IDEAS home Printed from https://ideas.repec.org/a/spr/svcbiz/v11y2017i4d10.1007_s11628-016-0332-3.html
   My bibliography  Save this article

Predicting direct marketing response in banking: comparison of class imbalance methods

Author

Listed:
  • Vera L. Miguéis

    (Universidade do Porto)

  • Ana S. Camanho

    (Universidade do Porto)

  • José Borges

    (Universidade do Porto)

Abstract

Customers’ response is an important topic in direct marketing. This study proposes a data mining response model supported by random forests to support the definition of target customers for banking campaigns. Class imbalance is a typical problem in telemarketing that can affect the performance of the data mining techniques. This study also contributes to the literature by exploring the use of class imbalance methods in the banking context. The performance of an undersampling method (the EasyEnsemble algorithm) is compared with that of an oversampling method (the Synthetic Minority Oversampling Technique) in order to determine the most appropriate specification. The importance of the attribute features included in the response model is also explored. In particular, discriminative performance was enhanced by the inclusion of demographic information, contact details and socio-economic features. Random forests, supported by an undersampling algorithm, presented very high prediction performance, outperforming the other techniques explored.

Suggested Citation

  • Vera L. Miguéis & Ana S. Camanho & José Borges, 2017. "Predicting direct marketing response in banking: comparison of class imbalance methods," Service Business, Springer;Pan-Pacific Business Association, vol. 11(4), pages 831-849, December.
    • Handle: RePEc:spr:svcbiz:v:11:y:2017:i:4:d:10.1007_s11628-016-0332-3
    DOI: 10.1007/s11628-016-0332-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11628-016-0332-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11628-016-0332-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Seyed Hosseini & Alireza Ziaei Bideh, 2014. "A data mining approach for segmentation-based importance-performance analysis (SOM–BPNN–IPA): a new framework for developing customer retention strategies," Service Business, Springer;Pan-Pacific Business Association, vol. 8(2), pages 295-312, June.
    2. David Olson & Qing Cao & Ching Gu & Donhee Lee, 2009. "Comparison of customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 3(2), pages 117-130, June.
    3. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(1), pages 151-159, February.
    4. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
    5. Alireza Abroud & Yap Choong & Saravanan Muthaiyah & David Fie, 2015. "Adopting e-finance: decomposing the technology acceptance model for investors," Service Business, Springer;Pan-Pacific Business Association, vol. 9(1), pages 161-182, March.
    6. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(5), pages 687-698, October.
    7. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
    8. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(3), pages 381-386, June.
    9. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(4), pages 525-537, August.
    10. ,, 1998. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 14(2), pages 285-292, April.
    11. Ming-Tsang Lu & Gwo-Hshiung Tzeng & Hilary Cheng & Chih-Cheng Hsu, 2015. "Exploring mobile banking services for user behavior in intention adoption: using new hybrid MADM model," Service Business, Springer;Pan-Pacific Business Association, vol. 9(3), pages 541-565, September.
    12. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bigler, T. & Kammermann, M. & Baumann, P., 2023. "A matheuristic for a customer assignment problem in direct marketing," European Journal of Operational Research, Elsevier, vol. 304(2), pages 689-708.
    2. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    3. Yen-Chun Chou & Howard Hao-Chun Chuang, 2018. "A predictive investigation of first-time customer retention in online reservation services," Service Business, Springer;Pan-Pacific Business Association, vol. 12(4), pages 685-699, December.
    4. Mahsa Samsami & Ralf Wagner, 2021. "Investment Decisions with Endogeneity: A Dirichlet Tree Analysis," JRFM, MDPI, vol. 14(7), pages 1-19, July.
    5. Santiago Carbo-Valverde & Pedro Cuadros-Solas & Francisco Rodríguez-Fernández, 2020. "A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-39, October.
    6. Murtaza Nasir & Nichalin Summerfield & Ali Dag & Asil Oztekin, 2020. "A service analytic approach to studying patient no-shows," Service Business, Springer;Pan-Pacific Business Association, vol. 14(2), pages 287-313, June.
    7. Ki-Kwang Lee & Hong-Hee Lee & Su-Ji Cho & Gyung-Su Min, 2022. "The context-based review recommendation system in e-business platform," Service Business, Springer;Pan-Pacific Business Association, vol. 16(4), pages 991-1013, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
    2. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    3. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
    4. Gubela, Robin M. & Lessmann, Stefan & Jaroszewicz, Szymon, 2020. "Response transformation and profit decomposition for revenue uplift modeling," European Journal of Operational Research, Elsevier, vol. 283(2), pages 647-661.
    5. Allen C. Goodman & Miron Stano, 2000. "Hmos and Health Externalities: A Local Public Good Perspective," Public Finance Review, , vol. 28(3), pages 247-269, May.
    6. Bettina Campedelli & Andrea Guerrina & Giulia Romano & Chiara Leardini, 2014. "La performance della rete ospedaliera pubblica della regione Veneto. L?impatto delle variabili ambientali e operative sull?efficienza," MECOSAN, FrancoAngeli Editore, vol. 2014(92), pages 119-142.
    7. Penn Loh & Zoë Ackerman & Joceline Fidalgo & Rebecca Tumposky, 2022. "Co-Education/Co-Research Partnership: A Critical Approach to Co-Learning between Dudley Street Neighborhood Initiative and Tufts University," Social Sciences, MDPI, vol. 11(2), pages 1-17, February.
    8. O'Brien, Raymond & Patacchini, Eleonora, 2003. "Testing the exogeneity assumption in panel data models with "non classical" disturbances," Discussion Paper Series In Economics And Econometrics 0302, Economics Division, School of Social Sciences, University of Southampton.
    9. YongSeog Kim & W. Nick Street & Gary J. Russell & Filippo Menczer, 2005. "Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms," Management Science, INFORMS, vol. 51(2), pages 264-276, February.
    10. Yanling Li & Zita Oravecz & Shuai Zhou & Yosef Bodovski & Ian J. Barnett & Guangqing Chi & Yuan Zhou & Naomi P. Friedman & Scott I. Vrieze & Sy-Miin Chow, 2022. "Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 376-402, June.
    11. Oscar J. Cacho & Robyn L. Hean & Russell M. Wise, 2003. "Carbon‐accounting methods and reforestation incentives," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 47(2), pages 153-179, June.
    12. Walter M. Cadette, 1999. "Financing Long-Term Care: Options for Policy," Economics Working Paper Archive wp_283, Levy Economics Institute.
    13. Eggli, Yves & Halfon, Patricia & Chikhi, Mehdi & Bandi, Till, 2006. "Ambulatory healthcare information system: A conceptual framework," Health Policy, Elsevier, vol. 78(1), pages 26-38, August.
    14. M. A. Noor & E.A. Al-Said, 2002. "Finite-Difference Method for a System of Third-Order Boundary-Value Problems," Journal of Optimization Theory and Applications, Springer, vol. 112(3), pages 627-637, March.
    15. Yong He & Zhiyi Tan, 2002. "Ordinal On-Line Scheduling for Maximizing the Minimum Machine Completion Time," Journal of Combinatorial Optimization, Springer, vol. 6(2), pages 199-206, June.
    16. Henderson, James E. & Dunn, Michael A., 2007. "Investigating the Potential of Fee-Based Recreation on Private Lands in the Lower Mississippi River Delta," 2007 Annual Meeting, February 4-7, 2007, Mobile, Alabama 34822, Southern Agricultural Economics Association.
    17. Eike Quilling & Birgit Babitsch & Kevin Dadaczynski & Stefanie Kruse & Maja Kuchler & Heike Köckler & Janna Leimann & Ulla Walter & Christina Plantz, 2020. "Municipal Health Promotion as Part of Urban Health: A Policy Framework for Action," Sustainability, MDPI, vol. 12(16), pages 1-10, August.
    18. Haeringer, Guillaume & Klijn, Flip, 2009. "Constrained school choice," Journal of Economic Theory, Elsevier, vol. 144(5), pages 1921-1947, September.
    19. Alireza Nili & Mary Tate & David Johnstone, 2019. "The process of solving problems with self-service technologies: a study from the user’s perspective," Electronic Commerce Research, Springer, vol. 19(2), pages 373-407, June.
    20. Chein-Shan Liu & Zhuojia Fu & Chung-Lun Kuo, 2017. "Directional Method of Fundamental Solutions for Three-dimensional Laplace Equation," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 9(6), pages 112-123, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:svcbiz:v:11:y:2017:i:4:d:10.1007_s11628-016-0332-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.