Predicting Customer Churn in Indonesian ISPs an MLP and Marketing Intelligence Approach
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Abstract
Customer churn is common in the telecommunication sector. One of the biggest Internet service providers (ISP) in Indonesia has had an increasing customer churn over the past five years. This research aims to integrate Multilayer Perceptron (MLP) neural networks with marketing intelligence to identify and anticipate the potential of churn, allowing for effective retention efforts. The research methods in this study comprise data preparation and preprocessing, exploratory data analysis (EDA), MLP design, and model evaluation. The EDA offers marketing insights and helps identify features to be incorporated into the MLP architecture development. Our results demonstrate the successful integration between exploratory data analysis and MLP, where the SMOTE application in the MLP model has the best outcomes with an AUC of 99% and 79% F1 score. This enables PT XYZ with the ability to better tailor their customer engagement and loyalty campaigns. The research results are expected to have an impact on Indonesian ISP business by offering a more precise and efficient approach to anticipating customer turnover.
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