Developing a prediction model for customer churn from electronic banking services using data mining

Abbas Keramati · Hajar Ghaneei · Seyed Mohammad Mirmohammadi

116 citations0/0 files storedFinancial Innovation

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Given the importance of customers as the most valuable assets of organizations, customer retention seems to be an essential, basic requirement for any organization. Banks are no exception to this rule. The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention. Being based on existing information technologies which allow one to collect data from organizations’ databases, data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data. In this research, the decision tree technique was applied to build a model incorporating this knowledge. The results represent the characteristics of churned customers. Bank managers can identify churners in future using the results of decision tree. They should be provide some strategies for customers whose features are getting more likely to churner’s features.

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References

2016An improved method to construct basic probability assignment based on the confusion matrix for classification problem 2004Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting 2010Building comprehensible customer churn prediction models with advanced rule induction techniques 2011Customer churn prediction in telecommunications 2005Computer assisted customer churn management: State-of-the-art and future trends 2009Customer churn prediction by hybrid neural networks 2011Credit card churn forecasting by logistic regression and decision tree 2014Improved churn prediction in telecommunication industry using data mining techniques

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