A Review on Classification Algorithm for Customer Churn Classification
Nurul Nadzirah Adnan1, Mohd Khalid Awang2
1Nurul Nadzirah Bt Adnan, Department of Informatic & Computing, University Sultan Zainal Abidin, Terengganu, Malaysia.
2Mohd Khalid Bin Awang, Department of Informatic & Computing University Sultan Zainal Abidin, Terengganu, Malaysia.
Manuscript received on 11 March 2024 | Revised Manuscript received on 27 March 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 5-15 | Volume-13 Issue-1, May 2024 | Retrieval Number: 100.1/ijrte.A803013010524 | DOI: 10.35940/ijrte.A8030.13010524
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Any sector faces a huge obstacle when it comes to retaining existing customers. The percentage of consumers who have quit using a product or service is referred to as customer churn, and it is a vital indication that offers reliable information about this percentage. When it comes to achieving long-term success in a market or industry, one of the most significant challenges that any company must face is the ability to keep their precious clients and to fulfill their needs. A review of the most significant studies on Customer Churn Prediction is presented in this paper so as to furnish the reader with an overview of frequently employed data mining methodologies and their respective performances. We provide the available statistics in addition to customer information in order to approximate customer attrition. The time period encompassing the survey extends from 2003 to 2023. During the process of Customer Churn Prediction, we identified the issues and difficulties that were linked with it and offered guidance and potential remedies.
Keywords: Artificial Neural Network, Churn Prediction, Survey, Customer
Scope of the Article: Classification