Customer segmentation based on buying and returning behaviour

Abstract

Purpose – The purpose of this paper is twofold: first, to empirically test whether a “one size fits all” strategy fits the fashion e-commerce business and second, to evaluate whether consumer returns are a central aspect of the creation of profitability and, if so, to discuss the role of returns management (RM) in the supply chain strategy. Design/methodology/approach – Transactional sales and return data were analysed and used to categorise customers based on their buying and returning behaviours, measuring each customer's net contribution margins. Findings – The e-commerce business collects a vast quantity of data, but these data are seldom used for the development of service differentiation. This study analysed behaviour patterns and determined that the segmentation of customers on the basis of both sales and return patterns can facilitate a differentiated service delivery approach. Research limitations/implications – This research empirically supports the theory that customer buying and returning behaviours can be used to appropriately categorise customers and thereby guide the development of a more differentiated service approach. Practical implications – The findings support a differentiated service delivery system that utilises a more dynamic approach, conserving resources and linking the supply chain and/or organisational strategies with customers' buying and returning behaviours to avoid over and underservicing customers. Originality/value – Consumer returns are often viewed as a negative aspect of doing business; interestingly, however, the authors revealed that the most profitable customer is a repeat customer who frequently returns goods.

Intermodal supply chains methodology and economic efficiency study of its implementation (using the example of an Iron and Steel Company)

“…the following study analyses behaviour patterns and determined that the segmentation of customers on the basis of both sales and return patterns can facilitate a differentiated service delivery approach [12]. key findings are that both innovative and noninnovative supply chain operators attribute supply chain synchronization as only a minor indicator of strategic supply chain capability, contrary to the literature; and they also indicate strategic supply chain capability has a minor influence in achieving beneficial outcomes from utilizing industry-led innovation [14].…”

Taking stock of consumer returns: A review and classification of the literature

“…Of course, returns can indeed be quite costly as evidenced by a plethora of industry sources (Boyajian, ; Dennis, ; Enright, ; National Retail Federation, ). Nevertheless, growing evidence shows that returning customers are more profitable than nonreturning customers (Hjort et al, ; Abbey et al, 2018b). In fact, customer profitability can actually increase as returns propensity increases, at least up to a threshold (Petersen & Kumar, ; Abbey et al, 2018b).…”

“…As stated by Kumar (2009) andHjort, Lantz, Ericsson, andGattorna (2013), if different customers return different amounts, retailers need to understand the underlying relationship among customer purchase behavior, customer characteristics, and return behavior. In effect, various customer segments require differentiated managerial consideration and treatment.…”

Competition Policy and Antitrust Law: Implications of Developments in Supply Chain Management

“…Research on multi-channel management (Herhausen, Binder, Schoegel & Herrmann, 2015;Rangaswamy & Van Bruggen, 2005), multi-channel distribution (Dutta, Bergen, Heide & John, 1995;Nunes & Cespedes, 2003;Perdikaki & Swaminathan, 2013), dual distribution (Huang & Swaminathan, 2009;David & Adida, 2015;Khouja, Pan, & Zhou, 2016;Chen, Liang, Yao, & Sun, 2017), omni-channel retailing (Zhang et al, 2010;Murfield, Boone, Rutner & Thomas, 2017;Wang, Hua, Wang, & Lai, 2017;Barwitz & Maas, 2018), retail management (Neslin & Shankar, 2009), and consumer-based SCM (Christopher & Gattorna, 2005;Ericsson, 2011;Gattorna, 2010;Godsell, Diefenbach, Clemmow, Towill & Christopher, 2011;Hjort, Lantz, Ericsson & Gattorna, 2013;Peinkofer, Esper, Smith & Williams, 2015;Stolze, Mollenkopf & Flint, 2016) documents developments in the management of distribution. Related research on cross-channel shopping (Hjort et al, 2013;Kumar & Venkatesan, 2005;Zhang et al, 2010), free riding (Bernstein et al, 2009;He, Xiong & Lin, 2016;Luo, Li & Cheng, 2016;Xing & Liu, 2012), and its manifestation through showrooming (Balakrishnan et al, 2014;Basak et al, 2017) examines the interplay of consumer shopping behavior and distribution strategy. Together, this research has potential to advance understanding of the use and effects of RPM to address free riding (i.e., the free rider thesis).…”