Dynamic Pricing in Name-Your-Own-Price Channels: Bidding Behavior, Seller Profit and Price Acceptance

Introduction Recent advances in the ability to track and analyze customer traces in electronic commerce have led to a lively debate about the promise of dynamic pricing. On the promise side, online retailers may use the customer’s browsing behavior to learn about aggregate and individual preferences. This type of information would enable online retailers to personalize pricing with coupons and promotional pricing through customized banner ads or pop-up windows (Shapiro and Varian 1998). Ideally, a retailer would learn the preferences and price sensitivity of his/her customers over time and subsequently be able to price discriminate perfectly. This type of price discrimination – called first-degree price discrimination after Pigou (1920) – allows the retailer to capture the entire consumer surplus and minimizes deadweight loss. Technology providers have realized that the analysis of click-stream data allows retailers to look over their customers’ shoulders while they shop. Nearly all e-commerce engines and host providers offer services to segment their customers; these range from descriptive statistics and capturing statistics of marketing campaigns to click-by-click retracing of a customer’s browsing path. Similarly, academics have made great strides in modeling online customer behavior (Montgomery et al. 2004, Johnson et al. 2004). In reality, dynamic pricing has been slow to take hold in mainstream e-commerce. In fact, deviations from a ‘one price’ policy in traditional online retailing seem to be the exception. One often cited reason appears to be consumer backlash and potential negative publicity. In one highly publicized example, Amazon upset its customers with a price discrimination policy by using buyer profiles to charge different prices in September 2000 (Baker et al. 2001). One buyer reportedly deleted the cookies on his computer that identified him as a regular Amazon customer. He watched the price of a DVD offered to him for sale drop from $26.24 to $22.74. Amazon.com ultimately ended up publicly apologizing and refunding all customers who had paid higher prices (Ramasastry 2005). On the other hand, vendors sell the same product at different prices on EBay without any fear of customer retribution. As Dickson and Kalapurakal (1994) and Cox (2001) argue, price discrimination is considered fair as long as all buyers have the possibility to achieve all price levels. Thus price discrimination can be achieved by exploiting different level of search costs (e.g., online booking), quantity discounts, haggling efforts (e.g., auctions) or time of transactions (last minute offers). Name-Your-Own-Price (NYOP) lets both, buyer and seller, influence the price of a product. At the outset, a seller defines a secret threshold price indicating the minimum price he is willing to sell the product for. Subsequently, a buyer is asked to place a bid indicating his willingness to pay for the product