Automation and AI-Enabled Customer Journey: A Bibliometric Analysis

Abstract

This article aims to scrutinize the dynamics of scientific publications devoted to optimizing the customer journey in the retail sector through the application of artificial intelligence (AI) and determines the status of existing research on the role of AI in the retail sector. The research objective was to analyse the pool of scientific publications from the Scopus database to analyse how emerging technologies have improved customer journey (pre-purchase, purchase, post-purchase). The research employed a publication search by article title, abstract and keywords to collect the data set from the Scopus database. The selected publications were visualized using the software VOSviewer 1.6.16.0, and Biblioshiny to perform bibliometric analyses. An initial sample of 356 research articles was collected. After reading the abstract and full article, a final corpus of 149 scientific publications over two decades (1989–2021) was analysed in the study. Our findings suggest the prominence of authors and journal publishing in implementing AI in retail to automate customer journey. The results highlight how the application of AI fosters in improving the different phases of customer journey among the retailing sectors. Banking and fashion are the retailing sectors that have major applications of AI in the customer journey. Further studies should be conducted to gain deeper insights into the emerging use of AI in other sectors of the economy. It was also found from the intensive literature review that in the purchasing and decision-making phase of the customer journey, the deployment of AI is the most. This study analysed the incorporation of AI into the customer journey to highlight its impact on retailing. This study contributes to the literature by adding the lenses of AI in key management areas in retailing.