2020/06/28 by Arnaud De Bruyn, Vijay Viswanathan, Yean Shan Beh +4 · 401 citations
Computer Science · Decision Sciences · #Explainable Artificial Intelligence (XAI) #Forecasting Techniques and Applications #Stock Market Forecasting Methods
paper · doi:10.1016/j.intmar.2020.04.007
published in Journal of Interactive Marketing 51(1), 91-105 (SAGE Publications)
openalex publication_date 2020/06/28 · crossref created 2020/06/28 · crossref issued 2020/08/01 · crossref published 2020/08/01 · crossref published-print 2020/08/01 · crossref published-online 2022/01/31 · openalex created_date 2025/10/10 · crossref deposited 2026/04/29 · openalex updated_date 2026/07/28 · crossref indexed 2026/08/06
This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations.