Markus Viljanen, A. Airola, T. Pahikkala, J. Heikkonen
{"title":"手机游戏的用户活动衰减取决于简单的微分方程?","authors":"Markus Viljanen, A. Airola, T. Pahikkala, J. Heikkonen","doi":"10.1109/CIG.2016.7860403","DOIUrl":null,"url":null,"abstract":"Decay of population level daily user activity in Tribeflame Ltd.'s mobile games is found to be determined by elementary differential equations. We describe practical methods for investigating laws underlying the decay of daily user activity in a given cohort, known as retention in the gaming industry. Simple decay patterns are found to accurately describe this evolution. In addition to being of academic interest in sharing parallels to population growth and decay dynamics, this finding has immediate applications in the mobile games industry. Utilizing this finding allows using smaller cohorts of users in intermittent paid acquisition tests and enables game performance forecasting over long timespans.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"31 1","pages":"1-8"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"User activity decay in mobile games determined by simple differential equations?\",\"authors\":\"Markus Viljanen, A. Airola, T. Pahikkala, J. Heikkonen\",\"doi\":\"10.1109/CIG.2016.7860403\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Decay of population level daily user activity in Tribeflame Ltd.'s mobile games is found to be determined by elementary differential equations. We describe practical methods for investigating laws underlying the decay of daily user activity in a given cohort, known as retention in the gaming industry. Simple decay patterns are found to accurately describe this evolution. In addition to being of academic interest in sharing parallels to population growth and decay dynamics, this finding has immediate applications in the mobile games industry. Utilizing this finding allows using smaller cohorts of users in intermittent paid acquisition tests and enables game performance forecasting over long timespans.\",\"PeriodicalId\":6594,\"journal\":{\"name\":\"2016 IEEE Conference on Computational Intelligence and Games (CIG)\",\"volume\":\"31 1\",\"pages\":\"1-8\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE Conference on Computational Intelligence and Games (CIG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIG.2016.7860403\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIG.2016.7860403","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
User activity decay in mobile games determined by simple differential equations?
Decay of population level daily user activity in Tribeflame Ltd.'s mobile games is found to be determined by elementary differential equations. We describe practical methods for investigating laws underlying the decay of daily user activity in a given cohort, known as retention in the gaming industry. Simple decay patterns are found to accurately describe this evolution. In addition to being of academic interest in sharing parallels to population growth and decay dynamics, this finding has immediate applications in the mobile games industry. Utilizing this finding allows using smaller cohorts of users in intermittent paid acquisition tests and enables game performance forecasting over long timespans.
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