DEVELOPMENT OF A COMPETITIVE ANALYSIS SYSTEM FOR ELECTRONIC COMMERCE ENTERPRISE (ECE)

This research addresses critical challenges in Nigerian e-commerce through the development of a web-based Competitive Analysis System (CAS). The system tackles persistent issues of product authenticity verification, inaccurate descriptions, and limited competitive intelligence that undermine consumer trust and vendor competitiveness. Built on a Django REST Framework and React.js architecture with MySQL database management, the system integrates computer vision technology using Convolutional Neural Networks and FAISS similarity search, achieving 97% accuracy in product image verification. Natural Language Processing pipelines validate description accuracy against visual content, while automated web scraping modules provide real-time competitor intelligence across major e-commerce platforms. The research successfully delivered a fully functional platform that enables multi-vendor product comparison, automated verification, and competitive analysis. The system's theoretical framework combines Technology Acceptance Model with trust theory, specifically addressing the unique socio-technical challenges of emerging e-commerce markets. Practical implementation provides SMEs with accessible competitive intelligence tools while establishing new standards for transparency in digital commerce. The CAS platform demonstrates that integrating artificial intelligence and real-time analytics can effectively address core e-commerce challenges in developing economies, offering a scalable solution for enhancing market transparency and consumer confidence through verified product listings and data-driven vendor insights.