document: 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 e-content markets is how much value the customers associate with the content . Though marketing surveys are useful , they can not 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 .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O