Machine learning in marketing: A literature review, conceptual framework, and research agenda

Eric W.T. Ngai and Yuanyuan Wu

Journal of Business Research, 2022, vol. 145, issue C, 35-48

Abstract: In recent years, machine learning (ML) and artificial intelligence (AI) have attracted considerable attention in different industry sectors, including marketing. ML and AI hold great promise for making marketing intelligent and efficient. In this study, we conduct a literature review of academic journal studies on ML in marketing applications and propose a conceptual framework highlighting the main ML tools and technologies that serve as the foundation of ML applications in marketing. We use the 7Ps marketing mix, that is, product, price, promotion, place, people, process, and physical evidence, to analyze these applications from 140 selected articles. The applications are supported by various ML tools (text, voice, image, and video analytics) and techniques such as supervised, unsupervised, and reinforcement learning algorithms. We propose a two-layer conceptual framework for ML applications in marketing development. This framework can serve future research and provide an illustration of the development of ML applications in marketing.

Keywords: Machine learning; Marketing; Literature review; Conceptual framework; Research agenda (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:145:y:2022:i:c:p:35-48

DOI: 10.1016/j.jbusres.2022.02.049

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