Customer Satisfaction and Service Experience in Big Data Analytics for Automotive Service Advisor

Abstract

Service Advisor in Automotive Service Centre plays an important role as the frontline in providing exceptional services. The automotive service centre has to adopt big data applications in understanding customers’ needs by collecting data promptly and analysing scientifically. The objective of this paper is to evaluate Customer Satisfaction (CS) and Service Advisor Experience (SAE) scores via an online survey based on big data analytics. Thus, applying a Quadrifid graph in identifying focus regions for improvement activities. The application of big data online survey platforms is an efficient way of gathering customer feedback for continuous improvement activities. The study focused on Service Advisor (SA) services throughout Malaysia with selected one automotive brand. It explains the definition of customer process and customer satisfaction by comparing high-density customer regions namely Central, Northern and Southern regions with low-density customer regions namely East Coast and East Malaysia regions. There are five steps in deriving the output, which are the consolidation of customer data, customer selection, survey execution, score calculation and analytical report. Thus, the big data applications analyse the expectation SA gap and propose recommendation actions. The online survey results achieved a minimum of 879.90 points for Customer Satisfaction while Service Advisor Experience was minimum at 73%. SA achieved a high score for portraying courtesy and professionalism, while a lack of performing the visual inspection is the main gap for all regions. Detailed analysis using Quadrifid graph interpreted Southern region recorded the lowest correlation with R-square value less than 0.1 and level of CS & SAE below the average value of 800 relates to response towards needs by SA. In this paper, the outcome of the execution is centralization of customer information, Service Level Agreement standard, customer handling norms and work efficiency improvement. Such indicators lead to the SA’s professionalism in managing customer expectations.