THE ROLE OF PERSONALIZED PRICE OFFERS IN INCREASING THE COMPETITIVENESS OF TELECOMMUNICATIONS COMPANIES
Abstract
The article is dedicated to the pressing issues of implementing personalized pricing offers in the telecommunications industry as a means of enhancing companies' competitiveness. The paper analyzes the impact of personalized pricing offers on the financial performance of telecommunications companies. In particular, it examines how the personalization of tariff plans and services affects such indicators as profitability, return on investment, and investment attractiveness of companies. Personalized strategies allow companies to significantly improve the effectiveness of their marketing efforts through more precise targeting of relevant audiences, leading to an increase in return on investment (ROI) and a reduction in marketing expenses. Special attention is paid to the use of modern technologies such as artificial intelligence and big data analytics to create personalized pricing offers. These technologies allow companies to gain deeper insights into the behavioral patterns of their customers, predict their needs, and, based on this data, develop individualized offers that better meet users' expectations. The article also outlines the regulatory and ethical aspects of implementing personalized strategies. The article discusses the requirements of data protection legislation, such as the General Data Protection Regulation (GDPR) and Ukrainian legislation in the field of personal data protection. Additionally, the impact of personalized offers on the competitiveness of telecommunications companies is examined. In conclusion, it is noted that personalized pricing offers are an effective tool for improving the financial performance and competitiveness of telecommunications companies, but their implementation requires compliance with regulatory norms and ethical standards. The prospects for further research in this area include studying the impact of personalization on long-term customer loyalty, developing new models of personalization based on artificial intelligence, and improving algorithms used for consumer data analysis.
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