Mathematical Models for Predicting Consumer Behavior in Dynamic Market Environments

Abstract

In today's rapidly evolving market environment, predicting consumer behavior has become crucial for businesses seeking to maintain competitiveness. The complexity of consumer decision-making is influenced by multiple factors, including economic conditions, technological advancements, and evolving social norms. Mathematical models have emerged as a vital tool for analyzing and predicting consumer behavior in such dynamic settings. This research paper explores various mathematical models used for predicting consumer behavior, including econometric models, agent-based models, and machine learning techniques. Each model's theoretical foundations, advantages, and limitations are discussed, with a focus on their applicability to real-world dynamic markets. The paper also highlights recent advancements in integrating data analytics with mathematical modeling to enhance predictive accuracy. By comparing different modeling approaches, the paper aims to provide insights into selecting the most suitable models for specific market conditions. Ultimately, the study underscores the significance of mathematical models in shaping effective marketing strategies and enhancing business decision-making.