A HYBRID DEMAND FORECASTING AND REINFORCEMENT LEARNING FRAMEWORK FOR DYNAMIC PRICING IN E-COMMERCE

Abstract

Dynamic pricing is becoming a hot topic these days, most importantly in e-commerce to increase the number of sales and customer satisfaction. This technique is mostly used in developed countries but in developing countries like Pakistan the applications of dynamic pricing are restricted. The main purpose of this research is to scrutinize the implementation of a hybrid dynamic pricing model in developing countries like Pakistan e-commerce sectors by the integration of reinforced learning (RL), demand forecasting, and price prediction methodologies. This study employs Reinforcement Q-Learning and Deep Q-Learning to simulate real-time pricing scenarios. Furthermore, in this study demand forecasting is performed using ARIMA and Prophet models, while Random Forest and XGBoost algorithms are implemented for accurate price prediction based on product-level features. The goal is to create a dynamic pricing structure that is fair, adaptable, and appropriate for the particulars of Pakistan's online marketplace. Dynamic pricing can bring big changes in online marketplace in developing countries. However, its success depends equally on the social and economic conditions as it relies on advances in technology. The ultimate goal of this study is to create a sustainable pricing model that takes into account the preferences of Pakistani consumers and, using data-driven insights and adaptive learning techniques, adapts product prices to shifting market conditions.