Paper Submission: 27 May 2020
Author Notification: 10 days
Journal Publication: May 2020
Awodele Oludele , Adeniyi Ben*, Ogbonna A.C., Kuyoro S.O., Ebiesuwa Seun
Prediction models are usually built by applying a supervised learning algorithm to historical data. This involves the use of data analytics system that uses real-time integration and dynamic real time responses data to detect churn risks. Subscribe are increasingly terminating their membership agreement with telecommunication companies through mobile number portability (MNP) in order to subscribe to another competitor companies.
To model the Customer prediction, a Markov Chain Model will be used. The Markov model allows for more flexibility than most other potential models, and can incorporate variables such as non-constant retention rate, which is not possible in the simpler models. The model allows looking at individual customer relationships as well as averages, and its probabilistic nature makes the uncertainty apprehensible. The Markov Decision Process is also appealing, but since dynamic decisions along the lifetime of the customer will not be evaluated the Markov Chain is the simplest model that still meets the requirements. Each state in the Markov Chain will represent a person being a customer for one month, with an infinite number of states. The transition probability to move from one state to the next is equivalent to a customer retaining with the operator to the next month. A customer that has churned will be considered lost forever.
Once the retention and churn rates are determined, the reference churn value for each customer will be computed. The churn rate will be calculated using MATLAB Monte Carlo simulations, running a large number of fictitious customer-company relationship processes, and extracting the results of the average customer.
Using simulation approach gives better result than analytical methods, since an indefinite number of states make matrix algebra complicated. It also allows visualizing the distribution of the results more easily than with algebraic calculation.
To the telecom companies the result of this analysis would improve the level at which they can predict customer churn, because, it will give insight on why a customer would choose to leave one telecommunication industry for another telecommunication industry. In other words, the cost of advertisement and loyalty programs as well as challenges face in retaining loyal customers would be identified. The researcher believes that the enhanced model for churn prediction developed from this study will lead to better retention strategy, improved telecom quality service, enhanced customer loyalty due to the improvement service from applying the information from this model. The study is also significant to researchers, behavioral scientist, business analysts as well as professionals in the computer science domain. The study will serve as a good reference material for research and contribute to the growing objective of developing enhanced model built on data mining techniques that can explain the churn behavior with more accuracy than using single methods.
Adebiyi, S.O., Oyatoye, E.O. & Amole, B.B. (2016). Relevant drivers for customers churn and retention decision in the Nigerian mobile telecommunication industry. JC, 8(3), 52-67
Adnan, A., Adnan, Z. Imran, A. Pir, S, Adeel, A., Basit, R., Ahmad, M. & Saif, M. (2017). Optimizing Coverage of Churn Prediction in Telecommunication Industry. International Journal of Advanced Computer Science and Applications, 8(5), 179 - 188
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
Ali Dehghan, Theodore B. Trafalis (2012. Examining churn and loyalty using support vector machine. Scienceedu Press, (4), 153161
Axelsson, R. & Notstan, A. (2017). Identify Churn. Unpublished Master’s Thesis
Azeem, M., Usman, M., & Fong, A. C. M. (2017). A churn prediction model for prepaid customers in telecom using fuzzy classifiers. Telecommunication Systems. 66(4), 603–614
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
Basha, S.M., Khare, A. & Gadipalli, J. (2018). Training and deploying churn prediction model using machine learning algorithms. IJERCSE. 5 (4): 59-64
Bryan, E. & Simmons, L.A. (2009). Family Involvement: Impacts on Post-secondary educational success for first-generation Appalachian college students. JCSD, 50 (4), 391-405
Canale, A. & Lunardon, N. (2014). Churn prediction in telecommunication industry: A study based on Bagging Classifiers. Cellegio Carlo Alberto 350
Chuanqi, W., Ruiqi, L. Peng, W., Zonghai, C. (2017). Partition costsensitive CART based on customer value for Telecom customer Churn Prediction, Control Conference (CCC),
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
Esteves, G.C. (2016). Churn Prediction in the Telecom Business. Unpublished Thesis.
Faris, H. (2018). A hybrid swarm intelligent neural network model for customer churn prediction and identifying the influencing factors. Information Journal. 9 (288)
Fei, T.Y., Shuan, L.H. & Yan, L.J. (2017). Prediction on customer churn in the telecommunications sector using discretization and Naïve Bayes Classifier. IJASCA, 9(3), 23-35
Jae Sik, Lee & Chun Lee, Jin. (2006). Customer churn Prediction by Hybrid Model.
Jamalian, E. & Foukerdi, R. (2018). A hybrid data mining method for customer churn prediction. ETASR 8(3), 2991-2997
Joshi, S. (2014). Customer experience management: An exploratory study on the parameters affecting customer experience for cellular mobile services of a telecom company. Social and Behavioral Sciences, 2(133), 392 – 399.
Karapinar, H.C., Altay, A., & Kayakutlu, G. (2016). Churn detection and prediction in automotive supply industry. IEEE, 1349-1354
Kau, F.M., Masethe, H.D. & Lepota, C.K. (2017). Service Provider churn prediction for telecoms company using data analytics. WCECS 1-4
Skymind (2019). A beginner’s guide to neural networks and deep learning. Retrieved from https://skymind.ai
Sufian, A., Khalid, L., Muhammad, M., & Kharbat, F. (2017). Telecom churn prediction model using data mining technique
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
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(2), 1–9.
Yu, R., An, X., Jin, B., Shi, J., Move, O. A., & Liu, Y. (2016). Particle classification optimization-based BP network for telecommunication customer churn prediction. Neural Computing and Applications, 1–14.
Zhao, L., Gao, Q., Dong, X. J., Dong, A., & Dong, X. (2017). K- local maximum margin feature extraction algorithm for churn prediction in telecom. Cluster Computing. 20(2), 1401–1409
Computer Science Department, Babcock University, Ilishan-Remo, Nigeria.
DOI: 10.21276/ijircst.2020.8.2.1 DOI URL: https://doi.org/10.21276/ijircst.2020.8.2.1
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