Revisiting customer analytics capability for data-driven retailing
Introduction
Advanced technologies are rapidly emerging in the ever-changing global retail landscape (Adapa et al., 2020; Daunt and Harris, 2017; Ferracuti et al., 2019). Some retailers are overwhelmed by technology-oriented opportunities and may utilize technologies without a clear understanding of both how they match into their plan and, probably, how customers see the relevance of those opportunities (Inman and Nikolova, 2017). A recent study conducted by the research firm ‘Periscope-McKinsey’ found that 78% of US retailers fail to provide unified brand experience to their customers due to the lack of customer analytics in various channels and inefficiency to engage customers across shopping journeys (Weinswig, 2018). Another study found that Australian fashion retailers struggle to remain competitive in the marketplace due to lack to customer focus and technology advancement (BBC, 2019; Euromonitor-International, 2019). The current study has taken an attempt to provide an explication to retailers by modelling customer analytics capability dimensions.
Customer analytics has been gaining momentum in recent years (Bonacchi and Perego, 2019; Hossain et al., 2020; Jayaram et al., 2015; Kitchens et al., 2018) because of the explosion of advanced innovative technologies and the ascent of new channels and new gadgets (Herhausen et al., 2019; Verhoef et al., 2015). There is a higher pressure than ever before to satisfy customers, leveraging a large amount of structured and unstructured customer data (Sun et al., 2014; Wedel and Kannan, 2016) through the use of customer analytics (Hossain et al., 2020). Customer analytics helps to capture value for retailers in terms of sales forecasting, remarketing, micro-segmentation, dynamic pricing, churn prediction, and so on.
Despite the importance of customer analytics, there is a dearth of studies on the specific capabilities relevant to customers' benefit. Capability refers to “the firm's capacity to leverage internal and external resources to create new value for stakeholders and maximize competitive advantage” (Rahman et al., 2019). In the research of big data analytics, Akter et al. (2016) defined capability as the firm's capacity in management, technology handling, and talent utilization. Davenport and Harris (2007) perceive the capability notion as the firm's ability to set the optimal price, identifying quality problems, selecting the optimal level of inventory in the data-rich environment. While scholars have emphasized on building capability in different domains such as marketing capability (Morgan et al., 2009; Vorhies and Morgan, 2005; Vorhies et al., 2009), big data analytics capability (Akter et al., 2016; Gupta and George, 2016; Wamba et al., 2017), business analytics capability (Cosic et al., 2015) and so on, they provide a limited view on accelerating customer engagement. Thus, this research addresses the following question to overcome the existing research gap.
RQ: What are the customer analytics capability dimensions for data-driven retailing?
The result of customer analytics capability unearths the issue of customer engagement. The concept of customer engagement generally focuses on customers and has gained momentum in recent years among the academics and marketing practitioners (Haumann et al., 2015; Hollebeek et al., 2019; Precourt, 2016). A firm should have the ability to engage customers by fulfilling their needs (Palvia, 2009; Yim et al., 2008). Furthermore, a firm's proficient customer analytics capability can also link up with customer equity as it has been recognized as the most significant determinant of the long-term values of the firm (Kim and Brandon, 2010; Lemon et al., 2001). The value a firm generates from a customer is not limited to the current transaction, but is the cumulative profit a company makes from the customer in the entire relationship over time (Kumar and George, 2007; Lee et al., 2014).
This research makes several significant contributions, both in theory and practice. Focusing on the outside-in view, this study combines market orientation (Day, 2011; Srivastava et al., 1998) and technology orientation (Trainor et al., 2011; Zhou et al., 2005) into the RBV research stream that generates RBV-capability to drive retail firms to respond effectively in market conditions. Besides, the outside-in view extends previous marketing capability research (Theodosiou et al., 2012; Vorhies and Morgan, 2005; Vorhies et al., 2011) and introduces a higher-order customer analytics capability by generating value in retail settings. Practically, the proposed customer analytics capability model is envisaged to connect customers, and ultimately maximize customer equity.
The paper progresses as follows: first, it focuses on the literature in the context of retail, customer analytics, and relevant theories in the capability research stream. Second, it discusses the methods and proposes a conceptual model. Subsequently, the study discusses the study findings, the theoretical and practical contributions, and provides guidelines for future researchers.
Section snippets
Retail and customer analytics context
Global retail sales value is nearly US$25 trillion, which is expected to strike US$30 trillion by 2023 (emarketer, 2019). The number of tech-savvy customers is growing globally (Hallikainen et al., 2019; Hwang and Oh, 2020; Kurata, 2019), and they demand substantial value from the retailers (Hinsch et al., 2020; Huang, 2019; Ladhari et al., 2019; Souiden et al., 2019) as they have intercommunication with them on a relatively regular basis. The conversations between the customer and firm produce
Research methods
This research was initiated with a literature review to recognize and evaluate comprehensive information informing the definitional aspect of customer analytics. In order to address the research question, this study further incorporated a systematic approach of literature review (SLR). SLR aims to “comprehensively locate and synthesize research that sails on a particular question, using organized, understandable, and replicable procedures at each step in the process” (Littell et al., 2008).
Conceptual model of customer analytics capability
This section proposes the conceptual model (See Fig. 2) of customer analytics capability for retailers and also explaining the customer analytics capability dimensions in detail.
A concise view of the study findings
The objective of this study was to identify the capability dimensions of customer analytics in a retail context. In order to serve the objective, a systematic literature review and thematic analysis were undertaken to develop six distinct primary dimensions and twelve sub-dimensions of customer analytics capability under the broad themes of value creation, value delivery, and value management. We contend that customer analytics capability in the retail environment enables customer engagement.
Conclusion
With the speedy technological advancement and the expansion of various analytics tools, an increasing number of firms are trying to adapt to analytics for staying competitive in the business. Despite the fact, there is a lack of guidelines about customer analytics capability deployment at the firm level. This research pinpoints customer analytics capabilities in the retail industry setting. Academics and practitioners always discuss on customer values when dealing with marketing issues. This
Acknowledgement
The authors acknowledge their sincere gratitude to the following marketing scholars for their valuable comments on the development of this customer analytics capability model.
(1) Michel Wedel is a Professor of consumer science at the University of Maryland, College Park, USA. His comments on our model and his article on marketing analytics published in the “Journal of Marketing” guided us to generate valuable insights.
(2) George S. Day is a Professor Emeritus at the University of Pennsylvania,
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