Abstract

Abundant consumer data has made decision making more complicated rather than simple for marketers. The abundance of consumer data raises an important question about which variables in the data contain reliable information for retailers to predict future consumer purchase value (CPV) to guide strategic decisions. The authors address this question by exploring the variables “distinctive choice of brand country of origin” (DBCOO) and “country of origin diversity” (COO diversity) as analytical tools to extract insights from consumer purchase data. Building on signaling theory, the authors theorize and empirically test that DBCOO and COO diversity in a consumer’s purchase history can signal, and therefore help predict CPV. Moreover, we explore high-involvement product categories and purchase frequency as boundary conditions to develop a comprehensive framework of COO signals as strategic analytical tools. We find that DBCOO in a consumer’s purchase history indeed increases CPV and that this relationship is enhanced for high-involvement product categories but moderated curvilinearly by purchase frequency. Moreover, we find that the COO diversity – CPV link is positive but depicts a negative interaction with both moderators. This allows retailers to successfully distinguish high- from low-CPV consumers and thus enables them to manage marketing mix and resources more effectively.

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