Paper Contents
Abstract
Data abstraction plays a crucial role in simplifying complex information, allowing for better understanding and analysis. It enhances the ability to extract meaningful insights from large datasets, ultimately improving decision-making processes in various fields. Overview of E-commerce and Dynamic Pricing. Dynamic pricing has become a fundamental aspect of e-commerce, enabling businesses to adjust prices in real-time based on market demand and consumer behavior. This adaptability not only maximizes revenue but also enhances customer satisfaction by providing competitive pricing options tailored to individual needs. Dynamic pricing strategies leverage advanced algorithms and machine learning techniques to optimize price adjustments, ensuring that businesses remain competitive in a rapidly evolving digital marketplace and responsive to changing market conditions. The integration of machine learning in dynamic pricing allows for more accurate predictions and personalized pricing strategies, significantly impacting revenue optimization and customer experience. Importance of Predictive Analysis in Pricing Strategies. Predictive analytics empowers businesses to anticipate consumer behavior and market trends, leading to more effective pricing strategies that enhance profitability and customer engagement. By utilizing historical data and machine learning algorithms, businesses can refine their pricing models, ensuring they effectively respond to market fluctuations and consumer preferences. This proactive approach not only drives sales but also fosters long-term customer loyalty by aligning pricing strategies with individual consumer needs and market dynamics. The integration of dynamic pricing with predictive analytics represents a transformative shift in e-commerce, enabling businesses to optimize pricing strategies while enhancing customer satisfaction through personalized experiences. This synergy between dynamic pricing and predictive analytics highlights the necessity for e-commerce businesses to adopt advanced data-driven strategies for sustained growth and competitiveness. To remain competitive, e-commerce businesses must embrace these innovative approaches that capitalize on data-driven insights and machine learning technologies. The review will focus on various predictive analysis techniques, including traditional and AI-based models, to determine their effectiveness in optimizing dynamic pricing strategies for e-commerce. The findings will highlight the advantages of AI-based models, such as Gradient Boosting Machines, over traditional pricing methods in enhancing revenue and customer satisfaction.
Copyright
Copyright © 2025 R.Sakthivel. This is an open access article distributed under the Creative Commons Attribution License.
