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Comparative Study on Customer Churn Prediction by Using Machine Learning Techniques

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Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1863))

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Abstract

Customer churn prediction is crucial for businesses in different industries, such as telecommunications, banking, insurance and e-commerce, because acquiring new customers is often more costly than retaining existing ones. Customer churn prediction (CCP) aims to identify customers who are likely to terminate their relationship with a business, enabling companies to take proactive measures to retain them. Machine learning approaches have emerged as a viable strategy for developing effective churn prediction models, employing past customer data to find churn predictors. A comparative study on the most popular supervised machine learning algorithms, including Logistic Regression, Decision tree and Ensemble approaches such as Bagging, Boosting, Stacking and Voting, was applied to predict customer churn in the telecommunications industry. Since the studied dataset is skewed towards non-churners, we investigated the SMOTE and SMOTEENN sampling strategies to balance the dataset. According to the findings of our study, machine learning is a viable method for predicting customer churn. Furthermore, our results show that ensemble learners outperform single-base learners, and a balanced training dataset is expected to improve the classifiers’ performance.

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Authors and Affiliations

  1. Frankfurt University of Applied Sciences, Frankfurt am Main, Germany

    Shashikant Kumar & Doina Logofatu

Authors
  1. Shashikant Kumar
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  2. Doina Logofatu
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Corresponding author

Correspondence to Doina Logofatu .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Siridech Boonsang

  3. Iwate Prefectural University, Iwate, Japan

    Hamido Fujita

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  6. King Mongkut's Institute of Technology, Ladkrabang, Thailand

    Kitsuchart Pasupa

  7. Malaysia Japan International Institute of Technology, Kuala Lumpur, Malaysia

    Ali Selamat

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Kumar, S., Logofatu, D. (2023). Comparative Study on Customer Churn Prediction by Using Machine Learning Techniques. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_28

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  • DOI: https://doi.org/10.1007/978-3-031-42430-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42429-8

  • Online ISBN: 978-3-031-42430-4

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