Bill Rand
I am an Associate Professor of Marketing at the Poole College of Management at North Carolina State University, where I focus on the application of computer science methods to marketing problems.
Research and Application
My research investigates the diffusion of information. I examine three large questions: (1) How do consumers find things out?, (2) What do consumers do with that information once they have it?, and (3) How do they pass their product insights along to others through online word-of-mouth? Given the explosion of new forms of Internet-based communication technologies, especially with the advent of social media, the answers to these questions are changing and new every day. Digital transformation is and will continue to fundamentally alter the way business is done, and at the base of this transformation is the flow of information among consumers. If we can understand that, then we can better prepare managers for the constantly changing future. I view this process through the lens of complex systems, i.e., that the best way to understand information diffusion is to model the consumers as individuals who are diffusing product insights and marketing information and their interactions, and then observe the emergent outcome of those models, such as product purchases, churn processes, and overall customer lifetime value.
I have applied my research to social media analytics, freemium network-based games, app adoption, not-for-profit donations, and innovation adoption. I have worked with a number of different companies and organizations while studying these research questions, including: Teradata, Expedia, American Red Cross, National Geographic, Brazil's Institute of Applied Economic Research, and many more.
Methods
Besides providing marketing insights to understand the diffusion of information, I am also interested in creating tools, pedagogy, and frameworks to help managers make more data-driven decisions. I teach classes, workshops, and MOOCs on agent-based modeling, digital marketing analytics, and data science, and the application of these methods to marketing and management decisions. In my research, I work extensively with machine learning and artificial intelligence, including causal state modeling, natural language processing, neural networks, and evolutionary computation, and I have written a textbook on agent-based modeling and teach an award winning MOOC based on this book.
Aug 5, 2022: Happy to be part of the program committee for Computational Social Science 2022.
Aug 1, 2022: "Evolution of Intent and Social Influence Networks and their Significance in Detecting COVID-19 Disinformation Actors on Social Media" was accepted for publication SBP-BRIMS.
July 22, 2022: Awarded a grant from DARPA to study "Influence Cascades Ecosystem: Entropy-Based Characterization and Modeling of Influence Patters and Pathways across Traditional and Social Media"
June 2, 2022: Presented an invited talk, "Social Media and Data Science" as part of St. Jude's Data Bootcamp.
May 24, 2022: Presented an invited talk at Loránd Eötvös University in Budapest on "Using Image Analytics and Deep Learning to Understand Consumer Decision-Making"
