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

Haythem Saad ()
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Haythem Saad: ESCT - École supérieure de commerce de Tunis

Working Papers from HAL

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. 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.

Keywords: Hybrid models; Machine learning; Data analytics; Business data analytics; Finance; Marketing; Modèles hybrides; Apprentissage automatique; Analyse de données commerciales; Marketing Hybrid models (search for similar items in EconPapers)
Date: 2025-07-03
Note: View the original document on HAL open archive server: https://hal.science/hal-05132125v1
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-05132125

DOI: 10.5281/zenodo.15470271

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