Evaluating the Performance of Deep Learning Methods and Its Impact on Digital Marketing

Abstract

This article maps and explains how two distinct areas of the marketing sciences are now and potentially related to computer science. It analyses the interplay of DL in the academy, and at the same time proposes a machine research framework that might be appreciated in many respects of the scientific field of digital marketing. In the fields of deep learning there are many research papers (DL). This quantity remains modest, however, with regard to digital marketing elements. Marketing intelligence may benefit in many ways from scientific studies on deep learning (DL). Today only a tiny proportion is linked to particular digital marketing techniques by scientific study on Digital Marketing and Deep Learning (DL). Generic aspects such as e-business, consumer behavior, e-commerce strategies, social media advertising, search engines and consumer prevision modelling are mostly discussed, and are not more closely dependent on specific marketing problems that are more aware of in business, such as social media consumption, target commercials, social media marketing, and transformation optimization. In spite of the extensive field of study and a lot of publications, it seems that scholarly papers on digital marketing and deep learning particularly lack (DL). Nevertheless, some highly comprehensive research efforts are quite promising in certain areas of digital marketing and deep learning. This article is by mapping applications in the field of digital marketing in the present state of deep learning (DL). It emphasizes the foundational element writings, identifies regions of absence or lack of their existence, and offers a learning engine that may fit into numeric marketing opportunities.