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Unsupervised Bayesian Network Learning for Non Obvious Marketing Insight

Unsupervised Bayesian Network Learning for Non-Obvious Marketing Insight

Presented at the 6th Annual BayesiaLab Conference in Chicago, November 1-2, 2018.

Abstract

Marketing researchers frequently deploy consumer surveys to test marketing hypotheses or to segment their potential audience. This paper shows examples of how Unsupervised Bayesian Networks unlock additional insights from consumer surveys beyond their original purposes. Unsupervised Learning approaches the data from an assumption-free perspective of knowledge representation. It discovers the underlying structures of consumer demand and expectations within the market.

The paper outlines the challenges and opportunities of Unstructured Learning, sets out best practices for analyzing consumer marketing data, and presents three case studies drawn from survey data on consumer packaged goods, restaurants, and Internet services. It draws some non-obvious conclusions in each of the three domains, suggests new directions in product development, and refines value propositions.

Presentations Slides

About the Presenters

Charles Hammerslough is the Discipline Lead for Data Science at VSA Partners, a design, marketing, and branding agency located in Chicago and New York. He oversees a wide range of projects involving marketing and predictive analytics. Prior to VSA, he was the Director of Modeling for the 2012 Obama for America campaign and a vice president of Research and Development at Nielsen. He received his Ph.D. in Sociology and Demography from Princeton University.

Praveen Singaraju is the Director of Strategy and Analytics at VSA Partners. He specializes in advanced analytics of marketing, pricing and trade, consumer and branding, and go-to-market strategies. Prior to VSA, he was a Senior Associate at PricewaterhouseCoopers and an Analytics Consultant at Nielsen Marketing Analytics. He received his MSBA from the University of Cincinnati.


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