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Forecasting client retention — A machine-learning approach
پیش بینی حفظ مشتری - یک رویکرد یادگیری ماشین-2020 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.
We work with monthly records of client transactions: each client is represented as a series of purchases and
consumptions. We vary (1) the length of the time period used to make the forecast, (2) the length of a period of
inactivity after which a client is assumed to be lost, and (3) how far in advance the forecast is made. Our
experimental work finds that current machine-learning techniques able to adequately predict, well in advance,
which clients will be lost. This knowledge permits a company to focus marketing efforts on such clients as early
as three months in advance. Keywords: Client retention | Sales forecasting | Machine learning | Prepaid unitary services |
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