Individual Marketing with Imperfect Targetability
Marketing Science
Consumer Addressability and Customized Pricing
Marketing Science
Competitive One-to-One Promotions
Management Science
Commissioned Paper: An Overview of Pricing Models for Revenue Management
Manufacturing & Service Operations Management
The Role of the Management Sciences in Research on Personalization
Management Science
The Dynamic Pricing Problem from a Newsvendor's Perspective
Manufacturing & Service Operations Management
Revenue Management Through Dynamic Cross Selling in E-Commerce Retailing
Operations Research
A Partially Observed Markov Decision Process for Dynamic Pricing
Management Science
Research NoteThe Benefits of Personalized Pricing in a Channel
Marketing Science
Dynamic Pricing Strategies for Multiproduct Revenue Management Problems
Manufacturing & Service Operations Management
Relationships Among Three Assumptions in Revenue Management
Operations Research
Intertemporal Pricing with Strategic Customer Behavior
Management Science
Manufacturing & Service Operations Management
Optimal Pricing of Seasonal Products in the Presence of Forward-Looking Consumers
Manufacturing & Service Operations Management
Pricing Promotional Products Under Upselling
Manufacturing & Service Operations Management
Dynamic Pricing Strategies with Reference Effects
Operations Research
Priority Assignment Under Imperfect Information on Customer Type Identities
Manufacturing & Service Operations Management
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Prior work has investigated time-and inventory-level-dependent pricing of limited inventories with finite selling horizons. We consider a third dimension---in addition to time and inventory level---that the firms can use in setting their prices: the information that the firm has at the individual customer level. An arriving customer provides a signal to the firm, which is an imperfect indicator of the customer's willingness to pay, and the firm makes a personalized price offer depending on the signal, inventory level, and time. We consider two different models: full personalization and partial personalization. In the full personalization model, the firm charges any price it wishes given the customer signal, while in the partial personalization model, the firm can charge one of two prices. We find that a mere correlation between the signals and customers' willingness to pay is not sufficient to ensure intuitive relationships between the signal and the optimal prices. We determine a stronger condition, which leads to several structural properties, including the monotonicity of the optimal price with respect to the signal in the full personalization model. For the partial personalization model, we show that the optimal pricing policy is of threshold-type and that the threshold is monotonic in the inventory level and time.