{"title":"分析免费手机游戏中高价值玩家的流失预测","authors":"Guan‐Yuan Wang","doi":"10.54694/stat.2022.18","DOIUrl":null,"url":null,"abstract":"Many game development companies use game data analysis for mining insights about users' behaviour and possible product growth. One of the most important analysis tasks for game development is user churn prediction. Effective churn prediction can help hold users in the game by initiating additional actions for their engagement. We focused on high-value user churn prediction as it is of particular interest for any business to keep paying customers satisfied and engaged. We consider the churn prediction problem as a classification problem and conduct the random undersampling approach to address imbalanced class distribution between churners and active users. Based on our real-life data from a freemium casual mobile game, although the best model was chosen as the final classification algorithm for extracted data, we can definitely say there is no general solution to the stated problem. Model performance highly depends on the churn definition, user segmentation and feature engineering, it is therefore necessary to have a custom approach to churn analysis in each specific case.","PeriodicalId":43106,"journal":{"name":"Statistika-Statistics and Economy Journal","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Churn Prediction for High-Value Players in Freemium Mobile Games: Using Random Under-Sampling\",\"authors\":\"Guan‐Yuan Wang\",\"doi\":\"10.54694/stat.2022.18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many game development companies use game data analysis for mining insights about users' behaviour and possible product growth. One of the most important analysis tasks for game development is user churn prediction. Effective churn prediction can help hold users in the game by initiating additional actions for their engagement. We focused on high-value user churn prediction as it is of particular interest for any business to keep paying customers satisfied and engaged. We consider the churn prediction problem as a classification problem and conduct the random undersampling approach to address imbalanced class distribution between churners and active users. Based on our real-life data from a freemium casual mobile game, although the best model was chosen as the final classification algorithm for extracted data, we can definitely say there is no general solution to the stated problem. Model performance highly depends on the churn definition, user segmentation and feature engineering, it is therefore necessary to have a custom approach to churn analysis in each specific case.\",\"PeriodicalId\":43106,\"journal\":{\"name\":\"Statistika-Statistics and Economy Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2022-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistika-Statistics and Economy Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54694/stat.2022.18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistika-Statistics and Economy Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54694/stat.2022.18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
Churn Prediction for High-Value Players in Freemium Mobile Games: Using Random Under-Sampling
Many game development companies use game data analysis for mining insights about users' behaviour and possible product growth. One of the most important analysis tasks for game development is user churn prediction. Effective churn prediction can help hold users in the game by initiating additional actions for their engagement. We focused on high-value user churn prediction as it is of particular interest for any business to keep paying customers satisfied and engaged. We consider the churn prediction problem as a classification problem and conduct the random undersampling approach to address imbalanced class distribution between churners and active users. Based on our real-life data from a freemium casual mobile game, although the best model was chosen as the final classification algorithm for extracted data, we can definitely say there is no general solution to the stated problem. Model performance highly depends on the churn definition, user segmentation and feature engineering, it is therefore necessary to have a custom approach to churn analysis in each specific case.