Optimal Pricing with New Models of Consumer Behavior

Abstract

Revenue management is a commonly used practice in many industries, such as airlines, hotels, fashion, and car rentals. It takes advantage of customers' different valuations for a product or products and charges different prices to different customers to extract customers' surplus. In revenue management, most literature assumes that customers are myopic and will buy immediately if the price is low and leave otherwise. In recent years there has been much research involving strategic customers who have the ability to predict future prices and thus make a purchase at the price that maximizes their utility. In Chapter 2 and 3, I will study a different type of customer behavior, which we call patient customer behavior. A patient customer will wait up to some fixed number of time periods for the price of the product to fall below his or her valuation at which point the customer will make a purchase. If the price does not fall below a patient customer's valuation at any time during those periods, then that customer will leave without buying. Chapter 4 describes a learning and pricing problem in which the seller does not know the fraction of patient customers. In practice, customers may wish to search for product information before making purchase decisions. That is, they may wish to research the product or products under consideration. This research behavior will introduce costs to customers, which may include time cost, travel cost, and mental processing cost. Since such research costs could be part of a customer's utility, they may affect a customer's purchasing behavior and thus the firm's strategy. However, most literature in revenue management does not consider the existence of customers' search cost. In Chapter 5, I consider a pricing problem in which customers face uncertainty about whether they will like certain products. Those customers can incur research costs to learn product information. In summary, I will focus on deriving optimal pricing decisions for companies that face customer behavior that is more complex than typically assumed in traditional models.