Predictive Business Analytics for Customer Attrition: A Machine Learning Approach to Customer Retention

Author

Harinandan Parashar

Abstract

 Customer attrition is a major problem for companies that are looking to grow their business, maintain profitability, and build a long lasting customer base. Machine learning powered predictive business analytics offers prospects for recognizing customers who are likely to churn, and formulating proactive customer retention strategies. The purpose of this study is to create a machine learning model for customer attrition prediction and assisting in data driven customer retention decision making. The study is designed in a quantitative predictive manner and is based on the publicly available data set of IBM Telco Customer Churn, which has 7043 customer records and 21 variables. Logistic regression, decision tree, random forest, support vector machine and the extreme gradient boosting (XGBoost) algorithms are suggested as classification methods to compare. Data preprocessing involves handling missing values, encoding categorical data, feature engineering, and avoiding data leakage. Stratified 80:20 training/testing split will be used for model development and hyper parameter optimization will be done using stratified 5-fold CV. The accuracy, precision, recall, F1-score, receiver operating characteristic-area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC) will be used to assess predictive performance. Feature importance and SHapley Additive exPlanations will help to interpret influential attrition predictors. The framework highlights the need to identify high risk customers while trying to not lose those who are not at risk but end up with a disproportionate number of retention efforts. If data on economic analysis of customer lifetime value and cost of intervention is available, economic analysis will be used alongside classification performance. The proposed research offer a replicable analytical structure to contrast the various machine learning methods and convert customer data into effective retention insights.

Keywords

Customer attrition; predictive business analytics; machine learning; customer retention; churn prediction; explainable artificial intelligence.

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References

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