INTELLIGENT PRICING STRATEGIES IN E-COMMERCE: AN AI-BASED FRAMEWORK FOR MARKET TREND ADAPTATION

Abstract

In In today’s fast-paced e-commerce landscape, pricing strategies play a pivotal role in driving customer acquisition, maximizing revenue, and adapting to volatile market conditions. This paper introduces an AI-powered dynamic pricing framework that combines supervised machine learning with real-time market intelligence to recommend optimal product prices. The system integrates Random Forest and Artificial Neural Network (ANN) classifiers to predict consumer purchase behavior, achieving an average accuracy of 94.6% and an F1-score of 0.92. While Random Forest delivered a precision of 93.1%, ANN exhibited superior recall, underscoring their complementary classification capabilities. For precise price estimation, a multivariate linear regression model was implemented, attaining a high coefficient of determination (R² = 0.917) and a low Mean Absolute Error (MAE) of 2.87. The framework maintains prediction latency under 300 ms, ensuring suitability for real-time applications. Market data ingestion from platforms such as BigBasket was automated using Python-based web scraping, while an intuitive Gradio-powered GUI enables seamless user interaction. Designed to be scalable and modular, the system adapts effectively to evolving market dynamics and product-specific attributes. This research demonstrates how the synergy of machine learning, real-time analytics, and automation can drive intelligent pricing strategies that enhance competitiveness, customer targeting, and operational efficiency in modern e-commerce ecosystems.