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Hybrid machine learning models for marketing business analytics: A selective review

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  • Saad, Haythem

    (Tunisian Ministry of Equipment and Housing)

Abstract

This is the original version in English of the paper with DOI: 10.5281/zenodo.15470272 published on Zenodo under the Creative Commons Attribution 4.0 International license. Link : https://doi.org/10.5281/zenodo.15470272 A French version is also available as a preprint on HAL under the identifier hal-05078079, version 1: https://hal.science/hal-05078079v1. This paper explores the use of hybrid machine learning models in business data analysis, based on a selective review of four landmark articles. The aim is to identify gaps in the existing literature and justify the importance of these models for enhancing decision-making across various fields, including finance and marketing. The findings indicate that hybrid models can optimize forecasting and campaign personalization, while highlighting the need for specific approaches to address the complexity of business data.

Suggested Citation

  • Saad, Haythem, 2025. "Hybrid machine learning models for marketing business analytics: A selective review," OSF Preprints xtahd_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:xtahd_v1
    DOI: 10.31219/osf.io/xtahd_v1
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    References listed on IDEAS

    as
    1. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.
    2. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    3. Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
    4. Xiao Zhong & David Enke, 2019. "Predicting the daily return direction of the stock market using hybrid machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-20, December.
    Full references (including those not matched with items on IDEAS)

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