پیش‌بینی رویگردانی جزئی مشتریان بانک‌ها با استفاده از مدل زنجیره وضعیت

نویسندگان

1 دانشجوی دکتری، مدیریت فناوری اطلاعات، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران.

2 عضو هیئت علمی، گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران. (نویسنده مسئول)؛ Taghva@atu.ac.ir

3 عضو هیئت علمی، گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران.

چکیده

 
بانک‌ها در فضای رقابتی شدید تلاش می‌کنند تا به منابع مالی بیشتری دست پیدا کنند. با توجه به بالاتر بودن هزینه‌های جذب مشتری جدید نسبت به نگهداری مشتریان موجود، عمده تلاش بانک‌ها روی حفظ سپرده‌های موجود مشتریان در بانک متمرکز است. لذا پیش‌بینی رویگردانی مشتریان پیش از وقوع برای بانک‌ها از اهمیت ویژه‌ای برخوردار است. تقریباً در تمامی تحقیقات مرتبط در بانک‌ها مشتریان به دو دسته رویگردان و غیر رویگردان با یک تعریف ثابت از رویگردانی تقسیم شده‌اند؛ اما در شرایط بانکداری ایران نمی‌توان از یک تعریف ثابت برای رویگردانی استفاده نمود؛ بنابراین لازم است که رویگردانی را به‌صورت دینامیک و در قالب وضعیت‌های مختلف تعریف کنیم. برای این منظور در این تحقیق مفهوم زنجیره وضعیت معرفی می‌شود که تغییرات وضعیت رویگردانی جزئی مشتریان طی زمان را مشخص می‌‌کند. با به‌کارگیری این زنجیره‌ها و استفاده ترکیبی از تکنیک‌های خوشه‌بندی سلسله‌مراتبی و همچنین ماشین‌های بردار پشتیبان، مدلی برای پیش‌بینی رویگردانی جزئی مشتریان بانک‌ها ساخته شد. برای ساختن نمونه عملی و ارزیابی دقت پیش‌بینی، پنج سال داده‌های واقعی مشتریان یک بانک اروپایی و همچنین سه سال داده‌های مشتریان سه بانک ایرانی مورد استفاده قرار گرفتند. نتایج حاکی از دقت بالای پیش‌بینی در مدل‌های ساخته‌شده روی هر چهار بانک به‌خصوص با افزایش طول زنجیره‌های وضعیت در داده‌های آزمون است.
 

کلیدواژه‌ها


عنوان مقاله [English]

Prediction of Bank Customers’ Partial Churn Using State Chain Model

نویسندگان [English]

  • Mohsen Asgari 1
  • Mohammadreza Taghva 2
  • Mohammad Taghi Taghavifard 3
1 Ph.D. Student, Information Technology Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran
2 Faculty Member, Industrial Management Department, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran.(Corresponding Author: Dr.taghavifard@gmail.com).
3 Faculty Member, Industrial Management Department, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran
چکیده [English]

Banks are endeavoring to gain more funds in a highly competitive environment. Given the higher costs of attracting new customers than retaining existing ones, most banks focus on maintaining their existing customers. Therefore, it is quite important for the banks to predict the customer churn in advance. In almost all related research works in banking, customers are divided into two types of static categories: “churners” and “loyal” customers. However, due to the nature of banking particularly in Iran, it is necessary to define churn in a dynamic manner in a variety of circumstances. In this study, the concept of state chain is introduced, which identifies changes in customers’ partial churn status over time. Using the sequence of chains and a combination of hierarchical clustering techniques as well as support vector machine, a model was developed to predict partial churn of bank customers. To construct a practical sample and to evaluate the prediction accuracy, 5 years of real European bank customers’ data as well as 3 years of customers’ data from three different Iranian banks were used. The results indicate a high level of prediction accuracy for the model in all 4 banks, particularly when longer sequences of states are used.

کلیدواژه‌ها [English]

  • : Partial Churn Prediction
  • State Chain Model
  • Bank Customers
  • Hierarchical Clustering
  • Support Vector Machine

 

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 Applications41(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. Neurocomputing237, 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 Research269(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 Applications25(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 Research66(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 Systems95, 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 Computing19, 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 Journal2014(1), 58-71.

Huang, Y., & Kechadi, T. (2013). An effective hybrid learning system for telecommunication churn prediction. Expert Systems with Applications40(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 Communication1(9), 720-725.

Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications39(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 Applications41(15), 6575-6584.

Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications policy28(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 Informatics10(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 Strategies9(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 applications39(12), 11250-11256.

Najarzadeh, R., Reed, M., & Mirzanejad, H. (2013). A Study of the Competitiveness of Iran's Banking System. Journal of Economic Cooperation & Development34(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 ICT8(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 Journal5(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 Research67(5), 990-997.

Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications36(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 Society69(1), 49-65.

Zorić, B. A. (2016). Predicting customer churn in banking industry using neural networks. Interdisciplinary Description of Complex Systems: INDECS14(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 Applications41(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. Neurocomputing237, 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 Research269(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 Applications25(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 Research66(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 Systems95, 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 Computing19, 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 Journal2014(1), 58-71.

Huang, Y., & Kechadi, T. (2013). An effective hybrid learning system for telecommunication churn prediction. Expert Systems with Applications40(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 Communication1(9), 720-725.

Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications39(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 Applications41(15), 6575-6584.

Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications policy28(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 Informatics10(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 Strategies9(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 applications39(12), 11250-11256.

Najarzadeh, R., Reed, M., & Mirzanejad, H. (2013). A Study of the Competitiveness of Iran's Banking System. Journal of Economic Cooperation & Development34(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 ICT8(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 Journal5(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 Research67(5), 990-997.

Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications36(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 Society69(1), 49-65.

Zorić, B. A. (2016). Predicting customer churn in banking industry using neural networks. Interdisciplinary Description of Complex Systems: INDECS14(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 Applications41(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. Neurocomputing237, 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 Research269(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 Applications25(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 Research66(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 Systems95, 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 Computing19, 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 Journal2014(1), 58-71.

Huang, Y., & Kechadi, T. (2013). An effective hybrid learning system for telecommunication churn prediction. Expert Systems with Applications40(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 Communication1(9), 720-725.

Khashei, M., & Bijari, M. (2012). A new class of hybrid models for time series forecasting. Expert Systems with Applications39(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 Applications41(15), 6575-6584.

Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications policy28(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 Informatics10(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 Strategies9(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 applications39(12), 11250-11256.

Najarzadeh, R., Reed, M., & Mirzanejad, H. (2013). A Study of the Competitiveness of Iran's Banking System. Journal of Economic Cooperation & Development34(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 ICT8(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 Journal5(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 Research67(5), 990-997.

Tsai, C. F., & Lu, Y. H. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications36(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 Society69(1), 49-65.

Zorić, B. A. (2016). Predicting customer churn in banking industry using neural networks. Interdisciplinary Description of Complex Systems: INDECS14(2), 116-124.