Designing next-level customer experiences using data-driven insights and emerging technologies

In recent years, numerous scholars and practitioners have indicated the importance of enhancing customer experience (CX) in a digital age of social media, ever-increasing competition, and data analytics. 32% of global consumer respondents stated that they would stop doing business with a brand they loved after just one bad experience, while 69% of respondents said that a good experience was the reason for their loyalty to a brand. CX seems to be critical for a brand to develop sustainable competitive advantage. There is also a growing body of academic research on customer CX and customer experience management (CEM), but few links are made between big data and data analytics, how they shape customers’ experiences across their journey, the types of technologies or tools used, the promises gained, and the strategies implemented to enhance customer experience (Sanchez-Iborra et al., 2016; Mach & Becvar, 2017; Nguyen, 2018). Therefore, there is a research opportunity and a gap in the literature.This chapter seeks to explore the role of data analytics in designing CX. Furthermore, the chapter focuses on the automotive industry by providing examples of CX that major automotive brands develop. The automotive industry is one of the most data-enabled industries. Indeed, vehicles collect and generate data through software applications, sensors, systems, and third-party services throughout their lifespan. Furthermore, OEMs and car manufacturers have developed a comprehensive understanding of the importance of data in their business model strategies. The industry has also long been affected by competition and CX issues. With new entrants in the fully electric vehicles (EV) segment, traditional OEMs are trying to reinvent their brand image and take advantage of a heritage they have built in car manufacturing and development over the decades. CX indeed seems to provide pointers in the quest for a sustainable competitive advantage (Santa et al., 2016; Shi et al., 2016).