The Value of Personalized Pricing

Increased availability of high-quality customer information has fueled interest personalized pricing strategies, i.e., strategies that predict an individual customer’s valuation for a product and then o↵er a customized price tailored to that customer. While the appeal of personalized pricing is clear, it may also incur large costs in the form of market research, investment in information technology and analytics expertise, and branding risks. In light of these tradeo↵s, in this work, we study the value of personalized pricing over simpler pricing strategies, such as charging a single price to all customers. In the first part of the work, we provide tight, closed-form upper bounds on the ratio of personalized pricing profits to single-pricing profits that depend on simple statistics of the valuation distribution. These bounds shed light on the types of markets for which personalized pricing has the most potential. In the second part of the work, we use these bounds to study the two key assumptions underlying personalized pricing: (i) the firm can charge a distinct price to each customer and (ii) the firm can perfectly predict customer valuations. Specifically, we bound the ratio of personalized pricing profits to the profits from a k-segmentation strategy where the firm is omniscient but can only charge customers one of k prices, and the ratio of personalized pricing profits to the profits of a feature-based pricing strategy, where the firm can charge a continuum of prices, but is no longer omniscient. These bounds help quantify the value of the operational capability of charging distinct prices and the value of additional predictive accuracy, respectively. Finally, we provide a general framework for computing an essentially tight bound on the ratio of personalized pricing profits to single-pricing profits in terms of the mean, support, and a generalized moment of the distribution.