Author(s) :

Aman Rawat.

Volume/Issue :

Abstract :

In the rapidly evolving landscape of e-commerce, AI-driven personalization has emerged as a pivotal strategy for enhancing customer engagement and driving sales. This paper explores the integration of big data analytics and artificial intelligence to create personalized shopping experiences tailored to individual consumer preferences and behaviors. By leveraging vast datasets from various sources, including browsing history, purchase patterns, and social media interactions, e-commerce platforms can utilize machine learning algorithms to predict customer needs and deliver targeted recommendations in real-time. The study highlights key methodologies for implementing AI-driven personalization, such as collaborative filtering, content-based filtering, and deep learning techniques. Furthermore, it examines the ethical considerations surrounding data privacy and consumer consent in the context of personalized marketing. The findings underscore the significant impact of AI-driven personalization on customer satisfaction and loyalty, ultimately contributing to increased conversion rates and revenue growth in the competitive e-commerce sector. This research aims to provide insights into best practices for e-commerce businesses seeking to harness the power of big data and AI to enhance their personalization strategies.

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