Optimal price strategy when facing privacy-concerned customers: uniform pricing vs. personalized pricing

The increased availability of customer information has inspired personalized pricing in recent years. However, the implementation of personalized pricing faces some challenges. On the one hand, personalized pricing is imperfect and its accuracy depends on firms’ big data capability. On the other hand, customers have growing privacy concerns about personalized pricing. The above two downward factors hinder the profitability of personalized pricing, but how these factors affect the interests of supply chain members and customers remains unclear. Moreover, when facing privacy-concerned customers, it is worth exploring which strategy is better: personalized or uniform pricing. Our findings indicate that (i) As big data capability increases, supply chain members’ profits increase and customer surplus decreases; as privacy concerns increase, both supply chain members’ profits and customer surplus decrease. (ii) Personalized pricing can reduce double marginalization compared to uniform pricing. Privacy-concerned customers always prefer uniform pricing than personalized pricing. (iii) Uniform pricing is a win-win strategy when the big data capability is low, and personalized pricing is a win-win strategy when the big data capability is high. These findings can provide practical insights for firms to choose an optimal price strategy when facing privacy-concerned customers.