When and how consumers are willing to exchange data with retailers: An exploratory segmentation
Introduction
It is common for retailers to acquire, store, and use customer data to generate insights and produce market intelligence (Campbell et al., 2020; Plangger and Montecchi, 2020; Plangger and Watson, 2015). While there is regulation pertaining to the collection and use of the personal information of consumers, the customer data retailers acquire transcends personal information. For instance, consumer data comprises information that can be collected from a wide variety of sources, including an individuals’ personal information, financial information, online and social media activity, and shopping behavior to name a few (Bradlow et al., 2017). For retailers, consumer data is a crucial resource and has been identified as a driving force for the future of retail (Grewal et al., 2017; Martin et al., 2020). The benefits of consumer data – such as the ability to personalize products, services, and communications – are increasingly expected by consumers (Rust, 2020; Shanahan et al., 2019). In fact, customer experience management now relies on retailers having a detailed view of who consumers are, their preferences, and their behavior (Holmlund et al., 2020). Recent technological advances provide these insights through unprecedented access to consumer data (Xiang, 2018) that can be collected from consumers across multiple channels and is fundamentally changing consumer-brand interactions (Moe and Ratchford, 2018).
Retailers rely on consumers participating in consumer-retailer data exchange; a symbiotic relationship between consumers and brands seeking mutual benefit. By exchanging data, consumers benefit from enhanced personalization (Rust, 2020) while retailers benefit through better customer intelligence (Grewal et al., 2017). Yet, data exchange also poses risks as evidenced by the increase in companies abusing consumer data (Walter, 2019). Two-thirds of consumers are unlikely to do business with a firm that has mismanaged consumer data, highlighting the importance of managing these risks (Gemalto, 2019).
Further, consumers find some forms of personalization unwelcome (Hayes et al., 2021). For instance, two-thirds of consumers have unfavorable views of personalization due to concerns related to privacy (Tran, 2017; Smith, 2014). For a consumer, privacy relates to their ability to “control the use, release, collection, storage, and access to their personal data” (Plangger and Montecchi, 2020, p.33). Privacy concerns lead some consumers to actively avoid marketers’ messages (Baek and Morimoto, 2012), even using technology to block online and mobile ads and tracking (Brinson et al., 2018). This contradiction between consumers' desire for personalized experiences and intentions to protect their privacy online is called the personalization-privacy paradox (Norberg et al., 2007).
The relative opportunities and risks of data exchange have led to heightened focus by academic researchers, social critics, and regulators (Martin and Murphy, 2017). Yet, questions remain regarding how consumers value consumer-retailer data exchange (Martin et al., 2020), and how different segments react to their private information being used, and potentially abused, by retailers (Plangger and Montecchi, 2020). Further research is required to explore how consumers trade off these potential benefits and risks (Rust, 2020). In this light, this research aims to provide insights on heterogeneity among consumer views towards retailer use and abuse of consumer data. We address two questions. First, what consumer segments exist among views towards consumer-retailer data exchange, and what are the antecedents of these segments? Second, how do consumer segments differ in their responses to the use and abuse of data by retailers?
Our findings contribute a better understanding of consumer-retailer data exchange from the perspective of consumers and advance knowledge in three ways. First, we draw on data collected from a cross-sectional quantitative survey, sampling 463 US consumers from an online panel provider. For statistical analysis we employ Latent Class Analysis, a segmentation approach that allows us to identify six consumer segments which differ in their perceptions of the consumer-retailer data exchange. Second, we identify the factors that explain consumer segment membership including personal views on privacy and technology as well as trust and engagement with retailers. Third, we demonstrate how the segments differ in response to retailer use and abuse of data, including willingness to exchange data, comfort with personalization and responses to data abuses. Taken together, these insights provide retailers an awareness of how consumers differ in terms of their view on data exchange, thereby informing the implementation and communication of appropriate data-exchange policies and practices.
Section snippets
Theoretical background
Consumer data is a crucial resource for retailers, having led to a range of data collection and surveillance practices as retailers' strive for competitive advantage (Plangger and Montecchi, 2020). The recognition of the role of data for retailers and the importance consumers place on privacy has led to an increased focus on the relationship between consumer data and the value retailers can extract from this data. This stream of research has more recently begun to focus on the interaction
Towards a data exchange consumer segmentation
The primary aim of this paper is to identify consumer segments based on views towards consumer-retailer data exchange and compare how these segments respond to the use and abuse of their data. The number and nature of these segments depend on heterogeneity within the consumer population, and so are best determined empirically (e.g. Konuş et al., 2008; Sands et al., 2016). Hence, we draw on existing literature to determine potentially relevant indicators, antecedents, and outcomes of consumer
Data collection
Following relevant University ethics clearance, an online quantitative survey was created with Qualtrics and survey invitations were distributed through Could Research, a popular social science research platform (Guttentag et al., 2018) which has also been utilized in recent data privacy studies (Plangger and Montecchi, 2020). Respondents were invited to participate in the study and advised their participation would be anonymous, no personal data would be collected, and that their agreement
Consumer segments and segment antecedents
We estimated the LCA using maximum likelihood, with 100 different random sets of starting parameters to reduce the chance of local maxima. The indicator variables in the model were: perceived value, perceived risk, vulnerability, transparency, and control. The covariates measured general privacy concerns, technology readiness, brand trust, brand engagement, and demographics (gender and age). With Latent Class segmentation, the optimal number of segments is not known prior to analysis, but
Discussion and conclusion
Understanding consumer perceptions of the data exchange process is key to the future of retail, and marketers broadly (Grewal et al., 2017; Martin et al., 2020). Emerging technologies have enabled retailers to collect new types of data and this data has empowered new technologies and applications (Bradlow et al., 2017). Yet, understanding of data exchange relationships has not kept pace with practical advancements, particularly regarding consumer views and preferences (Martin and Murphy, 2017).
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