DECIPHERING CUSTOMER SATISFACTION : A MACHINE LEARNING-ORIENTED METHOD USING AGGLOMERATIVE CLUSTERING FOR PREDICTIVE MODELING AND FEATURE SELECTION
(2025) In Management Systems in Production Engineering 33(2). p.60-70- Abstract
In contemporary enterprises, customer satisfaction analysis has become a critical area of concentration. Being able to understand and predict customer satisfaction is becoming more and more important as companies try to develop and launch new products. Leveraging customer data intelligently and employing robust data analytics techniques are essential for meeting this imperative. With this objective in mind, the study proposes a machine learning-based approach to analyze and discern the variables influencing customer satisfaction. Specifically, the study utilizes agglomerative clustering for data segmentation and feature identification, followed by a Random Forest Classifier as machine learning (ML) model for prediction. Performance... (More)
In contemporary enterprises, customer satisfaction analysis has become a critical area of concentration. Being able to understand and predict customer satisfaction is becoming more and more important as companies try to develop and launch new products. Leveraging customer data intelligently and employing robust data analytics techniques are essential for meeting this imperative. With this objective in mind, the study proposes a machine learning-based approach to analyze and discern the variables influencing customer satisfaction. Specifically, the study utilizes agglomerative clustering for data segmentation and feature identification, followed by a Random Forest Classifier as machine learning (ML) model for prediction. Performance metrics such as accuracy, recall, precision and F1-score are employed for model evaluation, ensuring robustness and reliability in the predictive process. Furthermore, it aims to predict the impact of enhancing specific product attributes on customer satisfaction. To provide a tangible demonstration of the proposed methodology, a comprehensive case study is conducted. By systematically integrating clustering techniques into the feature selection and modeling process, this framework furnishes a structured methodology for data-driven decision-making and predictive analytics. This holistic approach not only enriches the comprehension of intricate datasets but also facilitates the development of resilient predictive models characterized by enhanced accuracy and interpretability. By segmenting customers based on their responses, we discerned specific areas of satisfaction and dissatisfaction, providing actionable insights for targeted strategies aimed at improving overall satisfaction. The insights and customer clustering derived from this study can guide these targeted strategies to enhance customer satisfaction and inform future product development initiatives.
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- author
- Rezki, Nisrine ; Mansouri, Mohamed and Oucheikh, Rachid LU
- organization
- publishing date
- 2025-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Agglomerative Clustering, customer satisfaction, machine learning, Random Forest Classifier, SC disruption, SC management
- in
- Management Systems in Production Engineering
- volume
- 33
- issue
- 2
- pages
- 11 pages
- publisher
- Sciendo
- external identifiers
-
- scopus:105002689186
- ISSN
- 2299-0461
- DOI
- 10.2478/mspe-2025-0007
- language
- English
- LU publication?
- yes
- id
- 8dbfe427-8b73-4c36-9b0a-81908bc4f92b
- date added to LUP
- 2025-08-27 10:25:46
- date last changed
- 2025-10-14 11:31:03
@article{8dbfe427-8b73-4c36-9b0a-81908bc4f92b,
abstract = {{<p>In contemporary enterprises, customer satisfaction analysis has become a critical area of concentration. Being able to understand and predict customer satisfaction is becoming more and more important as companies try to develop and launch new products. Leveraging customer data intelligently and employing robust data analytics techniques are essential for meeting this imperative. With this objective in mind, the study proposes a machine learning-based approach to analyze and discern the variables influencing customer satisfaction. Specifically, the study utilizes agglomerative clustering for data segmentation and feature identification, followed by a Random Forest Classifier as machine learning (ML) model for prediction. Performance metrics such as accuracy, recall, precision and F1-score are employed for model evaluation, ensuring robustness and reliability in the predictive process. Furthermore, it aims to predict the impact of enhancing specific product attributes on customer satisfaction. To provide a tangible demonstration of the proposed methodology, a comprehensive case study is conducted. By systematically integrating clustering techniques into the feature selection and modeling process, this framework furnishes a structured methodology for data-driven decision-making and predictive analytics. This holistic approach not only enriches the comprehension of intricate datasets but also facilitates the development of resilient predictive models characterized by enhanced accuracy and interpretability. By segmenting customers based on their responses, we discerned specific areas of satisfaction and dissatisfaction, providing actionable insights for targeted strategies aimed at improving overall satisfaction. The insights and customer clustering derived from this study can guide these targeted strategies to enhance customer satisfaction and inform future product development initiatives.</p>}},
author = {{Rezki, Nisrine and Mansouri, Mohamed and Oucheikh, Rachid}},
issn = {{2299-0461}},
keywords = {{Agglomerative Clustering; customer satisfaction; machine learning; Random Forest Classifier; SC disruption; SC management}},
language = {{eng}},
number = {{2}},
pages = {{60--70}},
publisher = {{Sciendo}},
series = {{Management Systems in Production Engineering}},
title = {{DECIPHERING CUSTOMER SATISFACTION : A MACHINE LEARNING-ORIENTED METHOD USING AGGLOMERATIVE CLUSTERING FOR PREDICTIVE MODELING AND FEATURE SELECTION}},
url = {{http://dx.doi.org/10.2478/mspe-2025-0007}},
doi = {{10.2478/mspe-2025-0007}},
volume = {{33}},
year = {{2025}},
}