The object of this study is the process of optimizing digital marketing for agro-industrial enterprises under conditions of multi-criteria and uncertainty. A formal statement of the problem of optimizing marketing strategies for agricultural production has been given by using the genetic algorithm NSGA-III. A hybrid method was devised to solve the task of multi-criteria optimization of marketing strategies for agro-industrial enterprises. The method is based on the NSGA-III algorithm in combination with the XGBoost software library and adapted to industry constraints for marketing strategies in the agricultural markets of Ukraine and Kazakhstan Republic. This allows for the generation and interpretation of Pareto-optimal strategies taking into account such criteria as efficiency, coverage, return on investment (ROI), costs, and engagement. A cluster analysis of solutions has been performed; three characteristic scenarios were identified – balanced, cautious, and aggressive. Empirical validation by regression analysis demonstrated the high accuracy of the model, as well as its ability to extrapolate new solutions. In particular, the mean square error on the test sample was 0.0316 with the achieved coefficient of determination of 0.9041. The results confirm the effectiveness of the devised method to support decision-making under conditions of multi-criteria and limited resources. The proposed method was used as the basis for the development of software implemented in practice at enterprises of the agro-industrial complex. However, the scope of method application also includes the activities by other business entities that devise marketing strategies to achieve the efficiency of their activities

The theoretical and methodological foundations of the digital transformation of marketing and advertising in the agro-industrial complex of the Republic of Kazakhstan are explored. The need to transition from traditional marketing tools to digital communication models in the conditions of climate instability, territorial scale of markets and institutional constraints in the agricultural sector is justified. A systematic analysis of the economic nature of marketing costs is carried out and a functional classification is proposed, which reflects the main purpose of managing communication costs. Special attention is paid to the analysis of mathematical, statistical and evolutionary methods for optimizing marketing and advertising budgets. The scientific novelty of the study lies in substantiating the feasibility of using multi-criteria evolutionary algorithms (NSGA-II, NSGA-III) to manage digital marketing strategies of agricultural enterprises. A research hypothesis and practical recommendations are formulated for using the results obtained in the strategic management system of agricultural enterprises.

In response to the complexities of modern business processes, this study introduces an integrative framework designed to enhance the automation and management of business operations. Rooted in a multidimensional and hierarchical modelling approach, our framework amalgamates various local models into a cohesive, comprehensive system that reflects both the micro and macro aspects of business processes. The methodology we employed involves the synthesis of local models characterized by their hierarchical relationships, multidimensionality, and operational semantics. These models were subsequently integrated to form a universal model capable of addressing diverse business needs and facilitating robust process automation. Empirical evaluations demonstrate that our framework significantly improves the efficiency, accuracy, and scalability of business process management systems compared to traditional models. By providing a structured yet flexible modelling approach, our framework supports the dynamic and often unpredictable nature of business environments, enabling organizations to achieve higher operational efficiency and adaptability. The findings suggest that such an integrative approach not only simplifies the complexity inherent in business process management but also enhances the capacity for in-depth analytics and decision-making, ultimately contributing to sustained organizational growth and agility.

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.

In the context of the rapid development of digital technologies and global transformations of educational systems, the concept of a digital educational ecosystem is becoming increasingly relevant. This paper explores the concept and structure of a digital educational ecosystem, with a focus on higher education in Kazakhstan. It analyzes theoretical approaches to the formation of a digital ecosystem and summarizes global experiences in implementing it in the university environment. The paper also focuses on the current state and prospects for the development of the digital educational environment in Kazakhstan, including the use of online platforms, digital services, and policy initiatives in the field of digitalization. The paper provides practical recommendations for the development of digital platforms, infrastructure, and competencies. Comparative data on the level of digital maturity of leading Kazakhstani universities is presented. The results of the analysis allow us to identify the key challenges and opportunities facing the higher education system in the context of digital transformation

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.