1
Accounting Department, Faculty of Commerce, Kafr El Sheikh University, Egypt
(dina.abdelsalam@com.kfs.edu.eg)
2
Accounting Department, Faculty of Commerce, Mansoura University, Egypt
(metawee68@mans.edu.eg)
Abstract :
In today’s data driven world businesses face a challenge in protecting customer strategies from operational churn. This paper explores the realm of data driven business intelligence with a focus on predicting and managing customer churn through analysis of analytics methods. Recognizing that customer attrition poses a threat to business sustainability, our research aims to harness the power of methods and discriminant analysis techniques. We examine Gradient Boosting Classifier, Ada Boost Classifier and Linear Discriminant Analysis to unravel patterns in customer behavior and predict churn likelihood. By utilizing a dataset that includes details about customer services account specifics and demographics we adopt an approach. Our comparative analysis of machine learning classifiers underscores their effectiveness in identifying patterns within the dataset. Importantly our findings emphasize the potential of machine learning as a strategy for managing churn.
Keywords :
Business Intelligence; Operation Research; Customer Churn Management; Customer Segmentation.
References :
[1] Osman, C. C. (2019). Robotic Process Automation: Lessons Learned from Case Studies. Informatica Economica, 23(4).
[2] Dey, S., & Das, A. (2019). Robotic process automation: assessment of the technology for transformation of business processes. International Journal of Business Process Integration and Management, 9(3), 220-230.
[3] Devarajan, Y. (2018). A study of robotic process automation use cases today for tomorrow’s business. International Journal of Computer Techniques, 5(6), 12-18.
[4] Romao, M., Costa, J., & Costa, C. J. (2019, June). Robotic process automation: A case study in the banking industry. In 2019 14th Iberian Conference on information systems and technologies (CISTI) (pp. 1-6). IEEE.
[5] Carden, L., Maldonado, T., Brace, C., & Myers, M. (2019). Robotics process automation at TECHSERV: An implementation case study. Journal of Information Technology Teaching Cases, 9(2), 72-79.
[6] Flechsig, C., Lohmer, J., & Lasch, R. (2019). Realizing the full potential of robotic process automation through a combination with BPM. In Logistics Management: Strategies and Instruments for digitalizing and decarbonizing supply chains-Proceedings of the German Academic Association for Business Research, Halle, 2019 (pp. 104-119). Springer International Publishing.
[7] Mishra, S., Sree Devi, K. K., & Badri Narayanan, M. K. (2019). Technology dimensions of automation in business process management industry. International Journal of Engineering and Advanced Technology, 8(6), 1919-1926.
[8] Zhang, N., & Liu, B. (2019). Alignment of business in robotic process automation. International Journal of Crowd Science, 3(1), 26-35.
[9] Anagnoste, S. (2018). Setting up a robotic process automation center of excellence. Management Dynamics in the Knowledge Economy, 6(2), 307-332.
[10] Ansari, W. A., Diya, P., Patil, S., & Patil, S. (2019, April). A review on robotic process automation-the future of business organizations. In 2nd International conference on advances in science & technology (ICAST).
[11] Leshob, A., Bourgouin, A., & Renard, L. (2018, October). Towards a process analysis approach to adopt robotic process automation. In 2018 IEEE 15th international conference on e-business engineering (ICEBE) (pp. 46-53). IEEE.
[12] Torkhani, R., Laval, J., Malek, H., & Moalla, N. (2018, September). Intelligent framework for business process automation and re-engineering. In 2018 International Conference on Intelligent Systems (IS) (pp. 624-629). IEEE.
[13] Mendling, J., Decker, G., Hull, R., Reijers, H. A., & Weber, I. (2018). How do machine learning, robotic process automation, and blockchains affect the human factor in business process management?. Communications of the Association for Information Systems, 43(1), 19.
[14] Asatiani, A., & Penttinen, E. (2016). Turning robotic process automation into commercial success–Case OpusCapita. Journal of Information Technology Teaching Cases, 6(2), 67-74.
[15] Willcocks, L., Lacity, M., & Craig, A. (2017). Robotic process automation: strategic transformation lever for global business services?. Journal of Information Technology Teaching Cases, 7(1), 17-28.
[16] Habib, Muhammad Nauman, and Attaullah Shah. "Business process reengineering: Literature review of approaches and applications." In Proceedings of 3rd Asia-Pacific Business Research Conference, pp. 25-26. 2013.
[17] Zhang, Q., & Cao, M. (2002). Business process reengineering for flexibility and innovation in manufacturing. Industrial Management & Data Systems, 102(3), 146-152.
| Style | # |
|---|---|
| MLA | Dina K. Hassan, Ahmed K. Metawee. "Data-Driven Business Intelligence for Operational Customer Churn Management." American Journal of Business and Operations Research, Vol. 0, No. 2, 2019 ,PP. 104-111 (Doi : https://doi.org/10.54216/AJBOR.000205) |
| APA | Dina K. Hassan, Ahmed K. Metawee. (2019). Data-Driven Business Intelligence for Operational Customer Churn Management. Journal of American Journal of Business and Operations Research, 0 ( 2 ), 104-111 (Doi : https://doi.org/10.54216/AJBOR.000205) |
| Chicago | Dina K. Hassan, Ahmed K. Metawee. "Data-Driven Business Intelligence for Operational Customer Churn Management." Journal of American Journal of Business and Operations Research, 0 no. 2 (2019): 104-111 (Doi : https://doi.org/10.54216/AJBOR.000205) |
| Harvard | Dina K. Hassan, Ahmed K. Metawee. (2019). Data-Driven Business Intelligence for Operational Customer Churn Management. Journal of American Journal of Business and Operations Research, 0 ( 2 ), 104-111 (Doi : https://doi.org/10.54216/AJBOR.000205) |
| Vancouver | Dina K. Hassan, Ahmed K. Metawee. Data-Driven Business Intelligence for Operational Customer Churn Management. Journal of American Journal of Business and Operations Research, (2019); 0 ( 2 ): 104-111 (Doi : https://doi.org/10.54216/AJBOR.000205) |
| IEEE | Dina K. Hassan, Ahmed K. Metawee, Data-Driven Business Intelligence for Operational Customer Churn Management, Journal of American Journal of Business and Operations Research, Vol. 0 , No. 2 , (2019) : 104-111 (Doi : https://doi.org/10.54216/AJBOR.000205) |