Business & Management Research Centre
Information Systems Area
Predicting Music Sales: A Functional Data Analysis of Demand and Supply Side P2P Data
Professor Il-Horn Hann
Robert H. Smith School of Business
University of Maryland
Friday, October 30, 2009
10:30 AM – 12:00 PM
Samuel Bronfman Building
1001 Sherbrooke St. West
We predict the sales of music albums by utilizing demand and supply side P2P data using a functional data analysis (FDA) approach. We find that the characteristics of the functional form of downloading behavior explain first-week sales by more than 60% after controlling for album characteristics. By updating our forecasts from 4 to 1 week prior to the album release date, we examine the dynamic changes across different quantiles of the sales-distribution for the demand and supply side P2P data. We find that the gap between downloading effect on sales among high-quantile vs. low quantile albums reach the highest level one week before the release date. In a second approach, we combine functional data analysis with quantile regression to explore the heterogeneity effect of sales. We show that such an approach further our forecast results by reducing the mean absolute percentage error (MAPE) by 20% compared to OLS and nonlinear regression.
Il-Horn Hann is an Associate Professor at the Robert H. Smith School of Business at the University of Maryland. He received his Ph.D. from the University of Pennsylvania. His primary research interests focus on the intersection of information technology and markets. He has investigated issues regarding competition and pricing in electronic markets and online privacy. Il-Horn’s second research interest is in the area of open source software. His research has been published in Communication of the ACM, Journal of Management Information Systems and Management Science. He served as an Associate Editor at Information Systems Research and is currently on the editorial board of Management Science.