Statistical Challenges in eCommerce: Modeling Dynamic and Networked Data
Abstract
Empirical research in the field of electronic commerce (eCommerce) has been growing fast due to the availability of rich, high-quality data. eCommerce data originate from many different behavioral, social, or economic processes and interactions online that have not been observable and measurable in the offline world. This data-rich environment allows for the questioning of existing theories and the uncovering of new phenomena. However, eCommerce data and the new research questions associated with these data are often not supported by classic statistical machinery. New dependency structures arise due to factors such as online competition and user interaction. In this tutorial, we discuss three key aspects of eCommerce data: eCommerce process dynamics, competition between processes, and user networks. Each data structure raises new challenges for data representation, visualization, and modeling, and we describe each of them in detail. We also present three case studies that showcase the various statistical challenges and present some solutions.
This publication has no references to display.
Your Access Options
-
Login Options
Purchase Options
Save for laterOther Options
Token AccessClaim access using a tokenRestore guest accessApplies for purchases made as a guest
