Contemporary organizations have adopted business analytics (BA) to gain competitive advantage under rapidly changing business environment (Goes, 2014; McAfee & Brynjolfsson, 2012; Popovič, Hackney, Tassabehji, & Castelli, 2016). BA enables firms not only to better understand internal business processes through data-driven decision making but also to sense market opportunities and threats by strengthening customer relationship management (Kiron, 2013). BA continues to be a top priority for a number of organizations and the promise of BA is attracting many followers (Işık, Jones, & Sidorova, 2013; Verhoef, Kooge, & Walk, 2016). Although BA is one of the most essential technologies to sharpen innovation (Chen, Chiang, & Storey, 2012), considerable number of BA implementations have failed and the adopters still struggle to obtain the suitable benefits (Işık et al., 2013; Ransbotham, Kiron, & Prentice, 2016; Schick, Frolick, & Ariyachandra, 2011). However, the reasons of the failures of BA adoption have not been fully explored in the literature. From the resource based view, previous scholars have noted the impact of information technology (IT) competence on competitive advantage (Mata, Fuerst, & Barney, 1995), business value (Bharadwaj, 2000) and business performance (Lioukas, Reuer, & Zollo, 2016). Since BA is an IT-enabled technology, business analytics capability has been also considered a driver of business performance (Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016; Wamba et al., 2017). However, technology competence per se does not always affect the performance. IT alone does not enhance organizational performance clearly (El Sawy, Malhotra, Park, & Pavlou, 2010). IT business value is determined by the extent of IT usage in business activities (Zhu & Kraemer, 2005) and depends on a firm’s abilities to leverage IT than IT itself (Clemons & Row, 1991; Ross, Beath, & Goodhue, 1996). Applying the findings of prior studies on IT to BA, we focused on the use of BA rather than BA itself or capability. Analytics has been actively invested and widely adopted to support customer relationship management (CRM) technology (i.e. acquire and retain customers). CRM vendors and practitioners believe that BA-enabled CRM capability, an ability to analyze, integrate, and leverage information resources and customer feedback for decision-making support in CRM, will improve business value and are active in introducing BA systems with advanced statistical modeling, simulation, forecasting and machine learning (Coltman, 2007; Coltman, Devinney, & Midgley, 2011; Shanks & Bekmamedova, 2012). However, the results of the use of CRM technology are debatable (e.g., Reinartz, Krafft, & Hoyer, 2004), and there has been little research on the BA use to improve CRM performance. Since CRM is traditionally the basis of data analysis and is now being transformed into big data- or business analytics-based, it is necessary to study a series of processes that generate business value and the result can be a guide for BA use and application to practitioners. Thus, this study attempts to explore the nomological net by focusing on BA use in driving business value from IT competence in the context of CRM.