Dynamic pricing as a marketing strategy for customer retention in platform markets

Abstract

The relevance of the study is determined by the active development of the platform economy, the expansion of digital services, and the growing role of algorithmic pricing in the formation of competitive advantages and long-term customer retention. Under conditions of intense digital competition, rapid changes in consumer behavioral patterns, and increasing personalization of marketing communications, dynamic pricing is transforming into an important instrument for managing customer loyalty and adapting platform business models to changes in market conditions. The purpose of the study is to identify the specific features of using dynamic pricing as a marketing tool for customer retention in platform markets and to substantiate directions for improving the efficiency of adaptive pricing mechanisms within the digital environment. Methods. The study employed methods of analysis and synthesis to generalize theoretical approaches to dynamic pricing and systematize its functional characteristics, comparative analysis to evaluate the influence of algorithmic pricing models on consumer behavior and competitive interaction between platforms, and logical generalization to substantiate approaches to integrating digital analytics and AI tools into adaptive pricing processes. Results. The economic essence of dynamic pricing and its role in the system of marketing management of platform markets have been investigated. It has been revealed that algorithmic pricing management models ensure increased adaptability of pricing decisions, a higher level of personalization of consumer interaction, and improved demand forecasting efficiency. It has been proven that the integration of Big Data, behavioral analytics, and artificial intelligence tools contributes to improving the accuracy of pricing decisions and strengthening customer loyalty. At the same time, it has been established that the effectiveness of dynamic pricing is constrained by the lack of algorithm transparency, high dependence on data quality, risks of price discrimination, and the negative impact of excessive price volatility on user trust. Conclusions. The expediency of using controlled dynamic pricing models, combining adaptive pricing strategies with loyalty programs, and implementing explainable AI models in algorithmic pricing systems has been substantiated. It has been concluded that dynamic pricing should be considered not only as a revenue management instrument, but also as a component of a long-term marketing strategy for customer retention in the digital environment. Prospects for further research are associated with the development of hybrid adaptive pricing models based on behavioral economics, predictive digital analytics, and artificial intelligence tools.