Artificial Intelligence and Marketing: Pitfalls and Opportunities

Abstract

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.

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

“…Since then, the number of definitions had risen with the growing number of AI applications [ 6 ]. There are several specific understandings of AI such as by De Bruyn et al [ 7 ], who define AI as software that can “autonomously generate new constructs and knowledge structures” [ 7 ]. More general approaches describe and distinguish between weak AI, strong AI, and artificial general intelligence (AGI).…”

Commentary: Artificial Intelligence: The Marketer’s Dilemma

“…For example, the authors write that companies could “strive to develop AI that is less, rather than more, humanlike” (p. 175) and that managers should “design both debiased and antibias AI experiences” (p. 170). We found a similar perspective in De Bruyn et al (2020). For example, their idea that “marketing managers” need to “specify valid objective functions” (pp.…”

“…Artificial intelligence fundamentally alters the opportunities for relationship building and customer contact. Du Bruyn et al (2020, p. 100) invoke the “paradox of automation” to suggest that automating the mundane and repetitive tasks of marketing will deny “customer service agents, sales representatives, content marketing editors, CRM specialists, or target marketing experts” of valuable chances to initiate, develop, and hone their marketing skills. But what might be lost pertains to more than knowledge or skills—it is also about the mutual care, trust, relationships, and loyalty that come with meaningful and continued connection between marketers and customers.…”

Prediction of Online Video Advertising Inventory Based on TV Programs: A Deep Learning Approach

“…LSTM analysis is a form of recurrent neural network (RNN) that allows the discovery of long-term dependencies in temporal data [38]. The RNN has the characteristic of effectively analyzing time-series information because the hidden layer value for the existing input stored inside the neural network is considered at the output of the next input value [39].…”

“…Our usage of these techniques reflects the recent increase in interest in the use of big data, machine learning, and artificial intelligence (AI) in various fields, especially in online advertising and marketing [52], [53]. The analytical methods that learn from past data are relevant for marketing because they facilitate intelligent business practice [38]. Programmatic advertising based on data analysis has been in the spotlight of online advertising, which is used as a core technology for inventory prediction [54].…”