Earticle

초록

영어

Nowadays, the telecommunication industries are facing substantial competition among the providers in order to capture new customers. Many providers have faced a loss of profitability due to the existing customers migrating to other providers. Customer retention program is one of the main strategies adopted in order to keep customers loyal to their provider. However, it requires a high cost and therefore the best strategy that companies could practice is to focus on identifying the customers that have the potential to churn at an early stage. The limited amount of research on investigating customer churn using machine learning techniques has lead this research to explore the potential of an artificial neural network to improve customer churn prediction. The research proposes Multilayer Perceptron (MLP) neural network approach to predict customer churn in one of the leading Malaysian’s telecommunication companies. The results are compared against the most popular churn prediction techniques such as Multiple Regression Analysis and Logistic Regression Analysis. The result has proven the supremacy of neural network (91.28% of prediction accuracy) over the statistical models in prediction tasks. Overall, the findings suggest that a neural network learning algorithm could offer a viable alternative to statistical predictive approaches in customer churn prediction.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Customer Churn Prediction Algorithms
  2.2. Neural Network
  2.3. Regression Analysis
 3. Research Methodology
  3.1. Feature Extraction
  3.2. Development of Prediction Models
 4. Results and Discussion
  4.1. Neural Network Analysis
  4.2. Multiple Regression Analysis
  4.3. Logistic Regression Analysis
  4.4. Performance Comparison between Neural Network and Regression Analysis Tools
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Mohammad Ridwan Ismail [ Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Tembila Campus, Besut, Terengganu, Malaysia ]
• Mohd Khalid Awang [ Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Tembila Campus, Besut, Terengganu, Malaysia ]
• M Nordin A Rahman [ Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Tembila Campus, Besut, Terengganu, Malaysia ]
• Mokhairi Makhtar [ Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Tembila Campus, Besut, Terengganu, Malaysia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605