Machine Learning and Consumer Data
Hannah H. Chang Anirban Mukherjee
The digital revolution has led to the digitization of human behavior,
creating unprecedented opportunities to understand observable actions on an
unmatched scale. Emerging phenomena such as crowdfunding and crowdsourcing have
further illuminated consumer behavior while also introducing new behavioral
patterns. However, the sheer volume and complexity of this data present
significant challenges for marketing researchers and practitioners. Traditional
methods used to analyze consumer data fall short in handling the breadth,
precision, and scale of emerging data sources. To address this, computational
methods have been developed to manage the "big data" associated with consumer
behavior, which typically includes structured data, textual data, audial data,
and visual data. These methods, particularly machine learning, allow for
effective parsing and processing of multi-faceted data. Given these recent
developments, this review article seeks to familiarize researchers and
practitioners with new data sources and analysis techniques for studying
consumer behavior at scale. It serves as an introduction to the application of
computational social science in understanding and leveraging publicly available
consumer data.