Telecom Customer Churn Prediction Using Adaboost Classifier and Neural Network

Research Area: Volume 11, Issue 4, July 2022 Year: 2022
Type of Publication: Article Keywords: Big Data, Churn Prediction, Decision Tree, Quality of Experience
  • Shivani Vaidya
  • Rajesh Kumar Nigam
Journal: IJEIR Volume: 11
Number: 4 Pages: 82-93
Month: July
ISSN: 2277-5668
Churn is normally occurring today. Churn prediction is very tuff process in all industry. The Telecommunications (telecom) industry is saturated day by day 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. Customer churn is always a grievous issue for the Telecom industry as customers do not hesitate to leave if they don’t find what they are looking for. They certainly want competitive pricing, value for money and above all, high quality service. Customer churning is directly related to customer satisfaction. It’s a known fact that the cost of customer acquisition is far greater than cost of customer retention, that makes retention a crucial business prototype. Data preprocessing, data normalization and feature selection have shown to be prominently influential. Monthly data volumes have not shown much decision power. Average Quality, Churn Risk and to some extent, Annoyance scores may point out a probable churner. Weekly data volumes with customer’s recent history and necessary attributes like age, gender, tenure, bill, contract, data plan, etc., are pivotal for churn prediction. 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.

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