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Forecasting client retention — A machine-learning approach

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  • Schaeffer, Satu Elisa
  • Rodriguez Sanchez, Sara Veronica

Abstract

In the age of big data, companies store practically all data on any client transaction. Making use of this data is commonly done with machine-learning techniques so as to turn it into information that can be used to drive business decisions. Our interest lies in using data on prepaid unitary services in a business-to-business setting to forecast client retention: whether a particular client is at risk of being lost before they cease being clients. The purpose of such a forecast is to provide the company with an opportunity to reach out to such clients as an effort to ensure their retention.

Suggested Citation

  • Schaeffer, Satu Elisa & Rodriguez Sanchez, Sara Veronica, 2020. "Forecasting client retention — A machine-learning approach," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    • Handle: RePEc:eee:joreco:v:52:y:2020:i:c:s0969698919302668
    DOI: 10.1016/j.jretconser.2019.101918
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    References listed on IDEAS

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    Cited by:

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    2. Kim, Jina & Ji, HongGeun & Oh, Soyoung & Hwang, Syjung & Park, Eunil & del Pobil, Angel P., 2021. "A deep hybrid learning model for customer repurchase behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    3. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    4. Lewlisa Saha & Hrudaya Kumar Tripathy & Soumya Ranjan Nayak & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review," Sustainability, MDPI, vol. 13(9), pages 1-35, May.

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