A multi-layer perceptron approach for customer churn prediction
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| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 |
| date | 2015-09-03 10:26:18 |
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| id | 12267 |
| institution | UniSZA |
| internalnotes | [1] S. Portelaa and R. Menezes, “Modeling Customer Churn: An Application of Duration Models,” Proceedings of the Australian and New Zealand Marketing Academy (ANZMAC), (2009). [2] J. Hadden, “A Customer Profiling Methodology for Churn Prediction,” Cranfield University, (2008). [3] A. Sharma and P. K. Panigrahi, “A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services,” International Journal of Computer Applications, vol. 27 no. 11, (2011), pp. 26-31. [4] H. E. Chueh, “Analysis of marketing data to extract key factors of telecom churn management,” African Journal of Business Management, vol. 5 no. 20, (2011), pp 8242-8247. [5] A. Berson, S. J. Smith and K. Thearling, “Building Data Mining Applications for CRM,” McGraw-Hill, NewYork, (2000). [6] M. C. Mozer, R. Wolniewicz, D. B. Grimes, E. Johnson and H. Kaushansky, “Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry,” IEEE Transactions On Neural Networks, vol. 11 no. 3, (2000), pp. 690 - 696. [7] B. H. Chu, M. S. Tsai and C. S. Ho, “Toward a hybrid data mining model for customer retention,” Knowledge-Based Systems, vol. 20 no. 8, (2007), pp. 703-718. [8] M. K. Awang, M. N. A. Rahman and M. R. Ismail, “Data Mining for Churn Prediction: Multiple Regressions Approach,Computer Applications for Database, Education, and Ubiquitous Computing,” Springer Berlin Heidelberg, (2012), pp. 318-324. [9] D. Chiang, Y. Wang, S. Lee and C. Lin, “Goal-oriented sequential pattern for network banking and churn analysis,” Expert systems with applications, vol. 25, (2003), pp. 293-302. [10] A. T. Kearney, “European Mobile Industry Observatory 2011. In GSMA (Ed.),” Rising to the Challenge of Intense Competition, (2011). [11] A. D. Athanassopoulos, “Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior,” Journal of Business Research, vol. 47, (2000), pp. 191-207. [12] S. K. Abi, M. R. Gholamian, M. R. and M. Namvar, “Data Mining Applications in Customer Churn Management,” Paper presented at the International Conference on Intelligent Systems, Modelling and Simulation, (2010). [13] J. B. Ferreira, M. Vellasco,M. A. Pacheco and C. H. Barbosa, “Data Mining Techniques on the Evaluation of Wireless Churn,” Paper presented at the European Symposium on Artificial Neural Networks, (2004). [14] Y. He, Z. He and D. Zhang, “A Study on Prediction of Customer Churn in Fixed Communication Network Based on Data Mining,” Paper presented at the Sixth International Conference on Fuzzy Systems and Knowledge Discovery, (2009). [15] P. Datta, B. Masand, D. R. Mani and B. Li, “Automated Cellular Modeling and Prediction on a Large Scale,” Artificial Intelligence Review, vol. 14 no. 6, (2000), pp. 485-502. [16] C. Kang and S. P. Ji, “Customer Churn Prediction Based on SVM-RFE,” Paper presented at the International Seminar on Business and Information Management, (2008). [17] S. Haykin, “Neural Network : A Comparehsive Foundation. New Jersey Prentice Hall International, vol. 2, (1999). [18] A. Krogh, “What are artificial neural networks?,” Nature Publishing Group, vol. 26 no. 2, vol. (2008), pp. 195-197. [19] D. M. Levine, P. P. Ramsey and R. K. Smidt, “Applied statistics for engineers and scientists: using Microsoft Excel and Minitab,” Upper Saddle River, NJ: Prentice Hall, (2001). [20] S. A. Sweet and K. G. Martin, “Data Analysis with SPSS: A First Course in Applied Statistics,: Pearson, vol. 2, (2010). |
| originalfilename | 6567-01-FH02-FIK-15-03716.jpg |
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| spelling | 12267 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12267 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal UniSZA Unisza unisza image/jpeg inches 96 96 67 67 1425 779 2015-09-03 10:26:18 1425x779 6567-01-FH02-FIK-15-03716.jpg UniSZA Private Access A multi-layer perceptron approach for customer churn prediction International Journal of Multimedia and Ubiquitous Engineering Nowadays, the telecommunication industries are facing substantial competition among the providers in order to capture new customers. Many providers have faced a loss of profitability due to the existing customers migrating to other providers. Customer retention program is one of the main strategies adopted in order to keep customers loyal to their provider. However, it requires a high cost and therefore the best strategy that companies could practice is to focus on identifying the customers that have the potential to churn at an early stage. The limited amount of research on investigating customer churn using machine learning techniques has lead this research to explore the potential of an artificial neural network to improve customer churn prediction. The research proposes Multilayer Perceptron (MLP) neural network approach to predict customer churn in one of the leading Malaysian’s telecommunication companies. The results are compared against the most popular churn prediction techniques such as Multiple Regression Analysis and Logistic Regression Analysis. The result has proven the supremacy of neural network (91.28% of prediction accuracy) over the statistical models in prediction tasks. Overall, the findings suggest that a neural network learning algorithm could offer a viable alternative to statistical predictive approaches in customer churn prediction. 10 7 213-222 [1] S. Portelaa and R. Menezes, “Modeling Customer Churn: An Application of Duration Models,” Proceedings of the Australian and New Zealand Marketing Academy (ANZMAC), (2009). [2] J. Hadden, “A Customer Profiling Methodology for Churn Prediction,” Cranfield University, (2008). [3] A. Sharma and P. K. Panigrahi, “A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services,” International Journal of Computer Applications, vol. 27 no. 11, (2011), pp. 26-31. [4] H. E. Chueh, “Analysis of marketing data to extract key factors of telecom churn management,” African Journal of Business Management, vol. 5 no. 20, (2011), pp 8242-8247. [5] A. Berson, S. J. Smith and K. Thearling, “Building Data Mining Applications for CRM,” McGraw-Hill, NewYork, (2000). [6] M. C. Mozer, R. Wolniewicz, D. B. Grimes, E. Johnson and H. Kaushansky, “Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry,” IEEE Transactions On Neural Networks, vol. 11 no. 3, (2000), pp. 690 - 696. [7] B. H. Chu, M. S. Tsai and C. S. Ho, “Toward a hybrid data mining model for customer retention,” Knowledge-Based Systems, vol. 20 no. 8, (2007), pp. 703-718. [8] M. K. Awang, M. N. A. Rahman and M. R. Ismail, “Data Mining for Churn Prediction: Multiple Regressions Approach,Computer Applications for Database, Education, and Ubiquitous Computing,” Springer Berlin Heidelberg, (2012), pp. 318-324. [9] D. Chiang, Y. Wang, S. Lee and C. Lin, “Goal-oriented sequential pattern for network banking and churn analysis,” Expert systems with applications, vol. 25, (2003), pp. 293-302. [10] A. T. Kearney, “European Mobile Industry Observatory 2011. In GSMA (Ed.),” Rising to the Challenge of Intense Competition, (2011). [11] A. D. Athanassopoulos, “Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior,” Journal of Business Research, vol. 47, (2000), pp. 191-207. [12] S. K. Abi, M. R. Gholamian, M. R. and M. Namvar, “Data Mining Applications in Customer Churn Management,” Paper presented at the International Conference on Intelligent Systems, Modelling and Simulation, (2010). [13] J. B. Ferreira, M. Vellasco,M. A. Pacheco and C. H. Barbosa, “Data Mining Techniques on the Evaluation of Wireless Churn,” Paper presented at the European Symposium on Artificial Neural Networks, (2004). [14] Y. He, Z. He and D. Zhang, “A Study on Prediction of Customer Churn in Fixed Communication Network Based on Data Mining,” Paper presented at the Sixth International Conference on Fuzzy Systems and Knowledge Discovery, (2009). [15] P. Datta, B. Masand, D. R. Mani and B. Li, “Automated Cellular Modeling and Prediction on a Large Scale,” Artificial Intelligence Review, vol. 14 no. 6, (2000), pp. 485-502. [16] C. Kang and S. P. Ji, “Customer Churn Prediction Based on SVM-RFE,” Paper presented at the International Seminar on Business and Information Management, (2008). [17] S. Haykin, “Neural Network : A Comparehsive Foundation. New Jersey Prentice Hall International, vol. 2, (1999). [18] A. Krogh, “What are artificial neural networks?,” Nature Publishing Group, vol. 26 no. 2, vol. (2008), pp. 195-197. [19] D. M. Levine, P. P. Ramsey and R. K. Smidt, “Applied statistics for engineers and scientists: using Microsoft Excel and Minitab,” Upper Saddle River, NJ: Prentice Hall, (2001). [20] S. A. Sweet and K. G. Martin, “Data Analysis with SPSS: A First Course in Applied Statistics,: Pearson, vol. 2, (2010). |
| spellingShingle | A multi-layer perceptron approach for customer churn prediction |
| summary | Nowadays, the telecommunication industries are facing substantial competition among the providers in order to capture new customers. Many providers have faced a loss of profitability due to the existing customers migrating to other providers. Customer retention program is one of the main strategies adopted in order to keep customers loyal to their provider. However, it requires a high cost and therefore the best strategy that companies could practice is to focus on identifying the customers that have the potential to churn at an early stage. The limited amount of research on investigating customer churn using machine learning techniques has lead this research to explore the potential of an artificial neural network to improve customer churn prediction. The research proposes Multilayer Perceptron (MLP) neural network approach to predict customer churn in one of the leading Malaysian’s telecommunication companies. The results are compared against the most popular churn prediction techniques such as Multiple Regression Analysis and Logistic Regression Analysis. The result has proven the supremacy of neural network (91.28% of prediction accuracy) over the statistical models in prediction tasks. Overall, the findings suggest that a neural network learning algorithm could offer a viable alternative to statistical predictive approaches in customer churn prediction. |
| title | A multi-layer perceptron approach for customer churn prediction |
| title_full | A multi-layer perceptron approach for customer churn prediction |
| title_fullStr | A multi-layer perceptron approach for customer churn prediction |
| title_full_unstemmed | A multi-layer perceptron approach for customer churn prediction |
| title_short | A multi-layer perceptron approach for customer churn prediction |
| title_sort | multi-layer perceptron approach for customer churn prediction |