A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services

Anuj Sharma

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Customer churn and segmentation Customer Service Quality and Loyalty AI and HR Technologies

Abstract

Marketing literature states that it is more costly to engage a new customer than to retain an existing loyal customer. Churn prediction models are developed by academics and practitioners to effectively manage and control customer churn in order to retain existing customers. As churn management is an important activity for companies to retain loyal customers, the ability to correctly predict customer churn is necessary. As the cellular network services market becoming more competitive, customer churn management has become a crucial task for mobile communication operators. This paper proposes a neural network (NN) based approach to predict customer churn in subscription of cellular wireless services. The results of experiments indicate that neural network based approach can predict customer churn with accuracy more than 92%. Further, it was observed that medium sized NNs perform best for the customer churn prediction when different neural network’s topologies were experimented.

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References

1995Customer Switching Behavior in Service Industries: An Exploratory Study 1999Building Data Mining Applications for CRM 2005Applying data mining to telecom churn management 2002Turning telecommunications call details to churn prediction: a data mining approach 2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques 2003Customer attrition analysis for financial services using proportional hazard models 2004Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting 2004Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market

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2014Churn prediction for high-value players in casual social games 2018A big data analytics model for customer churn prediction in the retiree segment 2016Developing a prediction model for customer churn from electronic banking services using data mining 2023Customer churning analysis using machine learning algorithms 2021Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

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