Abstract
A customer relationship management system is used to manage company relationships with current and possible customers. Following a thorough review of contemporary literature, different data mining techniques employed in different types of business, corporate sectors and organizations are analyzed. A model that would be helpful to identify customers’ behavior in the banking sector is then proposed. Three classifiers, k-NN, decision tree and artificial neural networks are used to predict customer behavior and are assessed in order to determine which classifier performs better for predicting customer behavior in the banking sector.
Highlights
Understanding customer nature and characteristics is very important for successful business growth
Direct bank marketing dataset obtained from UCI repository is used for fuzzy logic model evaluation
The study investigates analyzed different classifiers such as multilayer perceptron neural network, nominal or logistic regression, Bayesian networks and decision tree model and applied on direct bank marketing dataset obtained from UCI repository
Summary
Abstract—A customer relationship management system is used to manage company relationships with current and possible customers. Following a thorough review of contemporary literature, different data mining techniques employed in different types of business, corporate sectors and organizations are analyzed. A model that would be helpful to identify customers’ behavior in the banking sector is proposed. K-NN, decision tree and artificial neural networks are used to predict customer behavior and are assessed in order to determine which classifier performs better for predicting customer behavior in the banking sector
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