# Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning
## 2 Literature Review  
Dynamic pricing can be understood as a pricing strategy that involves flexibly adjusting the prices of products or services in response to changing market conditions, demand, competition, and other external factors [5,6]. It is noted that dynamic pricing can have either a negative or positive impact from the consumer's perspective, depending on the quality of communication processes between the business and the customer [7]. However, when properly implemented, dynamic pricing leads to a win-win situation where both companies and consumers benefit compared to the alternative strategy of offering an optimal static price [8]. The beginning of intense scientific interest in this issue can be dated back to the 1970s [9], when numerous studies were conducted on the application of dynamic pricing concepts, especially in the airline and hotel industries [10,11]. Nowadays, the range of industries in which dynamic pricing strategies can be successfully applied is very broad [12,13]. Examples of the application of dynamic pricing models in various industries include  
* the airline industry--setting prices based on factors such as demand for a particular flight, departure date, seat availability, etc. [14];
* the hotel industry--setting prices to maximize occupancy and revenue, taking into account criteria such as seasonality, local events, or last-minute bookings [15];
* the e-commerce industry--setting prices in real-time based on customer behavior, competition, and other market data [16];
* the transportation industry--setting prices to optimize revenue based on the demand and supply for transportation services at a given moment [17]. [PERSON] and colleagues [18] presented an overview of dynamic pricing models used in e-business. The authors discussed the roles of inventory-based, data-driven, auction-based, and machine learning models used to calculate dynamic prices to maximize revenue. In particular, the authors emphasized the importance of data-driven models, which enable real-time price adjustments based on the analysis of consumer behavior [18]. [PERSON] and colleagues [19] conducted a systematic review of the literature on dynamic pricing in intelligent transportation systems (ITSs). The authors analyzed how dynamic pricing can help manage traffic, reduce peak loads, and optimize vehicle routes, highlighting the environmental benefits of such strategies. The literature review showed that dynamic pricing in ITSs can lead to more efficient resource management and reduce air pollution and noise [19]. [PERSON] and [PERSON] [20] examined dynamic quality and pricing decisions in customer-intensive service systems that are evaluated online. The authors constructed a dynamic programming model to determine optimal quality and pricing strategies. The research showed that online reviews impact pricing and quality decisions, forcing providers to offer higher quality at a higher price for a smaller number of customers, especially when customer intensity is high [20]. With the development of technology and increased access to data on consumer behavior, dynamic pricing has gained popularity and become a significant tool in revenue management [21]. However, dynamic pricing also presents several challenges, including managing consumer perception, adjusting prices in real time, and integrating advanced technologies such as artificial intelligence. [PERSON] and [PERSON] [22] emphasized the need for further research on variables affecting dynamic pricing and consumer perceptions of price fairness [22]. The ethics of dynamic pricing practices constitute an extensive research program in itself [23; 24]. Dynamic pricing, while beneficial for businesses in maximizing revenues, raises significant ethical concerns, particularly in terms of fairness and transparency. One major issue is price discrimination, where consumers are charged different prices based on their purchasing behavior or demographic data. This practice, although legally permissible, often elicits moral outrage from consumers who perceive it as unfair.