Specific Features and Trends in the Field of Application of Artificial Neural Networks in Marketing

Abstract

Purpose: The purpose of the article is to demonstrate the applied capabilities of neural networks in relation to the up-to-date task of marketing - predicting the effectiveness of buying a banner space for retargeting. Design/methodology/approach: The authors evaluate the development of neural networks, which are more and more involved in people's lives. They are becoming an essential tool in modern marketing, as well. The article considers the history of the development of neural networks and identifies the prerequisites for their wide use. The effectiveness of the use of neural networks in solving the tasks of a marketer in the following subject areas was evaluated: marketing analysis, the formation of ideas and strategies, content creation, automation of technical work and work with clients. The influence of neural networks and digitalization of marketing leads to the fact that for the marketer it is no longer important whether it is "a consumer or a person" as such. Findings: Modern marketing has been shown to increase its effectiveness with the help of neural networks. However, neural network marketing, considering all its advantages, has several disadvantages. The authors identified the following: the impossibility of creating new approaches by neural networks, the content of unpredictable cognitive distortions by neural networks. As a result of the use of neural networks, marketers will become less in demand, and their work will become significantly accelerated and simplified. Therefore, for marketers to remain competitive, it is necessary to study related areas. Originality / value: An example of the effective use of neural networks in marketing is the retargeting of site visitors through advertising sites. The neural network selects and estimates the price for displaying an advertising banner based on basic functions and preliminary deep training. The neural network presented in the article offers optimal solutions, evaluating a significant number of factors.