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

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 bottomup (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.