Uso de Redes Neurais em Big Data Aplicado ao Marketing e ao Comportamento do Consumidor

Abstract The convergence of Big Data and Deep Neural Networks is revolutionizing marketing and the understanding of consumer behavior. The growing volume of digital data, combined with the ability of neural networks to identify complex patterns, enables companies to personalize offers, predict trends, and automate decisions at an unprecedented scale. This article delves into the technical foundations of these technologies, explores contemporary use cases, and discusses ethical and regulatory challenges. The analysis is grounded in recent evidence, such as De Bruyn et al. (2020), Thakur e Kushwaha (2024), et al. (2024), among others, demonstrating how architectures like MLP (Multi-Layer Perceptron), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and Transformers are being applied in predictive marketing, segmentation, automation, and consumer behavior analysis. The article also addresses limitations such as algorithmic biases, model opacity, and legal requirements, as well as future perspectives involving generative AI, XAI (Inteligência Artificial Explicável), and the growing need for multidisciplinary teams. It concludes by highlighting the importance of an ethical and strategic approach to maximize the value of these technologies. Okeleke Keywords: Big Data, neural networks, predictive marketing, consumer behavior.