Abstract

Introduction

Research method

Literature analysis

Concluding remarks and limitations

Agenda for future research

Declaration of Competing Interest

Acknowledgements

References

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.

Introduction

     In recent years, the extensive development of information and communication technologies in the private and public sectors has initiated the emergence of a new digital marketing environment (Miklosik et al., 2019; Shah & Murthi, 2021). With the rapid advancement of information technology, a huge amount of marketing data is captured and used to generate meaningful insights. To make effective marketing decisions, corporations need to apply new data-oriented methods to process and analyze these data. Machine learning (ML) can be applied to predict consumer behavior and support marketing decision making by mining useful information from large amounts of generated data. As a result, the applications of ML and artificial intelligence (AI) have attracted considerable attention in the marketing field.

     Mitchell (1997, p. 2) describes ML as “a computer program [that] is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” ML is considered a subset of AI (Kumar et al., 2021). Its ability to look for patterns in data and enable better decision-making have attracted researchers and practitioners, such that it has been widely applied in different business functions, including marketing (Chen et al., 2017), accounting (Ding et al., 2020), finance (Yazdani et al., 2018), and customer service (Jain & Kumar, 2020)

     ML is a powerful tool used for data analysis; it automates analytical model building and can be used for mining large sets of data, providing marketers opportunities to gain new insights into consumer behavior and improve the performance of marketing operations (Cui et al., 2006). Research has presented how ML and AI are used in marketing (e.g., (Ascarza, 2018; Chatterjee et al., 2021; Huang & Rust, 2021)). Several studies have focused on understanding various ML technologies that support the use of ML in marketing (e.g., (Homburg et al., 2020; Alabdulrahman & Viktor, 2021)). Additionally, marketing theories that serve as the basis of applications have been discussed in a few studies (e.g., (Evgeniou et al., 2007; Fang & Hu, 2018)).