Churn Analysis for Factoring: An Application in Turkish Factoring Sector

Abstract: Due to the increasing competitive environment in many areas in recent years, customers can easily turn to alternative services. For this reason, it is very important to predict that customers will turn to another service, especially in sectors such as telecom and banking, which have a membership-based revenue model. As in many sectors, in the factoring sector churn prediction models are being developed which predict customers who plan to move to competitors. According to the prediction results, companies aim to prevent customers from leaving the company by developing various campaigns or different actions related to the customers to be lost and to increase the loyalty of the customer to the company. At this stage, focusing on the right customer is critical in order to reduce campaign costs and increase customer loyalty. In order to identify the correct customer, successful prediction models are being developed by using current classification algorithms. However, it would not be enough to treat customer churn prediction as just a classification model. Additional analyzes are needed to provide information to decision processes such as selecting the targeted customer, determining the types of actions to be taken for the customers, and personalizing the actions according to different customer groups. Therefore, it is necessary to consider customer churn prediction as a holistic customer relationship model, which includes the developed forecasting model, as well as analysis to recognize the customer, such as profiling, segmentation.

In this study, a profiling and risk segmentation study was conducted primarily to identify the customers in different dimensions through a data set containing information such as location, demography, transaction history and intelligence results of customers of a private factoring firm. Then, the customer churn prediction model was developed by adding engineered features to the existing data set. Tree based methods such as CatBoost, Random Forest, LightGBM, and XGBoost have been used for the prediction model. Furthermore, the methods used were compared over metrics such as accuracy, F1 score, sensitivity and precision.