Churn Customer Estimation Method based on LightGBM for Improving Sales

Abstract

Churn customer estimation method is proposed for improving sales. By analyzing the differences between customers who churn and customers who do not churn (returning), we will conduct a customer churn analysis to reduce customer churn and take steps to reduce the number of unique customers. By predicting customers who are likely to defect using decision tree models such as LightGBM, which is a machine learning method, and logistic regression, we will discover important feature values in prediction and utilize the knowledge obtained through Exploratory Data Analysis: EDA. As results for experiments, it is found that the proposed method allows estimation and prediction of churn customers as well as characteristics and behavior of churn customers. Also, it is found that the proposed method is superior to the conventional method, GradientBoostingClassifier: GBC by around 10%.