Real-Time Competitor Strategy Tracking and Analysis for E-commerce Using AI

In the dynamic e-commerce landscape, understanding and responding to competitor strategies in real time is critical for sustaining market advantage. This paper presents a unified AI-powered framework that enables real-time tracking and analysis of competitors’ pricing patterns, discount strategies, and customer sentiment. Leveraging a combination of web scraping, ARIMA time-series forecasting, and sentiment analysis using large language models (LLMs), the system processes both structured and unstructured data from e-commerce platforms such as Amazon and Flipkart. The insights are visualized using an interactive Streamlit dashboard, providing actionable intelligence for pricing and marketing decisions. Unlike traditional systems that analyze historical trends in isolation, the proposed solution integrates forecasting and emotional response analytics to provide a holistic view of market competition. This study serves as a practical bridge between AI-driven analytics and strategic business decision-making in the digital commerce sector, making it highly relevant for retailers, SMEs, and digital strategy researchers.