Abstract
Customer Relationship Management systems are used to enable organizations to acquire new customers, develop a continuous relationship with them, and increase customer retention for more profitability. Customer Loyalty is also known as Customer Churn. The main intention of churn prediction is to classify and find customers into churner and non-churner. A churned customer means there is more chance that the customer is about to leave the organization. So in order to find the churn customers will give more benefits to the organization. Thus, churn prediction can avoid the loss of revenue by retaining the existing customers. There are several techniques are available with ensemble and hybrid models. This paper aims to predict customer loyalty in banking sector with a novel method named mixed ensemble model and hybrid model. Ensemble acts as a wrapper for group of machine learning or deep learning methods. This paper proposes two methods to predict customer churn using ensemble method with a mixed group containing XGB Classifier, LightGBM Classifier, and MLP model. And also build a hybrid model with the combination of Multilayer Perceptron (MLP) model and Convolutional Neural Network (CNN) model. Toward this, churn data of banking sector is used and build the systems then compare the performance of two. Thus, the system with more accuracy is termed more useful for organizations to find the customers with more chances to become churn. The results of experiments showed that the two proposed systems for churn prediction perform with an accuracy of 86% to 87%.
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I would like to acknowledge the contribution and support from the Computer Science and Engineering Department of Rajiv Gandhi Institute of Technology, Kottayam.
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Latheef, J., Vineetha, S. (2021). Predicting Customer Loyalty in Banking Sector with Mixed Ensemble Model and Hybrid Model. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Computing Techniques and Applications. Smart Innovation, Systems and Technologies, vol 224. Springer, Singapore. https://doi.org/10.1007/978-981-16-1502-3_37
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