Many dynamic pricing models such as forward-auction and reverse-auction are deployed by a lot of e-Commerce companies. However, in case of digital contents, existing dynamic pricing models are not very suitable because their marginal cost is zero. In this thesis, a dynamic pricing model for digital contents is developed. According to a research conducted by Hanson and Putler (1996), consumers consider the download counts of a software as an indication of quality of that software. We assume that the consumers are willing to pay more for the popular contents that have sold a lot, and develop a forecasting model based on the sales history data. In addition, an application strategy for this model in a real business environment is considered.
