Kano Model Integration with Data Mining to Predict Customer Satisfaction
The Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model is focused on highlighting the most relevant attributes of a product or service along with customers’ estimation of how the presence of these at...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/5/4/66 |
| _version_ | 1827674097643945984 |
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| author | Khaled Al Rabaiei Fady Alnajjar Amir Ahmad |
| author_facet | Khaled Al Rabaiei Fady Alnajjar Amir Ahmad |
| author_sort | Khaled Al Rabaiei |
| collection | DOAJ |
| description | The Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model is focused on highlighting the most relevant attributes of a product or service along with customers’ estimation of how the presence of these attributes can be used to predict satisfaction about specific services or products. This research aims to develop a method to integrate the Kano model and data mining approaches to select relevant attributes that drive customer satisfaction, with a specific focus on higher education. The significant contribution of this research is to solve the problem of selecting features that are not methodically correlated to customer satisfaction, which could reduce the risk of investing in features that could ultimately be irrelevant to enhancing customer satisfaction. Questionnaire data were collected from 646 students from UAE University. The experiment suggests that XGBoost Regression and Decision Tree Regression produce best results for this kind of problem. Based on the integration between the Kano model and the feature selection method, the number of features used to predict customer satisfaction is minimized to four features. It was found that ANOVA features selection model’s integration with the Kano model gives higher Pearson correlation coefficients and higher R2 values. |
| first_indexed | 2024-03-10T04:34:51Z |
| format | Article |
| id | doaj.art-8a419e2b67854e8eaec8b3acdc231a1b |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T04:34:51Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-8a419e2b67854e8eaec8b3acdc231a1b2023-11-23T03:51:11ZengMDPI AGBig Data and Cognitive Computing2504-22892021-11-01546610.3390/bdcc5040066Kano Model Integration with Data Mining to Predict Customer SatisfactionKhaled Al Rabaiei0Fady Alnajjar1Amir Ahmad2Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University (UAEU), Al Ain P.O. Box 15551, United Arab EmiratesDepartment of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University (UAEU), Al Ain P.O. Box 15551, United Arab EmiratesDepartment of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University (UAEU), Al Ain P.O. Box 15551, United Arab EmiratesThe Kano model is one of the models that help determine which features must be included in a product or service to improve customer satisfaction. The model is focused on highlighting the most relevant attributes of a product or service along with customers’ estimation of how the presence of these attributes can be used to predict satisfaction about specific services or products. This research aims to develop a method to integrate the Kano model and data mining approaches to select relevant attributes that drive customer satisfaction, with a specific focus on higher education. The significant contribution of this research is to solve the problem of selecting features that are not methodically correlated to customer satisfaction, which could reduce the risk of investing in features that could ultimately be irrelevant to enhancing customer satisfaction. Questionnaire data were collected from 646 students from UAE University. The experiment suggests that XGBoost Regression and Decision Tree Regression produce best results for this kind of problem. Based on the integration between the Kano model and the feature selection method, the number of features used to predict customer satisfaction is minimized to four features. It was found that ANOVA features selection model’s integration with the Kano model gives higher Pearson correlation coefficients and higher R2 values.https://www.mdpi.com/2504-2289/5/4/66customer satisfactiondata miningfeature selectionthe Kano model |
| spellingShingle | Khaled Al Rabaiei Fady Alnajjar Amir Ahmad Kano Model Integration with Data Mining to Predict Customer Satisfaction Big Data and Cognitive Computing customer satisfaction data mining feature selection the Kano model |
| title | Kano Model Integration with Data Mining to Predict Customer Satisfaction |
| title_full | Kano Model Integration with Data Mining to Predict Customer Satisfaction |
| title_fullStr | Kano Model Integration with Data Mining to Predict Customer Satisfaction |
| title_full_unstemmed | Kano Model Integration with Data Mining to Predict Customer Satisfaction |
| title_short | Kano Model Integration with Data Mining to Predict Customer Satisfaction |
| title_sort | kano model integration with data mining to predict customer satisfaction |
| topic | customer satisfaction data mining feature selection the Kano model |
| url | https://www.mdpi.com/2504-2289/5/4/66 |
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