E-Commerce Customer Churn Prediction for the Marketplace in Malaysia

Abstract

Customer churn, or the loss of customers, is a significant challenge for e-commerce businesses, as it leads to revenue loss and increased marketing costs. Most online marketplaces have insufficient awareness of the e-commerce customer churn rate that can be analysed before conducting customer retention activities or management. It is crucial for an online marketplace to handle customer churn as it is more high-value to manage existing customers than to have new customers on the platform. This study uses machine learning algorithms to predict e-commerce customer churn. This project has three (3) goals that must be met, which include identifying the attributes with high association to e-commerce customer churn for the online marketplace in Malaysia, constructing e-commerce customer churn prediction model with the important attributes by using the machine learning techniques, and evaluating and comparing the performance of the e-commerce customer churn prediction models using evaluation metrics. The machine learning techniques chosen based on the literature review are Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistics Regression (LR), K-Nearest Neighbours (KNN), and eXtreme Gradient Boosting (XGB). The dataset involved in the project was compiled from a leading e-commerce platform in Malaysia – online marketplace ABC, and it contains customer demographic information, purchase history etc. The dataset and model will use Python as the analytics tool and extended CRISP-DM with TDSP as the methodology. There will be four (4) evaluation metrics to compare and evaluate the performance of the prediction models, including accuracy, precision, recall, and F1-score. The desired outcome will show that machine learning can predict Malaysian e-commerce customer churn. This study can help Malaysian e-commerce companies identify at-risk customers and retain them. Additionally, this study can inform e-commerce customer churn prediction research.