Commodity Price Optimization based on Price Elasticity of Demand

Abstract: Pricing strategies are essential for optimizing revenue, profitability, and customer happiness in the fiercely competitive retail sector of today. The goal of this project is to create a machine learning-based price optimization model that will allow merchants to identify the best prices for their products by examining a number of influencing factors, such as market circumstances, competition pricing, demand trends, and historical sales data. The suggested solution makes use of predictive analytics to comprehend how pricing and demand elasticity are related, determining the price points that optimize profits without offending clients. To forecast sales success at various price points and suggest the most lucrative pricing strategies, sophisticated regression algorithms like Gradient Boosting (XGBoost) are used. To improve the accuracy of the model, feature engineering will take into account consumer segmentation, inventory levels, promotions, and seasonality. The model will be trained and validated using publicly available or retailer-provided data, and its performance will be evaluated using metrics such as Mean Absolute Error (MAE) and Revenue Growth Rate (RGR). One of the top priorities will be creating a dynamic and adaptable system that can respond to changes in the market in real time. The expected outcome is a data-driven pricing strategy that helps businesses increase profit margins, reduce inventory costs, and improve customer retention. This initiative may have practical benefits for retail chains, e-commerce sites, and other consumer-focused firms seeking to enhance their pricing tactics.

Keywords: Demand prediction, price optimization, data driven machine learning, retailing.