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Top1. Introduction
Worldwide retail sales worth is approximately $25 trillion and is expected to reach $30 trillion by 2023 (emarketer, 2019). Evidence suggests that tech-savvy customers who engage with retailers frequently are rising all over the world (Hallikainen, Alamäki, & Laukkanen, 2019; Hwang & Oh, 2020; Kurata, 2019), and expect spontaneous value from their retailers (Hinsch, Felix, & Rauschnabel, 2020; Huang, 2019; Ladhari, Rioux, Souiden, & Chiadmi, 2019; Sebald & Jacob, 2018; Souiden, Chaouali, & Baccouche, 2019). The communications between the customer and firm generate a significant amount of customer data (Hofacker, Malthouse, & Sultan, 2016; Kunz et al., 2017; Liu, Shin, & Burns, 2019; Xie, Wu, Xiao, & Hu, 2016). These advantageous customers’ data/ (insights) exhibit a new pathway to the retailers to serve the customer more efficiently (Aloysius, Hoehle, Goodarzi, & Venkatesh, 2018; Aloysius, Hoehle, & Venkatesh, 2016; Germann, Lilien, Fiedler, & Kraus, 2014).
Customer insights are underlying information related to customers' attitudes, behaviours, beliefs, desires, emotions, involvement, lifestyles, motives, needs, perceptions, preferences, psychographics, tastes, values, wants, and more (Varadarajan, 2020). The extant literature shows that customer insight management is a traditionally manual process that involves analysing the customers' data and grabbing the customer insights manually from the available information (Bailey, Baines, Wilson, & Clark, 2009; Chew & Gottschalk, 2009), which may be time-consuming and inaccurate (Mahmood & Szewczak, 1999). In contrast, a firm's capability in customer analytics involves technology that is argued to manage customer insights accurately and quickly (Hossain, Akter, & Yanamandram, 2020a). In other words, a firm will likely be able to grab meaningful customer insights quickly due to such advanced analytics capability that ensures the extraction of quality data from various channels. Further, AI-enabled CA capability allows firms to transmit solutions to customers through seizing and analysing customer insights in real-time. This view is consistent with the academic literature, which suggests that AI-enabled analytics enhances substantial competitive advantage for the firm as it create more real-time solutions for customers by grabbing the customer's insights (Davenport, Guha, Grewal, & Bressgott, 2020; Dubey et al., 2019).
In a report, Juniper Research acknowledged that the retail sector invested $2 billion on the advanced version of analytics such as AI in 2018 and expected to spend $7.3 billion by 2022 (Adair, 2019). A most recent survey suggests that the majority of the retailers do not know how or what type of customer insights they have to acquire and use profoundly in this competitive business environment (Inman & Nikolova, 2017). Thus, the study on CA attached to AI capability has significant potential from both a theoretical and managerial lens. While earlier academic studies focused on big data analytics capability (Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016; Dubey, Gunasekaran, & Childe, 2018; Gupta & George, 2016; Mikalef, Framnes, Danielsen, Krogstie, & Olsen, 2017; Wamba et al., 2017) and business analytics capability (Cosic, Shanks, & Maynard, 2012; Cosic, Shanks, & Maynard, 2015; Nam, Lee, & Lee, 2019; Vidgen, Shaw, & Grant, 2017), there is now a need to investigate AI-enabled CA capability. This conceptual paper addresses the following research question: What are the AI-enabled CA capability dimensions for retailers?