ARTIFICIAL INTELLIGENCE IN OPTIMIZING MARKETING PRICING

Abstract

The article studies implementing artificial intelligence tools to optimize marketing pricing. The research aims to analyze, systematize, and improve the existing theoretical and methodological foundations for using artificial intelligence tools in this process.

The article examines marketing pricing as a key element in forming an effective pricing policy for enterprises. The research presents a systematization of factors influencing the pricing process, categorized into internal and external ones, and their interaction in the modern market environment. Internal factors of marketing pricing include production costs, long-term enterprise goals, profitability, brand quality, and competitive advantages. External factors encompass competition levels, consumer needs, economic conditions, regulatory norms, technology, and environmental requirements. The study substantiates the role of marketing pricing in achieving an enterprise's market and strategic goals, particularly ensuring competitiveness, maximizing profits, and enhancing customer loyalty.

An analysis of the literature allowed for the systematization of artificial intelligence tools applicable to analyzing the influence of these factors, including machine learning, neural networks, big data analysis, optimization algorithms, and forecasting techniques. The research demonstrates that integrating artificial intelligence enables enterprises to automate cost management processes, adapt prices to market environment changes, and predict economic risks. It confirms that artificial intelligence tools can help businesses respond more effectively to market challenges and implement flexible pricing strategies. The study also identifies promising directions for improving marketing pricing, such as integrating environmental standards, utilizing innovative technologies for market monitoring, and optimizing costs. These measures enhance enterprises' investment attractiveness, which is crucial in today's competitive environment. The research findings may be helpful for practitioners in the field of pricing management and for researchers studying the issues of marketing pricing in the context of business digital transformation.