Abstract
Nowadays, the consistency of customer relationship is not guaranteed. Since organizations are faced with many costs with losing their customers and to generate stable profits, the main focus of the organizations is based on customer retention.
This research aims to develop a three-phase framework for fuzzy dynamic churn prediction of high-value customers. Three steps include identification of high-value customers, determination of the degree of churn with the help of fuzzy inference system, and prediction of their future churn.
Proposed method was implemented on the database of a finance and credit institution successfully and provided us the ability to define churn rate in the banking industry, considering its changes over the time and select a suitable prediction model to predict the future churn rate.
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Safinejad, F., Noughabi, E.A.Z., Far, B.H. (2018). A Fuzzy Dynamic Model for Customer Churn Prediction in Retail Banking Industry. In: Moshirpour, M., Far, B., Alhajj, R. (eds) Applications of Data Management and Analysis . Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-95810-1_7
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DOI: https://doi.org/10.1007/978-3-319-95810-1_7
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