The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry

Abstract

This research aims to examine a neural network (artificial intelligence) as an alternative model to examine the neuromarketing phenomenon. Neuromarketing is comparatively new as a technique for designing marketing strategies, especially advertising campaigns. Marketers have used a variety of different neuromarketing tools, for instance functional magnetic resonance imaging (fMRI), eye tracking, electroencephalography (EEG), steady-state probe topography (SSPT), and other expensive gadgets. Similarly, researchers have been using these devices to carry out their studies. Therefore, neuromarketing has been an expensive project for both companies and researchers. We employed 585 human responses and used the neural network (artificial intelligence) technique to examine the predictive consumer buying behavior of an effective advertisement. For this purpose, we employed two neural network applications (artificial intelligence) to examine consumer buying behavior, first taken from a 1–5 Likert scale. A second application was run to examine the predicted consumer buying behavior in light of the neuromarketing phenomenon. The findings suggest that a neural network (artificial intelligence) is a unique, cost-effective, and powerful alternative to traditional neuromarketing tools. This study has significant theoretical and practical implications for future researchers and brand managers in the service and manufacturing sectors.