Leveraging Recurrent Neural Networks for Analyzing Consumer Behavior in the Digital Economy

Abstract

The digital economy has revolutionized the landscape of consumer behavior, creating a complex ecosystem driven by data insights. This study delves into the transformative impact of the digital economy on consumer behavior dynamics, examining how digital transformation has reshaped the way consumers engage with products, services, and brands. As consumer behaviors continue to evolve in digital spaces, traditional analytical methods struggle to keep pace with the complexity and volume of data generated, necessitating advanced modeling techniques that effectively decipher these patterns and provide actionable insights. This research aims to explore the potential of Recurrent Neural Networks (RNN) in analyzing consumer behavior in the digital economy. With their inherent ability to process sequential data and capture temporal dependencies, RNNs are particularly well-suited for modeling the chronological nature of consumer interactions. The research objectives include harnessing the predictive power of RNN to enhance understanding of consumer decision-making and develop more effective marketing strategies. Additionally, the study investigates methodologies for integrating RNN into personalized recommendation systems to improve recommendation accuracy and user engagement. This paper lays the groundwork for a detailed exploration of how RNN can be strategically employed to analyze and predict consumer behavior within the digital economy, expanding on the theoretical foundations, practical applications, and potential for businesses to gain a competitive edge in digital markets.