Using Machine Learning to Extract Insights From Consumer Data

Abstract

Advances in digital technology have led to the digitization of everyday activities of billions of people around the world, generating vast amounts of data on human behavior. A parallel trend has been the emergence of computational methods and analysis techniques needed to deal with these new sources of behavioral data—which tend to be more unstructured, of much larger scale, and noisier. As they are recent and emerging developments, many behavioral scientists and practitioners may be unaware or unfamiliar with these recent developments and opportunities to extract behavioral insights. The main objective of this article is to discuss machine learning methods for researchers and practitioners interested in addressing customer-relevant questions using new secondary data sources that are publicly available, such as data from crowdfunding, video streaming, crowdsourcing, and social media platforms. This article offers a primer on the application of computational social science for understanding consumer data for researchers and practitioners.

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