Research On Ctrip Customer Churn Prediction Based On Customer Segmentation And XGBoost Algorithm

Customers are the fuel that promotes the long-term development of enterprises.With the intensified competition in the consumer market,companies have become increasingly difficult to retain customers.Especially for many service companies,acquiring new customers often requires more effort than maintaining old customers.Times the energy and cost.Therefore,customer churn is an issue that any company needs to focus on monitoring.But so far,customer churn prediction models are generally concentrated on traditional machine learning algorithms(such as logistic regression,decision trees,etc.),and the application of some algorithms that have become popular in recent years such as XGBoost(e Xtreme Gradient Boosting)is still lacking.In addition,customer churn prediction,as an important research topic in customer relationship management,should be combined with other related theories to further improve the predictive performance of the model.In response to the above problems,by analyzing and modeling the Ctrip customer churn data set,the following tasks have been completed: first,the original data set is preprocessed,including missing value filling,data standardization,and data category balance;and second,the customer Based on the value theory,the RFM model was improved and extended to obtain the four clustering characteristics of the total number of historical orders,the time of the last order,the spending power index,and the price sensitivity index.Then,the customer segmentation was performed using the K-means algorithm.,And build a customer churn prediction model based on the XGBoost algorithm on each type of customer group;finally,evaluate the XGBoost algorithm with logistic regression,decision tree,and deep neural network as benchmark models,and set up two sets of comparative experiments to verify customer segmentation The effect of model performance improvement,and then based on the customer segmentation results to locate customers and give targeted churn management recommendations.The research results show that compared with the other three algorithms,the prediction performance of XGBoost algorithm has significant advantages.Furthermore,the XGBoost model based on customer segmentation has improved accuracy,recall,and AUC values by 2.75%,0.80%,and 3.00%,respectively,compared to before the customer segmentation.