Utilizing Business Intelligence and Machine Learning in CRM Data to Reduce Customer Churn in E-commerce Platforms

Abstract

In the ever-changing world of online business, keeping clients to achieve long-term growth and profitability is crucial. This work uses customer relationship management (CRM) data and machine learning techniques to reduce customer turnover. Initially, the previous CRM data is employed to identify clients no longer affiliated with the platform. Through analyzing prior encounters, such as reviews and purchase histories, valuable information about them preferences and complaints can be obtained. More precisely, negative comments are isolated to identify areas where the product might be improved or eliminated from the e-commerce catalogue. Furthermore, predictive analytics methods are utilized to improve client involvement and contentment. The proposed approach combines CRM data analysis with machine learning algorithms such as Logistic Regression, Decision Tree, SVM, Random Forest, and XGBoost to provide a proactive strategy for reducing customer churn in e-commerce platforms.