# Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning
## 6 Conclusions  
This study contributes to the advancement of dynamic pricing in the e-commerce sector by presenting a novel machine learning-based approach that enhances decision-making processes. The model integrates multiple perspectives, including customer preferences, enterprise objectives, and market conditions, to optimize pricing strategies. By leveraging machine learning techniques such as regression and classification algorithms, the model analyzes extensive datasets encompassing customer behavior, purchase history, competitor pricing, and internal factors like inventory levels. This approach not only supports person alized pricing decisions but also provides a practical decision support system for managers aiming to balance profitability and competitiveness. The empirical results confirm the effectiveness of the proposed model in improving pricing accuracy and revenue optimization. In testing scenarios with real-world data from an e-commerce platform, the model achieved an average prediction accuracy of over 85% in determining optimal pricing adjustments. These adjustments led to a measurable increase in revenue, with test cases showing revenue growth of 12% on average compared to static pricing strategies. Additionally, the model demonstrated significant potential in mitigating inventory issues by dynamically adjusting prices based on stock levels, reducing instances of overstocking and understocking by 18%. Personalized pricing recommendations aligned closely with customer preferences, resulting in improved conversion rates, with observed increases in purchase likelihood of 15% for targeted customer segments. The analysis reveals that while the model is effective, its reliance on extensive data presents challenges, particularly for smaller businesses with limited access to customer and market information. Furthermore, its sensitivity to rapid changes in consumer preferences and market conditions suggests the need for continuous updates and recalibration to maintain accuracy and relevance. For instance, scenarios with abrupt shifts in demand due to external factors (e.g., seasonal trends or economic disruptions) required retraining the model to sustain performance levels. From a theoretical perspective, this research contributes to the growing body of knowledge on the intersection of machine learning and dynamic pricing, offering insights into the application of advanced computational techniques to optimize pricing strategies. It highlights the potential of integrating personalized approaches with broader market analyses to address the complexities of modern e-commerce environments. Practically, this study underscores the importance of data-driven decision-making tools for managers, providing actionable frameworks to implement machine learning in pricing strategies effectively. Future research could explore the integration of advanced machine learning techniques such as deep learning or reinforcement learning to enhance the model's adaptability and precision further. Additionally, investigating the influence of external macroeconomic factors, such as fiscal policies or legal regulations, on pricing dynamics could provide valuable insights. Another promising avenue lies in developing models capable of operating under limited data conditions, making dynamic pricing accessible to smaller e-commerce businesses. Understanding the long-term impact of dynamic pricing on customer loyalty and brand perception also remains a critical area for further study, offering a holistic view of the implications of this pricing strategy. Conceptualization, M.N. and M.P.-N.; Methodology, M.N. and M.P.-N.; Software, M.N. ; Formal analysis, M.N. ; Writing--review & editing, M.P.-N.; Visualization, M.N. All authors have read and agreed to the published version of the manuscript. This research was funded by Poznan University of Technology grant number 0813/SBAD/2985. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.