A multi-layer perceptron approach for customer churn prediction

International Journal of Multimedia and Ubiquitous Engineering

Volumn 10, Issue 7, 2015, Pages 213-222

  Ismail, Mohammad Ridwan ^a   Awang, Mohd Khalid ^a   Rahman, M Nordin A ^a   Makhtar, Mokhairi ^a  

^a UNIVERSITI SULTAN ZAINAL ABIDIN   (Malaysia)

Author keywords

Churn; Data mining; Logistic regression; Multiple regression; Neural network; Regression analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPETITION; DATA MINING; FORECASTING; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; REGRESSION ANALYSIS; TELECOMMUNICATION INDUSTRY;

CHURN; LOGISTIC REGRESSION ANALYSIS; LOGISTIC REGRESSIONS; MULTILAYER PERCEPTRON NEURAL NETWORKS; MULTIPLE REGRESSION ANALYSIS; MULTIPLE REGRESSIONS; NEURAL NETWORK LEARNING ALGORITHM; TELECOMMUNICATION COMPANIES;

SALES;

EID: 84938598440     PISSN: 19750080     EISSN: None     Source Type: Journal    
DOI: 10.14257/ijmue.2015.10.7.22     Document Type: Article

References (20)

1. [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. [2] J. Hadden, “A Customer Profiling Methodology for Churn Prediction,” Cranfield University, (2008).
3. [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. [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. [5] A. Berson, S. J. Smith and K. Thearling, “Building Data Mining Applications for CRM,” McGraw-Hill, NewYork, (2000).
6. [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. [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. [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. [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. [10] A. T. Kearney, “European Mobile Industry Observatory 2011. In GSMA (Ed.),” Rising to the Challenge of Intense Competition, (2011).
11. [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. [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. [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. [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. [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. [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. [17] S. Haykin, “Neural Network: A Comparehsive Foundation. New Jersey Prentice Hall International, vol. 2, (1999).
18. [18] A. Krogh, “What are artificial neural networks?,” Nature Publishing Group, vol. 26 no. 2, vol. (2008), pp. 195-197.
19. [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. [20] S. A. Sweet and K. G. Martin, “Data Analysis with SPSS: A First Course in Applied Statistics,: Pearson, vol. 2, (2010).