Authors : Dr. P. Manivannan; Dr. Uthira D.

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/2bcz6h45

Scribd : https://tinyurl.com/ytzwcc8

DOI : https://doi.org/10.38124/ijisrt/26mar1270

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The growing synergy between machine learning (ML) and the evolving domain of targeted advertising has created a transformative shift in the way businesses interact with their customers. In today’s competitive marketplace, organizations can no longer afford to adopt generalized marketing strategies; instead, they are increasingly turning toward machine learning algorithms to design highly personalized and data-driven marketing campaigns. This research paper explores a range of significant machine learning techniques, including collaborative filtering, content-based filtering, clustering, and predictive modeling, emphasizing how each contributes to delivering tailored customer experiences. The study further investigates practical applications of these approaches across diverse industries such as e-commerce, social media platforms, email marketing, and retail, showcasing real-world examples of successful implementations. Findings reveal that machine learning has not only enhanced personalization but has also contributed to measurable improvements in customer satisfaction, engagement, and conversion rates. However, alongside these benefits, the paper critically examines the challenges associated with personalized marketing, including concerns regarding data privacy, algorithmic bias, and ethical implications of intrusive targeting.

Keywords : Personalized Marketing, Machine Learning, Algorithms, Customer Engagement, E-Commerce Optimization, Predictive Modeling, Consumer Satisfaction.

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