Abstract
Customer churn prediction is one of the areas in Customer Relationship Management that differentiates loyal customers from factors that have a negative impact on business growth. Hence, various machine learning-based methods have been developed by researchers to accurately predict customer churn. However, high dimensionality and low prediction accuracy are problems in identifying averse customers. This paper presents a new system called PCA-PSO-K Means algorithm, which combines three algorithms: principal component analysis (PCA) for data set feature reduction, K Means algorithm for classification, and particle swarm optimization (PSO) algorithm to optimize K Means in providing initial centroids. The experimental results in the data set of one of the fixed internet providers in Isfahan Province show the improvement of the accuracy of customer churn prediction. The proposed system has an accuracy of 99.77%, a sensitivity of 75%, a specificity of 99.81% and a correlation coefficient of 0.443 ± 0.271. Found.
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Change history
18 March 2023
Affiliation 2 is updated from “Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran” to “Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad,Iran”.
16 January 2023
A Correction to this paper has been published: https://doi.org/10.1007/s11227-022-05015-z
31 March 2023
A Correction to this paper has been published: https://doi.org/10.1007/s11227-023-05204-4
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The original online version of this article was revised: In this article the affiliation ‘Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran’ for Naser Khani was missing.
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Sadeghi, M., Dehkordi, M.N., Barekatain, B. et al. Improve customer churn prediction through the proposed PCA-PSO-K means algorithm in the communication industry. J Supercomput 79, 6871–6888 (2023). https://doi.org/10.1007/s11227-022-04907-4
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DOI: https://doi.org/10.1007/s11227-022-04907-4