Many retailers have collected large amounts of customer data using, for example, loyalty programs. We provide an overview of the extant literature on customer relationship management (CRM), with a specific focus on retailing. We discuss how retailers can gather customer data and how they can analyze these data to gain useful customer insights. We provide an overview of the methods predicting customer responses and behavior over time. We also discuss the existing knowledge on the application of marketing actions in a CRM context, while providing an in-depth discussion on CRM and firm value. We outline future research directions based on the literature review and retail practice insights.
Over the past decade, retailers have been able to collect enormous amounts of information at the customer level measuring customer purchases, marketing activities, and customer attitudes. An important example is Tesco, which is using its Loyalty Card as a core element of its marketing strategy (e.g., Humby and Hunt 2003). Despite this trend, many retailers have also decided not to invest in building large customer databases. One reason is to be able to focus on low prices and operational excellence as exemplified by discount retailers such as Aldi, Lidl, and Wal-Mart. While these retailers still collect large amounts of data, it is often not at the customer level. The ubiquity of retail data, regardless of whether at the customer level or not, has created tremendous opportunities as well as challenges for both retail practitioners and researchers in retailing.
On the practitioner side, the results of using customer data are mixed. Tesco is one of the successful retailers that extensively use a customer database and is frequently cited as a successful benchmark in textbooks and the business press (Humby and Hunt 2003; Kumar and Reinartz 2005). However, other retailers have not been successful at leveraging their customer databases. A McKinsey study reports that the majority of retailers are unable to recover the investments in loyalty programs, especially because only less than 50% of customers increase their spending after enrolling in a loyalty program (Cigliano et al. 2000).
The practitioner dilemma has been reflected in multiple discussions that have arisen within the academic community on the effectiveness of loyalty programs in retailing (e.g., Dowling and Uncles 1997; Shugan 2005). Numerous empirical studies that use large customer databases examine how firms can increase loyalty metrics such as retention rates, cross-buying and customer share (e.g., Verhoef 2003; Verhoef, Frances, and Hoekstra 2001; Kumar, Venkatesan, and Reinartz 2008), and/or how firms can predict these metrics (e.g., Fader, Hardie, and Lee 2005; Neslin et al. 2006a). Other studies have specifically focused on how firms can influence and optimize customer value (e.g., Rust and Verhoef 2005; Venkatesan and Kumar 2004; Venkatesan, Kumar, and Bohling 2007). In sum, there is an existing knowledge base on how to influence and predict customer loyalty and how to optimize customer value (for overviews, see Gupta and Zeithaml 2006; Verhoef, van Doorn, and Dorotic 2007; Blattberg, Malthouse, and Neslin 2009).
In addition to customer value management, multichannel retailing has gained importance as a consequence of the ability of the retailers to amass large customer databases and more broadly ability to obtain a view of the customers across several channels. Multichannel retailing presents the retailer with the opportunity to improve customer profitability by offering a variety of transaction options for the customer. At the same time, the increasing multichannel orientation of retailing practice has created huge challenges for retailers in having real-time access to reliable data across different channels and in understanding and predicting customer behavior across different channels (e.g., Ansari, Mela, and Neslin 2008; Arikan 2008; Dholakia et al. 2010; Kushwaha and Shankar 2007; Neslin et al. 2006b; Neslin and Shankar 2009; Verhoef, Neslin, and Vroomen 2007; Venkatesan, Kumar and Ravishanker 2007). For example, several stores such as Best Buy offer customers the option of ordering products online and picking up the products in a nearby offline store. The A CNET.com research shows that execution of this option still remains a challenge for several retailers1. Specifically, the ability to immediately recognize a customer’s online order in the offline store still remains a challenge for retailers.
While there is research in marketing that has looked at the impact of various aspects of the customer relationship management (CRM) process on customer outcomes (Reinartz and Kumar 2003; Du, Kamakura, and Mela 2007), many retailers do not collect the right data, analyze the data appropriately, or initiate the optimal marketing actions to achieve the best customer outcomes, possibly leading to many failed CRM implementations. In this paper, we discuss the application of CRM in retail environments. We elaborate on current knowledge from the academic marketing literature and discuss its relevance in the increasingly multichannel and multimedia retail environment. Furthermore, we provide new research directions on CRM in data-rich retail environments.
The remainder of this paper is structured as follows. We first discuss the conceptual role of CRM in retail environments and present our conceptual model. Subsequently, we address the specific topics within the conceptual model, such as data usage and the application of marketing actions. We address specific research questions within each topic. We also provide a discussion of future research opportunities within each topic covered by our conceptual model.