Prediction of the Effectiveness of Bank Marketing Strategies Using the XGBoost Model

Research Article
Open access

Prediction of the Effectiveness of Bank Marketing Strategies Using the XGBoost Model

Yuanzi Zheng 1*
  • 1 Institute of Mathematics, Jilin University, Changchun, China    
  • *corresponding author zhengyz1023@mails.jlu.edu.cn
AEMPS Vol.170
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-019-1
ISBN (Online): 978-1-80590-020-7

Abstract

Currently, major banks are encountering significant challenges in a highly competitive environment characterized by declining interest rates and the underperformance of traditional marketing strategies. These challenges include customer churn, insufficient appeal to new customers, reduced profitability, lower marketing success rates, and increasing marketing costs. To enhance the effectiveness of bank marketing and reduce associated costs, this study leverages the bank marketing data set from Alibaba Cloud Tianchi. It introduces and analyzes key parameter concepts within the data set and performs data cleaning. After evaluating various models and employing research methodologies such as feature selection, imbalanced data handling, and correlation analysis, the XGBoost model combined with the RandomUnderSampler method for addressing data imbalance was selected. The findings indicate that, compared to traditional models, this approach achieves higher precision, recall, and accuracy rates. Furthermore, considering the primary objective of bank marketing and prioritizing recall rate, this method attains a recall rate of 84.3% for marketing customers within the data set. Consequently, this approach holds substantial significance for banks in predicting customer deposit demands and optimizing deposit marketing strategies, thereby assisting banks in reducing marketing costs and enhancing marketing success rates.

Keywords:

Bank marketing, Machine learning, XGBoost model, RandomUnderSampler

Zheng,Y. (2025). Prediction of the Effectiveness of Bank Marketing Strategies Using the XGBoost Model . Advances in Economics, Management and Political Sciences,170,17-28.
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References

[1]. Jin, J. (2025) Research on marketing digital transformation strategy of city commercial bank.Market Modernization, (02):138-140.

[2]. Li, J. (2024) Research on digital precision marketing strategy of commercial banks. Market Modernization, (24):106-108.

[3]. Lu L. (2022) Research on customer precision marketing of housing savings banks in China and Germany based on data mining.University of International Business and Economics.

[4]. Tang X, Zhu Y. (2024) Enhancing bank marketing strategies with ensemble learning: Empirical analysis. PLoS One. Jan 11;19(1):e0294759.

[5]. Zhang X, Liu J, Zhang W. (2025) Prediction of mechanical properties of PVC-P geomagnetic film with scratch damage based on XGBoost algorithm[J/OL].Water Resourses and Power,(05):111-115.

[6]. Assalé P Y F, Kouao A F A, Kessé T M. (2025) Machine learning and neural networks in predicting grain-size of sandy formations. Results in Earth Sciences, 3100084-100084.

[7]. Confusion Matrix. Retrieved from https://blog.csdn.net/seagal890/article/details/105059498

[8]. Imani M, Beikmohammadi A, Arabnia R H. (2025) Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels.Technologies, 13(3):88-88.

[9]. Liu S,Tian Q , Liu Y, et al. (2024) Joint Statistical Inference for the Area under the ROC Curve and Youden Index under a Density Ratio Model. Mathematics, 12(13):2118-2118.

[10]. Zou Q, Wang J, Li Q, et al. (2025) The accurate estimation of soil available nutrients achieved by feature selection coupled with preprocessing based on MIR and pXRF fusion. European Journal of Agronomy, 168127633-127633.

Cite this article

Zheng,Y. (2025). Prediction of the Effectiveness of Bank Marketing Strategies Using the XGBoost Model . Advances in Economics, Management and Political Sciences,170,17-28.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 9th International Conference on Economic Management and Green Development

ISBN：978-1-80590-019-1(Print) / 978-1-80590-020-7(Online)
Editor：Florian Marcel Nuţă
Conference website: https://2025.icemgd.org/
Conference date: 26 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.170
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Jin, J. (2025) Research on marketing digital transformation strategy of city commercial bank.Market Modernization, (02):138-140.

[2]. Li, J. (2024) Research on digital precision marketing strategy of commercial banks. Market Modernization, (24):106-108.

[3]. Lu L. (2022) Research on customer precision marketing of housing savings banks in China and Germany based on data mining.University of International Business and Economics.

[4]. Tang X, Zhu Y. (2024) Enhancing bank marketing strategies with ensemble learning: Empirical analysis. PLoS One. Jan 11;19(1):e0294759.

[5]. Zhang X, Liu J, Zhang W. (2025) Prediction of mechanical properties of PVC-P geomagnetic film with scratch damage based on XGBoost algorithm[J/OL].Water Resourses and Power,(05):111-115.

[6]. Assalé P Y F, Kouao A F A, Kessé T M. (2025) Machine learning and neural networks in predicting grain-size of sandy formations. Results in Earth Sciences, 3100084-100084.

[7]. Confusion Matrix. Retrieved from https://blog.csdn.net/seagal890/article/details/105059498

[8]. Imani M, Beikmohammadi A, Arabnia R H. (2025) Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels.Technologies, 13(3):88-88.

[9]. Liu S,Tian Q , Liu Y, et al. (2024) Joint Statistical Inference for the Area under the ROC Curve and Youden Index under a Density Ratio Model. Mathematics, 12(13):2118-2118.

[10]. Zou Q, Wang J, Li Q, et al. (2025) The accurate estimation of soil available nutrients achieved by feature selection coupled with preprocessing based on MIR and pXRF fusion. European Journal of Agronomy, 168127633-127633.

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