The Promise and Perils of Sensitive Consumer Data: A Relational Exchange Perspective

To cope with growing market demands, retailers increasingly collect and utilize consumer data in order to provide personalized shopping experiences to their consumers and to increase marketing returns (e.g., Schumann et al., 2014). Personalizing retail-shopping experiences, however, involves collecting, analysing, and leveraging consumers’ private data beyond the original transaction (Kim et al., 2015; Narayanan and Manchanda, 2009), creating a challenging trade-off situation for consumers: On the one hand, consumers might comply with retailers’ data requests in an exchange for better offers and greater convenience, such as personalized product offerings, prize discounts or personalized marketing communications (Bleier and Eisenbeiss, 2015; Shanakar et al., 2016). On the other hand, providing sensitive data implies higher levels of risk or vulnerability, as private customer data may be misused by retailers themselves or passed along to third parties (e.g., Martin et al. 2017). This, in turn, could have adverse effects for retailers, with customers either avoiding personalized offers or encouraging switching behaviour (Drèze and Hussherr, 2003; Martin et al., 2017).
While the risk-related facets of providing sensitive data have been thoroughly addressed by marketing researchers (e.g., Martin et al., 2017), little is known about the relational benefits retailer’s may or may not derive from utilizing customers’ personal data. This research attempts to address this gap and seeks to contribute to a better understanding of several relationship-related aspects influenced by consumers’ provision of sensitive data to retailers: First, the study aims to explore in how far consumers are sensitive to differences in terms of personalized offerings made by retailers, based on the data customers made available to their retailer. Second, if such benefits occur, it can be assumed that different types of benefits might be relevant to consumers, and, subsequently, impact customer attitudes and behaviours in diverse ways. For example, benefits related to preferential treatment in response to particular interests might be evaluated differently than pure economic benefits such as cheaper prices. Third, by shedding light on different types of benefits, this study attempts to contribute to a better understanding of how retailers can facilitate customers’ decision-making process in a retail context based on customer data (Grewal et al., 2017). Finally, this research examines switching barriers arising from sensitive data provided by customers to retailers. In particular, this study assumes that providing sensitive data represents an investment into the customer-retailer relationship increasing customers’ relationship setup costs as well performance loss costs, as potential benefits related to the provision of sensitive data would be lost in case of switching to an alternative retailer.
The conceptual model developed in the study is planned to be tested using field data of actual consumers of a set of large UK retailers. To address the issue of common method bias, the a survey-based sample of about 1,500 customers will be combined with actual purchase data of these customers with their particular retailer, based on a time-series of purchases in one product category used as an anchor or representative product category for grocery shopping in general.