Title | A Stacking-Based Data Mining Solution to Customer Churn Prediction |
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Type | Journal article |
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Authors | Shabankareh, M.J., Shabankareh, M.A., Nazarian, A., Ranjbaran, A. and Seyyedamiri, N. |
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Abstract | Abstract In today’s competitive world, organizations are in a constant struggle to retain their current customers while attracting new customers through various methods. Customer churn is a major challenge in different industries and companies. Despite their initial successful attempts at attracting customers, organizations soon face the fact that their current customers may turn away towards their rivals. By identifying churn candidates, organizations will be able to guarantee their future success by revising their customer relationship management policy. Analyzing the data of the telecommunications industries, this study provided an effective early-churn-detection solution using modern techniques by stacking data mining algorithms. Research findings indicate that integrating support vector machines (SVMs) with the chi-square automatic interaction detection (CHAID) decision tree can yield the best outcome. The results show the proper accuracy of the proposed churn prediction solution. In addition, stacking contributed to improved customer churn detection results. |
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Keywords | Marketing |
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Journal | Journal of Relationship Marketing |
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Journal citation | 21 (2), pp. 124-147 |
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ISSN | 1533-2667 |
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| 1533-2675 |
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Year | 2022 |
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Publisher | Taylor & Francis |
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Accepted author manuscript | File Access Level Open (open metadata and files) |
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Digital Object Identifier (DOI) | https://doi.org/10.1080/15332667.2021.1889743 |
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Web address (URL) | https://doi.org/10.1080/15332667.2021.1889743 |
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Publication dates |
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Published online | 29 Jul 2021 |
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Published in print | 2022 |
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Page range | 1-24 |
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