Machine Learning in Marketing discusses the central role that artificial intelligence (AI) and, more specifically, machine learning can play as a research method in the marketing field. The fundamental goal of machine learning is to generalize beyond the examples provided by training data, looking for generalizability. Thus, one of the potential contributions of machine learning to marketing lies in its robustness for the generation, testing, and generalization of scientific discoveries. With these different academic and practical perspectives in mind, the goal of this monograph is to provide marketing with an overview of machine learning and to analyze required learning, applications, and future developments involved in applying machine learning to marketing.
After a short introduction, the following section provides an overview of machine learning, including a review of its most relevant types, algorithms, and relevance to marketing. The next section presents a typical machine learning workflow, followed by a section that proposes two different learning strategies that can be used by management/marketing researchers interested in machine learning. That section is followed by a descriptive analysis of applications of machine learning published in top-tier marketing and management journals, books, book chapters, and recent working papers that explore a few of the most promising marketing research sub-fields. Next, the author discusses how trends and future developments of machine learning can impact the field of marketing. The last section summarizes the contributions, limitations, and suggestions for future research.