The week's pick
Reseach Article
Comparison of Artificial Neural Network and SPSS Model in Predicting Customers Churn of Iran’s Insurance Industry
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 32 |
| Year of Publication: 2020 |
| Authors: Gholamreza Pirmohammadi, Maryam Mast Zohouri |
10.5120/ijca2020920345
|
Gholamreza Pirmohammadi, Maryam Mast Zohouri . Comparison of Artificial Neural Network and SPSS Model in Predicting Customers Churn of Iran’s Insurance Industry. International Journal of Computer Applications. 176, 32 ( Jun 2020), 14-21. DOI=10.5120/ijca2020920345
Abstract
This paper has three aims. The first aim is Prediction of Iran’s insurance industry customers churn using data mining techniques and SPSS software. In this way, on the one place, in data mining part, multi-layer perceptron ANN with 8 neurons in hidden layer has applied and the best performance of this network appears in epoch 10. Plus, the structural model of network is added. On the other place, Regression test has used in order to prediction customer churn by SPSS. As a result, the performance of predicting regression and neural network model are compared. Second, the difference between target and output values is presented based on the Root Mean Square Error (RMSE) and Mean Square Error (MSE) codes in Matlab. Indeed, MSE have less value rather than RMSE. Finally, in order to prevent the waste of financial and human resources, the K-Means method has used for clustering customers into two groups of churn and non-churn.
References
- Alizadeh, S., Ghazanfarian, M., and Teimourpour, B. 2011 Data Mining and knowledge discovery. Tehran: Publication of Iran University of Science and Technology, 2nd ed, p.p. 70-250.
- Bagheri, M. and Valipour, M., and Amin, V. 2012 The bankruptcy prediction in Tehran share holding using neural network and it’s comparison with logistic regression. TJMCS, Vol. 5, No. 3, p.p. 219-228.
- Baicker, K. and Chandra, A. 2006 The labour market effects of rising health insurance premiums. Journal of Labour Economics, Vol. 24, No. 3.
- Berry, M.J.A. and Linoff. G.S. Data Mining Techniques for Marketing, Sales and Customers Relationship Management, 2nd edition, Wiley Publishing Inc, Indinana, USA.
- Bigus, J.P. Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support Paperback, McGraw-Hill, Inc. Hightstown, NJ, USA.
- Chih, R. and Yu.Hsin, L. 2009 Customer churn prediction by hybrid neural networks. Expert Systems with Applications, Vol. 36, p.p. 12547-12553.
- Dasu, T. and Johnson, T. 2003 Exploratory Data Mining and Data Cleaning, John Wiley.
- Han, J., Kamber, M., and Pei, J. 2011 Data mining: Concepts and techniques, 3rd ed. United States, Morgan Kaufman.
- Ismail, M.R., Awang, M.Kh., Rahman, M., and Makhtar. M. 2015 A multi-layer perceptron approach for customer churn prediction. International Journal of Multimedia and ubiquitous engineering, Vol. 10, No. 7, p.p. 213-222.
- Izadparast, S. M., Farahi, A., Fath Nejad, F., and Teimourpour, B. 2012 Using data mining thechniques to predict the detriment level of car insurance customers. Iranian Journal of Information Processing and Management, Vol. 27, Issue 3.
- Mehregan, S., and Samizadeh, R. 2012 Customers retention based on the number of purchase: A data mining approach. Int. J. Manag. Bus. Res., 2 (1), p.p. 41-50.
- Ngai, E.W.T., Xiu, L., and Chau, D.C.K. 2009 Application of data mining techniques in customer relationship management: A Literature review and classification. Expert Systems with Applications, Vol. 36, p.p. 2592-2602.
- Pendar, M., and Haji, M. 2017 Comparison of neural network models, vector auto regression (VAR), Bayesian vector-autoregressive (BVAR), generalized auto regressive conditional heteroskedasticity (GARCH) process and time series in forecasting inflation in Iran. IJIM, Vol. 9, No. 2.
- Sabbeh, S.F. 2018 Machine-learning techniques for customer retention: A comparative study. IJACSA, Vol. 9, No. 2.
- Sharma, A., and Panigrahi, P.K. 2011 A neural network based approach for predicting customer churn in cellular network services. International Journal of Computer Applications, Vol. 27, No. 11.
- Tarokh, M.J., and Sharifiyan, 2007 K. Usage of data mining in improvement CRM. Industrial management studies, Vol. 6, p.p. 153-181.
Index Terms
Keywords