Optimal privacy service strategies for omnichannel retailers: A combination of nonlinear optimization and evolutionary game approaches

Abstract

As consumer concerns about privacy grow in the digital age, omnichannel retailers face increasing pressure to adopt privacy strategies that balance customer trust with business performance. This paper offers a theoretical analysis of optimal privacy service strategies for omnichannel retailers considering consumers' privacy concern. We develop a nonlinear constrained optimization model to identify the equilibrium operational decisions of an omnichannel retailer under various cooperation scenarios. Using this framework, we analyze an evolutionary game between the retailer and consumers, ultimately deriving optimal cooperation agreements. Our findings indicate that offering value-added privacy services can enhance the performance of omnichannel businesses. However, the effectiveness of these strategies is limited by privacy-related costs. Additionally, customers' perceptions of the benefits and risks associated with privacy significantly influence the retailer’ operational decisions. While fully cooperative privacy services are ideal, partial cooperation may still yield higher market returns. Finally, we propose that, over the long term, consumers’ perceptions of product value could impact cooperation strategies between omnichannel retailer and consumers. The incentives provided by value-added privacy services may also promote greater collaboration between the two parties.