AI-Driven Marketing Success Stories: A Case Note of Industry Pioneers

1. Introduction

Artificial intelligence (AI) is characterized as the utilization of computational machinery to replicate human capabilities, encompassing physical or mechanical tasks, cognitive processes such as thinking, and even emotions (Huang & Rust, 2021, p. 31). Its historical roots coincide with the advent of the first computers, but recent times have witnessed a surge in AI's prominence owing to rapid advancements in computer power and diverse technologies like computer vision, machine learning, and natural language processing. The proliferation of available data for algorithm training has also played a pivotal role in driving AI's recent momentum (Mariani et al., 2022). Domingos, a computer science professor at the University of Washington, highlights a crucial distinction between the machine learning he discusses—operating as an infinite key—and conventional programming methods (Conick, 2017). In the world of traditional programming, think of creating new keys as a necessity for every different lock. For marketers tracking specific customer groups, it means going through the hassle of developing a new algorithm for each case. Now, enter machine learning—a part of AI that changes the game. It lets computers learn just like humans do, but faster and without the need for explicit programming. According to Domingos, this is the core of AI, granting computers the ability to learn, hold conversations, appear human-like, and potentially come up with their own marketing strategies. Domingos dreams of a future where we combine algorithms from the five schools of thought in machine learning, creating what he calls the “master algorithm.” This idea has the potential to not only transform marketing but could extend its influence to entire companies within the next five to ten years (Conick, 2017). Big players in the industry, like Amazon and Google, have already embraced machine learning in all aspects of their operations. Take Netflix, for instance, where about three-fourths of the movies you watch are recommended by the platform's machine learning system—an impressive example of how this technology is shaping our experiences (Conick, 2017).

The academic interest in AI and its impact on marketing practices has seen a notable surge in recent years, especially from 2017 onward. While early studies in the 1980s delved into expert systems and robotics (e.g., Chablo, 1994; Davenport, 2018; Gill, 1995), the subject experienced a relatively quiet period for nearly two decades. The revival of interest among both researchers and marketing professionals can be attributed to significant factors like the rise of Big Data, increased computational power, and advancements in AI techniques and technological support (Bock et al., 2020; Overgoor et al., 2019). Although expert-based surveys (Davenport et al., 2020) underscore the importance of AI applications in marketing, they often lack a robust quantitative foundation and may carry interpretative biases (Furrer et al., 2020). To address this, our study aims to complement current research by exploring the historical intersection of AI and marketing, offering insights into the evolution of this dynamic relationship. Using a multiple correspondence analysis (MCA) method, we delve into the foundational aspects of this research field, providing a visual representation of its intellectual structure. MCA is a trusted content analysis approach acknowledged for mapping the structures of diverse research fields, including international strategic alliances (López-Duarte et al., 2016), service marketing (Furrer et al., 2020), and immigrant entrepreneurship (Vlačić et al., 2021). This chapter serves to unveil the remarkable achievements and innovative strategies adopted by key industry leaders, notably Dominos, Nike, and Coca-Cola, in the realm of marketing powered by advanced technology.