Customer mobile behavioral segmentation and analysis in telecom using machine learning
This study aims to identify telecom customer segments by utilizing machine learning and subsequently develop a web-based dashboard. The dashboard visualizes the cluster analysis based on demographics, behavior, and region features. The study applied analytic pipeline that involved five stages i.e. d...
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| Format: | Article |
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Taylor and Francis
2021
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| _version_ | 1825937736463286272 |
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| author | Sharaf Addin, Eman Hussein Admodisastro, Novia Indriaty Mohd Ashri, Siti Nur Syahirah Kamaruddin, Azrina Chew, Yew Chong |
| author_facet | Sharaf Addin, Eman Hussein Admodisastro, Novia Indriaty Mohd Ashri, Siti Nur Syahirah Kamaruddin, Azrina Chew, Yew Chong |
| author_sort | Sharaf Addin, Eman Hussein |
| collection | UPM |
| description | This study aims to identify telecom customer segments by utilizing machine learning and subsequently develop a web-based dashboard. The dashboard visualizes the cluster analysis based on demographics, behavior, and region features. The study applied analytic pipeline that involved five stages i.e. data generation, data pre-processing, data clustering, clusters analysis, and data visualization. Firstly, the customer’s dataset was generated using Faker Python package. Secondly was the pre-processing which includes the dimensionality reduction of the dataset using the PCA technique and finding the optimal number of clusters using the Elbow method. Unsupervised machine learning algorithm K-means was used to cluster the data, and these results were analyzed and labeled with labels and descriptions. Lastly, a dashboard was developed using Microsoft Power BI to visualize the clustering results in meaningful analysis. According to the results, four customer clusters were obtained. An interactive web-based dashboard called INSIGHT was developed to provide analysis of customer segments based on demographic, behavioral, and regional traits; and to devise customized query for deeper analysis. The correctness of the clustering results was evaluated and achieved a satisfactory Silhouette Score of 0.3853. Hence, the telecom could target their customers accurately based on their needs and preferences to increase service satisfaction. |
| first_indexed | 2024-03-06T11:04:03Z |
| format | Article |
| id | upm.eprints-96586 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:04:03Z |
| publishDate | 2021 |
| publisher | Taylor and Francis |
| record_format | dspace |
| spelling | upm.eprints-965862023-01-11T08:23:11Z http://psasir.upm.edu.my/id/eprint/96586/ Customer mobile behavioral segmentation and analysis in telecom using machine learning Sharaf Addin, Eman Hussein Admodisastro, Novia Indriaty Mohd Ashri, Siti Nur Syahirah Kamaruddin, Azrina Chew, Yew Chong This study aims to identify telecom customer segments by utilizing machine learning and subsequently develop a web-based dashboard. The dashboard visualizes the cluster analysis based on demographics, behavior, and region features. The study applied analytic pipeline that involved five stages i.e. data generation, data pre-processing, data clustering, clusters analysis, and data visualization. Firstly, the customer’s dataset was generated using Faker Python package. Secondly was the pre-processing which includes the dimensionality reduction of the dataset using the PCA technique and finding the optimal number of clusters using the Elbow method. Unsupervised machine learning algorithm K-means was used to cluster the data, and these results were analyzed and labeled with labels and descriptions. Lastly, a dashboard was developed using Microsoft Power BI to visualize the clustering results in meaningful analysis. According to the results, four customer clusters were obtained. An interactive web-based dashboard called INSIGHT was developed to provide analysis of customer segments based on demographic, behavioral, and regional traits; and to devise customized query for deeper analysis. The correctness of the clustering results was evaluated and achieved a satisfactory Silhouette Score of 0.3853. Hence, the telecom could target their customers accurately based on their needs and preferences to increase service satisfaction. Taylor and Francis 2021 Article PeerReviewed Sharaf Addin, Eman Hussein and Admodisastro, Novia Indriaty and Mohd Ashri, Siti Nur Syahirah and Kamaruddin, Azrina and Chew, Yew Chong (2021) Customer mobile behavioral segmentation and analysis in telecom using machine learning. Applied Artificial Intelligence, 36 (1). pp. 1-26. ISSN 0883-9514; ESSN: 1087-6545 https://www.tandfonline.com/doi/full/10.1080/08839514.2021.2009223 10.1080/08839514.2021.2009223 |
| spellingShingle | Sharaf Addin, Eman Hussein Admodisastro, Novia Indriaty Mohd Ashri, Siti Nur Syahirah Kamaruddin, Azrina Chew, Yew Chong Customer mobile behavioral segmentation and analysis in telecom using machine learning |
| title | Customer mobile behavioral segmentation and analysis in telecom using machine learning |
| title_full | Customer mobile behavioral segmentation and analysis in telecom using machine learning |
| title_fullStr | Customer mobile behavioral segmentation and analysis in telecom using machine learning |
| title_full_unstemmed | Customer mobile behavioral segmentation and analysis in telecom using machine learning |
| title_short | Customer mobile behavioral segmentation and analysis in telecom using machine learning |
| title_sort | customer mobile behavioral segmentation and analysis in telecom using machine learning |
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