A Method of Bank Telemarketing Customer Prediction based on Hybrid Sampling and Stacked Deep Networks

혼성 표본 추출과 적층 딥 네트워크에 기반한 은행 텔레마케팅 고객 예측 방법

  • 이현진 (숭실사이버대학교 ICT공학과)
  • Received : 2019.08.02
  • Accepted : 2019.09.03
  • Published : 2019.09.30


Telemarketing has been used in finance due to the reduction of offline channels. In order to select telemarketing target customers, various machine learning techniques have emerged to maximize the effect of minimum cost. However, there are problems that the class imbalance, which the number of marketing success customers is smaller than the number of failed customers, and the recall rate is lower than accuracy. In this paper, we propose a method that solve the imbalanced class problem and increase the recall rate to improve the efficiency. The hybrid sampling method is applied to balance the data in the class, and the stacked deep network is applied to improve the recall and precision as well as the accuracy. The proposed method is applied to actual bank telemarketing data. As a result of the comparison experiment, the accuracy, the recall, and the precision is improved higher than that of the conventional methods.


  1. P. Kotler, K. L. Keller, "Framework for Marketing Management, 6th edition," Pearson, 2015.
  2. S. Moro, P. Cortez, and P. Rita, "A data-driven approach to predict the success of bank telemarketing," Decision Support Systems, Vol. 62, 2014, pp. 22-31.
  3. E. Turban, R. Sharda, and D. Delen, " Decision Support and Business Intelligence Systems, 9th edition," Pearson, 2010, pp. 2-35.
  4. S. L. France, and S. Ghose, "Marketing analytics: Methods, practice, implementation, and links to other fields," Expert Systems with Applications, Vol. 119, 2019, pp. 456-475.
  5. G. Marinakos, and S. Daskalaki, "Imbalanced customer classification for bank direct marketing," Journal of Marketing Analytics, Vol. 5, Issue 1, 2017, pp. 14-30.
  6. 김승수.김종우, "비정형 정보와 CNN 기법을 활용한 이진 분류 모델의 고객 행태 예측," 지능정보연구, Vol. 24, No. 2, 2018, pp. 221-241.
  7. F. Kaefer, C. M. Heilman, and S. D. Ramenofsky, "A neural network application to consumer classification to improve the timing of direct marketing activities," Computers & Operations Research, Vol. 32, No. 10, 2005, pp. 2595-2615.
  8. S. Liao, Y. Chen, and H. Hsieh, "Mining customer knowledge for direct selling and marketing," Expert Systems with Applications, Vol. 38, 2011, pp. 6059-6069.
  9. S. H. Javaheri, M. M. Sepehri, and B. Teimourpour, "Response modeling in direct marketing: a data mining based approach for target selection," Data Mining Applications with R, 2014, pp. 153-178.
  10. P. Ladyzinski, K. Zbikowski, and P. Gawrysiak, "Direct marketing campaign in retail banking with the use of deep learning and random forests," Expert Systems with Applications, Vol. 134, 2019, pp. 28-35.
  11. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, Vol. 16, 2002, pp. 321-357.
  12. G. Menardi, and N. Torelli, "Training and assessing classification rules with imbalanced data,"Data Mining and Knowledge Discovery, Vol. 28, No. 1, 2014, pp. 92-122.
  13. 김창식.김남규.곽기영, "머신러닝 및 딥러닝 연구동향 분석: 토픽모델링을 중심으로," 디지털산업정보학회 논문지, 제15권, 제2호, 2019, pp.19-28.
  14. 주명길.윤성욱, "워드 임베딩과 CNN을 사용하여 영화 리뷰에 대한 감성 분석," 디지털산업정보학회 논문지, 제15권, 제1호, 2019, pp.87-97.
  15. S. Moro, P. Cortez, and P. Rita, "A Data-Driven Approach to Predict the Success of Bank Telemarketing," Decision Support Systems, Vol. 62, 2014, pp. 22-31.