Advancements in Retail Price Optimization: Leveraging Machine Learning Models for Profitability and Competitiveness

Abstract

Retail price optimization is essential for maximizing profitability and maintaining competitiveness in today's dynamic retail landscape. This study addresses retail price optimization as a regression problem, utilizing machine learning models to predict optimal price points for products. Leveraging factors such as product attributes, competitor pricing dynamics, and customer behaviors, regression analysis provides a structured approach to understanding the intricate relationships between variables. Among various regression techniques, the Random Forest Regressor emerges as a potent strategy, offering robustness and versatility in tackling complex pricing tasks. Results indicate that Random Forest outperforms Decision Tree and Logistic Regression models regarding accuracy, precision, recall, and overall predictive performance. With Random Forest achieving an accuracy of 94%, it demonstrates superior capability in capturing complex data patterns and making accurate predictions of retail prices. By leveraging advanced analytics and machine learning techniques, retailers can optimize pricing strategies, maximize profits, and maintain competitiveness in the market. This study underscores the importance of continuously analyzing and refining pricing strategies to gain a competitive edge in the retail landscape.