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

Haythem Saad

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Haythem Saad. Hybrid machine learning models for marketing business analytics: A selective review.
2025. hal-05132125

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

By : Haythem Saad

Engineering Sciences Centre
Tunisian Ministry of Equipment and Housing

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.

**Abstract :**

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.

**Résumé :**

Ce document explore l'utilisation de modèles hybrides d'apprentissage automatique dans l'analyse de données commerciales comme une revue sélective de quatre articles phares de la littérature existante. L'objectif est d'identifier les lacunes dans la littérature et de justifier l'importance de ces modèles pour améliorer la prise de décision dans divers domaines, notamment la finance et le marketing. Les résultats montrent que les modèles hybrides peuvent optimiser les prévisions et la personnalisation des campagnes, tout en soulignant la nécessité d'approches spécifiques pour traiter la complexité des données commerciales.

**ملخص :**

تتطرق هذه الوثيقة لاستخدام نماذج التعلم الآلي الهجينة في تحليل البيانات التجارية، استنادًا إلى مراجعة انتقائية لأربعة مقالات بارزة. الهدف هو تحديد الفجوات في الأدبيات الحالية وتبرير أهمية هذه النماذج لتحسين اتخاذ القرار في مجالات متنوعة بما في ذلك المالية والتسويقية. تشير النتائج إلى أن النماذج الهجينة يمكن أن تعزز التنبؤات وتخصص الحملات (التجارية)، مع تسليط الضوء على الحاجة إلى مناهج محددة لمعالجة تعقيد البيانات التجارية.

**Mots clés :** Modèles hybrides, Apprentissage automatique, Analyse de données commerciales, Finance, Marketing.

**Keywords :** Hybrid models, Machine learning, Business data analytics, Finance, Marketing.

الكلمات المفتاحية : النماذج الهجينة، التعلم الآلي، تحليل البيانات التجارية، المالية، التسويق.
Summary

<table><tr><td>1. Introduction</td><td>2</td></tr><tr><td>2. Historical Context</td><td>2</td></tr><tr><td>2.1. Machine Learning</td><td>2</td></tr><tr><td>2.2. Machine Learning in Marketing</td><td>2</td></tr><tr><td>2.3. Hybrid Machine Learning models</td><td>2</td></tr><tr><td>2.4. Hybrid Machine Learning models in Marketing</td><td>2</td></tr><tr><td>3. Goals of the review</td><td>3</td></tr><tr><td>3.1. Overall research question</td><td>3</td></tr><tr><td>3.2. Specific goals of the review</td><td>3</td></tr><tr><td>4. Research protocol and methodology</td><td>4</td></tr><tr><td>5. Selected articles</td><td>4</td></tr><tr><td>6. Interpretation of the results</td><td>5</td></tr><tr><td>7. Discussion</td><td>5</td></tr><tr><td>7.1. Hybrid machine learning models applied in financial prediction</td><td>5</td></tr><tr><td>7.1.1. Credit risk prediction</td><td>5</td></tr><tr><td>7.1.2. Stock market prediction</td><td>7</td></tr><tr><td>7.2. Hybrid machine learning models applied in marketing</td><td>8</td></tr><tr><td>7.2.1. Decision support systems in marketing</td><td>8</td></tr><tr><td>7.2.2. Demand forecasting in the retail field</td><td>10</td></tr><tr><td>8. Recapitulation</td><td>11</td></tr><tr><td>9. Conclusion</td><td>11</td></tr><tr><td>10. References</td><td>11</td></tr></table>
# 1. Introduction:

The present document aims to explore relevant articles in the field of business intelligence using hybrid machine learning models, with a particular emphasis on business data analysis. The selected articles offer a variety of applications of hybrid models ranging from finance to marketing. The objective is to identify current gaps in the literature and to justify the relevance of using these models in business data analysis to address these gaps.

# 2. Historical Context:

## 2.1. Machine Learning:

- **Origins:** The concept of machine learning has deep roots in the 1940s and 1950s.

- **Evolution:** Over the decades, the field has developed with significant contributions in the 1980s and 1990s, notably with the advent of neural networks and supervised learning algorithms.

- **Recent Advances:** Recent advances are closely linked to the increase in computing power and the enormous amount of available data, enabling significant progress in areas such as deep learning.

## 2.2. Machine Learning in Marketing:

- **Beginning of Application:** The application of Machine Learning in Marketing gained popularity in the 2000s with the growth and globalization of ERPs, the rise of the Internet, and the massive collection of online data (Big Data).

- **Customer Segmentation:** Early applications were often related to customer segmentation, personalized recommendations, and optimization of advertising campaigns.

- **Current Evolution:** Today, Machine Learning in Marketing is ubiquitous, used for demand forecasting, sentiment analysis, customer experience personalization...

## 2.3. Hybrid Machine Learning models:

- **Emergence:** The idea of combining different machine learning models emerged in the context of seeking better performance using complementary approaches.

- **Motivations:** Hybrid models aim to leverage the strengths of different techniques to overcome the individual limitations of isolated models.

- **Initial Application:** Early applications focused on specific domains such as finance, weather forecasting, and more recently, business data analysis.

## 2.4. Hybrid Machine Learning models in Marketing:

- **Rationalization:** The application of hybrid models in Marketing stems from the need to address the complexity of business data, which can be heterogeneous and multidimensional.

- **Decision Optimization:** The objective is often to optimize Marketing decisions by combining traditional machine learning techniques with newer approaches such as deep learning.
- **Current Evolution:** Hybrid models in Marketing represent an emerging concept and are at the forefront of research, aiming to improve forecast accuracy, campaign personalization, and overall understanding of consumer behaviour.

- **Limitations of Existing Literature:** It is important to note that the existing literature on hybrid models applied in Marketing is not as extensive as in other areas, as it is a relatively recent discipline. While several literature reviews focus on machine learning in marketing in general (without the application of hybrid models), such as the one by EWT Ngai et al. (2022) [1], as well as other reviews on hybrid models in general (without application in business data analysis), there is no review that combines these two aspects (a literature review focused on hybrid models applied in marketing and business analysis). There are only a few scattered articles that address this theme but no literature review. This gap can be attributed to the increased complexity of business data and the need to develop specific approaches to address these challenges. Current research is attempting to bridge this gap by exploring new hybrid methodologies tailored to the unique needs of Marketing. Given the importance of filling this gap, it is essential to review these existing scientific papers. With this aim, we attempt to provide a selective literature synthesis based on four relevant articles for this theme.

### 3. Goals of the review:

By structuring the review around these questions and objectives, we aim to provide a thorough understanding of hybrid machine learning models in the context of business intelligence and their specific application to business data analysis.

#### 3.1. Overall research question:

What are the recent developments in the field of business intelligence using hybrid machine learning models with a particular focus on their application in business data analysis?

#### 3.2. Specific goals of the review:

- **Analysing hybrid machine learning models:**

* Thoroughly examine the hybrid machine learning models presented in existing literature.

* Identify the specific components of these models and their application in the context of business intelligence (even in domains other than Marketing).

- **Evaluate the application in business data analysis:**

* Study how hybrid models are specifically applied to business data analysis and in Marketing.
- **Identify the hybrid integrated aspects:**

o Examine, in relation to the application domain (financial, commercial, etc.), the integrated aspects of each hybrid model in the context of business intelligence.

o Identify the specific areas where these models show advantages or limitations.

- **Explore the limitations and perspectives specific to research work:**

o Analyse the limitations of each reviewed article.

o Explore the suggested perspectives to guide further research.

4. **Research protocol and methodology:**

- **Identification of relevant articles:**

o Perform a comprehensive search in academic databases such as PubMed, IEEE Xplore, Google Scholar, and ScienceDirect.

o Use of specific keywords such as "hybrid," "Machine Learning," "Marketing," "Business Intelligence," "data analysis," "decision," and other relevant terms.

- **Criteria of selection:**

o Include articles published in the last five years to ensure the relevance of the information.

o Select articles that focus on the application of hybrid models in the field of business intelligence, setting aside the application context.

- **Analysis of models:**

o Examine in detail the hybrid models presented in each selected article.

o Identify the specific components of these models and the combined machine learning techniques.

- **Application evaluation:**

o Analyse how each article applies hybrid models.

o Identify specific sectors such as finance and marketing.

- **Identification of analysis aspects:**

o Enumerate the analysis aspects integrated by each hybrid model.

o Examine how these models address specific challenges related to business intelligence in the context of application.

- **Analysis of limitations and perspectives:**

o Carefully examine the limitations in each article.

o Explore the perspectives to guide further research.

5. **Selected articles:**

- Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble
  Machine Learning approach by Y Zhu et al. (2019) [2].
- Predicting the daily return direction of the stock market using hybrid Machine Learning algorithms by X Zhong et D Enke (2019) [3].

- Data mining based Marketing decision support system using hybrid Machine Learning algorithm by DTS Kumar (2020) [4].

- A comparative study of demand forecasting models for a multi-channel retail company: A novel hybrid Machine Learning approach by A Mitra et al. (2022) [5].

**6. Interpretation of the results:**

- **Article 1: "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble Machine Learning approach "**: The results indicate that the hybrid approach combining XGBoost, Random Forest, Decision Trees, SVM, and others improves credit risk prediction for SMEs in supply chain finance.

- **Article 2: "Predicting the daily return direction of the stock market using hybrid Machine Learning algorithms"**: The results suggest that using aggregation-based hybrid models, such as Random Forest, Bagging, Boosting, allows for better anticipation of the daily stock market return direction, although sensitivity to market conditions is emphasized.

- **Article 3: "Data mining based Marketing decision support system using hybrid Machine Learning algorithm"**: The article does not specify the models used. The results focus on the development of a Marketing decision support system based on data mining, highlighting the importance of contextual usage.

- **Article 4: "A comparative study of demand forecasting models for a multi-channel retail company: A novel hybrid Machine Learning approach"**: The results highlight that the hybrid approach involving Random Forest, Gradient Boosting, ARIMA, and others, offers a promising perspective for commercial demand forecasting in a multi-channel retail company despite challenges related to seasonal variability.

**7. Discussion:**

**7.1. Hybrid machine learning models applied in financial prediction:**

Hybrid machine learning models combine different techniques to improve the accuracy and robustness of financial predictions. Among these techniques, the hybrid approach typically combines methods such as Random Forest, XGBoost, and Linear Regression. This combination leverages the strengths of each model by providing a more comprehensive approach to data modelling.
### 7.1.1. Credit risk prediction:

In the context of small and medium-sized enterprises (SMEs), hybrid models have proven effective in financial applications, as mentioned in "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble Machine Learning approach". These models offer superior performance, thus opening opportunities for more informed decisions in the financial domain.

This article proposes a hybrid approach to assess the credit risk of SMEs in supply chain finance. While the context differs from business data analysis, the hybrid methodologies used to improve forecast accuracy can be extrapolated to applications in the business domain.

The article provides insight into the effectiveness of hybrid models for evaluating credit risk in complex financial contexts. It also demonstrates the adaptability of these models to specific situations such as supply chain finance.

While the article effectively addresses credit risk in a financial context, it does not delve into the adaptability of these models to broader contexts, particularly those related to business data analysis. Therefore, one could explore how to adapt these financial models to business domains. Research could focus on developing specific hybrid models for business data analysis by drawing inspiration from the effective methodologies identified in the financial context through this article.

#### 7.1.1.1. Discussed models and aspects:

##### - Hybrid models:

**Used models:** Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Tree (DT).

The article proposes a hybrid model that combines several ensemble machine learning techniques. It integrates methods such as Random Forest, boosting, and other ensemble algorithms. These methods are synergistically combined to improve the overall accuracy of the model.

##### - Analysis aspects:

**Financial aspects mentioned:** Financial health of SMEs, Payment histories, Supplier relationships, Supply chain-specific financial indicators.

In the financial context of the supply chain, the hybrid model considers various aspects. This could include financial analyses such as the financial health of SMEs, payment histories, supplier relationships, and other supply chain-specific financial indicators. The ensemble approach enables the handling of complex and multidimensional data by providing a more comprehensive view of credit risk.
**7.1.1.2. Contributions:**

- Methodologies and hybrid models to improve forecast accuracy in a specific and purely financial context (credit risk assessment).

- Adaptability of hybrid models to business contexts.

**7.1.1.3. Limits:**

- The specificity of models for the financial context requires adaptation for business data.

- Lack of transferability of these models to business domains.

**7.1.1.4. Perspectives:**

- Exploration of adaptation mechanisms of these financial models for commercial applications.

**7.1.1.5. Issues and Challenges:**

- How to adapt credit risk prediction models to business domains except finance?

- What specific methodologies can be borrowed from the proposed hybrid approach to enhance the effectiveness of models in business environments?

**7.1.2. Stock market prediction:**

The applications of hybrid models in financial prediction are diverse, especially in the context of the stock market. Indeed, most studies on hybrid approaches focus on this context. Studies such as "Predicting the daily return direction of the stock market using hybrid Machine Learning algorithms" have demonstrated the effectiveness of these models in predicting financial market trends. The results highlight the adaptability of these models to specific situations such as the supply chain, thus offering promising perspectives for applications in various contexts. This article focuses on the daily prediction of stock market direction through hybrid algorithms. Although centred on finance, the use of hybrid models underscores their effectiveness in prediction, which is a key skill for prediction-focused business data analysis. The article provides valuable insights into the application of hybrid models to volatile financial data (stock market). It highlights the importance of model flexibility in forecasting market trends. While the article excels in predicting stock market trends, it does not specifically address applications in the field of business data analysis. One could explore how these models can be adapted to anticipate business trends. Research perspectives could focus on adapting hybrid models to predict business trends based on successful methodologies identified in the financial context.

**7.1.2.1. Discussed models and aspects:**

- **Hybrid models:**

**Used models:** Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), SVM, Stochastic Oscillator, DT.
The article uses hybrid machine learning algorithms to predict stock returns. These models likely combine time series approaches with ensemble methods such as bagging or stacking. Hybridization aims to improve the predictive capacity of the model.

### - Analysis aspects:

**Financial aspects mentioned:** Stock prices. Transaction volumes. Historical trends. Macroeconomic data influencing financial markets.

The financial aspects analysed include market indicators such as stock prices, transaction volumes, historical trends, and macroeconomic data influencing financial markets. The hybrid approach allows for consideration of the complexity of interactions among these different factors for a more robust prediction.

### 7.1.2.2. Contributions:

- Importance of prediction in data analysis.

- Effectiveness of hybrid models in dynamic contexts such as the stock market.

### 7.1.2.3. Limits:

- The volatility of financial markets can make prediction challenging (Necessity of models adapted to rapid changes in data).

- Lack of research on prediction in the business context.

### 7.1.2.4. Perspectives:

- Adapting these methods to predict relevant business indicators.

### 7.1.2.5. Issues and Challenges:

- How to apply hybrid models to business data to anticipate trends?

- What specific characteristics of business data need to be considered in the development of these models?

## 7.2. Hybrid machine learning models applied in marketing:

### 7.2.1. Decision support systems in marketing:

The use of hybrid models in Marketing decision support systems offers an interesting perspective, as highlighted in "Data mining based Marketing decision support system using hybrid Machine Learning algorithm". These systems can enhance decision-making by leveraging the predictive power of hybrid models in Marketing data analysis. This article introduces a Marketing decision support system based on data exploration and hybrid algorithms. The focus is on improving Marketing decision-making through complex models, emphasizing their applicability to business data analysis. The article makes significant contributions to our topic by highlighting the application of hybrid models in Marketing decision-making. It underscores the importance of data exploration in optimizing business strategies.
While the article is robust in its application to Marketing, it does not explicitly explore the broader commercial dimension with its various aspects. Efforts could be made to generalize these approaches for broader business data analysis contexts. Perspectives could involve expanding the application of hybrid models by examining how these methods can be adapted to encompass broader business aspects.

### 7.2.1.1. **Discussed models and aspects:**

- **Hybrid models:**

**Used models:** Not specified (The article provides generalities about the approach to follow without delving into technical details).

The article presents a Marketing decision support system based on data exploration and using a hybrid algorithm. Specific details about the hybridization are not provided.

- **Analysis aspects:**

**Commercial aspects mentioned:** Consumer behaviour and preferences, Buying habits, Marketing campaign performance.

The commercial aspects analysed include consumer behaviour, preferences, buying habits, marketing campaign performance, etc. The hybridization of algorithms enables the processing of complex datasets and provides more accurate recommendations for decision-making.

### 7.2.1.2. **Contributions:**

- Application of hybrid models in Marketing decision-making.

- Relevance of models in commercial environments.

### 7.2.1.3. **Limits:**

- The application is limited (requirement for customization for other aspects of business analysis and specific sectors).

### 7.2.1.4. **Perspectives:**

- Adapt these models for decision-making in companies across different aspects and specific sectors.

### 7.2.1.5. **Issues and Challenges:**

- How can hybrid models be adjusted to support overall business decisions?

- What are the specificities of commercial data analysis that require adaptation of existing models?
### 7.2.2. Demand forecasting in the retail field:

In the context of retail, hybrid models are successfully applied for demand forecasting. "A comparative study of demand forecasting models for a multi-channel retail company: A novel hybrid Machine Learning approach" highlights the improvement in demand forecasting performance by emphasizing the importance of these models for effective inventory management. This article focuses on demand forecasting using a hybrid approach. The results show a significant improvement in accuracy compared to other models, underscoring the importance of forecasting in commercial data analysis overall. The article makes a significant contribution to our topic by presenting a hybrid modeling approach for demand forecasting in a complex commercial context. It highlights the importance of combining multiple models to enhance forecast accuracy. While the article is robust in its methodology, it specifically focuses on one aspect: demand forecasting in the retail sector. We could aim to generalize these hybrid approaches to apply to other aspects of commercial data analysis. The perspectives offered by the article include the possibility of adapting hybrid models to other areas of commercial data analysis. It also suggests that specific adjustments may be necessary based on the distinct properties of products in different sectors.

#### 7.2.2.1. Discussed models and aspects:

- **Hybrid models:**

**Used models:** SARIMA (Seasonal Autoregressive Integrated Moving Average), Long Short-Term Memory (LSTM), Prophet.

The article proposes a hybrid modelling approach for forecasting. Specific details are not provided, but this could involve a combination of methods to be explored further in research within this context.

- **Analysis aspects:**

**Commercial aspects mentioned:** Consumer purchasing trends, impact of promotions, seasonality, retail-specific variables.

Hybridization of models aims to capture the complexity of these factors and provide more accurate forecasts to optimize inventory management, distribution channels, and other various aspects of business analysis.

#### 7.2.2.2. Contributions:

- Hybrid methodology for commercial demand forecasting.

- Relevance of data analysis in the context of retail.

#### 7.2.2.3. Limits:

- The need for sector-specific knowledge for effective customization.
- The application is limited: demand forecasting represents only one aspect (need for customization for other aspects of business analysis and specific sectors).

#### 7.2.2.4. Perspectives:

- Explore customization for different industries in the business context.

- Explore customization for different aspects of business analysis.

#### 7.2.2.5. Issues and Challenges:

- How can the hybrid models developed for demand forecasting be extended to other aspects of business data analysis?

- What specific considerations should be considered when applying these models to different industries?

### 8. Recapitulation:

The diversity of applications in the examined articles reinforces the relevance of hybrid machine learning models in the field of business intelligence for commercial data analysis. These models offer promising solutions to improve prediction accuracy, optimize decision-making in marketing, and address specific challenges in various application domains. The identified limitations provide starting points for future research, justifying the need to continue exploring hybrid approaches for application in commercial analysis. The identified perspectives suggest promising avenues for ongoing development and adaptation in this subject.

### 9. Conclusion:

The existing literature provides interesting perspectives and approaches for specific domains such as finance, stock markets, decision support, and sales. However, developing a hybrid approach that applies more specifically to business data analysis could make a significant contribution by combining best practices from various sectors and addressing methodological gaps identified in the literature.

### 10. References:

1. Ngai, E. W., & Wu, Y. (2022). Machine learning in marketing: A literature review, conceptual framework, and research agenda. Journal of Business Research, 145, 35-48.

2. Zhu, Y., Zhou, L., Xie, C., Wang, G. J., & Nguyen, T. V. (2019). Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble Machine Learning approach. International Journal of Production Economics, 211, 22-33.

3. Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid Machine Learning algorithms. Financial Innovation, 5(1), 1-20.
4. **Kumar, D. T. S. (2020).** Data mining based Marketing decision support system using hybrid Machine Learning algorithm. Journal of Artificial Intelligence and Capsule Networks, 2(3), 185-193.

5. **Mitra, A., Jain, A., Kishore, A., & Kumar, P. (2022, September).** A comparative study of demand forecasting models for a multi-channel retail company: A novel hybrid Machine Learning approach. In Operations Research Forum (Vol. 3, No. 4, p. 58). Cham: Springer International Publishing.