A Model for Discovering Customer Value for E-Content

There exists a huge demand for multimedia goods and services in the Internet. Currently available bandwidth speeds can support sale of downloadable content like CDs, e-books, etc. as well as services like video-on-demand. In the future, such services will be prevalent in the Internet. Since costs are typically fixed, maximizing revenue can maximize profits. A primary determinant of revenue in such econtent markets is how much value the customers associate with the content. Though marketing surveys are useful, they cannot adapt to the dynamic nature of the Internet market. In this work, we examine how to learn customer valuations in close to real-time. Our contributions in this paper are threefold: (1) we develop a probabilistic model to describe customer behavior, (2) we develop a framework for pricing e-content based on basic economic principles, and (3) we propose a price discovering algorithm that learns customer behavior parameters and suggests prices to an e-content provider. We validate our algorithm using simulations. Our simulations indicate that our algorithm generates revenue close to the maximum expectation. Further, they also indicate that the algorithm is robust to transient customer behavior. 1