Telco Customer Churn Prediction Using ML Models

Authors

  • Padmanabh Wanikar Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India.
  • Sudhanshu Maurya Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
  • Muktinath Vishvakarma Visvesvaraya National Institute of Technology Nagpur, India.
  • Karimisetty Sujatha Dadi Institute of Engineering & Technology, Anakapalle, India
  • Nitin Rakesh Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India.
  • Vrince Vimal Graphic Era Hill University, Dehradun; Graphic Era Deemed to be University, Dehradun, India
  • Nilesh Shelke Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India

Keywords:

Artificial Neural Network, Customer, Churn, Machine Learning, Predictive Models, Telecommunications

Abstract

This study aims to develop a robust customer churn prediction model in the communications industry. Using various machine learning and analysis methods, we aim to improve the accuracy and efficiency of customer churn prediction. Our research explores integration techniques such as bundling and bracing to combine predictions from multiple models and reduce variability. We likewise lead awareness examination to assess the effect of various factors on the presentation model. We calibrate the model through factors like learning rate, number of layers, and group size and decide the best arrangement to assess the misfortune. We likewise broaden the existing examination of client rivalry forecasts in the correspondence business by consolidating the utilization of vast amounts of information. By utilizing the force of extensive information examination, we expect to build the versatility and effectiveness of client-agitate forecast models. Analysis of this study's consequences included using different AI calculations. The fundamental reason for this study is to anticipate client beat in the correspondence business utilizing AI and extensive information. Research has shown that client agitates can be precisely expected using this procedure.

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References

Anindito De "Telecom Revenue Enhancement and Churn Prevention - Wipro," wipro.com, (Accessed 28 Oct. 2023).

Geetha, S. & Kaliappan, Vishnu. (2018). Tweet Analysis Based on Distinct Opinions of Social Media Users. 1-6. 10.1109/ICSNS.2018.8573619.

Sun, Yuechi & Liu, Haiyan & Gao, Yu. (2023). Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model. Heliyon. 9. e13384. 10.1016/j.heliyon.2023.e13384.

Peng, Ke & Peng, Yan. (2022). Research on Telecom Customer Churn Prediction Based on GA-XGBoost and SHAP. Journal of Computer and Communications. 10. 107-120.10.4236/jcc.2022.1011008.

Sana, Joydeb & Abedin, M. & Rahman, Mohammad & Rahman, Mohammad. (2022). Using data transformation methods and feature selection, a novel customer churn prediction model for the telecommunication industry. PLOS ONE.17.e0278095.10.1371/journal.pone.0278095.

Börühan, G. (2022). Churn Customer Management in Retail Industry: A Case Study. İzmir İktisat Dergisi , 37 (4) , 1094-1118 . DOI: 10.24988/ije.1070830

hen, Hao & Tang, Qian & Wei, Yifei & Song, Mei. (2021). Churn Prediction Model of Telecom Users Based on XGBoost. Journal on Artificial Intelligence. 3. 115-121. 10.32604/jai.2021.026851.

Hou, Hongwei & Tang, Kunzhi & Liu, Xiaoqian & Zhou, Yue. (2022). Application of Artificial Intelligence Technology Optimized by Deep Learning to Rural Financial Development and Rural Governance. Journal of Global Information Management. 30. 1-23. 10.4018/JGIM.289220.

Fareniuk, Yana & Zatonatska, Tetiana & Dluhopolskyi, Oleksandr & Kovalenko, Oksana. (2022). Customer Churn Prediction Model: A Case of the Telecommunication Market. ECONOMICS. 10. 10.2478/eoik-2022-0021.

Nalatissifa, Hiya & Pardede, Hilman. (2021). Customer Decision Prediction Using Deep Neural Network on Telco Customer Churn Data. Jurnal Elektronika dan Telekomunikasi. 21. 122. 10.14203/jet.v21.122-127.

Kostić, Stefan & Simic, Mirjana & Kostić, Miroljub. (2020). Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory. Entropy. 22. 753. 10.3390/e22070753.

Suh, Youngjung. (2023). Machine learning-based customer churn prediction in-home appliance rental business. Journal of Big Data. 10. 10.1186/s40537-023-00721-8.

Óskarsdóttir, María & Bravo, Cristián & Verbeke, Wouter & Sarraute, Carlos & Baesens, Bart & Vanthienen, Jan. (2020). A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry.

Koumetio Tekouabou, Cédric Stéphane & Gherghina, Ştefan Cristian & Toulni, Hamza & Mata, Pedro & Martins, José. (2022). Towards Explainable Machine Learning for Bank Churn Prediction Using Data Balancing and Ensemble-Based Methods. Mathematics. 10. 2379. 10.3390/math10142379.

Lee, Seonghye & Oh, Hae & Choi, Jeongil. (2020). Service Design Management and Organizational Innovation Performance. Sustainability. 13. 4. 10.3390/su13010004.

Rai, Shivam & Nayak, Jogendra. (2019). Hospitality branding in emerging economies: an Indian perspective. Journal of Tourism Futures. 5. 10.1108/JTF-07-2018-0047.

Han, Shaoyong. (2018). A Study on a Predictive Model of Customer Defection in a Hotel Reservation Website. MATECWeb of Conferences.228.01009. 10.1051/matecconf/201822801009.

Usman, Muhammad & Azeem, Muhammad. (2018). A fuzzy-based churn prediction and retention model for prepaid customers in the telecom industry. International Journal of Computational Intelligence Systems. 11. 66-78. 10.2991/ijcis.11.1.6.

Li, X.L., Li, Z. (2019). A hybrid prediction model for e-commerce customer churn based on logistic regression and extreme gradient boosting algorithm. Ingénierie des Systèmes d’Information, Vol. 24, No. 5, pp. 525-530. https://doi.org/10.18280/isi.240510

Arshad, Soban & Iqbal, Khalid & Naz, Sheneela & Yasmin, Sadaf & Rehman, Zobia. (2022). A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning. Computers, Materials & Continua. 72. 4283-4301.10.32604/cmc.2022.025442.

Devi, M. & Saharia, Jyotikinkar & Kumar, Shubham & Chansoriya, Aayushi & Yadav, Prashant. (2019). Machine Learning Based Suspicion of Customer Detention in Banking with Diverse Solver Neighbors and Kernels. International Journal of Recent Technology and Engineering (IJRTE). 8. 3244-3249.10.35940/ijrte.D8043.118419.

Zhao, Ming & Zeng, Qingjun & Chang, Ming & Tong, Qian & Su, Jiafu. (2021). A Prediction Model of Customer Churn Considering Customer Value: An Empirical Research of Telecom Industry in China. Discrete Dynamics in Nature and Society. 2021. 1-12. 10.1155/2021/7160527.

Darshil Gandhi "How to Improve Retention with Churn Prediction Analytics," amplitude.com, (Accessed 28 Oct. 2023).

Chu, Charles & Xu, Guandong & Brownlow, James & Fu, Bin. (2016). Deployment of a churn prediction model in financial services industry.12.10.1109/BESC.2016.7804486.

[25Devi, M. & Krishna, G. & Sowmya, K. & Pavan, T.. (2019). Postulation of Customer Retention in the Banking Sector using Machine Learning and Principal Components. International Journal of Recent Technology and Engineering (IJRTE). 8. 3178-3182. 10.35940/ijrte.D8020.118419.

Coussement, Kristof & Van den Poel, Dirk. (2008). Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Systems with Applications. 36. 6127-6134. 10.1016/j.eswa.2008.07.021.

Ribeiro, Hugo & Barbosa, Belem & Moreira, Antonio & Rodrigues, Ricardo. (2022). Churn in services – A bibliometric review. Cuadernos de Gestión. 22. 10.5295/cdg.211509hr.

Hudaib, Amjad & Dannoun, Reham & Harfoushi, Osama & Obiedat, Ruba & Faris, Hossam. (2015). Hybrid Data Mining Models for Predicting Customer Churn. International Journal of Communications, Network and System Sciences. 08. 91-96. 10.4236/ijcns.2015.85012.

Gan, Lin. (2022). XGBoost-Based E-Commerce Customer Loss Prediction. Computational Intelligence and Neuroscience. 2022. 1-10. 10.1155/2022/1858300.

Bahmani, Bahram & Mohammadi, Golshan & Mohammadi, Mehrdad & Tavakkoli-Moghaddam, Reza. (2013). Customer churn prediction using a hybrid method and censored data. Management Science Letters. 3. 1345-1352. 10.5267/j.msl.2013.04.017.

Ibitoye, Ayodeji & Onifade, Olufade. (2020). Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management.6.56-59.10.11648/j.ijdst.20200602.12.

Thein, Si Thu & Khin, Hnin & Thi, Aung. (2017). Anti-malarial landscape in Myanmar: Results from a nationally representative survey among community health workers and the private sector outlets in 2015/2016. Malaria Journal. 16. 10.1186/s12936-017-1761-8.

Zhang, Yali & Tian, Yichao & Li, Ying & Wang, Donghua & Tao, Jin & Yang, Yongwei & Lin, Junliang & Zhang, Qiang & Wu, Luhua. (2022). Machine learning algorithm for estimating karst rocky desertification in a peak-cluster depression basin in southwest Guangxi, China. Scientific Reports. 12. 10.1038/s41598-022-21684-5.

Shen, Jiquan & Shi, Jiawei & Luo, Junwei & Zhai, Haixia & Liu, Xiaoyan & Wu, Zhengjiang & Yan, Chaokun & Luo, Huimin. (2022). Deep learning approach for cancer subtype classification using high-dimensional gene expression data. BMC Bioinformatics. 23. 10.1186/s12859-022-04980-9.

Li, Shujuan & Ren, Hongyan & Hu, Wensheng & Lu, Liang & xu, Xinliang & Zhuang, Dafang & Liu, Qi-Yong. (2014). Spatiotemporal Heterogeneity Analysis of Hemorrhagic Fever with Renal Syndrome in China Using Geographically Weighted Regression Models. International journal of environmental research and public health. 11. 12129-47. 10.3390/ijerph111212129.

Melo, Esthefani & Nobre, Reginaldo & Pinheiro, Francisco & Souza, Leandro & Lima, Geovani & Gheyi, Hans & Elias, Jutahy & Silva, Wandra. (2018). Evaluation of West Indian cherry (Malpighia emarginata) rootstock under saline water irrigation and nitrogen fertilization. Australian Journal of Crop Science. 12. 1034-1040. 10.21475/ajcs.18.12.06.PNE1314.

Behera, Tapan & Tripathi, Kumud. (2023). Root Cause Analysis Bot using Machine Learning Techniques. 10.36227/techrxiv.21588159.v3.

Rushd, Sayeed & Hafsa, Noor & Al-Faiad, Majdi & Arifuzzaman, Md. (2021). Modeling the Settling Velocity of a Sphere in Newtonian and Non-Newtonian Fluids with Machine-Learning Algorithms. Symmetry. 13. 71. 10.3390/sym13010071.

Sarker, Iqbal & Hoque, Moshiul & Uddin, Kafil & Alsanoosy, Tawfeeq. (2021). Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions. Mobile Networks and Applications. 26. 10.1007/s11036-020-01650-z.

Sharifzadeh, Mostafa & Benali, Habib & Rivaz, Hassan. (2020). Phase Aberration Correction: A Convolutional Neural Network Approach. IEEE Access. 8. 162252-162260. 10.1109/ACCESS.2020.3021685.

Iqbal, Umair & Barthélemy, Johan & Perez, Pascal. (2022). Prediction of hydraulic blockage at culverts from a single image using deep learning. Neural Computing and Applications. 34. 1-17. 10.1007/s00521-022-07593-8.

Rahagiyanto, Angga & Basuki, Achmad & Sigit, Riyanto. (2017). Moment Invariant Features Extraction for Hand Gesture Recognition of Sign Language based on SIBI. EMITTER International Journal of Engineering Technology. 5. 10.24003/emitter.v5i1.173.

Qin, Chuanbo & Lin, JingYin & Zeng, Junying & Zhai, Yikui & Tian, LianFang & Peng, ShuTing & Li, Fang. (2022). Joint Dense Residual and Recurrent Attention Network for DCE-MRI Breast Tumor Segmentation. Computational Intelligence and Neuroscience. 2022. 1-16. 10.1155/2022/3470764.

Bao, Shuai & Liu, Jiping & Wang, Liang & Zhao, Xizhi. (2022). Application of Transformer Models to Landslide Susceptibility Mapping. Sensors. 22. 9104. 10.3390/s22239104.

Sokolovsky, Artur & Hare, David & Mehnen, Jorn. (2021). Cost-Effective Vibration Analysis through Data-Backed Pipeline Optimisation. Sensors (Basel, Switzerland). 21. 10.3390/s21196678.

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Published

25.12.2023

How to Cite

Wanikar, P. ., Maurya, S. ., Vishvakarma, M. ., Sujatha, K. ., Rakesh, N. ., Vimal, V. ., & Shelke, N. . (2023). Telco Customer Churn Prediction Using ML Models. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 644 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4309

Issue

Vol. 12 No. 2 (2024)

Section

Research Article

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