The Value of Personalized Pricing
46 Pages Posted: 2 Mar 2018 Last revised: 15 Oct 2018
Date Written: October 1, 2018
Increased availability of high-quality customer information has fueled interest in personalized pricing strategies, i.e., strategies that predict an individual customer's valuation for a product and then offer 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 tradeoffs, our work studies the value of idealized personalized pricing over a spectrum of pricing strategies varying in pricing flexibility and prediction model accuracy.
We first provide tight, closed-form upper bounds on the ratio between the profits of an idealized personalized pricing strategy and a single price strategy. These bounds depend on simple statistics of the valuation distribution and shed light on the types of markets for which personalized pricing has the most potential. Next, we consider two stylized price discrimination strategies that isolate the key assumptions underlying idealized personalized pricing: (i) a k-market segmentation strategy where the firm knows all customer valuations precisely but can only charge customers one of k prices and (ii) a feature-based pricing strategy, where the firm can charge a continuum of prices, but no longer knows customer valuations precisely. For each strategy, we bound the ratio of idealized personalized pricing profits to the profits of that strategy. These bounds quantify the value of the operational capability of charging distinct prices and the value of additional predictive accuracy, respectively. Finally, we synthesize these results to study a more realistic personalization strategy in which the seller neither knows customer valuations precisely nor is able to offer a continuum of prices. Computational evidence suggests that our bounds are both qualitatively and quantitatively representative under several common valuation distributions.
Keywords: price discrimination, personalization, market segmentation
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