Customer Churn Scrutiny and Prediction Using Data Extraction  Models in Funding Sectors

P Ramalingamm; G Subba Rao; K Hari; T R Chaitanya

doi:10.55524/

Authors

P Ramalingamm Assistant Professor, Department of Information Technology, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author
G Subba Rao Assistant Professor, Department of Information Technology, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author
K Hari Assistant Professor, Department of Information Technology, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author
T R Chaitanya Professor, Department of Information Technology, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Customer churn, prediction, data Extraction, catalog, machine learning

Abstract

A new method for customer churn analysis and prediction has been proposed. The method uses data Extraction m o d e l in Funding industries. This has been inspired by the fact that there are around 1,5 million churn customers in a year which is increasing every year. Churncustomer prediction is an activity carried out to predict whether the customer will leave the company or not. One way to predict this customer churn is to use a catalog technique from data Extraction that produces amachine learning model. This study tested 5 different catalog methods with a dataset consisting of 57 attributes. Experiments were carried out several times using comparisons between different classes. Support Vector Machine (SVM) with a comparison of 50:50 Class sampling data is the best method for predicting churn customers at a private bank in Indonesia. The results of this modeling can be utilized by company who will apply strategic action to prevent customer churn.

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