KOREASCHOLAR

Marketing becomes more and more data driven and hence enables machine learning to empower instruments to foster the interaction between firms and consumers to a new level of customization. Replacement and redirection of workforce through machine learning powered devices is not anymore a mere myth (Huang and Rust 2018). Adaption of machine learning has remained low in the recent years even though disposability was given. Nevertheless the acceptance and the implementation of machine learning based marketing efforts experience currently an exponential increase (Syam and Sharma 2018). In this article, the authors aim to develop a stronger understanding of machine learning in the context of marketing as well as to provide an overview about already established usage and implementation of machine learning in the interactions between firms and customers. To achieve this objective, the authors discuss and study machine learning in marketing from both the management and the consumer perspective. This is supported by survey data retrieved from managers out of varies industries as well as consumers, which reveal great variety in usage of machine learning not only among different marketing activities but also among industries. The authors examine the roots of machine learning in marketing and evaluate inferential state-of-the-art instruments. Predictions of what can and will evolve in the marketing context with the help of machine learning in the near future are also connected with concerns and safety issues related to the increasingly transparent consumer-firm-relationship. To conclude, the article the authors present a summary of state-of-the-art mechanisms in machine learning in marketing and propose a research agenda for upcoming research.

• Florian Stahl(University of Mannheim, Germany)
• Maximilian Beichert(University of Mannheim, Germany)
• Sabrina Haas(University of Mannheim, Germany)