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Imbalanced customer classification for bank direct marketing

Author

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  • Georgios Marinakos

    (University of Patras)

  • Sophia Daskalaki

    (University of Patras)

Abstract

This paper aims to contribute insights on data analytics methodologies when applied to direct marketing. From a business perspective, the objective is to unveil those banking customers who are most likely to respond positively to a term deposit marketing campaign. Mathematically, this is a typical classification problem; however, in our case, the class of interest is relatively rare and the dataset imbalanced. The paper offers a comparison of performance between statistical, distance-based, induction and Machine Learning classification algorithms on predicting potential depositors, when trained with imbalanced datasets. The main effort focuses on rebalancing effectively the datasets during training so as to reverse the negative effect of imbalance and to increase the correct classifications for the under-represented class. Distance-based and cluster-based resampling techniques are applied in comparison and in combination in order to understand how customer targeting could become more effective for practitioners. Using a publicly available dataset for direct marketing of bank products, we study the influence of resampling techniques on the different algorithms and conclude that our proposed cluster-based technique is overall the most effective in relation to other well-established techniques.

Suggested Citation

  • Georgios Marinakos & Sophia Daskalaki, 2017. "Imbalanced customer classification for bank direct marketing," Journal of Marketing Analytics, Palgrave Macmillan, vol. 5(1), pages 14-30, March.
    • Handle: RePEc:pal:jmarka:v:5:y:2017:i:1:d:10.1057_s41270-017-0013-7
    DOI: 10.1057/s41270-017-0013-7
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    Cited by:

    1. Omar H. Fares & Irfan Butt & Seung Hwan Mark Lee, 2023. "Utilization of artificial intelligence in the banking sector: a systematic literature review," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 835-852, December.
    2. Jinping Hu, 2023. "Customer feature selection from high-dimensional bank direct marketing data for uplift modeling," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 160-171, June.
    3. Stéphane C. K. Tékouabou & Ștefan Cristian Gherghina & Hamza Toulni & Pedro Neves Mata & José Moleiro Martins, 2022. "Towards Explainable Machine Learning for Bank Churn Prediction Using Data Balancing and Ensemble-Based Methods," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    4. Stéphane Cédric Koumétio Tékouabou & Ştefan Cristian Gherghina & Hamza Toulni & Pedro Neves Mata & Mário Nuno Mata & José Moleiro Martins, 2022. "A Machine Learning Framework towards Bank Telemarketing Prediction," JRFM, MDPI, vol. 15(6), pages 1-19, June.
    5. Pritha Ghosh & Subrata Saha & Shamindra Nath Sanyal & Swati Mukherjee, 2021. "Positioning of private label brands of men’s apparel against national brands," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(3), pages 210-227, September.
    6. Marco Vriens & Nathan Bosch & Chad Vidden & Jason Talwar, 2022. "Prediction and profitability in market segmentation typing tools," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(4), pages 360-389, December.

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