Customer Churn Prediction in Telecommunication Industry Using Data Certainty
DOI:
https://doi.org//10.32628/IJSRSET207142Keywords:
Regression, Linear Classifier, Random Forest, Decision Tree, CNN, Support Vector Machine, Naive Bayes, Neural networks, Multilayer Perceptron, and Performance metrics.Abstract
Customer churn is a major problem affecting large companies, especially in telecommunication field. So the telecom industries have to take the necessary steps to retain their customers, to maintain their market value. So companies are seeking to develop methods that predict potential churned customers. We have to find out the factors that increase customer churn for making necessary actions to reduce churn. In the past, different data mining techniques have been used for predicting the churners. Here the most popular machine learning algorithms used for churn predicting are analysed. The conclusions are stated with the help of suitable tables.
References
- Bingquan Huang, Mohand Tahar Kechadi, Brian Buckley, “Customer Churn Prediction in Telecommunication”, Expert Systems with Applications 39 (2012) 1414–1425.
- Ionut Brandusoiu, Gavril Toderean, "Churn Prediction in Telecommunication Sector using Support Vector Machine", May 2013.
- A.Keramatia, R.Jafari-Marandia, M.Aliannejadib, I.Ahmadianc, M. Mozaffaria, U. Abbasia Makhtar, “Improved churn prediction in telecommunication industry using data mining techniques”, Applied Soft Computing 24 (2014) 994–1012.
- Mohammad Ridwan Ismail, Mohd Khalid Awang, Mohd Nordin Abdul Rahman, Mokhairi Makhtar, "A multilayer Perceptron Approach for Customer Churn Prediction”, Article in International Journal of Multimedia and Ubiquitous Engineering, July 2015.
- Mohd Khalid Awang, Mokhairi Makhtar, Mohd Nordin Abdul Rahman, "Improving Accuracy and Performance of Customer Churn Prediction Using Feature Reduction Algorithms", Journal of Telecommunication, Electronic and Computer Engineering, Vol. 9,2017.
- Sanket Agrawal, Aditya Das, Sudhir Dhage, Amit Gaikwad, “Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning”, International Conference on Smart Computing and Electronic Enterprise, 2018 IEEE.
- Shrisha Bharadwaj, Adhiraj Pahargarh, Anil B.S., P S Gowra, Sharath Kumar, “Customer Churn Prediction in Mobile Networks using Logistic Regression and Multilayer Perceptron”, Second International Conference on Green Computing and Internet of Things.
- Irfan Ullah, Basit Raza, Ahmad Kamran Malik, Muhammad Imran, Saif Ul Islam, Sung Wonkim," A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector", 2019 IEEE Translation.
- Adnan Amin, Feras Al-Obeidat, Babar Shah, Awais Adnan, Jonathan Loo, Sajid Anwar, “ Customer churn prediction in telecommunication industry using data certainty”, Journal of Business Research, 94 (2019) 290-301.
- Adnan Amin, Babar Shah, Ali Abbas, Sajid Anwar, Omar Alfandi, Fernando Moreira, “Features Weight Estimation Using a Genetic Algorithm for Customer Churn Prediction in the Telecom Sector”, Springer pp. 483-491, 2019.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET
This work is licensed under a Creative Commons Attribution 4.0 International License.