ALGORITHMIC APPROACHES TO MARKETING RISK MANAGEMENT

Abstract

The article examines the theoretical and applied foundations of using algorithmic approaches to marketing decision-making under conditions of digital transformation, the growing volume of consumer data, and increasing uncertainty in the marketing environment. The relevance of the study is substantiated by the transition of enterprises from intuitive and predominantly retrospective approaches to analytically supported marketing management, within which artificial intelligence, machine learning, and predictive analytics enable the identification of hidden patterns in consumer behaviour, personalization of offers, optimization of pricing decisions, management of communication channels, and improvement of customer interaction effectiveness. The study summarizes approaches to understanding the role of artificial intelligence and machine learning in marketing. It is determined that the algorithmic approach in marketing should be considered not only as a tool for improving forecasting accuracy, but also as a comprehensive management technology that combines data collection and preparation, model development, recommendation generation, implementation of marketing actions, performance evaluation, and risk monitoring. The specific features of algorithmic technologies are revealed in demand forecasting, customer segmentation, customer lifetime value assessment, churn prediction, communication personalization, dynamic pricing, and the identification of marketing activity risks. The risks accompanying the use of artificial intelligence in marketing activities are systematized, including data quality risks, algorithmic bias, model opacity, privacy violations, excessive automation, reputational risks, regulatory risks, and cyber risks. Based on the provisions of risk management, international standards ISO 31000, ISO/IEC 31010, ISO/IEC 42001, the NIST AI RMF approach, and risk-oriented regulation, an approach to marketing risk management is proposed. This approach integrates the stages of context establishment, risk identification, analysis, evaluation, treatment, and monitoring. It is proved that the effectiveness of algorithmic approaches in marketing depends not only on the quality of machine learning models, but also on the level of data governance, transparency of decision-making, the presence of control mechanisms, ethical constraints, a system of performance indicators, and regular audit procedures. The practical significance of the study lies in the possibility of applying the proposed approach in the development of digital marketing strategies, marketing analytics systems, customer experience management programs, and marketing risk control procedures.