The article considers that the digital transformation of Kazakhstan's agro-industrial complex has created an urgent need for scientifically grounded methods that can optimize marketing strategies under conditions of resource limitations, production seasonality, and heterogeneous consumer behavior. Existing approaches often rely on simplified analytical models that fail to capture the nonlinear and multi-criteria relationships inherent in agricultural marketing. This study addresses that gap by proposing a hybrid decision-support framework that integrates a modified NSGA-III evolutionary algorithm with machine learning techniques to optimize the allocation of marketing budgets across digital and traditional channels. The approach incorporates three key performance criteria—channel efficiency, target audience reach, and marketing cost—while embedding sector-specific constraints related to the AIC of Kazakhstan. A comparative analysis of current digital marketing optimization practices reveals that most methods lack adaptability to sector-specific production cycles, do not sufficiently analyze trade-offs, or fail to integrate machine learning for post-optimization interpretation. The results of the hybrid model show the formation of three distinct clusters of Pareto-optimal strategies, demonstrating the need for differentiated and adaptive budget allocation patterns. The outcomes confirm that the proposed hybrid methodology provides a rigorous scientific basis for designing cost-effective and high-impact marketing strategies within the digital transformation agenda, AIC.

This study considers the digital transformation of Kazakhstan’s agro-industrial complex, which has created an urgent need for scientifically grounded methods that can optimize marketing strategies under conditions of resource limitations, production seasonality, and heterogeneous consumer behavior. This study proposes a hybrid decision-support framework integrating a modified NSGA-III algorithm with machine learning techniques for optimizing digital marketing strategies in the agro-industrial complex of Kazakhstan. The model considers three objectives: maximizing channel efficiency and audience reach while minimizing marketing costs. Experimental results based on a dataset of N = 1200 observations demonstrate that the proposed approach improves the composite performance indicator by 12.4% compared to baseline single-objective optimization methods. Pareto front analysis reveals three distinct clusters of strategies, corresponding to (1) high-impact integrated digital TV strategies, (2) cost-efficient traditional channel strategies, and (3) high-risk high-return allocations. The clustering validity is confirmed by a silhouette score of 0.624, indicating strong separation between strategy groups. The results highlight the practical significance of adaptive budget allocation and demonstrate the effectiveness of combining evolutionary optimization with machine learning for decision support in complex marketing environments.

The study is aimed at using the NSGA-III algorithm for multi-criteria optimization based on machine learning to solve marketing and advertising problems in the agro-industrial complex (AIC). In the context of increasing competitiveness and digitalization of the economy of the Republic of Kazakhstan, the relevance of the work is due to the need to optimize the distribution of marketing budgets of enterprises, taking into account such criteria as the effectiveness of advertising campaigns, coverage of the target audience and cost reduction. The methodology used in the work involves formalization of objective functions, constraints and the use of NSGA-III to find Pareto-optimal solutions. The results obtained during cybernetic modeling usually indicate high quality of solutions (hyper-volumetric), a compromise between the criteria and the dominant role of digital channels (up to 15% increase in the budget). The data obtained during the study confirm the potential use of NSGA-III for marketing planning tasks in the agro industrial complex and can serve as a basis for making management decisions at agro-industrial enterprises. The results of the study prove that the NSGA-III algorithm has significant potential in improving marketing planning processes in the agro-industrial complex and supporting management decisions of enterprises. This approach allows for efficient use of resources in the agribusiness sector and increased competitiveness in the market.