Study On Optimal Pricing Strategy Of A Name-Your-Own-Price Retailer

The emergence of Internet and its extensive usage for electronic commerce has given enterprises the opportunity to experiment with a number of innovative pricing models. A well-known example for this is the Name-Your-Own-Price (NYOP) model. In the NYOP setting, instead of posting a price, the seller waits for potential buyers to submit offers for a given product. In the NYOP model, a customer contacts the retailer through the NYOP system. After providing identification (or registering a new user name), customer may give his/her offer for a product needed, and the system will compare the offer with the internal threshold price. If the offer exceeds the threshold price, the transaction occurs at the price offered by the customer; otherwise the system will reject it, and customer will leave. Priceline.com is such a seller who sells air tickets and new cars and offers housing loans by NYOP mode. The important question for the NYOP seller is how to set the threshold price. This paper discusses the optimal pricing strategy in a variety of situations.First, the tickets and hotel rooms sold by the typical NYOP retailer such as priceline.com are perishable goods, for which there is a problem of inventory and whose residual values will sharply fall and even become zero if not sold out in a certain period of time. In respect of this, it is assumed that random customers give their offers randomly and customers stochastic demand and the probability distribution of pricing or customers' reserve prices are known, so as to establish a model of expected revenue of a NYOP retailer and to determine the relationship between optimal price and limited inventory, sale time, and other factors by numerical analysis.Second, in real life, sellers often have multiple objectives. Seeking maximum sales in ensuring obtain a certain profit is the most commom model of the multi-objective models. In this model, the original method does not apply. Given that the retailers'inventory limitation and produce market share, the online retailers maximum expected revenue model is put forward and numerically analyzed when the probability distribution of customers'reserve prices are known. The impact of the probability threshold of actual sales volume above the target value and different levels of target sales on retailers' optimal threshold prices and retailers' maximum expected revenue is discussed.Third, we study how to set the optimal threshold price in single-channel and dual-channel scenarios. In the single-channel market configuration, the retailer only offers an NYOP channel to the consumers, who will buy from other competitor's list-price channel(s) if they fail to win in the NYOP channel. In the dual-channel market configuration, the retailer provides an NYOP channel along with a list-price channel. The consumers who do not win from the NYOP channel might not only buy from the NYOP-retailer-own list-channel but also buy from other competitor's list-price channel(s). We establish a double-bid business model in which the consumers can bid twice and a single-bid model in which the consumers can only bid once in order to analysis the consumer behavior. We also study the optimal threshold prices in a variety of situations, including different combinations of single-bid, double-bid, dual-channel, single-channel, abundant inventory capacity and constrained inventory capacity in the Name-Your-Own-Price (NYOP) channel.Finally, we assume that customers prefer list-price channels and establish a game model to find the equilibrium of the NYOP retailer's optimal threshold price and list-price sellers' optimal posted price in simple-dual-channel and complicated-dual-channel scenarios.