Author(s): Blanchard Wayne
Artificial intelligence (AI) and machine learning (ML) have yet to make a significant impact on many aspects of society, including marketing. Despite this shortcoming, ML has a number of potential advantages, including the ability to use more robust approaches for generalizing scientific results. This monograph has four objectives in order to address this deficiency. First, to give marketing an overview of machine learning (ML), including a look at the many types (supervised, unsupervised, and reinforcement learning) and algorithms, as well as the relevance of ML to marketing and the overall process. Second, to examine two possible ML learning strategies for marketing researchers: bottom-up (which requires a strong background in general math and calculus, statistics, and programming languages) and top-down (which focuses on the implementation of ML algorithms to improve explanations and/or predictions given within the researcher's domain of knowledge). The third purpose is to look at machine learning applications that have been published in top-tier marketing and management journals, books, and book chapters, as well as recent working papers on a few interesting marketing research sub-fields. Finally, the monograph's final goal is to discuss the potential impact of ML trends and future developments on the field of marketing.