Smarter Stores, Better UX: An Engineering Framework for AI-Driven E-Commerce Optimization

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Naresh Suthar

Abstract

The rapid evolution of e-commerce demands smarter, faster, and more adaptive digital storefronts. Consumers expect seamless, intelligent, and hyper-personalized shopping experiences across platforms and devices. To meet this expectation, the integration of Artificial Intelligence (AI) within a structured engineering framework is key to optimizing both performance and user experience (UX). This paper presents a comprehensive exploration of how AI technologies—such as machine learning, computer vision, natural language processing, and predictive analytics—can be systematically applied within an engineering framework to build smarter, more responsive e-commerce systems. It also examines the intersection of performance engineering, user-centric design, and adaptive intelligence in creating digital storefronts that are not only technologically sophisticated but also intuitive and emotionally resonant. The framework outlined emphasizes modularity, scalability, and real-time responsiveness, enabling businesses to continuously learn, adapt, and optimize for both operational efficiency and customer satisfaction.

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How to Cite
Suthar, N. (2024). Smarter Stores, Better UX: An Engineering Framework for AI-Driven E-Commerce Optimization. Pioneer Research Journal of Computing Science, 1(4), 1–9. Retrieved from http://prjcs.com/index.php/prjcs/article/view/52

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