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Customer mobile behavioral segmentation and analysis in telecom using machine learning


Citation

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

Abstract

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.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1080/08839514.2021.2009223
Publisher: Taylor and Francis
Keywords: Telecom; Machine; Faker Python; PCA technique
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 11 Jan 2023 08:23
Last Modified: 11 Jan 2023 08:23
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/08839514.2021.2009223
URI: http://psasir.upm.edu.my/id/eprint/96586
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