Dynamic Pricing for E-Commerce
Prithviraj Dasgupta (University of Nebraska, Omaha, USA), Louise E. Moser (University of California - Santa Barbara, USA) and P. Michael Melliar-Smith (University of California - Santa Barbara, USA)
Copyright: © 2009
Over the last decade, e-commerce has significantly changed the traditional forms of interaction among humans in conducting business by automating business processes over the Internet. Early seller Web sites consisted of passive text-based catalogs of products that could be manually browsed by potential customers. Online passive catalogs were soon replaced by dynamically updated catalogs containing detailed product descriptions using combinations of text and images that could be searched in various formats and according to different search criteria. E-commerce techniques used by sellers for operations such as price setting, negotiation, and payment have matured from manual off-line processing of sales data to automated algorithms that dynamically determine prices and profits for sellers. Modern e-commerce processes for trading goods between buyers and sellers can be divided into five stages: search, valuation, negotiation, payment, and delivery. Depending on the type of market in which the goods are traded, some of the above stages are more important than others. There are three principal market models that are used for online trading. The most common market model used by online sellers for trading goods over the Internet is the posted-price market model. The other two market models, the auction model (Sandholm, Suri, Gilpin, & Levine, 2002) and the marketplace model (Chavez & Maes, 1996), are used for markets in which niche or specialty items with sporadic or uncertain demand are traded. In the posted-price market model, a seller announces the price of a product on its Web site. Buyers visiting the seller’s Web site request a quote from the seller. The seller responds with a quote in response to the buyers’ requests, and the buyers examine the seller’s quote to make a purchase decision. Unlike auctions and market places, products traded in posted-price markets are no-niche items and exhibit continuous demand over time. The Web site of online book merchant Amazon (http://www.amazon.com) is an example of a posted-price market. A buyer interested in a particular book enters the necessary information through a form on Amazon’s Web site to request the price of the book and receives the price in response. Modern seller Web sites employ automated techniques for the different stages of e-commerce. Intermediaries called intelligent agents are used to automate trading processes by implementing different algorithms for selling products. For example, Web sites such as MySimon (http://www. mysimon.com) and PriceGrabber (http://www. pricegrabber.com) automate the search stage by employing the services of intelligent agents called shopbots. Shopbots enable buyers to make an informed purchase decision by comparing the prices and other attributes of products from thousands of online sellers. Automated price comparison by buyers has resulted in increased competition among sellers. Sellers have responded to this challenge by using intelligent agents called pricebots that dynamically determine the price of a product in response to varying market conditions and buyers’ preferences. Intelligent agents are also used to enable other e-commerce processes, such as supply-chain management and automated negotiation. In this article, we focus on the different algorithms that sellers’ pricebots can use for the dynamic pricing of goods in posted-price markets.