Customer Attrition Detection Using the LGBM Model - Huang Jie (SHS Web of Conferences, 2025)
In internet service industries, such as competitive industries, it costs more to attract new consumers to become customers of the company than saving the consumers who already are customers. Therefore, detecting the running off customers and finding a way to keep the customers from leaving is extremely important. This study addresses the problem of customer attrition in the internet service industry by choosing the best-performing model to detect the customers who are going to run off in advance. To select the most suitable model for accurately detecting customer churn, this study performs preprocessing, including data cleaning, feature engineering, and feature selection. The dataset is then split into training, testing, and validation sets. Various models are built and evaluated based on their performance, measured by calculating the mean and standardized values of the detection rate. The result is that the Light Gradient Boosting Machine (LGBM) model has superior performance in detection rate scoring.