Machine Learning in Clinical Research: Transforming the Future of Evidence Generation

Many parts of society, including marketing, have yet to be
significantly impacted by Artificial Intelligence (AI) and Machine
Learning (ML). Regardless of this flaw, ML has a lot of potential
benefits, including the capacity to apply more robust methodologies
for generalizing scientific findings. In order to overcome this deficit,
this monograph has four goals. To begin, present a marketing
overview of Machine Learning (ML), including an examination of the
various types (supervised, unsupervised, and reinforcement learning)
and algorithms, as well as the significance of ML to marketing and the
whole process. Second, we'll look at two different ML learning
strategies for marketers: Bottom-up (which requires a strong
background in general math and calculus, statistics, and programming
languages) and top-down (which focuses on using ML algorithms to
improve explanations and/or predictions given within the researcher's
domain of knowledge). The third goal is to examine machine learning
applications that have been published in prestigious marketing and
management journals, books, and book chapters, as well as recent
working papers on a few intriguing marketing research subfields.
Finally, the monograph's purpose is to discuss the potential impact of
machine learning trends and future advances on the marketing
industry.