Bill Rand

I am the McLauchlan Distinguished Professor of Marketing and Analytics and University Faculty Scholar at the Poole College of Management at North Carolina State University, where I focus on the application of data-driven methods to business problems.

Research and Application

My research investigates the diffusion of information. I examine four large related questions: (1) How do people find things out?, (2) What do people do with that information once they have it?, (3) How do they pass their insights along to others?, and (4) How do modern digital communications methods affect information diffusion?  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 stakeholders 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 individual decision-makers and their interactions, and then observe the emergent outcome of those behaviors, such as viral trends, the spread of misinformation, and purchase decisions.

I have applied my research to social media, text-based and image-based communication, 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: Clinton Health Access Initiative, Teradata, Expedia, American Red Cross, National Geographic, Brazil's Institute of Applied Economic Research, and many more.


Besides understanding 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, business 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, large language models, neural networks, and evolutionary computation, and I have written a textbook on agent-based modeling and taught an award winning MOOC based on this book.

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