MERCHANDISING MANAGEMENT SYSTEM (CORE 2): PRODUCT POSITIONING, MARKETING STRATEGY CUSTOMER RELATIONSHIP MANAGEMENT PERSONALIZED PRODUCTS SHOWCASE USING AI

Abstract

This capstone project aims to modernize a local client’s outdated and underperforming e-commerce webstore by addressing common challenges encountered by small and medium enterprises (SMEs), including limited technical resources and inadequate security features. The existing platform lacked essential functionalities such as secure login, CAPTCHA, OTP verification, product reviews, customer rewards, and personalized recommendations—resulting in diminished user trust and poor sales performance. To address these issues, the project introduced a secure, user-friendly, and intelligent web-based merchandising management system. Key features included OTP authentication via PHP Mailer, CAPTCHA integration, password hashing, a review and rating system, a voucher-based loyalty program, and an AI-powered product recommendation engine. The platform was developed using PHP, MySQL, HTML/CSS, JavaScript, Bootstrap, and TensorFlow.js, aligning technological solutions with business strategy to improve user experience and operational efficiency. To upgrade the platform’s security and performance, multiple modules were integrated. OTP verification using PHP Mailer, Google reCAPTCHA, and password hashing was implemented to ensure account protection and prevent unauthorized access. Session timeout protocols were also established to enhance data security. Payment options were expanded through PayPal for international transactions and PayMongo for local payments, supporting GCash, Maya, and bank transfers. These enhancements aimed to create a reliable, secure, and convenient purchasing experience for users. The system achieved its primary goal of transforming the outdated webstore into a secure and intelligent e-commerce platform. Security modules such as OTP, CAPTCHA, and hashed passwords successfully protected user data, while payment integrations streamlined both local and global transactions. Customer engagement increased with the introduction of product reviews, voucher-based rewards, and personalized product suggestions powered by TensorFlow.js. These upgrades contributed to improved platform usability and customer satisfaction. This project demonstrated the effectiveness of integrating robust security features, diverse payment gateways, customer engagement tools, and AI-driven personalization into an e-commerce platform. The use of TensorFlow.js for product recommendation and the incorporation of CRM-related features supported a more strategic approach to merchandising. As a result, the redesigned system not only addressed previous inefficiencies but also provided a scalable model for future digital retail solutions tailored to SMEs.