Operation Management Optimization Of X Online Game Of S Company Based On LTV

After ten years of rapid development of the domestic online game industry,the competition is becoming increasingly fierce in online game market.All of online game developers and operators are more and more concerned about the game quality,and try every effort to optimize the operation and management constantly,eventually to improve the competitiveness of products.This paper introduces the X online game product in S company,which is running in Tencent Game platform.To collect the information of the installation of the software and all the activities of the users in the game,X product has a powerful Log System.From the data in the Log System,the analysis system will generate the result for the user retention rate(URR,User Retention Rate),the average revenue per user(ARPU,Average Revenue Per User),the average number of online(ACU,Average Concurrent Users)and project life cycle value(LTV,Life Time Value).Through the analyst data,some serious problems about 4 core indicators can be seen and must be resolved by the time of X product commercial launch.On this basis,through the analysis of the behavior of the online game users,and the characteristics of the virtual world itself,this paper promotes that,the operators should adjust their view to the users' Life Time Value,not only just concern about the paying users who have contributed by economic value,but also concern about the free users who continue to create the time value.In this paper,it's promoted that within the same virtual world,all of the paying users are transferred by the free users,and the free users themselves are the new contents of the game,which are exactly consumed by the paying users in the game.Therefore,the balance between the number of free users and the number of paying users,is the key to ensure the stability of foundation of the virtual world.Based on the historical operation data,by regression analysis method,the paper established a unified user life cycle value computing model for both paying users and users free.It provides the technical feasibility to evaluate the life cycle value for all the users,including paying users and free users,based on the same criterion.By computing Life Time Value for every user once a day,there is a new daily value for all users.Based on the Life Time Value,by performing a user segmentation,analyzing the needs of different user groups,focusing their attention on the game design and contents,this paper proposes some specific optimization methods to improve the operation.Finally,based on the actual operation data of 1 month after the implementation of the optimization methods,this paper verifies the effectiveness of the optimization methods for the operation based on the user life cycle value.