2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 8 , Issue 1 , PP: 43-54, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Abedallah Zaid Abualkishik 1 * , Rasha Almajed 2 , William Thompson 3
Doi: https://doi.org/10.54216/JISIoT.080104
Business intelligence (BI) mentions to the technical and procedural structure which gathers, supplies, and examines the data formed by company action. BI is a wide term that includes descriptive analytics, procedure analysis, data mining, and performance benchmarking. Customer churn is a general problem across businesses from several sectors. Companies are working always for improving their supposed quality by way of providing timely and quality service to its customer. Customer churn is developed most initial challenges which several firms were facing currently. Many churn prediction techniques and methods were presented before in literature for predicting customer churn from the domains like telecom, finance, banking, and so on. Researchers are also working on customer churn prediction (CCP) from e-commerce utilizing data mining and machine learning (ML) approaches. This manuscript focuses on the development of Stacked Deep Learning with Wind Driven Optimization based Business Intelligence for Customer Churn Prediction model. The proposed model is considered an intelligent system that applies golden sine algorithm (GSA) based feature selection approach to derive a set of features. In addition, the stacked gated recurrent unit (SGRU) model is applied for the prediction of customer churns.
Business intelligence , Customer churn prediction , Deep learning , Feature selection , Machine learning , Intelligent Systems
[1] Shakir, T.K. and Al Masri, A.N. 2021. Intelligent Optimal Deep Learning based Customer Churn Prediction Model in Telecom Industry. American Journal of Business and Operations Research, 4(2), pp. 57-64. doi: 10.54216/AJBOR.040202.
[2] Rai, S., Khandelwal, N. and Boghey, R., 2020. Analysis of customer churn prediction in telecom sector using cart algorithm. In First International Conference on Sustainable Technologies for Computational Intelligence (pp. 457-466). Springer, Singapore.
[3] Thakkar, H.K., Desai, A., Ghosh, S., Singh, P. and Sharma, G., 2022. Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction. Computational Intelligence and Neuroscience, 2022.
[4] Nwaogu, V.C. and Dimililer, K., 2021, June. Customer Churn Prediction For Business Intelligence Using Machine Learning. In 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-7). IEEE.
[5] Shirazi, F. and Mohammadi, M., 2019. A big data analytics model for customer churn prediction in the retiree segment. International Journal of Information Management, 48, pp.238-253.
[6] Banday, S.A. and Khan, S., 2021. Evaluation Study of Churn Prediction Models for Business Intelligence. In Big Data Analytics (pp. 201-213). Auerbach Publications.
[7] Gursoy, U.F., Yildiz, E.M., Okay, M.E. and Aktas, M.S., 2021, September. Customer Churn Prediction and Promotion Models in the Telecom Sector: A Case Study. In Proceedings of SAI Intelligent Systems Conference (pp. 276-286). Springer, Cham.
[8] Günesen, S.N., Şen, N., Yıldırım, N. and Kaya, T., 2021, January. Customer Churn Prediction in FMCG Sector Using Machine Learning Applications. In IFIP International Workshop on Artificial Intelligence for Knowledge Management (pp. 82-103). Springer, Cham.
[9] Dai, M. and Wang, Y., 2020. Research on the application of business intelligence based on data mining technology in the new industry. J. Simul, 8(6).
[10] Çelik, O. and Osmanoglu, U.O., 2019. Comparing to techniques used in customer churn analysis. Journal of Multidisciplinary Developments, 4(1), pp.30-38.
[11] Wu, Z., Jing, L., Wu, B. and Jin, L., 2022. A PCA-AdaBoost model for E-commerce customer churn prediction. Annals of Operations Research, pp.1-18
[12] Ramesh, P., Jeba Emilyn, J. and Vijayakumar, V., 2022. Hybrid Artificial Neural Networks Using Customer Churn Prediction. Wireless Personal Communications, 124(2), pp.1695-1709
[13] Pustokhina, I.V., Pustokhin, D.A., Aswathy, R.H., Jayasankar, T., Jeyalakshmi, C., Díaz, V.G. and Shankar, K., 2021. Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms. Information Processing & Management, 58(6), p.102706
[14] Rahmaty, M., Daneshvar, A., Salahi, F., Ebrahimi, M. and Chobar, A.P., 2022. Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks. Discrete Dynamics in Nature and Society, 2022
[15] Xiahou, X. and Harada, Y., 2022. B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), pp.458-475
[16] Perišić, A. and Pahor, M., 2020. Extended RFM logit model for churn prediction in the mobile gaming market. Croatian Operational Research Review, pp.249-261
[17] Tariq, M.U., Babar, M., Poulin, M. and Khattak, A.S., 2021. Distributed model for customer churn prediction using convolutional neural network. Journal of Modelling in Management
[18] Liu, Q., Li, N., Jia, H., Qi, Q., Abualigah, L. and Liu, Y., 2022. A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems. Mathematics, 10(9), p.1567.
[19] Al Wazrah, A. and Alhumoud, S., 2021. Sentiment Analysis Using Stacked Gated Recurrent Unit for Arabic Tweets. IEEE Access, 9, pp.137176-137187.
