Prediction Model Using Machine Learning: Analysis Of Determinants Of Customer Churn At PT XYZ - Riena Pribadi Gronloh; Hendra Achmadi (SEAN Institute, 2024)
This study aims to identify the factors influencing customer churn at PT XYZ, a B2B application-based company selling essential goods. Machine learning algorithms such as Random Forest and Logistic Regression were used to predict churn based on demographic and behavioral variables, including age, membership duration, monthly transaction averages, spending value, and product variety. Transaction data from January 2023 to August 2024 was analyzed to understand partner behavior patterns. The results indicate that the Random Forest algorithm provides more accurate predictions than Logistic Regression, based on evaluation metrics such as accuracy, precision, recall, and ROC-AUC. This study provides strategic insights for PT XYZ to reduce churn and maintain customer purchase retention through a data-driven approach.