Benchmarking study for customer churn prediction: A case study in the e-commerce industry

The e-commerce sector has grown and continues to grow with an acceleration in the last decade, and the data produced and used in this sector has also prepared the environment for important work areas for the big data world. The data produced and collected in the e-commerce sector is used in many decision-making mechanisms. One of them is customer churn prediction. This study aimed to prepare a benchmark for estimating customer churn using traditional and current machine learning approaches, using data obtained from an e-commerce company operating in Turkey. It also aimed to enrich the benchmarking study by offering alternative solutions to the target class imbalance problem, which is frequently encountered in data sets used in customer churn prediction. A total of 24 different models were created, including 6 forecasting models and 4 sampling strategies. In this study, the best performing model pair was the XGBoost Algorithm and the No Sampling. This model duo produced values of 91%, 79% on the basis of accuracy, F2-Score respectively.