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Improve customer churn prediction through the proposed PCA-PSO-K means algorithm in the communication industry

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A Correction to this article was published on 31 March 2023

A Correction to this article was published on 16 January 2023

This article has been updated

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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Acknowledgements

The authors thank the data provider for their extensive support.

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Authors and Affiliations

  1. Department of Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Maryam Sadeghi & Naser Khani

  2. Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Mohammad Naderi Dehkordi & Behrang Barekatain

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  1. Maryam Sadeghi
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  2. Mohammad Naderi Dehkordi
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  3. Behrang Barekatain
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  4. Naser Khani
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Correspondence to Mohammad Naderi Dehkordi.

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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

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