Smart AI-Powered E-Commerce Platform with Personalized Recommendation and Sales Analytics

Abstract: Online retail has become a primary shopping channel, and customers increasingly expect experiences that are tailored to them [1]. Many small and mid-sized merchants, however, still operate storefronts that show every visitor the same catalog and give owners little insight into what is selling, what is running out, and why. This paper presents a Smart AI-Powered E-Commerce Platform that combines personalized product recommendation, a database-grounded multilingual shopping assistant, and an analytics-driven administration console in a single full-stack web application. The recommendation module ranks products with an explainable score built from category affinity, popularity, customer rating, and price suitability, and derives “frequently bought together” suggestions from real co-purchase history. The shopping assistant interprets free-text queries in English, Hindi, and Marathi, retrieves products only from the store database, and phrases its replies through a language-model layer with an automatic rule-based fallback, so it never invents items or prices. For the merchant, the platform provides sales-trend, category, and regional analytics together with an inventory module that estimates sales velocity and proposes restock quantities. The system is implemented with React, Node.js/Express, and MongoDB, and includes JWT-based sessions, e-mail one-time-password two-factor login, server- side price computation, Razorpay online payment, and cash on delivery. A working prototype comprising 89 REST endpoints and 12 data collections demonstrates the design across customer and administrator interfaces. The platform is expected to improve product discovery, reduce stock-outs, and support data-driven decisions for local retailers; future work covers collaborative filtering, payment-gateway webhooks, and large-scale usability and load evaluation.

Keywords: E-Commerce, Personalized Recommendation, Sales Analytics, AI Shopping Assistant, Inventory Intelligence, MERN Stack