Specifics of pricing on marketplaces

Marketplaces have transformed traditional mechanisms of market interaction by converting them to a digital format, allowing buyers to access more products and sellers to a wider audience. This transition has led to a change in consumer behavioral patterns - buyers have the opportunity to compare prices, read reviews and make decisions based on more information, and sellers, in an effort to successfully compete for consumer attention, faced the need to quickly adapt to new conditions. The aim of this article is to study the features of pricing within the framework of a platform business model with an emphasis on the analysis of factors specific to marketplaces. The process of setting prices for goods within the framework of electronic trading platforms has significant differences from traditional sales channels. This is due to both the features of the platforms themselves and the approaches used by the marketplaces. Based on the analysis, it was revealed that the most important systemic factors in the organization of sales on marketplaces include: platforms' high dynamism and interactivity, specific behavioral models due to network effects, as well as global reach and price transparency. Unlike traditional retail, where prices are changed manually and often only during promotional periods, pricing on marketplaces is more dynamic and flexible. Sellers can quickly adjust prices based on emerging market conditions: in response to price changes from competitors, fluctuations in demand or customer behavior. Globalization not only expands access to new markets, but also attracts participants from other countries and regions, often with lower costs or preferential tax conditions. Positive reviews and high ratings, being sources of network effects, act as social proof, encouraging customers to make purchases and strengthen their trust. On the contrary, products with negative reviews or low ratings rarely attract customers, even if they are competitive in other ways. Thus, sellers are not only forced to operate in a highly competitive market, but also face the need to maintain their prices in accordance with the principles of price transparency. One way to respond to these challenges may be to apply an algorithmic approach to setting prices for goods. This approach involves the use of automated systems that regulate prices based on various input data, such as demand, competitor prices, inventory quantity, customer behavior, and so on. The simulation results made it possible to clearly demonstrate that the use of a dynamic pricing strategy leads to a significant increase in total marginal profit. The study concludes that the integration of machine learning and data analysis algorithms into business pricing processes opens up new opportunities to improve the efficiency and accuracy of forecasting, as well as provides the opportunity to quickly adapt to dynamic market conditions within marketplaces, taking into account the competitive environment and consumer behavioral models. The author declares no conflicts of interests.

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