Intelligent marketing personalization systems: application of AI-technologies to predict consumer behavior in the services market

Abstract

The purpose of the article is to theoretically generalize approaches to building intelligent marketing personalization systems in the service sector based on artificial intelligence technologies, with an emphasis on predicting consumer behavior, increasing the relevance of interactions and the effectiveness of marketing decisions. The object of the study is the processes of personalization of marketing communications and service interaction with customers in the services market, which are formed and optimized using data on customer behavior and predictive models based on artificial intelligence technologies. The article examines the role of AI-based technologies in the formation of intelligent marketing personalization systems in the services market, where the key determinants of competitiveness are contact relevance, trust, and the quality of the customer experience. It is determined that personalization in the service sector goes beyond the customization of advertising messages and turns into a tool for managing the customer journey, which includes the choice of channel, moment of interaction, content of the offer, and parameters of service support. The information basis of such systems is characterized, which includes first-party data on purchases, appeals, reactions to communications, digital actions in channels, as well as contextual factors that enhance the ability of predictive models to reflect real customer needs. It is established that not only the complexity of algorithms, but also the quality of data, the coordination of customer identification between channels, the discipline of forming behavioral characteristics, and the management of the life cycle of models are of decisive importance for effectiveness. The feasibility of using forecasting for tasks of assessing the probability of purchase, risk of churn, customer value during the life cycle, choosing the best next action, recommendations of services and additions, as well as demand forecasting for resource planning is substantiated. It is proved that the practical effect of personalization is ensured only with the presence of correct impact assessment, the use of experiments and control groups, as well as the coordination of marketing decisions with the operational capabilities of the company in order to reduce the gap between the promise and the performance of the service.