# Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning
## 2 Literature Review
Strategic purchasing behavior in response to dynamic pricing. 3. The effect of dynamic pricing on customers' perception of fairness. 4. Personalized dynamic pricing (PDP). 5. Channel-differentiated pricing. 6. The effectiveness of various types of communication used by companies. 7. The role and significance of consumers' personal characteristics in their perception of price changes. 8. Practical implications for managers and researchers to optimize pricing strategies for increased revenue. Given the stated objectives, this article aligns with two key research trends in dynamic pricing: personalized dynamic pricing (the target model considers customer-related criteria) and the development of systems to support managers in optimizing pricing strategies to increase revenue (using machine learning methods). Table 1 presents a summary of the strengths and limitations of selected dynamic pricing models. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Strengths** & **Limitations** \\ \hline \multirow{4}{*}{Dynamic Toll Pricing Model} & \(-\) & efficient traffic management through & \(-\) & focused solely on the transportation sector \\  & & real-time toll adjustments & \(-\) & limited applicability to other \\  & & optimization of road infrastructure via & & economic sectors \\  & & precise traffic flow control & & \\ \hline \multirow{4}{*}{Dynamic Pricing with Demand Learning and Reference Effect} & \(-\) & incorporates historical sales data and reference effects for more accurate & \(-\) & high data requirements and the need for the \\  & & pricing decisions & & continuous monitoring of customer preferences \\  & & ability to adapt prices to changing & \(-\) & potential ethical concerns regarding the use \\  & & consumer preferences & & of consumer data \\ \hline \multirow{4}{*}{Deep Reinforcement Learning-Based Dynamic Pricing Strategy} & \(-\) & automation of real-time pricing adjustments & \(-\) & high computational demands, especially \\  & & effective handling of demand uncertainty & & with large datasets \\ \cline{1-1}  & & effective handling of demand uncertainty & \(-\) & potential lack of interpretability in model \\ \cline{1-1}  & & & decisions (black-box nature) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of dynamic pricing models: strengths and limitations. This literature review discusses key dynamic pricing models, highlighting their applications across various sectors such as transportation, e-commerce, and services. It identifies strengths, such as precise real-time price management, and limitations, including dependence on large datasets, lack of interpretability in model decisions, and sector-specific constraints. Current dynamic pricing models are often focused on specific industries or lack flexibility in adapting to diverse market conditions. Additionally, many of these models rely on algorithms that are difficult to interpret, which may limit their usefulness in managerial decision-making. There is also a lack of comprehensive models that integrate customer, business, and market criteria while remaining scalable and adaptable across industries; this represents a research gap that the present article seeks to address. The proposed machine learning-based dynamic pricing method addresses the identified research gap by offering  
* a comprehensive approach that incorporates three perspectives: customer, business, and market;
* flexibility to adapt to various economic sectors;
* transparency through the use of interpretable algorithms (e.g., decision trees), enhancing managerial trust in decision-making;
* scalability, enabling its application to both small and large datasets with appropriate technical infrastructure. This method not only fills the gap in the literature but also introduces innovative aspects that enhance its practical value, addressing the evolving demands of managers in a dynamically changing e-commerce environment.