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

Author

Listed:
  • Abbas Keramati

    (University of Tehran)

  • Hajar Ghaneei

    (Payame Noor University)

  • Seyed Mohammad Mirmohammadi

    (Payame Noor University)

Abstract

Background 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. Methods 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. Results The results represent the characteristics of churned customers. Conclusions 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.

Suggested Citation

  • Abbas Keramati & Hajar Ghaneei & Seyed Mohammad Mirmohammadi, 2016. "Developing a prediction model for customer churn from electronic banking services using data mining," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-13, December.
    • Handle: RePEc:spr:fininn:v:2:y:2016:i:1:d:10.1186_s40854-016-0029-6
    DOI: 10.1186/s40854-016-0029-6
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    References listed on IDEAS

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    1. Risselada, Hans & Verhoef, Peter C. & Bijmolt, Tammo H.A., 2010. "Staying Power of Churn Prediction Models," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 198-208.
    2. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
    3. Gigliarano, Chiara & Figini, Silvia & Muliere, Pietro, 2014. "Making classifier performance comparisons when ROC curves intersect," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 300-312.
    4. Vijay M. KUMBHAR, 2011. "Factors Affecting On Customers’ Satisfaction In E-Banking: Some Evidences Form Indian Banks," Management Research and Practice, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 3(4), pages 1-14, December.
    5. V. L. Miguéis & D. Van Den Poel & A.S. Camanho & J. Falcao E Cunha, 2012. "Modeling Partial Customer Churn: On the Value of First Product-Category Purchase Sequences," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/790, Ghent University, Faculty of Economics and Business Administration.
    6. Keramati, Abbas & Ardabili, Seyed M.S., 2011. "Churn analysis for an Iranian mobile operator," Telecommunications Policy, Elsevier, vol. 35(4), pages 344-356, May.
    7. Dudyala Anil Kumar & V. Ravi, 2008. "Predicting credit card customer churn in banks using data mining," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(1), pages 4-28.
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    Cited by:

    1. Abedin, Mohammad Zoynul & Hajek, Petr & Sharif, Taimur & Satu, Md. Shahriare & Khan, Md. Imran, 2023. "Modelling bank customer behaviour using feature engineering and classification techniques," Research in International Business and Finance, Elsevier, vol. 65(C).
    2. Maldonado, Sebastián & Domínguez, Gonzalo & Olaya, Diego & Verbeke, Wouter, 2021. "Profit-driven churn prediction for the mutual fund industry: A multisegment approach," Omega, Elsevier, vol. 100(C).

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