What question did this study set out to answer?

The study aims to optimize marketing budget allocation in Kazakhstan’s agro-industrial sector using a hybrid framework.

July 29, 2026SustainabilityOpen Access

A Hybrid NSGA-II and Machine Learning Framework for Multi-Objective Marketing Budget Optimization in Kazakhstan’s Agro-Industrial Complex

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Authors

ZKZhuldyz KalpeyevaSatbayev University ZAZhansaya AbildaevaSatbayev University RURaissa UskenbayevaSatbayev University

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Implication

Randomized trial evaluates marketing budget optimization strategies in Kazakhstan's agro-industrial sector, indicating effective resource allocation approaches.

Key Points

The study aims to optimize marketing budget allocation in Kazakhstan’s agro-industrial sector using a hybrid framework.
Utilized NSGA-II for generating Pareto-optimal budget allocation strategies.
Implemented K-means clustering for classifying solutions into interpretable strategy groups.
Evaluated framework using synthetic and enterprise-level marketing data.
Strategies combining digital media and television showed the highest effectiveness and coverage, requiring greater expenditure.
Radio and print media proved effective for cost-sensitive strategies, especially in resource-limited enterprises.
Identified three strategy groups: cost-efficient, balanced, and high-performance configurations.

Cite This Study

Kalpeyeva et al. (2026) studied this question.

synapsesocial.com/papers/6a69a2a3c8da07d9defa653f https://doi.org/10.3390/su18157632

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Implication

Randomized trial evaluates marketing budget optimization strategies in Kazakhstan's agro-industrial sector, indicating effective resource allocation approaches.

A Hybrid NSGA-II and Machine Learning Framework for Multi-Objective Marketing Budget Optimization in Kazakhstan’s Agro-Industrial Complex

Discussion

Member takes

Implication

Key Points

Cite This Study

A Hybrid NSGA-II and Machine Learning Framework for Multi-Objective Marketing Budget Optimization in Kazakhstan’s Agro-Industrial Complex

Discussion

Member takes

Implication

Key Points

Cite This Study

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