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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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. Jeff Allen & Santiago Carbo-Valverde & Sujit Chakravorti & Francisco Rodriguez-Fernandez & Oya Pinar Ardic, 2022. "Assessing incentives to increase digital payment acceptance and usage: A machine learning approach," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-29, November.
    2. 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.
    3. 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.
    4. Jeff Allen & Santiago Carbo Valverde & Sujit Chakravorti & Francisco Rodriguez-Fernandez & Oya Pinar Ardic, 2022. "Assessing Incentives to Increase Digital Payment Acceptance and Usage," World Bank Publications - Books, The World Bank Group, number 37487, April.
    5. 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.
    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.
    8. Mahsa Samsami & Ralf Wagner, 2021. "Investment Decisions with Endogeneity: A Dirichlet Tree Analysis," JRFM, MDPI, vol. 14(7), pages 1-19, July.
    9. 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.

    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. 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.
    2. Juin-Hao Ho & Gwo-Guang Lee & Ming-Tsang Lu, 2020. "Exploring the Implementation of a Legal AI Bot for Sustainable Development in Legal Advisory Institutions," Sustainability, MDPI, vol. 12(15), pages 1-17, July.
    3. 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.
    4. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges," Management Science, INFORMS, vol. 66(6), pages 2495-2522, June.
    5. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    6. Van den Poel, Dirk & Lariviere, Bart, 2004. "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
    7. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    8. Jiacong Wu & Yu Wang & Ru Zhang & Jing Cai, 2018. "An Approach to Discovering Product/Service Improvement Priorities: Using Dynamic Importance-Performance Analysis," Sustainability, MDPI, vol. 10(10), pages 1-26, October.
    9. Noopur Saxena & Navneet Gera & Mayur Taneja, 2023. "An empirical study on facilitators and inhibitors of adoption of mobile banking in India," Electronic Commerce Research, Springer, vol. 23(4), pages 2573-2604, December.
    10. Baesens, Bart & Verstraeten, Geert & Van den Poel, Dirk & Egmont-Petersen, Michael & Van Kenhove, Patrick & Vanthienen, Jan, 2004. "Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers," European Journal of Operational Research, Elsevier, vol. 156(2), pages 508-523, July.
    11. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    12. 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.
    13. Seret, Alex & Verbraken, Thomas & Versailles, Sébastien & Baesens, Bart, 2012. "A new SOM-based method for profile generation: Theory and an application in direct marketing," European Journal of Operational Research, Elsevier, vol. 220(1), pages 199-209.
    14. Danijel Bratina & Armand Faganel, 2023. "Using Supervised Machine Learning Methods for RFM Segmentation: A Casino Direct Marketing Communication Case," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(1), pages 7-22.
    15. Luis Castro-Martín & Maria del Mar Rueda & Ramón Ferri-García, 2020. "Inference from Non-Probability Surveys with Statistical Matching and Propensity Score Adjustment Using Modern Prediction Techniques," Mathematics, MDPI, vol. 8(6), pages 1-19, June.
    16. Benjamin Lev, 2007. "Book Reviews," Interfaces, INFORMS, vol. 37(3), pages 300-304, June.
    17. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.
    18. Stephen C. Wingreen & Natasha C. H. L. Mazey & Stephen L. Baglione & Gordon R. Storholm, 2019. "Transfer of electronic commerce trust between physical and virtual environments: experimental effects of structural assurance and situational normality," Electronic Commerce Research, Springer, vol. 19(2), pages 339-371, June.
    19. Tsai, Pei-Hsuan, 2020. "Strategic evaluation criteria to assess competitiveness of the service industry in Taiwan," Journal of Policy Modeling, Elsevier, vol. 42(6), pages 1287-1309.
    20. Viaene, Stijn & Dedene, Guido, 2005. "Cost-sensitive learning and decision making revisited," European Journal of Operational Research, Elsevier, vol. 166(1), pages 212-220, October.

    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.