Revolutionizing market surveillance: customer relationship management with machine learning
In the telecommunications industry, predicting customer churn is essential for retaining clients and sustaining profitability. Traditional CRM systems often fall short due to their static models, limiting responsiveness to evolving customer behaviors. To address these gaps, we developed the SmartSur...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-12-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2583.pdf |
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| _version_ | 1846114258698371072 |
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| author | Xiangting Shi Yakang Zhang Manning Yu Lihao Zhang |
| author_facet | Xiangting Shi Yakang Zhang Manning Yu Lihao Zhang |
| author_sort | Xiangting Shi |
| collection | DOAJ |
| description | In the telecommunications industry, predicting customer churn is essential for retaining clients and sustaining profitability. Traditional CRM systems often fall short due to their static models, limiting responsiveness to evolving customer behaviors. To address these gaps, we developed the SmartSurveil CRM model, an ensemble-based system combining random forest, gradient boosting, and support vector machine to enhance churn prediction accuracy and adaptability. Using a comprehensive telecom dataset, our model achieved high performance metrics, including an accuracy of 0.89 and ROC-AUC of 0.91, surpassing baseline approaches. Integrated into a decision support system (DSS), SmartSurveil provides actionable insights to improve customer retention, enabling telecom companies to tailor strategies dynamically. Additionally, this model addresses ethical concerns, including data privacy and algorithmic transparency, ensuring a robust and responsible CRM approach. The SmartSurveil CRM model represents a substantial advancement in predictive accuracy and practical applicability within CRM systems. |
| format | Article |
| id | doaj-art-b62828f63ade43b1ada2ae0d0a09d144 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-b62828f63ade43b1ada2ae0d0a09d1442024-12-20T15:05:09ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e258310.7717/peerj-cs.2583Revolutionizing market surveillance: customer relationship management with machine learningXiangting Shi0Yakang Zhang1Manning Yu2Lihao Zhang3Industrial Engineering and Operations Research Department, Columbia University, New York, United StatesIndustrial Engineering and Operations Research Department, Columbia University, New York, United StatesDepartment of Statistics, Columbia University, Amsterdam Avenue New York, New York, United StatesDepartment of Information Engineering, Chinese University of Hong Kong, Ho Sin Hang Engineering Building, The Chinese University of Hong Kong, Shatin, N.T, Hong KongIn the telecommunications industry, predicting customer churn is essential for retaining clients and sustaining profitability. Traditional CRM systems often fall short due to their static models, limiting responsiveness to evolving customer behaviors. To address these gaps, we developed the SmartSurveil CRM model, an ensemble-based system combining random forest, gradient boosting, and support vector machine to enhance churn prediction accuracy and adaptability. Using a comprehensive telecom dataset, our model achieved high performance metrics, including an accuracy of 0.89 and ROC-AUC of 0.91, surpassing baseline approaches. Integrated into a decision support system (DSS), SmartSurveil provides actionable insights to improve customer retention, enabling telecom companies to tailor strategies dynamically. Additionally, this model addresses ethical concerns, including data privacy and algorithmic transparency, ensuring a robust and responsible CRM approach. The SmartSurveil CRM model represents a substantial advancement in predictive accuracy and practical applicability within CRM systems.https://peerj.com/articles/cs-2583.pdfMarket surveillanceCustomer relationship managementMachine learningPredictive analyticsPersonalization |
| spellingShingle | Xiangting Shi Yakang Zhang Manning Yu Lihao Zhang Revolutionizing market surveillance: customer relationship management with machine learning PeerJ Computer Science Market surveillance Customer relationship management Machine learning Predictive analytics Personalization |
| title | Revolutionizing market surveillance: customer relationship management with machine learning |
| title_full | Revolutionizing market surveillance: customer relationship management with machine learning |
| title_fullStr | Revolutionizing market surveillance: customer relationship management with machine learning |
| title_full_unstemmed | Revolutionizing market surveillance: customer relationship management with machine learning |
| title_short | Revolutionizing market surveillance: customer relationship management with machine learning |
| title_sort | revolutionizing market surveillance customer relationship management with machine learning |
| topic | Market surveillance Customer relationship management Machine learning Predictive analytics Personalization |
| url | https://peerj.com/articles/cs-2583.pdf |
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