Deep Learning Applications for Interactive Marketing in the Contemporary Digital Age

研究成果: Chapter同行評審

2 引文 斯高帕斯(Scopus)


This chapter reviews recent research deploying deep learning (DL) to resolve problems in interactive marketing. It takes stock of what marketers know, how and why they do that. From an engagement marketing perspective, this chapter catalogues DL application cases in (1) customer acquisition, expansion, and retention, (2) marketing communication, and (3) product innovation. By identifying challenges from technological advancement and interactive marketing needs, it characterizes eleven issues that marketers have to deal with. They include the curse of dimensionality, complex data processing, language-image nexus, and customer privacy. This chapter elucidates the DL logic and solution techniques to correspondingly address these issues. It also introduces some technical terms for communication with data scientists. They include generative adversarial networks, transfer learning, and federated learning. A wide range of typical DL models is presented, from simple classification to sophisticated language models. Marketers have to choose from them according to their application matters. This chapter concludes that DL is indispensable to fulfilling today’s customer needs for performance and interactivity. The review shall help formulate DL projects and it can act as a handy reference for exotic marketing innovations upon new technology trends, like the Metaverse. Implications and challenges will be discussed in the ending section. Hopefully, marketers will find DL easier, for implementation or outsourcing.

主出版物標題The Palgrave Handbook of Interactive Marketing
發行者Springer International Publishing
出版狀態Published - 1 1月 2023


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