Authors : Maxwell Nortey; Joy Onma Enyejo; Victoria Bukky Ayoola

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/ms6346ey

Scribd : https://tinyurl.com/ydzm8wna

DOI : https://doi.org/10.38124/ijisrt/26mar131

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study develops a unified analytical framework for evaluating the impact of analytics-driven marketing strategies on stakeholder engagement within public agricultural markets. The approach integrates structured data acquisition, preprocessing pipelines, and predictive modeling to derive a composite understanding of how exposure intensity, price transparency, and real-time notifications influence engagement outcomes. Transactional, communication, and behavioral datasets were normalized, encoded, and transformed into a Stakeholder Engagement Index to capture multidimensional participation patterns. Ensemble learning models, including Gradient Boosting and Random Forest, consistently outperformed linear baselines, demonstrating superior predictive accuracy and robustness under controlled perturbation scenarios. Sensitivity analysis revealed gradual performance degradation with increasing data noise, confirming model reliability even in low-quality data environments typical of rural market settings. Comparative assessment with existing literature indicates strong alignment with broader digital-agriculture findings while extending methodological rigor through unified feature-engineering and robustness evaluation. The results highlight clear operational pathways for market authorities, including structured communication protocols, real-time advisory systems, and evidence-based segmentation strategies. The study concludes that analytics-driven marketing offers a scalable mechanism for strengthening transparency, enhancing stakeholder participation, and modernizing public agricultural systems through data-informed decision-making.

Keywords : Evaluating, Impact, Analytics-Driven, Marketing Strategies, Stakeholder Engagement, Public Agricultural Markets.

References :

1. Abiola, K. J., & Grant, R. S. (2026). Predictive modeling for stakeholder engagement in agricultural markets. Journal of Agri-Digital Analytics, 8(1), 25–41.
2. Adewale, L.D. (2026). Machine Learning Surroagte Models Replacing Physics Simulations International Journal of Computer Applications Technology and Research  Volume 12–Issue 12, 341 - 352, DOI:10.7753/IJCATR1212.1030
3. Adewale, L.D. (2025). Applying Supply Chain 4.0 to Vertical Supply Chain Integration: A key to revitalizing US automotive manufacturing sector. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.6.0225.0940
4. Adewale, L.D. (2025). Lifecycle Assessment and Circular Economy Strategies for Sustainable Automotive Materials: Optimizing Recycling, Waste Reduction, and Cost Efficiency. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.6.0225.0953
5. Adewale, L.D. (2025). Sustainable and high-performance materials in automotive manufacturing: enhancing durability, lightweighting, and lifecycle optimization through data-driven material science International Research Journal of Modernization in Engineering Technology and Science Volume:07/Issue:02 DOI : https://www.doi.org/10.56726/IRJMETS67497
6. Adewumi, T. K., & Hartman, S. R. (2026). Data-driven governance models for public agricultural markets: Enhancing transparency and participation. Journal of Agricultural Systems Innovation, 18(1), 44–59.
7. Adeyeri, J. M., & Solano, R. P. (2026). Behavioral analytics for digital participation in agricultural systems. Journal of Smart Market Technologies, 9(1), 31–48.
8. Akinsola, J. D., & Reyes, M. L. (2026). Optimization-driven marketing strategies in digital agricultural platforms. Journal of Agri-Decision Systems, 11(1), 39–57.
9. Animasaun, J.B., Ijiga, O.M., Ayoola, V.B., & Enyejo, L.A. (2026). Development of a Rapid GC-MS Workflow for Simultaneous Quantification of Volatile Terpenes and Cannabinoids in Industrial Hemp Extracts. International Journal of Innovative Science and Research Technology.
10. Awolola, O. J., Azonuche, T. I., Enyejo, J. O., Ononiwu, M. & Ayoola, V. B. (2025). Innovation-Focused Business Models for Scaling Small and Medium-Sized Engineering Firms Through Technology  Adoption and Process Standardization International Journal of Scientific Research in Science, Engineering and Technology  Volume 12, Issue 5, PG 497-519  doi : https://doi.org/10.32628/IJSRSET25125416
11. Awolola, O. J., Azonuche, T. I., Enyejo, J. O., Ononiwu, M. & Ayoola, V, B. (2026). “Integrating Digital Project Controls and Risk Mitigation Frameworks to Improve Decision-Making in Complex Civil Engineering Projects". Volume. 11 Issue.1, January 2026 International Journal of Innovative Science and Research Technology (IJISRT) 2869-2886 https://doi.org/10.38124/ijisrt/26jan1455
12. Awolola, O. J., Azonuche, T. I., Enyejo, J. O., Ononiwu, M. & Ayoola, V. B. (2026). “Innovation-Led Construction Management Strategies for Improving Procurement, Contractor Coordination, and Regulatory Compliance in Emerging Economics". Volume. 11 Issue.1, January 2026 International Journal of Innovative Science and Research Technology (IJISRT) 2853-2868 https://doi.org/10.38124/ijisrt/26jan1454
13. Baffour, K. T., & Linde, S. M. (2026). Structural dynamics of public agricultural markets: Institutional roles and actor interactions. Journal of Agribusiness Systems, 14(1), 11–29.
14. Bello, F. O., & Murata, K. H. (2026). Probabilistic modeling for stakeholder engagement classification in public markets. International Journal of Agricultural Data Analytics, 14(1), 22–38.
15. Chen, R. X., & Gariba, S. M. (2026). Predictive analytics for monitoring agricultural market participation. Journal of Agri-Data Modeling, 10(1), 22–40.
16. DaCosta, L. M., & Moreno, J. P. (2026). Cross-validated experimental designs for agricultural engagement prediction models. Journal of Rural Data Engineering, 9(1), 15–32.
17. Daramola, S. T., & Mensink, R. L. (2026). Weighted composite indices for stakeholder participation assessment in agricultural systems. Journal of Rural Analytics, 12(1), 18–36.
18. Fasuba, T. K., & Leighton, P. M. (2026). Methodological gaps in empirical studies on agricultural market modernization. Review of Rural Data Systems, 14(1), 51–69.
19. Gideon, K. A., & Velez, M. S. (2026). Behavioral metrics and multi-dimensional engagement modeling in public markets. Agricultural Data Science Advances, 7(1), 41–58.
20. Hernandez, J. P., & Boateng, K. L. (2026). Data-centric approaches for improving public agricultural market transparency. Journal of Market Intelligence Systems, 12(1), 40–57.
21. Karanja, D. T., & Silva, J. R. (2026). Communication-centered engagement models in public agricultural systems. Journal of Market Interaction Studies, 15(1), 33–49.
22. Kim, J. W., & Duarte, P. L. (2026). Challenges in modernizing engagement measurement in public agricultural ecosystems. Journal of Rural Data Systems, 14(1), 21–37.
23. Kumar, R. T., & Seidel, H. R. (2026). Error metrics and performance evaluation for machine-learning tools in public market analytics. International Journal of Agri-Informatics Evaluation, 11(1), 41–58.
24. Kwesi, R. T., & Romero, J. P. (2026). Categorical encoding strategies for market behavior modeling. Journal of Applied Agricultural Informatics, 9(1), 27–45.
25. Lamidi, S. O., & Ortega, M. L. (2026). Participation metrics and inclusiveness in rural market governance. International Journal of Agricultural Participation Research, 11(1), 12–28.
26. Lawani, R. S., & Herrera, A. P. (2026). Machine-learning architectures for predicting participation trends in agricultural ecosystems. Agricultural Informatics and Modeling Review, 10(1), 13–31.
27. Lawson, M. A., & Chen, R. Y. (2026). Stakeholder participation challenges in public-sector agricultural programs. International Review of Agricultural Governance, 8(1), 19–34.
28. Lopez, M. A., & Osei, B. K. (2026). Predictive analytics for stakeholder behavior modeling in emerging agricultural economies. International Journal of Market Informatics, 9(1), 15–33.
29. Mensah, P. L., & Rodriguez, A. M. (2026). Descriptive analytics frameworks for public agricultural market intelligence. Agricultural Information Systems Review, 13(1), 9–23.
30. Mensah, S. L., & Alvarez, D. Q. (2026). Normalization and temporal aggregation techniques in agricultural analytics. International Journal of Data-Driven Agriculture, 13(1), 10–26.
31. Morris, D. H., & Zhang, Q. L. (2026). Evidence-based engagement strategies for improving public sector agricultural marketing performance. Public Data Analytics Review, 11(1), 72–88.
32. Moyo, K. P., & Alvarez, S. G. (2026). Digital engagement constructs for emerging agricultural platforms. Agricultural Informatics Perspectives, 9(1), 41–58.
33. Obeng, J. A., & Mitchell, C. L. (2026). Evaluating digital information systems in public agricultural markets. International Journal of Agricultural Information Science, 8(1), 17–34.
34. Okafor, T. C., & Mensah, D. S. (2026). Predictive marketing analytics for enhancing stakeholder engagement in agricultural value chains. Applied Agricultural Informatics Journal, 11(1), 63–82.
35. Okonkwo, F. R., & Tesfaye, H. G. (2026). Information asymmetry and participation barriers in emerging agricultural market environments. International Review of Agricultural Policy and Data, 9(1), 44–62.
36. Okoye, P. C., & Hartfield, J. M. (2026). Evaluating index-based frameworks for engagement optimization in agricultural governance. International Journal of Market Intelligence, 9(1), 29–47.
37. Olaoye, M. B., & Hastings, L. J. (2026). Managing heterogeneous datasets for predictive engagement analysis in public markets. Agricultural Data Engineering Review, 8(1), 52–68.
38. Olatunji, R. M., & Mercer, A. J. (2026). Limitations of traditional marketing in public sector agricultural markets: A diagnostic assessment. International Journal of Agricultural Communication, 7(1), 58–74.
39. Rahman, F. I., & Opoku, J. D. (2026). Communication log analysis for optimizing stakeholder alignment in public markets. International Journal of Agricultural Data Exchange, 12(1), 14–29.
40. Santos, L. R., & Ibrahim, Y. K. (2026). Developing analytics-driven engagement frameworks for inclusive agricultural market participation. Agricultural Informatics Review, 10(1), 33–52.
41. Takyi, E. O., & Montero, J. V. (2026). Prescriptive analytics for optimizing marketing interventions in rural commodity markets. International Journal of Data-Driven Agriculture, 10(1), 47–63.
42. Twumasi, G. W., & Delgado, H. R. (2026). Leveraging transactional datasets for engagement modeling in rural commodity markets. Agricultural Data Science Review, 11(1), 7–24.
43. Wang, Y. Z., & Arhin, D. S. (2026). Robustness testing and temporal drift assessment in engagement modeling systems. Agricultural Predictive Systems Review, 13(1), 23–40.
44. Zhou, A. Y., & Kareem, M. O. (2026). Digital transformation and market governance: Reducing asymmetry in public agricultural systems. Agricultural Data Innovation Journal, 7(1), 55–71.