Applying Data Mining to Customer Churn Prediction in an Internet Service Provider

Afaq Alam Khan · Sanjay Jamwal · Mohammad Mehdi Sepehri

68 citations0/0 files storedInternational Journal of Computer Applications

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

A business incurs much higher charges when attempting to win new customers than to retain existing ones.As a result, much research has been invested into new ways of identifying those customers who have a high risk of churning.However, customer retention efforts have also been costing organizations large amounts of resources.Same is the situation in ISP industry in I.R.Iran.Exploiting the use of demographic, billing and usage data, this study tends to identify the best churn predictors on the one hand and evaluates the accuracy of different data mining techniques on the other.Clustering users as per their usage features and incorporating that cluster membership information in classification models is another aspect which has been addressed in this study.

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2004Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management 2005Applying data mining to telecom churn management 2002Turning telecommunications call details to churn prediction: a data mining approach 2003Customer attrition analysis for financial services using proportional hazard models 2004Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market 2003An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry 2005Computer assisted customer churn management: State-of-the-art and future trends 2003Evaluating consumer loans using neural networks

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2017An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing 2014Churn Prediction in Telecommunication Industry Using Rough Set Approach 2017Data-driven agent-based exploration of customer behavior 2021Customer churn analysis in banking sector: Evidence from explainable machine learning models 2022

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