Bill Rand is an Associate Professor of Marketing at the Poole College of Management at NC State University, specializing in the intersection of marketing and computer science. His research focuses on data-driven decision-making and the diffusion of information among consumers and organizations. To do this he examines the use of computational modeling techniques, such as agent-based modeling, machine learning, network analysis, natural language processing, and geographic information systems, to help understand and analyze complex systems, such as the social media marketing, organizational behavior, and predictive analytics. He has applied his methods to analyze big data sets that have been drawn from social media platforms, marketing communications, and large-scale software systems. He works to develop methods, create pedagogy, and build frameworks to allow researchers and marketing practitioners to use analytics and data-intensive methods in their own work. He has received funding for his research from the NSF, DARPA, ARL, Google, WPP, and the Marketing Science Institute. His work has been published in JM, JMR, IJRM, Management Science, and JOM. He received his doctorate in Computer Science from the University of Michigan in 2005 and prior to coming to NCSU was at the University of Maryland for eight years.
While completing his PhD in computer science at the University of Michigan, Bill became interested in the application of computer science to social science problems. As a result, while finishing his dissertation he also became heavily involved with a project, at the Center for the Study of Complex Systems (CSCS), that was using agent-based modeling, a form of computer simulation, to study urban policy and its relation to how residents choose which homes to buy. While there he was an architect on one of the first large-scale agent-based models of suburban sprawl. After graduating, Bill was awarded a postdoctoral research fellowship by the Northwestern Institute on Complexity (NICO). There he expanded his work on agent-based modeling of social systems, and became interested in the study of information diffusion among consumers. How do people find things out about new products? What causes them to make a purchase? When do they decide to recommend products to others?
Based on this work, he was offered a position at the University of Maryland's Robert H. Smith School of Business, where he was asked to help run a brand new research center, called the Center for Complexity in Business. The goal of this Center was to capitalize on the quickly increasing amount of data in the world and the rapidly decreasing computational costs to create models of complex systems that would aid managers in making business decisions. The Center was successful, publishing papers in Marketing, Management Science, and Computer Science, raising around two million dollars in funding, and holding eight conferences that attracted participants from all over the world. The Center became known as a prominent data science and predictive analytics center. Bill served as the Director of Research, and eventually became the Director of the Center. He still serves as Director Emeritus.
Most recently, Bill has brought his skills to NC State's Poole College of Management where he is focusing even more on the growing importance of analytics in the Marketing domain. He has developed a new MBA class on Digital Marketing, that is analytically focussed, and is providing both teaching and curriculum advice on a Business Analytics Honors program for the undergraduates. His research has also continued to be focussed on the application of computational methods to marketing and management problems. Bill's research provides computational methods, such as machine learning, artificial intelligence, and big data approaches, to deal with the large-scale data that is now available for decision-making, which is a key component of future marketing research. Marketing practitioners are demanding more and more in terms of analytics, and Bill's research aims to provide tools, pedagogy, and frameworks to help marketing practitioners develop and use descriptive, predictive, and prescriptive analytics in a data-driven environment.