Abstract

Decision-making in marketing has changed dramatically in the past decade. Companies increasingly use algorithms to generate predictions for marketing decisions, such as which consumers to target with which offers. Such algorithmic decision-making promises to make marketing more intelligent, efficient, consumer-friendly, and, ultimately, more effective. Not surprisingly, machine learning is a trending topic for marketing researchers and practitioners. However, machine learning also introduces important challenges to the marketing landscape. We discuss this development by outlining recent progress and future research directions of machine learning in marketing. Specifically, we provide an overview of typical machine learning applications in marketing and present a guiding framework. We position the articles in the Journal of Business Research’s Special Issue on “Machine Learning in Marketing” within this framework and conclude by putting forward a research agenda to further guide future research in this area.

Introduction

Marketing decision-makers today often struggle to adequately capture and transform (big) customer data into meaningful insights (e.g., De Luca et al., 2021, Sheth and Kellstadt, 2021). Recent research indicates that machine learning (ML)—a field of computer science dedicated to developing learning algorithms, often using big data, to generate predictions needed to make decisions (Agrawal, Gans, & Goldfarb, 2018)—can help companies manage the flood of data (e.g., Davenport et al., 2020, Hagen et al., 2020, Ma and Sun, 2020, Vermeer et al., 2019). ML has been a trending topic in many industries for quite a while now, the marketing industry will be no exception, and it is being used in various industries in the context of both B2C and B2B (e.g., Herhausen et al., 2020, Kumar et al., 2020, Luo et al., 2019, Rust, 2020). ML promises to make marketing more intelligent, efficient, consumer-friendly, and, ultimately, more effective (Huang & Rust, 2021). To put it more directly, proficiency in ML could become an essential skill for numerous marketing researchers and practitioners rather than just a desirable one.

Faced with this development, we aim to highlight recent progress and future research directions on ML in marketing. Such an endeavor is both important and timely, given the dramatic increase in publications on ML in marketing in recent years, as summarized in Fig. 1. Indeed, from 2012 to 2022, the number of publications has increased by almost 600 percent to more than 50,000 yearly publications. Rather than provide an exhaustive literature review, our purpose here is to discuss the most fundamental concepts and topics from past and present research that will drive future research on ML in marketing.1 In doing so, we position the articles of this special issue as an overarching framework that touches upon three key themes, namely employing ML in marketing, benchmarking ML in marketing, and managing ML in marketing.

Conclusion and acknowledgments

We hope this overview article and the papers in this Special Issue serve as an impetus for further research on the important topic of ML in marketing. In addition to summarizing the insights from the published papers, we also discussed important future research directions related to the themes of (1) automated machine learning, (2) data privacy and security, (3) model interpretability, (4) algorithm fairness, and (5) causal machine learning. Selected research questions for ML in marketing derived from these discussions are summarized in Table 2. In addition, broader ML themes might also benefit from a marketing perspective. For example, how can we ensure that ML models are producing ethical and socially responsible models and predictions (De Cremer, 2020; De Cremer & Kasparov, 2022)? Deep learning seems to change how prediction models are developed (Kaur & Sharma, 2023) – what will these techniques’ impact on marketing analytics be in the near future? And, importantly, how will ChatGPT and similar advanced AI technologies change marketing research (van Dis, Bollen, Zuidema, van Rooij, & Bockting, 2023)?