Retail Purchase Intelligence System

Abstract

Context and Motivation: The growing popularity of e-commerce platforms has transformed consumer behavior, with modern buyers increasingly relying on digital channels to compare prices before making purchases. However, manual price checking across multiple websites remains inefficient, time-consuming, and error-prone. This research presents a Retail Purchase Intelligence System, an automated price comparison framework that aggregates product pricing information from various e-commerce sources and displays it in a unified interface. The system utilizes web scraping techniques through Python libraries such as Beautiful Soup and Requests, combined with a centralized MySQL database for structured data storage. A lightweight front-end interface built with HTML, CSS, and JavaScript enables intuitive search and quick visualization of comparative results. Experimental validation demonstrates that the system can accurately extract and normalize pricing data across multiple online retailers, significantly reducing consumer effort and time in finding optimal deals. The proposed model also outlines scalability for dynamic websites through Selenium-based scraping and highlights future extensions such as price-trend analysis, alert notifications, and browser integration. Overall, the system provides an effective, low-cost solution for real-time price intelligence and contributes to advancing consumer-centric automation in digital retail.