivity Coefficient and Customer Satisfaction Index respectively. A total of 11,721 customers responded to the survey with overall Customer Satisfaction (CS) score of 893.39 points and a Customer Service Standard Transaction (CSST) score between 84.60% to 98.59%. The correlation of the quadrifid graph shows the response towards customer needs is the highest point. The big data model analysis trigger immediate recovery action by the automotive company upon receiving the analysis results. The modelling method is simple and practical for automotive company users in evaluating customer satisfaction and service experience.