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Customer Churn Prediction for Financial Institutions Using Deep Learning Artificial Neural Networks in Zimbabwe

Domain:

socioeconomic

Record type:

paper
Creator:
PanAgripah KandieroSab
Publisher:
IGI Global
Host:
The research was conducted to develop a customer churn predictive modelling using deep neural networks for financial institutions in Zimbabwe using a local leading financial institution. This was based on a need to perform a customer churn analysis and develop a very high accurate and reliable customer churn predictive model. In this era, every customer counts, hence once acquired a business should do everything in its power to keep that customer because the cost of acquiring a new customer is far greater than the cost of keeping an existing one. Therefore the need to ascertain customers who have churned and also be at a position to anticipate those who are churning or are about to churn then take corrective measures to keep such customers on board. The study followed one of the data science research methodologies called CRoss industry standard process for data mining (CRISP-DM) which involves understanding the business, understanding the data, data preparation, modelling, validating the model then deployment of the model.

Visit

doi.org

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