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A big data analytics model for customer churn prediction in the retiree segment

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  • Shirazi, Farid
  • Mohammadi, Mahbobeh

Abstract

Undoubtedly, the change in consumers’ choices and expectations, stemming from the emerging technology and also significant availability of different products and services, created a highly competitive landscape in various customer service sectors, including the financial industry. Accordingly, the Canadian banking industry has also become highly competitive due to the threats and disruptions caused by not only direct competitors, but also new entrants to the market.

Suggested Citation

  • Shirazi, Farid & Mohammadi, Mahbobeh, 2019. "A big data analytics model for customer churn prediction in the retiree segment," International Journal of Information Management, Elsevier, vol. 48(C), pages 238-253.
    • Handle: RePEc:eee:ininma:v:48:y:2019:i:c:p:238-253
    DOI: 10.1016/j.ijinfomgt.2018.10.005
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    Citations

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    Cited by:

    1. Lewlisa Saha & Hrudaya Kumar Tripathy & Tarek Gaber & Hatem El-Gohary & El-Sayed M. El-kenawy, 2023. "Deep Churn Prediction Method for Telecommunication Industry," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
    2. Shivam Gupta & Théo Justy & Shampy Kamboj & Ajay Kumar & Eivind Kristoffersen, 2021. "Big data and firm marketing performance: Findings from knowledge-based view," Post-Print hal-03609916, HAL.
    3. Anwar, Muhammad Azfar & Zong, Zupan & Mendiratta, Aparna & Yaqub, Muhammad Zafar, 2024. "Antecedents of big data analytics adoption and its impact on decision quality and environmental performance of SMEs in recycling sector," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    4. Morimura, Fumikazu & Sakagawa, Yuji, 2023. "The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    5. Ebru Pekel Ozmen & Tuncay Ozcan, 2022. "A novel deep learning model based on convolutional neural networks for employee churn prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 539-550, April.
    6. Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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