Abstract
Customer segmentation is an essential process that leads a bank to gain more insight and better understand their customers. In the past, this process requires analyses of data, both customer demographic and offline financial transactions. However, from the advancement of mobile technology, mobile banking has become more accessible than before. With over 10 million digital users, SCB easy app by Siam Commercial Bank receives an enormous volume of transactions each day. In this work, we propose a method to classify mobile user’s click behaviour into two groups, i.e. ‘SME-like’ and ‘Non-SME-like’ users. Thus, the bank can easily identify the customers and offer them the right products. We convert a user’s click log into an image that aims to capture temporal information. The image representation reduces the need for feature engineering. Employing ResNet-18 with our image data can achieve 71.69% average accuracy. Clearly, the proposed method outperforms the conventional machine learning technique with hand-crafted features that can achieve 61.70% average accuracy. Also, we discover a hidden insight behind ‘SME-like’ and ‘Non-SME-like’ user’s click behaviour from these images. Our proposed method can lead to a better understanding of mobile banking user behaviour and a novel way of developing a customer segmentation classifier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), San Francisco, CA, USA, pp. 785–794 (2016). https://doi.org/10.1145/2939672.2939785
De Bock, K., Van den Poel, D.: Predicting website audience demographics for web advertising targeting using multi-website clickstream data. Fundamenta Informaticae 98(1), 49–70 (2010). https://doi.org/10.5555/1803672.1803677
Dullaghan, C., Rozaki, E.: Integration of machine learning techniques to evaluate dynamic customer segmentation analysis for mobile customers. Int. J. Data Mining Knowl. Manag. Process 7, 13–24 (2017). https://doi.org/10.5121/ijdkp.2017.7102
Florez, R., Ramon, J.: Marketing segmentation through machine learning models: an approach based on customer relationship management and customer profitability accounting. Soc. Sci. Comput. Rev. 27, 96–117 (2008). https://doi.org/10.1177/0894439308321592
Friedman, J.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001). https://doi.org/10.2307/2699986
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, NV, USA, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Kim, E., Kim, W., Lee, Y.: Combination of multiple classifiers for customer’s purchase behavior prediction. Decis. Support Syst. 34, 167–175 (2003). https://doi.org/10.1016/S0167-9236(02)00079-9
Li, W., Wu, X., Sun, Y., Zhang, Q.: Credit card customer segmentation and target marketing based on data mining. In: Proceedings of the International Conference on Computational Intelligence and Security (CIS 2010), Nanning, China, pp. 73–76 (2011). https://doi.org/10.1109/CIS.2010.23
Mihova, V., Pavlov, V.: A customer segmentation approach in commercial banks. In: AIP Conference Proceedings, vol. 2025, p. 030003 (2018). https://doi.org/10.1063/1.5064881
Ngai, E., Xiu, L., Chau, D.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36, 2592–2602 (2009). https://doi.org/10.1016/j.eswa.2008.02.021
Pasupa, K., Chatkamjuncharoen, P., Wuttilertdeshar, C., Sugimoto, M.: Using image features and eye tracking device to predict human emotions towards abstract images. In: Bräunl, T., McCane, B., Rivera, M., Yu, X. (eds.) PSIVT 2015. LNCS, vol. 9431, pp. 419–430. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29451-3_34
Pasupa, K., Sunhem, W., Loo, C.K., Kuroki, Y.: Can eye movement information improve prediction performance of human emotional response to images? In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) ICONIP 2017. LNCS, vol. 10637, pp. 830–838. (2017). https://doi.org/10.1007/978-3-319-70093-9_88
Pasupa, K., Szedmak, S.: Utilising Kronnecker decomposition and tensor-based multi-view learning to predict where people are looking in images. Neurocomputing 248, 80–93 (2017). https://doi.org/10.1016/j.neucom.2016.11.074
Sunhem, W., Pasupa, K.: A scenario-based analysis of front-facing camera eye tracker for UX-UI survey on mobile banking app. In: Proceedings of the 12th International Conference on Knowledge and Smart Technology (KST 2020), Pattaya, Thailand, pp. 80–85 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tungjitnob, S., Pasupa, K., Thamwiwatthana, E., Suntisrivaraporn, B. (2020). SME User Classification from Click Feedback on a Mobile Banking Apps. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-63820-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63819-1
Online ISBN: 978-3-030-63820-7
eBook Packages: Computer ScienceComputer Science (R0)