پیش‌بینی رویگردانی جزئی مشتریان بانک‌ها با استفاده از مدل زنجیره وضعیت

 

Ali, Ö. G., & Arıtürk, U. (2014). Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Systems with Applications, 41(17), 7889-7903.

Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242-254.

Babu, S., & Ananthanarayanan, N. R. (2018). Enhanced Prediction Model for Customer Churn in Telecommunication Using EMOTE. In International Conference on Intelligent Computing and Applications (pp. 465-475). Springer, Singapore.

Barbará, D., & Wu, X. (2001, July). Finding dense clusters in hyperspace: an approach based on row shuffling. In International Conference on Web-Age Information Management (pp. 305-316). Springer, Berlin, Heidelberg.

Caigny, A., Coussement, K., & 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), 760-772.

Chiang, D. A., Wang, Y. F., Lee, S. L., & Lin, C. J. (2003). Goal-oriented sequential pattern for network banking churn analysis. Expert Systems with Applications, 25(3), 293-302.

Chu, C., Xu, G., Brownlow, J., & Fu, B. (2016, November). Deployment of churn prediction model in financial services industry. In Behavioral, Economic and Socio-cultural Computing (BESC), 2016 International Conference on (pp. 1-2). IEEE.

Coussement, K., & De Bock, K. W. (2013). Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research, 66(9), 1629-1636.

Coussement, K., Lessmann, S., & 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, 27-36.

Dahiya, K., & Bhatia, S. (2015, September). Customer churn analysis in telecom industry. In Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 2015 4th International Conference on (pp. 1-6). IEEE.

Farquad, M. A. H., Ravi, V., & Raju, S. B. (2014). Churn prediction using comprehensible support vector machine: An analytical CRM application. Applied Soft Computing, 19, 31-40.

Guo, H., & Viktor, H. L. (2006, August). Mining relational data through correlation-based multiple view validation. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 567-573). ACM.

Günther, C. C., Tvete, I. F., Aas, K., Sandnes, G. I., & Borgan, Ø. (2014). Modelling and predicting customer churn from an insurance company. Scandinavian Actuarial Journal, 2014(1), 58-71.

Huang, Y., & Kechadi, T. (2013). An effective hybrid learning system for telecommunication churn prediction. Expert Systems with Applications, 40(14), 5635-5647.

Ikonomovska, E., & Džeroski, S. (2011, March). Regression on evolving multi-relational data streams. In Proceedings of the 2011 Joint EDBT/ICDT Ph. D. Workshop (pp. 1-7). ACM.

Kaur, M., Singh, K., & Sharma, N. (2013). Data Mining as a tool to Predict the Churn Behaviour among Indian bank customers. International Journal on Recent and Innovation Trends in Computing and Communication, 1(9), 720-725.

Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications, 39(4), 4344-4357.

Kim, K., Jun, C. H., & Lee, J. (2014). Improved churn prediction in telecommunication industry by analyzing a large network. Expert Systems with Applications, 41(15), 6575-6584.

Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications policy, 28(9-10), 751-765.

Lu, N., Lin, H., Lu, J., & Zhang, G. (2014). A customer churn prediction model in telecom industry using boosting. IEEE Transactions on Industrial Informatics, 10(2), 1659-1665.

Mahajan, V., Misra, R., & Mahajan, R. (2017). Review on factors affecting customer churn in telecom sector. International Journal of Data Analysis Techniques and Strategies, 9(2), 122-144.

Miguéis, V. L., Van den Poel, D., Camanho, A. S., & e Cunha, J. F. (2012). Modeling partial customer churn: On the value of first product-category purchase sequences. Expert systems with applications, 39(12), 11250-11256.

Najarzadeh, R., Reed, M., & Mirzanejad, H. (2013). A Study of the Competitiveness of Iran's Banking System. Journal of Economic Cooperation & Development, 34(1), 93.

Oyeniyi, A. O., Adeyemo, A. B., Oyeniyi, A. O., & Adeyemo, A. B. (2015). Customer churn analysis in banking sector using data mining techniques. Afr J Comput ICT, 8(3), 165-174.

Perlich, C., & Huang, Z. (2005). Relational learning for customer relationship management. In Proceedings of international workshop on customer relationship management: data mining meets marketing.

PKDD'99. 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD99) Discovery Challenge: http://lisp.vse.cz/pkdd99/chall.htm

Prasad, U. D., & Madhavi, S. (2012). Prediction of churn behavior of bank customers using data mining tools. Business Intelligence Journal, 5(1), 96-101.

Qureshi, S. A., Rehman, A. S., Qamar, A. M., Kamal, A., & Rehman, A. (2013, September). Telecommunication subscribers' churn prediction model using machine learning. In Digital Information Management (ICDIM), 2013 Eighth International Conference on (pp. 131-136). IEEE.

Riebe, E., Wright, M., Stern, P., & Sharp, B. (2014). How to grow a brand: Retain or acquire customers?. Journal of Business Research, 67(5), 990-997.

Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547-12553.

Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research, 218(1), 211–229.

Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1-9.

Yang, C., Shi, X., Luo, J., & Han, J. (2018). I Know You’ll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application.

Zhu, B., Xiao, J., & He, C. (2014). A Balanced Transfer Learning Model for Customer Churn Prediction. In Proceedings of the Eighth International Conference on Management Science and Engineering Management (pp. 97-104). Springer, Berlin, Heidelberg.

Zhu, B., Baesens, B., Backiel, A. E., & vanden Broucke, S. K. (2018). Benchmarking sampling techniques for imbalance learning in churn prediction. Journal of the Operational Research Society, 69(1), 49-65.

Zorić, B. A. (2016). Predicting customer churn in banking industry using neural networks. Interdisciplinary Description of Complex Systems: INDECS, 14(2), 116-124.

 

 

Ali, Ö. G., & Arıtürk, U. (2014). Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Systems with Applications, 41(17), 7889-7903.

Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242-254.

Babu, S., & Ananthanarayanan, N. R. (2018). Enhanced Prediction Model for Customer Churn in Telecommunication Using EMOTE. In International Conference on Intelligent Computing and Applications (pp. 465-475). Springer, Singapore.

Barbará, D., & Wu, X. (2001, July). Finding dense clusters in hyperspace: an approach based on row shuffling. In International Conference on Web-Age Information Management (pp. 305-316). Springer, Berlin, Heidelberg.

Caigny, A., Coussement, K., & 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), 760-772.

Chiang, D. A., Wang, Y. F., Lee, S. L., & Lin, C. J. (2003). Goal-oriented sequential pattern for network banking churn analysis. Expert Systems with Applications, 25(3), 293-302.

Chu, C., Xu, G., Brownlow, J., & Fu, B. (2016, November). Deployment of churn prediction model in financial services industry. In Behavioral, Economic and Socio-cultural Computing (BESC), 2016 International Conference on (pp. 1-2). IEEE.

Coussement, K., & De Bock, K. W. (2013). Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research, 66(9), 1629-1636.

Coussement, K., Lessmann, S., & 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, 27-36.

Dahiya, K., & Bhatia, S. (2015, September). Customer churn analysis in telecom industry. In Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 2015 4th International Conference on (pp. 1-6). IEEE.

Farquad, M. A. H., Ravi, V., & Raju, S. B. (2014). Churn prediction using comprehensible support vector machine: An analytical CRM application. Applied Soft Computing, 19, 31-40.

Guo, H., & Viktor, H. L. (2006, August). Mining relational data through correlation-based multiple view validation. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 567-573). ACM.

Günther, C. C., Tvete, I. F., Aas, K., Sandnes, G. I., & Borgan, Ø. (2014). Modelling and predicting customer churn from an insurance company. Scandinavian Actuarial Journal, 2014(1), 58-71.

Huang, Y., & Kechadi, T. (2013). An effective hybrid learning system for telecommunication churn prediction. Expert Systems with Applications, 40(14), 5635-5647.

Ikonomovska, E., & Džeroski, S. (2011, March). Regression on evolving multi-relational data streams. In Proceedings of the 2011 Joint EDBT/ICDT Ph. D. Workshop (pp. 1-7). ACM.

Kaur, M., Singh, K., & Sharma, N. (2013). Data Mining as a tool to Predict the Churn Behaviour among Indian bank customers. International Journal on Recent and Innovation Trends in Computing and Communication, 1(9), 720-725.

Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications, 39(4), 4344-4357.

Kim, K., Jun, C. H., & Lee, J. (2014). Improved churn prediction in telecommunication industry by analyzing a large network. Expert Systems with Applications, 41(15), 6575-6584.

Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications policy, 28(9-10), 751-765.

Lu, N., Lin, H., Lu, J., & Zhang, G. (2014). A customer churn prediction model in telecom industry using boosting. IEEE Transactions on Industrial Informatics, 10(2), 1659-1665.

Mahajan, V., Misra, R., & Mahajan, R. (2017). Review on factors affecting customer churn in telecom sector. International Journal of Data Analysis Techniques and Strategies, 9(2), 122-144.

Miguéis, V. L., Van den Poel, D., Camanho, A. S., & e Cunha, J. F. (2012). Modeling partial customer churn: On the value of first product-category purchase sequences. Expert systems with applications, 39(12), 11250-11256.

Najarzadeh, R., Reed, M., & Mirzanejad, H. (2013). A Study of the Competitiveness of Iran's Banking System. Journal of Economic Cooperation & Development, 34(1), 93.

Oyeniyi, A. O., Adeyemo, A. B., Oyeniyi, A. O., & Adeyemo, A. B. (2015). Customer churn analysis in banking sector using data mining techniques. Afr J Comput ICT, 8(3), 165-174.

Perlich, C., & Huang, Z. (2005). Relational learning for customer relationship management. In Proceedings of international workshop on customer relationship management: data mining meets marketing.

PKDD'99. 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD99) Discovery Challenge: http://lisp.vse.cz/pkdd99/chall.htm

Prasad, U. D., & Madhavi, S. (2012). Prediction of churn behavior of bank customers using data mining tools. Business Intelligence Journal, 5(1), 96-101.

Qureshi, S. A., Rehman, A. S., Qamar, A. M., Kamal, A., & Rehman, A. (2013, September). Telecommunication subscribers' churn prediction model using machine learning. In Digital Information Management (ICDIM), 2013 Eighth International Conference on (pp. 131-136). IEEE.

Riebe, E., Wright, M., Stern, P., & Sharp, B. (2014). How to grow a brand: Retain or acquire customers?. Journal of Business Research, 67(5), 990-997.

Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547-12553.

Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research, 218(1), 211–229.

Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1-9.

Yang, C., Shi, X., Luo, J., & Han, J. (2018). I Know You’ll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application.

Zhu, B., Xiao, J., & He, C. (2014). A Balanced Transfer Learning Model for Customer Churn Prediction. In Proceedings of the Eighth International Conference on Management Science and Engineering Management (pp. 97-104). Springer, Berlin, Heidelberg.

Zhu, B., Baesens, B., Backiel, A. E., & vanden Broucke, S. K. (2018). Benchmarking sampling techniques for imbalance learning in churn prediction. Journal of the Operational Research Society, 69(1), 49-65.

Zorić, B. A. (2016). Predicting customer churn in banking industry using neural networks. Interdisciplinary Description of Complex Systems: INDECS, 14(2), 116-124.

 

 

Ali, Ö. G., & Arıtürk, U. (2014). Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Systems with Applications, 41(17), 7889-7903.

Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242-254.

Babu, S., & Ananthanarayanan, N. R. (2018). Enhanced Prediction Model for Customer Churn in Telecommunication Using EMOTE. In International Conference on Intelligent Computing and Applications (pp. 465-475). Springer, Singapore.

Barbará, D., & Wu, X. (2001, July). Finding dense clusters in hyperspace: an approach based on row shuffling. In International Conference on Web-Age Information Management (pp. 305-316). Springer, Berlin, Heidelberg.

Caigny, A., Coussement, K., & 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), 760-772.

Chiang, D. A., Wang, Y. F., Lee, S. L., & Lin, C. J. (2003). Goal-oriented sequential pattern for network banking churn analysis. Expert Systems with Applications, 25(3), 293-302.

Chu, C., Xu, G., Brownlow, J., & Fu, B. (2016, November). Deployment of churn prediction model in financial services industry. In Behavioral, Economic and Socio-cultural Computing (BESC), 2016 International Conference on (pp. 1-2). IEEE.

Coussement, K., & De Bock, K. W. (2013). Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. Journal of Business Research, 66(9), 1629-1636.

Coussement, K., Lessmann, S., & 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, 27-36.

Dahiya, K., & Bhatia, S. (2015, September). Customer churn analysis in telecom industry. In Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 2015 4th International Conference on (pp. 1-6). IEEE.

Farquad, M. A. H., Ravi, V., & Raju, S. B. (2014). Churn prediction using comprehensible support vector machine: An analytical CRM application. Applied Soft Computing, 19, 31-40.

Guo, H., & Viktor, H. L. (2006, August). Mining relational data through correlation-based multiple view validation. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 567-573). ACM.

Günther, C. C., Tvete, I. F., Aas, K., Sandnes, G. I., & Borgan, Ø. (2014). Modelling and predicting customer churn from an insurance company. Scandinavian Actuarial Journal, 2014(1), 58-71.

Huang, Y., & Kechadi, T. (2013). An effective hybrid learning system for telecommunication churn prediction. Expert Systems with Applications, 40(14), 5635-5647.

Ikonomovska, E., & Džeroski, S. (2011, March). Regression on evolving multi-relational data streams. In Proceedings of the 2011 Joint EDBT/ICDT Ph. D. Workshop (pp. 1-7). ACM.

Kaur, M., Singh, K., & Sharma, N. (2013). Data Mining as a tool to Predict the Churn Behaviour among Indian bank customers. International Journal on Recent and Innovation Trends in Computing and Communication, 1(9), 720-725.

Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications, 39(4), 4344-4357.

Kim, K., Jun, C. H., & Lee, J. (2014). Improved churn prediction in telecommunication industry by analyzing a large network. Expert Systems with Applications, 41(15), 6575-6584.

Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications policy, 28(9-10), 751-765.

Lu, N., Lin, H., Lu, J., & Zhang, G. (2014). A customer churn prediction model in telecom industry using boosting. IEEE Transactions on Industrial Informatics, 10(2), 1659-1665.

Mahajan, V., Misra, R., & Mahajan, R. (2017). Review on factors affecting customer churn in telecom sector. International Journal of Data Analysis Techniques and Strategies, 9(2), 122-144.

Miguéis, V. L., Van den Poel, D., Camanho, A. S., & e Cunha, J. F. (2012). Modeling partial customer churn: On the value of first product-category purchase sequences. Expert systems with applications, 39(12), 11250-11256.

Najarzadeh, R., Reed, M., & Mirzanejad, H. (2013). A Study of the Competitiveness of Iran's Banking System. Journal of Economic Cooperation & Development, 34(1), 93.

Oyeniyi, A. O., Adeyemo, A. B., Oyeniyi, A. O., & Adeyemo, A. B. (2015). Customer churn analysis in banking sector using data mining techniques. Afr J Comput ICT, 8(3), 165-174.

Perlich, C., & Huang, Z. (2005). Relational learning for customer relationship management. In Proceedings of international workshop on customer relationship management: data mining meets marketing.

PKDD'99. 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD99) Discovery Challenge: http://lisp.vse.cz/pkdd99/chall.htm

Prasad, U. D., & Madhavi, S. (2012). Prediction of churn behavior of bank customers using data mining tools. Business Intelligence Journal, 5(1), 96-101.

Qureshi, S. A., Rehman, A. S., Qamar, A. M., Kamal, A., & Rehman, A. (2013, September). Telecommunication subscribers' churn prediction model using machine learning. In Digital Information Management (ICDIM), 2013 Eighth International Conference on (pp. 131-136). IEEE.

Riebe, E., Wright, M., Stern, P., & Sharp, B. (2014). How to grow a brand: Retain or acquire customers?. Journal of Business Research, 67(5), 990-997.

Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36(10), 12547-12553.

Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research, 218(1), 211–229.

Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1-9.

Yang, C., Shi, X., Luo, J., & Han, J. (2018). I Know You’ll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application.

Zhu, B., Xiao, J., & He, C. (2014). A Balanced Transfer Learning Model for Customer Churn Prediction. In Proceedings of the Eighth International Conference on Management Science and Engineering Management (pp. 97-104). Springer, Berlin, Heidelberg.

Zhu, B., Baesens, B., Backiel, A. E., & vanden Broucke, S. K. (2018). Benchmarking sampling techniques for imbalance learning in churn prediction. Journal of the Operational Research Society, 69(1), 49-65.

Zorić, B. A. (2016). Predicting customer churn in banking industry using neural networks. Interdisciplinary Description of Complex Systems: INDECS, 14(2), 116-124.