Yasser Khan,Zeeshan Rasheed,Naeem Ahmed,Minhaj Ullah,Malik Taimur Ali,Farrukh Hassan,Sheeraz Ahmed,



Artificial Neural Network,Prediction,Churn management,Telecom Churn,


Telecommunication customer churn is considered as major cause for dropped revenue and customer baseline of voice, multimedia and broadband service provider. There is strong need on focusing to understand the contributory factors of churn. Now considering factors from data sets obtained from Pakistan major telecom operators are applied for modeling. On the basis of results obtained from the optimal techniques, comparative technical evaluation is carried out. This research study is comprised mainly of proposition of conceptual frame work for telecom customer churn that lead to creation of predictive model. This is trained tested and evaluated on given data set taken from Pakistan Telecom industry that has provided accurate & reliable outcomes. Out of four prevailing statistical and machine learning algorithm, artificial neural network is declared the most reliable model, followed by decision tree. The logistic regression is placed at last position by considering the performance metrics like accuracy, recall, precision and ROC curve.  The results from research has revealed main parameters found responsible for customer churn were data rate, call failure rate, mean time to repair and monthly billing amount. On the basis of these parameter artificial neural network has achieved 79% more efficiency as compare to low performing statistical techniques.


I. Asmaa Jamal Awad, Ahmed Abdulrasool Ahmed, Osamah Abdallatif. : ‘ESTIMATION TYPES OF FAILURE FOR THERMO-ELECTRIC UNIT BY USING ARTIFICIAL NEURAL NETWORK (ANN)’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-7, July (2020) pp 47-69. DOI : 10.26782/jmcms.2020.07.00005
II. Al-Weshah, Ghazi A., Excimirey Al-Manasrah, and Manar Al-Qatawneh. “Customer relationship management systems and organizational performance: Quantitative evidence from the Jordanian telecommunication industry.” Journal of Marketing Communications 25, no. 8 (2019): 799-819.
III. Bhattacharyya, Jishnu, and Manoj Kumar Dash. “Investigation of customer churn insights and intelligence from social media: a netnographic research.” Online Information Review (2020).
IV. DARMA, Jufri, Azhar SUSANTO, Sri MULYANI, and Jadi SUPRIJADI. “The Role of Top Management Support in the Quality of Financial Accounting Information Systems.” Journal of Applied Economic Sciences 13, no. 4 (2018).
IV. Dridi, Amna, Mohamed Medhat Gaber, R. Muhammad Atif Azad, and Jagdev Bhogal. “Scholarly data mining: A systematic review of its applications.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (2020)

V. Gordini, Niccolò, and Valerio Veglio. “Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry.” Industrial Marketing Management 62 (2017): 100-107.
VI. Idris, Adnan, Aksam Iftikhar, and Zia ur Rehman. “Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO under sampling.” Cluster Computing 22, no. 3 (2019): 7241-7255.
VII. Jere, Mlenga G., and Alick Mukupa. “Customer satisfaction and loyalty drivers in the Zambian mobile telecommunications industry.” Journal of Business and Retail Management Research 13, no. 2 (2018).
VIII. Khdour, Naser, and Atef Al-Raoush. “The impact of organizational storytelling on organizational performance within Jordanian telecommunication sector.” Journal of Workplace Learning (2020).
IX. Lee, Hyunsong, Hyunhong Choi, and Yoonmo Koo. “Lowering customer’s switching cost using B2B services for telecommunication companies.” Telematics and Informatics 35, no. 7 (2018): 2054-2066.
X. Mashchak, Nataliia, and Oksana Dovhun. “Modern Marketing and Logistics Approaches in the Implementation of E-Commerce.” In Integration of Information Flow for Greening Supply Chain Management, pp. 375-391. Springer, Cham, 2020.
XI. Muthu, BalaAnand, C. B. Sivaparthipan, Gunasekaran Manogaran, Revathi Sundarasekar, Seifedine Kadry, A. Shanthini, and Antony Dasel. “IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector.” Peer-to-peer networking and applications 13, no. 6 (2020): 2123-2134.
XII. Özata, Hatice Işık, Önder Demir, and Buket Doğan. “Analysis of Patents in Cyber Security with Text Mining.” International Journal of Computer Theory and Engineering 13, no. 1 (2021).
XIII. Pant, Laxmi Prasad, and Helen Hambly Odame. “Broadband for a sustainable digital future of rural communities: A reflexive interactive assessment.” Journal of Rural Studies 54 (2017): 435-450.
XIV. Rachid, Ait Daoud, Amine Abdellah, Bouikhalene Belaid, and Lbibb Rachid. “Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context.” International Journal of Electrical and Computer Engineering 8, no. 4 (2018): 2367.
XV. Stiassny, Alfred, Agnes Somosi, and Krisztina Kolos. “Enhancing customer retention in case of service elimination? An empirical investigation in telecommunications.” (2019).
XVI. Taniguchi, Tadanari. “Self-organizing map analysis and classification of consumption trends of foreigners visiting Japan using a questionnaire survey.” Journal of Global Tourism Research 3, no. 2 (2018).
XVII. Thuethongchai, Nopsaran, Tatri Taiphapoon, Achara Chandrachai, and Sipat Triukose. “Adopt big-data analytics to explore and exploit the new value for service innovation.” Social Sciences 9, no. 3 (2020): 29.
XVIII. Van den Poel, Dirk, and Bart Lariviere. “Customer attrition analysis for financial services using proportional hazard models.” European journal of operational research 157, no. 1 (2004): 196-217.
XIX Wang, Li, Chaochao Chen, Jun Zhou, and Xiaolong Li. “Time-sensitive Customer Churn Prediction based on PU Learning.” arXiv preprint arXiv:1802.09788 (2018).
XX. Yasser Khan, Shahryar Shafiq, Sheeraz Ahmed, Nadeem Safwan, Mehr-e-Munir, Alamgir Khan. : ‘Factors affecting Service Quality, Customer Satisfaction and Customer Churn in Pakistan Telecommunication Services Market’. J. Mech. Cont.& Math. Sci., Vol.-14, No.-4, July-August (2019) pp 576-594. DOI : 10.26782/jmcms.2019.08.00048

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