Skip to main content
SpringerLink
Log in

Data Mining Approach for Direct Marketing of Banking Products with Profit/Cost Analysis

  • Article
  • Published:
The Review of Socionetwork Strategies Aims and scope Submit manuscript

Abstract

Nowadays, many businesses, such as banks, use direct marketing methods to reach customers to minimize the campaigning cost and maximize the return rate. To achieve this, huge customer data should be analyzed to determine the most appropriate product offer for each customer and the most effective channel to reach her/him. However, since only a very small amount of responses collected from the customers are positive to the offers, the dataset is very imbalanced. This decreases sensitivity ratio of prediction results and makes it difficult to make a successful product and channel selection for the offer. In this paper, we propose a hybrid system, which first classifies the customers to decide if s/he is interested in the offered product, and then clusters them for product and channel suggestions. Experiments with real life banking data show very promising accuracy results for predicting the proper product and channel for the customers. Moreover, cost-profit analysis is also added to this problem. Our experiment results show that the proposed method decreases a fraction of the total profit, but since the decrease in the total cost is very large, there is a huge increase in the overall profit/cost ratio.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Shih, J.-Y., Chen, W.-H., Chang, Y.-J. (2014). Developing target marketing models for personal loans. Industrial Engineering and Engineering Management (IEEM), IEEE International Conference.

  2. Nachev, A., Hogan, M. (2014). Application of multilayer perceptrons for response modeling. Proceedings on the International Conference on Artificial Intelligence, Springfield, The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing.

  3. Nueno, Jose Luis, & John, A. (1998). Quelch. The mass marketing of luxury. Business Horizons, 41(6), 61–68.

    Article  Google Scholar 

  4. Ling, C. X., & Li, C. (1998). Data mining for direct marketing: Problems and solutions. KDD, 98, 73–79.

    Google Scholar 

  5. Kotler, Philip. (1989). From mass marketing to mass customization. Planning Review, 17(5), 10–47.

    Article  Google Scholar 

  6. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.

    Article  Google Scholar 

  7. Almana, A. M., Mehmet, S. A., & Rasheed, A. (2014). A survey on data mining techniques in customer churn analysis for telecom industry. Journal of Engineering Research and Applications, 4(5), 165–171.

    Google Scholar 

  8. Zubi, Z. S., & Rema, A. S. (2014). Improves treatment programs of lung cancer using data mining techniques. Journal of Software Engineering and Applications, 7(2), 69.

    Article  Google Scholar 

  9. Ozkan, M. B., et al. (2013). A data mining-based wind power forecasting method: results for wind power plants in Turkey. Data warehousing and knowledge discovery (pp. 268–276). Berlin Heidelberg: Springer.

    Google Scholar 

  10. Lopez-Yanez, Itzama, Sheremetov, Leonid, & Camacho-Nieto, Oscar. (2013). Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering, database and expert systems applications. Berlin Heidelberg: Springer.

    Google Scholar 

  11. Ngai, E. W. T., Li, X., & Dorothy, C. K. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592–2602.

    Article  Google Scholar 

  12. Cohen, M.-D. (2004). Exploiting response models: Optimizing cross-sell and upsell opportunities in banking. Information Systems, 29, 327–341.

    Article  Google Scholar 

  13. Elsalamony, H. A. (2014). Bank direct marketing analysis of data mining techniques. International Journal of Computer Applications, 85, 975–8887.

    Article  Google Scholar 

  14. Bahari, T. F., & Elayidom, M. S. (2015). An efficient CRM-data mining framework for the prediction of customer behaviour. Procedia Computer Science, 46, 725–731.

    Article  Google Scholar 

  15. Pan, Y., Tang, Z. (2014). Ensemble methods in bank direct marketing. Service Systems and Service Management (ICSSSM), 2014 11th International Conference on. IEEE.

  16. Nobibon, F.T., Roel L., Frits S. (2008). Models for the optimization of promotion campaigns: Exact and heuristic algorithms. Social Science Research Network, Available at SSRN 1290503.

Download references

Author information

Authors and Affiliations

  1. Middle East Technical University, 06800, Ankara, Turkey

    Merve Mitik, Ozan Korkmaz, Pinar Karagoz & Ismail Hakki Toroslu

  2. Intertech Bilgi Islem Ve Pazarlama Ticaret A.S., Istanbul, Turkey

    Ferhat Yucel

Authors
  1. Merve Mitik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ozan Korkmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pinar Karagoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ismail Hakki Toroslu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ferhat Yucel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pinar Karagoz.

Additional information

This work is partially supported by Intertech within the scope of research collaboration project.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mitik, M., Korkmaz, O., Karagoz, P. et al. Data Mining Approach for Direct Marketing of Banking Products with Profit/Cost Analysis. Rev Socionetwork Strat 11, 17–31 (2017). https://doi.org/10.1007/s12626-017-0002-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12626-017-0002-5

Keywords

Search

Navigation