CUSTOMER CHURN PREDICTION
Keywords:
Customer churn, telecommunication, services, rate, revenueAbstract
The main thing is to directly estimate client survival rates in the telecom diligence and client threat serves as a tool to completely understand client churn over time. relating to the guests who are on the edge of leaving and estimating when they will do so is another thing. Client churn vaticination has drawn further attention from businesses, especially those working in the telecommunications industry. multitudinous authors have offered colorful duplications of churn vaticination models that are heavily grounded on data mining principles and employ machine literacy and meta- heuristic algorithms. The purpose of this paper is to examine some of the most significant churn vaticination styles created in recent times. The thing of this paper is to dissect churn vaticination ways in order to fetch churn addresses and confirm the causes of client churn. This article summarizes churn prediction methods in order to gain a better understanding of client churn. It also demonstrates that mongrel models, as opposed to single algorithms, give the most accurate churn prognostications, allowing telecom diligence to more understand the requirements of high- threat guests and modify their services consequently.
Downloads
References
Irfan Ullah, Basit Raza, Ahmad Kamran Malik, Muhamad Imran, Saif Ul Islam and Sung Won Kim., “A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector”, In the proceedings of IEEE Access, vol. 07, no. 2169-3536, pp. 60134 - 60149, 2019.
Kavitha V, Hemanth G, Mohan S.V and Harish M, “Churn Prediction of Customer in Telecom Industry using Machine Learning Algorithms”, International Journal of Engineering Research & Technology (2278-0181), Vol. 9, Issue 05, pp. 181-184, 2020.
Kiran and Surbhi, “Customer Churn Analysis in Telecom”, Industry International Conference for Reliablity, Noida, India, 2015.
Krishna B.N, and Sasikala,“Predictive Analysis and Modeling of Customer Churn in Telecom using Machine Learning Technique ,”In the proceedings of International Conference on Trends in Electronics and Informatics , Tirunelveli, India, pp. 6-11, 2019.
Rahul J and Usharani T,“Churn Prediction in Telecommunication Using Data Mining Technology”, International Journal of Advanced Computer Science and Applications, Vol. 2, No.2, pp. 17-19, 2013.
Roshin Reji, Rohit Zacharias , Sebin Antony and Merlin Mary James, “Churn Prediction in Telecom sector using Machine Learning”, International Journal of Information Systems and Computer Sciences, Vol. 8, No.2, pp. 832–937, 2019
Sato T, Huang B.Q, Huang Y, Kechadi M.T and Buckley B , “Using PCA to Predict Customer Churn in Telecommunication Dataset”, International conference on Advanced data mining and applications, Vol. 2,pp. 26-27, 2010
Jadhav, Rahul Pawar, Usharani. (2011). Churn Prediction in Telecommunication Using Data Mining Technology. International Journal of Advanced Computer Sciences and Applications. 2.10.14569/IJACSA.2011.020204
Kirui, Clement K. et al. “Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining.” (2013)
Lazarov, Vladislav and Marius Capota. “Churn Prediction.” (2007)
Kirui, Clement K. et al. “Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining.” (2013)
Umaparvathi, V. and K. Iyakutti. “A Survey on Customer Churn Prediction in Telecom Industry: Datasets, Methods and Metrics.” (2016).
Canale, Antonio and Nicola Lunardon. “CHURN PREDICTION IN TELECOMMUNICATIONS INDUSTRY. A STUDY BASED ON BAGGING CLASSIFIERS.” (2014).
Liu, Y. and Yongrui Zhuang. “Research Model of Churn Prediction Based on Customer Segmentation and Misclassification Cost in the Context of Big Data.” Journal of Computational Chemistry 03 (2015): 87-93.
Hashmi, Nabgha Butt, Naveed Anwer Iqbal, Dr. Muddesar. (2013). Customer Churn Prediction in Telecommunication A Decade Review and Classification. IJCSI. 10. 271-282
L. F. Khalid, A. Mohsin Abdulazeez, D. Q. Zeebaree, F. Y. H. Ahmed and D. A. Zebari, “Customer Churn Prediction in Telecommunications Industry Based on Data Mining,” 2021 IEEE Symposium on Industrial Electronics Applications (ISIEA), 2021, pp. 1-6, doi: 10.1109/ISIEA51897.2021.9509988
Babu, Pr. Sathesh et al. “A Review on Customer Churn Prediction in Telecommunication Using Data Mining Techniques.” (2016)
B, Senthil Nayaki M, Swetha D, Nivedha. (2021). CUSTOMER CHURN PREDICTION. IARJSET. 8. 527-531. 10.17148/IARJSET.2021.8692
Y. Kavyarshitha, V. Sandhya and M. Deepika,” Churn Prediction in Banking using ML with ANN,” 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), 2022, pp. 1191-1198, doi: 10.1109/ICICCS53718.2022.9788456
Ahmad, A.K., Jafar, A. & Aljoumaa, K. (2019). Customer churn prediction in telecom using machine learning in big data platform. JBD, 6(28), 1-24
Albadawi, S. et al (2017). Telecom churn prediction model using data mining techniques. BUJICT, 10(2), 8-14
Axelsson, R. & Notstan, A. (2017). Identify Churn. Unpublished Master’s Thesi
Kau, F.M., Masethe, H.D. & Lepota, C.K. (2017). Service Provider churn prediction for telecoms company using data analytics. WCECS 1-4
Tsymbalov, E. (2016). Churn Prediction for Game Industry Based on Cohort Classification Ensemble. MPRA 82871
Umayaparvathi, V. & Iyakutti, K. (2016). A Survey on Customer Churn Prediction in Telecom Industry: Datasets, Methods and Metrics. IRJET 3(4), 1065-1070
Babu, S. & Ananthanarayanan, N.R. (2016). A review on customer churn prediction in telecommunication using data mining techniques. IJSER 4(1), 35-40
Backiel, A., Baesens, B. & Claeskens, G. (n.d.). Predicting time-to-churn of prepaid mobile Telephone Customers using Social Network Analysis
Balasubramanian, M. & Selvarani, M. (2014). Churn Prediction in Mobile Telecom System using Data Mining Techniques. IJSRP, 4(4), 1-5
Chuanqi, W., Ruiqi, L. Peng, W., Zonghai, C. (2017). Partition costsensitive CART based on customer value for Telecom customer Churn Prediction, Control Conference (CCC),
Canale, A. & Lunardon, N. (2014). Churn prediction in telecommunication industry: A study based on Bagging Classifiers. Cellegio Carlo Alberto 350
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(2), 27–36.
Diaz-Aviles, E. et al (2015). Towards real-time customer experience prediction for telecommunication operators.
Eria, K. & Marikannan, B.P. (2018). Systematic review of customer churn prediction in the Telecom. JATI, 2(1), 7-14
Karapinar, H.C., Altay, A., & Kayakutlu, G. (2016). Churn detection and prediction in automotive supply industry. IEEE, 1349-1354
María Óskarsdóttir, Cristian Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, and Jan Vanathien. "A comparative study of social network classifiers for predicting churn in the telecommunication industry." 2016.
Abbas Keramati, and RuhollaJafariMarandi. "Addressing churn prediction problem with Meta-heuristic, Machine learning, Neural Network and data mining techniques: a case study of a telecommunication company." International Journal of Future Computer and Communication, vol. 4, no. 5, pp. 350, 2015.
Abbas Keramati, RuhollaJafari-Marandi, Mohammed Aliannejadi, ImanAhmadian, MahdiehMozaffari, and UldozAbbasi. "Improved churn prediction in telecommunication industry using data mining techniques." Applied Soft Computing, vol. 24, pp. 994- 1012, 2014.
Thanasis Vafeiadis, Konstantinos I. Diamantaras, G. Sarigiannidis, and K. Ch Chatzisavvas. "A comparison of machine learning techniques for customer churn prediction." Simulation Modelling Practice and Theory, vol. 55, pp. 1-9, 2015.
Shin-Yuan Hung, David C. Yen, and Hsiu-Yu Wang. "Applying data mining to telecom churn management." Expert Systems with Applications, vol. 31, no. 3, pp. 515-524, 2006.
Pretam Jayaswal, BakshiRohit Prasad, DivyaTomar, and SonaliAgarwal. "An Ensemble Approach for Efficient Churn Prediction in Telecom Industry." International Journal of Database Theory and Application, vol. 9, no. 8, pp. 211-232, 2016.