Selection of Mathematical Models Using Fractional Differential Equations in Economics for Projections

Abstract

Personalized marketing has emerged as a pivotal strategy in modern business, leveraging advanced data analytics and artificial intelligence to tailor marketing efforts to individual consumer preferences. This paper analyzes the impact of personalized marketing techniques on business performance, with a detailed case study of Amazon, a leader in e-commerce and personalization. By examining Amazon’s use of recommendation systems, targeted advertising, and dynamic pricing, the study highlights how these techniques enhance customer engagement, increase conversion rates, and drive revenue growth. The paper also addresses the challenges associated with personalized marketing, including privacy concerns and algorithmic bias, and explores the implications for business operations and customer relationships. Through this case study, the paper provides insights into the effectiveness and limitations of personalized marketing strategies, offering valuable lessons for businesses aiming to implement similar approaches.