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An Analysis of Customer Churn Predictions in the Telecommunications Sector

Shivani Vaidya; Rajesh kumar Nigam
The Telecommunications (telecom) Industry is saturated and marketing strategies are focusing on customer retention and churn prevention. Churning is when a customer stops using a company’s service thereby opting for the next available service provider. This churn threat has led to various Customer Churn Prediction (CCP) studies and many models have been developed to predict possible churn cases for timely retention strategies. So customer churn is an important area of concern. This research work aims at carrying out a literature review for the past decade reviewing around 50 research papers in the area of telecom churn with two perspectives: mining technique applied and publication year. This review looks at the existing models in literature, using 30 selected CCP studies particularly from 2014 to 2020. Data preparation methods and churn prediction challenges have also been explored. This study reveals that Support Vector Machines, Naïve Bayes, Decision Trees and Neural Networks are the mostly used CCP techniques. Feature selection is the mostly used data preparation method followed by Normalization and Noise removal. Under sampling is the mostly preferred technique for handling telecom data class imbalances. Imbalanced, large and high dimensional datasets are the key challenges in telecom churn prediction.
Select Volume / Issues:
Year:
2022
Type of Publication:
Article
Keywords:
Big Data; Churn Prediction; Decision Tree; Quality of Experience
Journal:
IJECCE
Volume:
13
Number:
4
Pages:
37-43
Month:
July
ISSN:
2249-071X
Hits: 413

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