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

Haythem Saad
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Haythem Saad: Tunisian Ministry of Equipment and Housing

No xtahd_v1, OSF Preprints from Center for Open Science

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.

Date: 2025-05-19
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:xtahd_v1

DOI: 10.31219/osf.io/xtahd_v1

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