Customer Churn Prediction Using Data Mining Techniques for an Iranian Payment Application

Olya Rezaeian · Seyedhamidreza Shahabi Haghighi · Jamal Shahrabi

11 citations0/0 files storedInternational Conference on Information and Knowledge Technology (IKT)

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

Abstract

Customer Relationship Management (CRM) and data-driven marketing have become of paramount importance in this age of evolved markets and fierce competition among businesses. One of the most important branches of CRM is retaining existing customers. Since customer acquisition is about 5 to 6 times more costly than retaining customers, achieving an accurate model for customer churn prediction is essential to devise marketing retention strategies. Therefore, in this study, ensemble models are proposed to predict customer churn. Since customer churn is a rare occurrence in an organization and causes an imbalanced distribution in the target variable, ensemble learning algorithms, one of the most efficient and widely used methods, have been used to deal with this problem. With regard to the case study, the dataset was generated on demographic and 13-month transactions of users of an Iranian payment application. In this study, the best model to predict customer churn is the bagging version of Decision Tree, reaching the highest accuracy, f-measure and AUC.

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References

2008Handling class imbalance in customer churn prediction 2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees 2015A comparison of machine learning techniques for customer churn prediction 2021Customer churn prediction system: a machine learning approach 2014Improved churn prediction in telecommunication industry using data mining techniques 2016Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry 2017An empirical comparison of techniques for the class imbalance problem in churn prediction 2017Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior

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