PREDICTIVE ANALYTICS AND MARKETING MANAGEMENT: IMPLEMENTING BUSINESS STRATEGIES THROUGH INTELLIGENT MODELS

Abstract

INTRODUCTION. In today's digital economy and highly competitive market conditions, companies are faced with the need to quickly adapt to changes in consumer behavior, market trends, and technological trends. In these conditions, marketing management ceases to be an exclusively intuitive art and is transformed into a systematic activity based on data analysis, customer behavior prediction, and customer interaction management based on objective patterns.

One of the key tools that allows for such a transformation is predictive analytics, which provides companies with the ability to proactively make decisions aimed at meeting customer needs, optimizing resources, and creating sustainable competitive advantages.

The relevance of combining predictive analytics and marketing management is due to the need to increase the accuracy of demand forecasts, adapt marketing strategies to market changes and personalize communications with consumers, which in turn helps to strengthen the positions of enterprises in the market and form long-term relationships with customers. The use of intelligent models, such as decision trees, allows you to structure data, highlight key factors that influence consumer behavior, and create understandable and practically applicable predictive models that are the basis for effective management of marketing processes.

THE HYPOTHESIS OF THE STUDY is to apply predictive analytics tools in marketing management, in the practical sphere of business, despite the existing challenges associated with the lack of readiness of enterprises for digital transformation, limited data, lack of competencies in the field of analytics, as well as the need to ensure the ethical and lawful use of personal data. Substantiation of theoretical and applied aspects of integrating predictive analytics into business marketing management with an emphasis on the use of intelligent models, in particular decision trees, to improve the effectiveness of management decisions, personalize interaction with customers and form sustainable competitive advantages in the activities of enterprises. Achieving this goal requires studying the essence and role of marketing management in modern business, analyzing tools and methods of predictive analytics, as well as assessing the opportunities and challenges of integrating intelligent models into the marketing activities of enterprises.

PURPOSE OF THE STUDY of predictive analytics tools in marketing management for business efficiency and competitiveness.

METHODS GENERAL scientific and special analytical methods: analysis, synthesis, generalization Decision tree method, which is based on determining the criteria for splitting data to maximize information gain or minimize entropy, which allows identifying significant factors that influence consumer behavior or the results of marketing campaigns. In business analytics, a decision tree is used to predict the probability of purchase, customer churn, response to marketing offers, and optimize communication strategies. Its advantage is not only the ability to work with large amounts of data, but also the ease of explaining management results to make informed management decisions (Basu et al., 2023).

The integration of predictive analytics into marketing management creates the prerequisites for the formation of intelligent marketing strategies based on data and allowing to achieve high accuracy in forecasts, increase the level of personalization of communications, adapt offers to the needs of individual consumer segments, and promptly respond to changes in the market environment. This transforms marketing activities from a cost function into a strategic asset that ensures sustainable business development and forms long-term consumer loyalty.

Among the tools of predictive analytics, a special place is occupied by the decision tree method, which is widely used in business analytics due to its interpretability and effectiveness in solving classification and forecasting problems. A decision tree is an algorithmic model that allows you to divide data into subsets based on the values of certain attributes, creating a visual structure where nodes reflect conditions, and branches – the results of checking these conditions (Lee et al., 2022).

CONCLUSIONS. Analysis of the theoretical foundations of the integration of predictive analytics into marketing management allows us to draw conclusions that modern enterprises are moving from intuitive decision-making to the systematic use of data as a strategic resource for forming effective marketing decisions. Predictive analytics, based on the analysis of large volumes of structured and unstructured data, allows you to build predictive models that increase the accuracy of planning and adaptability of marketing strategies to changes in consumer behavior and market dynamics.

The decision tree method, as one of the leading tools of predictive business analytics, provides visual interpretation of data and allows you to identify key factors that affect the effectiveness of marketing activities, increasing the accuracy of forecasts and facilitating the process of making management decisions. The use of predictive analytics in marketing management contributes not only to increasing the effectiveness of advertising campaigns, but also to optimizing the use of enterprise resources, creating conditions for creating personalized communications with consumers and developing long-term relationships with them.

The integration of predictive analytics into marketing management is a strategic necessity for modern business, which seeks to achieve sustainable competitive advantages and ensure adaptability in an environment of high market turbulence. Based on these theoretical provisions, there is a need to study intellectual models, in particular decision trees, as tools for implementing marketing strategies in the practice of enterprises.

KEYWORDS: business; business analytics; decision tree; business efficiency; predictive analytics; management; marketing management.