Data, measurement, and causal inferences in machine learning: opportunities and challenges for marketing

Hair, Joseph F. and Sarstedt, Marko (2021): Data, measurement, and causal inferences in machine learning: opportunities and challenges for marketing. In: Journal of Marketing Theory and Practice, Vol. 29, No. 1: pp. 65-77

Abstract

The emergence of digital data and the methods used to analyze them are revolutionizing marketing research. The vast quantity of data offers marketing researchers countless opportunities to better predict and potentially explain consumer behavior. Yet, as we will argue in this paper, marketing researchers should not prematurely abandon cognitive and methodological procedures that have been refined during centuries of philosophical and scientific thought. Merging the literatures from various hard sciences, we discuss recent challenges in data management and measurement in the era of digital data and the role of machine learning in causal inference.

• Item Type: Journal article
• Keywords: Data, Machine Learning, consumer behavior, consumer behaviour
• Faculties: Munich School of Management > Institute for Marketing
• Subjects: 300 Social sciences > 330 Economics
• ISSN: 1069-6679
• Language: English