How to Create a Fave and Catch the Fake: Generative Adversarial Networks in Marketing

Abstract

Today's marketing is dominated by decision-making based on artificial intelligence and machine learning. This study focuses on one semi- and unsupervised machine learning technique, generative adversarial networks (GANs). GANs are a type of deep learning architecture capable of generating new data similar to the training data that were used to train it, and thus, it is designed to learn a generative model that can produce new samples. GANs have been used in multiple marketing areas, especially in creating images and video and providing customized consumer contents. Through providing a holistic picture of GANs, including its advantage, disadvantage, ethical considerations, and its current application, the study attempts to provide business some strategical orientations, including formulating strong marketing positioning, creating consumer lifetime values, and delivering desired marketing tactics in product, promotion, pricing, and distribution channel. Through using GANs, marketers will create unique experiences for consumers, build strategic focus, and gain competitive advantages. This study is an original endeavor in discussing GANs in marketing, offering fresh insights in this research topic.

Citation

Bose, M., Ye, L. and Zhuang, Y. (2024), "How to Create a Fave and Catch the Fake: Generative Adversarial Networks in Marketing", Matosas-López, L. (Ed.) The Impact of Digitalization on Current Marketing Strategies (Marketing & Technology: New Horizons and Challenges), Emerald Publishing Limited, Leeds, pp. 39-55. https://doi.org/10.1108/978-1-83753-686-320241003