IMPACT OF AI-DRIVEN PERSONALIZATION ON CONSUMER PURCHASE DECISIONS IN ECOMMERCE

rtificial intelligence (AI) has become a key driver of
innovation in the e-commerce sector, particularly through personalized
recommendations, targeted advertisements, and customized shopping
experiences. This study examines the impact of AI-driven
personalization on consumer purchase decisions in e-commerce,
focusing on how machine learning algorithms influence consumer
behavior, trust, and purchase intentions. The research analyzes major
personalization techniques—such as recommendation systems,
predictive analytics, and behavior-based segmentation—and evaluates
their effectiveness in enhancing customer engagement and conversion
rates. Data collected through surveys and online consumer analytics
reveal that personalized product suggestions significantly improve user
satisfaction, reduce search time, and increase impulse buying
tendencies. However, the study also identifies consumer concerns
related to privacy, data usage transparency, and algorithmic bias.
Overall, the findings suggest that AI-driven personalization plays a
critical role in shaping modern online shopping behavior, offering
substantial benefits to e-commerce platforms while highlighting the
need for ethical data practices. This research contributes to the growing
body of literature on digital marketing and provides insights for ecommerce managers aiming to optimize personalization strategies for
improved customer experience and business performance.