Abstract

The widespread impacts of artificial intelligence (AI) and machine learning (ML) in many segments of society have not yet been felt strongly in the marketing field. Despite such shortfall, ML offers a variety of potential benefits, including the opportunity to apply more robust methods for the generalization of scientific discoveries. Trying to reduce this shortfall, this monograph has four goals. First, to provide marketing with an overview of ML, including a review of its major types (supervised, unsupervised, and reinforcement learning) and algorithms, relevance to marketing, and general workflow. Second, to analyze two potential learning strategies for marketing researchers to learn ML: the bottom-up (that requires a strong background in general math and calculus, statistics, and programming languages) and the top-down (focused on the implementation of ML algorithms to improve explanations and/or predictions given within the domain of the researcher’s knowledge). The third goal is to analyze the ML applications published in top-tier marketing and management journals, books, book chapters, as well as recent working papers on a few promising marketing research sub-fields. Finally, the last goal of the monograph is to discuss possible impacts of trends and future developments of ML to the field of marketing.

Machine Learning in Marketing: Overview, Learning Strategies, Applications, and Future Developments

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.