OPTIMIZING REAL-TIME DYNAMIC PRICING STRATEGIES IN RETAIL AND E-COMMERCE USING MACHINE LEARNING MODELS

Аннотация

INTRODUCTION balances computational efficiency and prediction Dynamic pricing has become a crucial strategy in accuracy remains a challenge. retail and e-commerce, where businesses aim to optimize prices in real-time to maximize profits, This study explores the application of supervised improve customer satisfaction, and maintain machine learning models—Linear Regression, competitiveness. The ability to adjust prices Random Forest, and Gradient Boosting dynamically depends on factors such as market Machines—to real-time dynamic pricing strategies demand, competitor pricing, product availability, in the retail and e-commerce sectors. The primary and customer preferences (McKinsey & Company, objective is to evaluate the models based on 2020). Traditional static pricing models often fail metrics such as R-squared (R²), Mean Absolute to capture these dynamic interactions, leading to Error (MAE), and Root Mean Square Error (RMSE) suboptimal business outcomes (Zhang et al., 2018). to determine their effectiveness in real-time dynamic pricing optimization. The study also In recent years, machine learning (ML) techniques simulates a controlled environment to test these have emerged as powerful tools for dynamic models’ real-world applicability, demonstrating pricing strategies, offering the ability to analyze their integration with e-commerce platforms. large datasets, detect patterns, and make accurate predictions. Machine learning models such as The Concept of Dynamic Pricing Linear Regression, Random Forest, and Gradient Dynamic pricing is the practice of adjusting prices Boosting Machines (GBM) have been increasingly in real-time based on market conditions, consumer applied in e-commerce and retail to forecast behavior, and competitive factors (Kannan & optimal pricing strategies (Choi et al., 2019). Kopalle, 2001). Dynamic pricing strategies have However, selecting the most suitable model that been extensively studied in the context of e- https://www.theamericanjournals.com/index.php/tajet 164 THE USA JOURNALS THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN – 2689-0984) VOLUME 06 ISSUE12 commerce and retail, with research highlighting its Challenges in Model Selection for Dynamic Pricing importance in maximizing revenue and optimizing Despite these advantages, selecting the right customer satisfaction (Gal-Or, 1985). According to machine learning model for dynamic pricing Chen et al. (2001), dynamic pricing algorithms remains challenging. Factors such as scalability, incorporate demand elasticity, competitor prices, computational efficiency, and interpretability play and inventory levels to adjust prices effectively. a significant role in decision-making (McKinsey & Recent studies have also emphasized the Company, 2020). Real-time deployment of these significance of real-time adjustments based on models requires integration with e-commerce machine learning predictions to address demand platforms and cloud infrastructure, which adds fluctuations and competitor actions (Kumar et al., complexity to system architecture and data 2019). processing (Yuan et al., 2019). Machine Learning in Dynamic Pricing Furthermore, the performance of machine Strategies learning models can be evaluated through various Machine learning has transformed dynamic metrics. The R-squared (R²) value measures the pricing strategies by enabling businesses to proportion of variance explained by the model, process and analyze large volumes of data while Mean Absolute Error (MAE) and Root Mean efficiently. Supervised learning models, Square Error (RMSE) are standard error metrics particularly regression-based and ensemble (Hyndman & Athanasopoulos, 2018). Accurate techniques, are often employed to forecast optimal assessment of these metrics is essential to prices (Waller & Leigh, 2009). Linear Regression determine the practical viability of machine remains one of the foundational techniques due to learning models in real-world dynamic pricing its simplicity and interpretability, but it often environments. struggles to capture non-linear patterns in