PROSPECTS OF USING ARTIFICIAL INTELLIGENCE FOR PRICING IN INTERNET TRADE

Abstract

The conditions of online trade, where buyers can compare prices for a particular product across numerous online stores, necessitate the use of dynamic pricing technologies that consider a vast array of data, such as prices of competitors, stock levels, seasonal demand fluctuations, and more. Optimizing prices for the extensive product ranges of large online stores, considering a multitude of data points for each item, is excessively labor-intensive for manual pricing. This necessitates the deployment of artificial intelligence tools that can discern patterns from large datasets and adapt to new information, swiftly testing various hypotheses and making optimal decisions.

A study of one of the leading online retailers, the Amazon marketplace, demonstrates that algorithmic pricing tools are employed not only to the marketplace's own products, but also to the products of third-party sellers' products listed for sale on amazon.com. When multiple sellers offer the same product, Amazon facilitates preferential traffic for potential buyers towards sellers with the most competitive sales conditions. Furthermore, all third-party vendors have access to certain automation and analytics tools. Conversely, the leader of Ukrainian online retail, the Rozetka marketplace does not yet provide similar opportunities for third-party sellers using the rozetka.com.ua site for sales.

Based on the competitive advantages provided by the implementation of algorithmic pricing to the participants of the Internet market, we can anticipate an increasingly widespread adoption of artificial intelligence for price formation in online trade. However, this process may also lead to the displacement of smaller market participants due to the technical, financial and organizational challenges of implementing such dynamic pricing mechanisms.