Statistical Methods in e-Commerce Research

This groundbreaking book introduces the application of statistical methodologies to e-Commerce data

With the expanding presence of technology in today's economic market, the use of the Internet for buying, selling, and investing is growing more popular and public in nature. Statistical Methods in e-Commerce Research is the first book of its kind to focus on the statistical models and methods that are essential in order to analyze information from electronic-commerce (e-Commerce) transactions, identify the challenges that arise with new e-Commerce data structures, and discover new knowledge about consumer activity.

This collection gathers over thirty researchers and practitioners from the fields of statistics, computer science, information systems, and marketing to discuss the growing use of statistical methods in e-Commerce research. From privacy protection to economic impact, the book first identifies the many obstacles that are encountered while collecting, cleaning, exploring, and analyzing e-Commerce data. Solutions to these problems are then suggested using established and newly developed statistical and data mining methods. Finally, a look into the future of this evolving area of study is provided through an in-depth discussion of the emerging methods for conducting e-Commerce research.

Statistical Methods in e-Commerce Research successfully bridges the gap between statistics and e-Commerce, introducing a statistical approach to solving challenges that arise in the context of online transactions, while also introducing a wide range of e-Commerce applications and problems where novel statistical methodology is warranted. It is an ideal text for courses on e-Commerce at the upper-undergraduate and graduate levels and also serves as a valuable reference for researchers and analysts across a wide array of subject areas, including economics, marketing, and information systems who would like to gain a deeper understanding of the use of statistics in their work.

Wolfgang Jank, PhD, is Associate Professor in the Department of Decision, Operations & Information Technologies at the Robert H. Smith School of Business, the University of Maryland. He has authored or coauthored over fifty refereed articles in his areas of research interest, which include stochastic optimization and Monte Carlo methods; nonparametric statistics and functional data analysis; and the application of statistical problem-solving to electronic commerce, marketing, aviation, and operations management.

Galit Shmueli, PhD, is Associate Professor and Director of the eMarkets Research Laboratory in the Department of Decision, Operations & Information Technologies at the Robert H. Smith School of Business, the University of Maryland. She has authored or coauthored over fifty refereed articles in her main area of research: the development and adoption of statistical methods to nonstandard data with applications to the fields of electronic commerce and biosurveillance. Dr. Shmueli is the coauthor of Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel® with XLMiner®, also published by Wiley.