Data Driven Retail Price Optimization using XGBoost and Predictive Modeling

Retail price optimization is a critical challenge in contemporary commerce, requiring robust methodologies to balance profitability and market competitiveness. This study introduces a data-driven framework employing advanced machine learning models, particularly XGBoost, to predict sales quantities, assess price elasticity, and determine optimal pricing strategies for diverse product categories. By leveraging historical sales data, seasonality indices, and category-specific attributes, the proposed approach effectively captures complex non-linear relationships that traditional models fail to address. Comprehensive experiments reveal XGBoost’s superior performance in comparison to linear regression and other baseline methods, achieving lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) across multiple datasets. Furthermore, optimal price simulations indicate significant opportunities for revenue enhancement through targeted pricing adjustments. Visual and tabular analyses underscore the importance of seasonality and category-specific dynamics in shaping sales trends and pricing strategies. The findings validate the efficacy of the proposed framework in addressing the multifaceted demands of retail price optimization, paving the way for more informed and profitable decision-making in retail operations.