Deep Learning in Marketing: A Review and Research Agenda

Abstract

The expansion of marketing data is encouraging the growing use of deep learning (DL) in marketing. I summarize the intuition behind deep learning and explain the mechanisms of six popular algorithms: three discriminative (convolutional neural network (CNN), recurrent neural network (RNN), and Transformer), two generative (variational autoencoder (VAE) and generative adversarial networks (GAN)), and one RL (DQN). I discuss what marketing problems DL is useful for and what fueled its growth in recent years. I emphasize the power and flexibility of DL for modeling unstructured data when formal theories and knowledge are absent. I also describe future research directions.

Citation

Liu, X. (2023), "Deep Learning in Marketing: A Review and Research Agenda", Sudhir, K. and Toubia, O. (Ed.) Artificial Intelligence in Marketing (Review of Marketing Research, Vol. 20), Emerald Publishing Limited, Leeds, pp. 239-271. https://doi.org/10.1108/S1548-643520230000020014