E-commerce companies think about long-term customer relationships in terms of conversion rates and repeat purchase rates. It is more cost-effective to retain existing customers than to acquire new ones, which is why it is crucial to track customers at high risk of turnover and target them with retention strategies. This research aims to identify what factors play a significant role in e-commerce to the customer churn rate in the next month. This study is based on 77,841 transactions data collected from Indonesia's fashion company through their e-commerce sales channel. In processing the data, descriptive statistics and predictive analytics with logistic regression and random forest models are used to achieve the research's objective, as both models have a good level of accuracy in making predictions and classifications. This study shows several factors such as gender, customer length of stay, order amount, and shipping cost significantly influencing the churn rate. This study recommends that the company and the fashion e-commerce industry manage customer churn by made appropriate strategic and business steps after knew the factors that cause it. Keywords: Customer Churn Rate; E-Commerce; Big Data; Logistic Regression; Random Fores
