1
Department of Control & Automation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq
(akbal.o.salman@mtu.edu.iq )
2
College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, Anbar, Iraq
(Mazinalshujeary@uoanbar.edu.iq)
Abstract :
Recent innovation in business intelligence (BI) assists companies to stay successful and competitive with the increasing business trend. Businesses have started to examine the succeeding level of data analytics and BI solution. At the same time, Customer Churn Prediction (CCP) is an essential procedure involved in business decision making that effectually determines the churn of clients and performs adequate processes to retain customers. With this motivation, this paper presents a sandpiper optimization with the bidirectional gated recurrent unit (SPO-BiGRU) for CCP on BI applications. The SPO-BiGRU model aims for determining the occurrence of customers into churners or non-churner. In addition, the SPO-BiGRU technique involves pre-processing, classification, and hyperparameter optimization. Followed by, the BiGRU model is applied to perform the predictive process. At last, the SPO algorithm is applied to optimally adjust the hyperparameters involved in the BiGRU model. For validating the enhanced performance of the SPO-BiGRU method, a wide range of simulations take place and the results are inspected under varying aspects. The experimental results portrayed the supremacy of the SPO-BiGRU technique over the recent state of art approaches.
Keywords :
Business intelligence , Customer churn prediction , Deep learning , Sandpiper optimization , BiGRU model.
References :
[1] Ahmad, A.K., Jafar, A. and Aljoumaa, K., 2019. Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data, 6(1), pp.1-24.
[2] Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J. and Anwar, S., 2019. Customer churn prediction in telecommunication industry using data certainty. Journal of Business Research, 94, pp.290-301.
[3] Verbeke, W., Martens, D., Mues, C. and Baesens, B., 2011. Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert systems with applications, 38(3), pp.2354-2364.
[4] Burez, J. and Van den Poel, D., 2009. Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36(3), pp.4626-4636.
[5] Huang, B., Kechadi, M.T. and Buckley, B., 2012. Customer churn prediction in telecommunications. Expert Systems with Applications, 39(1), pp.1414-1425.
[6] Lu, N., Lin, H., Lu, J. and Zhang, G., 2012. A customer churn prediction model in telecom industry using boosting. IEEE Transactions on Industrial Informatics, 10(2), pp.1659-1665.
[7] Stripling, E., vanden Broucke, S., Antonio, K., Baesens, B. and Snoeck, M., 2018. Profit maximizing logistic model for customer churn prediction using genetic algorithms. Swarm and Evolutionary Computation, 40, pp.116-130.
[8] De Caigny, A., Coussement, K., De Bock, K.W. and Lessmann, S., 2020. Incorporating textual information in customer churn prediction models based on a convolutional neural network. International Journal of Forecasting, 36(4), pp.1563-1578.
[9] Vafeiadis, T., Diamantaras, K.I., Sarigiannidis, G. and Chatzisavvas, K.C., 2015. A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, pp.1-9.
[10] Coussement, K., Lessmann, S. and Verstraeten, G., 2017. A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decision Support Systems, 95, pp.27-36.
[11] Tsai, C.F. and Chen, M.Y., 2010. Variable selection by association rules for customer churn prediction of multimedia on demand. Expert Systems with Applications, 37(3), pp.2006-2015.
[12] Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A. and Huang, K., 2017. Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, pp.242-254.
[13] De Bock, K.W. and Van den Poel, D., 2011. An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. Expert Systems with Applications, 38(10), pp.12293-12301.
[14] 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.
[15] De Caigny, A., Coussement, K. and De Bock, K.W., 2018. A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research, 269(2), pp.760-772.
[16] Calzada-Infante, L., Óskarsdóttir, M. and Baesens, B., 2020. Evaluation of customer behavior with temporal centrality metrics for churn prediction of prepaid contracts. Expert Systems with Applications, 160, p.113553.
[17] Vo, N.N., Liu, S., Li, X. and Xu, G., 2021. Leveraging unstructured call log data for customer churn prediction. Knowledge-Based Systems, 212, p.106586.
[18] Abdelgwad, M.M., Soliman, T.H.A., Taloba, A.I. and Farghaly, M.F., 2021. Arabic aspect based sentiment analysis using bidirectional GRU based models. arXiv preprint arXiv:2101.10539.
[19] Metan, J., Prasad, A.Y., Kumar, K.A., Mathapati, M. and Patil, K.K., 2021. Cardiovascular MRI image analysis by using the bio inspired (sand piper optimized) fully deep convolutional network (Bio-FDCN) architecture for an automated detection of cardiac disorders. Biomedical Signal Processing and Control, 70, p.103002.
[20] 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.
| Style | # |
|---|---|
| MLA | Akbal Omran Salman , Mazin Abed Mohammed. "Customer Churn Prediction using Sandpiper Optimization with Bidirectional Gated Recurrent Unit for Business Intelligence." American Journal of Business and Operations Research, Vol. 6, No. 1, 2021 ,PP. 36-47 (Doi : https://doi.org/10.54216/AJBOR.060103) |
| APA | Akbal Omran Salman , Mazin Abed Mohammed. (2021). Customer Churn Prediction using Sandpiper Optimization with Bidirectional Gated Recurrent Unit for Business Intelligence. Journal of American Journal of Business and Operations Research, 6 ( 1 ), 36-47 (Doi : https://doi.org/10.54216/AJBOR.060103) |
| Chicago | Akbal Omran Salman , Mazin Abed Mohammed. "Customer Churn Prediction using Sandpiper Optimization with Bidirectional Gated Recurrent Unit for Business Intelligence." Journal of American Journal of Business and Operations Research, 6 no. 1 (2021): 36-47 (Doi : https://doi.org/10.54216/AJBOR.060103) |
| Harvard | Akbal Omran Salman , Mazin Abed Mohammed. (2021). Customer Churn Prediction using Sandpiper Optimization with Bidirectional Gated Recurrent Unit for Business Intelligence. Journal of American Journal of Business and Operations Research, 6 ( 1 ), 36-47 (Doi : https://doi.org/10.54216/AJBOR.060103) |
| Vancouver | Akbal Omran Salman , Mazin Abed Mohammed. Customer Churn Prediction using Sandpiper Optimization with Bidirectional Gated Recurrent Unit for Business Intelligence. Journal of American Journal of Business and Operations Research, (2021); 6 ( 1 ): 36-47 (Doi : https://doi.org/10.54216/AJBOR.060103) |
| IEEE | Akbal Omran Salman, Mazin Abed Mohammed, Customer Churn Prediction using Sandpiper Optimization with Bidirectional Gated Recurrent Unit for Business Intelligence, Journal of American Journal of Business and Operations Research, Vol. 6 , No. 1 , (2021) : 36-47 (Doi : https://doi.org/10.54216/AJBOR.060103) |