INNOVATIVE APPROACHES TO AGRICULTURAL MARKETING: NSGA-II AND K-MEANS FOR STRATEGIES IN THE AGRO-INDUSTRIAL COMPLEX

Authors

DOI:

https://doi.org/10.37943/23ORHF6321

Keywords:

agro-industrial complex (AIC), digital marketing, agromarketing, strategy, target functions, optimization, hybrid method, machine learning, agricultural products

Abstract

The relevance of this study is determined by the urgent need to improve marketing strategies in the agro-industrial complex (AIC) of Kazakhstan, where competitiveness and sustainability depend not only on production efficiency but also on effective promotion of agricultural products. Rapid digitalization and regional market heterogeneity create new challenges for enterprises that cannot be solved by traditional heuristic or single-objective approaches. The purpose of the research is the development of a hybrid method for multi-criteria optimization of marketing budget allocation in the AIC of Kazakhstan. The objective of the experiment is to test the hypothesis that such a method provides more balanced solutions in terms of efficiency, coverage, and cost compared to baseline approaches. The methodology is based on combining the evolutionary NSGA-II algorithm with K-means clustering. The first stage identifies Pareto-optimal distributions of marketing resources, while the clustering procedure segments the obtained strategies into groups with distinct efficiency-cost trade-offs. Input data were derived from synthetic simulations reflecting typical market conditions and real indicators of several agricultural enterprises. The results of computational experiments demonstrate that the proposed method outperforms single-objective optimization. In particular, it achieved higher average efficiency (1.56 vs. 1.10), wider coverage (1.39 vs. 0.95), and greater hypervolume (0.67 vs. 0.45). Clusters with combined use of digital and television channels provided the most effective balance of performance indicators, while radio and print media remained relevant for enterprises with moderate budgets. The novelty of the study lies in integrating evolutionary optimization with machine learning for marketing strategy design in the AIC. The obtained data can be applied by managers and policymakers for media planning, budget allocation, and the development of adaptive strategies that strengthen competitiveness and contribute to export growth.

Author Biographies

Zhansaya Abildaeva, K.I.Satbayev Kazakh National Research Technical University, Kazakhstan

PhD candidate, Department of Software Engineering

Raissa Uskenbayeva, K.I.Satbayev Kazakh National Research Technical University, Kazakhstan

Professor, Vice-Rector

Nurbek Konyrbaev, Korkyt Ata Kyzylorda University, Kazakhstan

PhD, Head of the Department of Computer Science of the Institute of Engineering and Technology

Gulzhanat Beketova, G. Daukeev University of Energy and Communications, Kazakhstan

PhD

Valerii Lakhno, National University of Life and Environmental Sciences, Ukraine

Professor, Department of Computer Systems, Networks and Cybersecurity

Alona Desiatko, University of Trade and Economics, Ukraine

PhD, Associate Professor at the Department of Software Engineering and Cyber Security

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Published

2025-09-30

How to Cite

Abildaeva, Z., Uskenbayeva, R., Konyrbaev, N., Beketova, G., Lakhno, V., & Desiatko, A. (2025). INNOVATIVE APPROACHES TO AGRICULTURAL MARKETING: NSGA-II AND K-MEANS FOR STRATEGIES IN THE AGRO-INDUSTRIAL COMPLEX. Scientific Journal of Astana IT University, 23, 35–45. https://doi.org/10.37943/23ORHF6321

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Section

Information Technologies