Forecasting client retention — A machine-learning approach

Satu Elisa Schaeffer and Sara Veronica Rodriguez Sanchez

Journal of Retailing and Consumer Services, 2020, vol. 52, issue C

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.

Keywords: Client retention; Sales forecasting; Machine learning; Prepaid unitary services (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:52:y:2020:i:c:s0969698919302668

DOI: 10.1016/j.jretconser.2019.101918

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