Bill Rand

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.

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