A algum tempo eu postei no Github (sorry por sonegar amigos, em breve postarei por aqui) uma proposta de análise de sobrevivência para Telecom e esse paper vem em boa hora para jogar mais luz sobre o tema.
Em momentos em que temos estreitamento de margens de lucro em aplicativos móveis é de fundamental importância o entendimento em relação à dinâmica relativa à saída dos usuários da base ativa.
Esse artigo mostra uma ótima perspectiva em relação à aplicação de análise de sobrevivência para maximização do tempo em que os jogadores permanecem no aplicativo.
Abstract: Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various duration variables quantifying product use: total playtime or session playtime are popular research targets, and active playtime is well-suited for subscription games. Such research often has the goal of increasing player retention or conversely decreasing player churn. Survival analysis is a framework of powerful tools well suited for retention type data. This paper contributes new methods to game analytics on how playtime can be analyzed using survival analysis without covariates. Survival and hazard estimates provide both a visual and an analytic interpretation of the playtime phenomena as a funnel type nonparametric estimate. Metrics based on the survival curve can be used to aggregate this playtime information into a single statistic. Comparison of survival curves between cohorts provides a scientific AB-test. All these methods work on censored data and enable computation of confidence intervals. This is especially important in time and sample limited data which occurs during game development. Throughout this paper, we illustrate the application of these methods to real world game development problems on the Hipster Sheep mobile game.
Conclusions: In this study, we demonstrated that survival analysis can be used to measure retention in games. Positive, skewed and censored duration data make it a very natural and powerful tool for this purpose. Duration variables quantifying retention such as playtime, session time and subscription time, even game progression, may be analyzed with the methods of survival analysis. In this study we used a real world game development example with focus on total playtime. We presented the basic foundation of survival analysis, which argued that the phenomena may be analyzed in a simple way through the churn rate or its complement, the retention rate. The study focused on three key motivations for survival analysis based measurement: computing survival curves, deriving survival metrics and comparing survival data. These methods contribute towards scientific data analysis by presenting methods new to game analytics, which are also able to deal with censoring and utilize statistical significance tests. For computing survival curves and cumulative hazards, we presented the Kaplan-Meier and the Nelson-Aalen estimate. Kernel methods may be used to compute the churn rate and produce smooth nonparametric survival curves. For metrics, we discussed how the hazard is an improvement over using the survival curve as a funnel type estimate. Utilized widely in reliability engineering, adopting it for game analytics is especially useful in retention and progression analysis to detect deviations from the natural pattern of constant rates. Furthermore, the mean and the median playtime metrics were derived from the survival curve with confidence intervals. For survival comparison, we used the log-rank statistical test to perform a test of the null hypothesis that the survival curves are equal. The test may be extended to stratify over covariates and compare multiple cohorts. This method enables scientific AB testing of game version quality, for example The reader may take advantage of Table 8 to use the methods for applications. It lists the methods we have presented and the R software functions implementing them. In summary, survival analysis motivated functions, metrics and comparisons provide multiple tools to utilize for retention and progression measurement in game development. We think that the field has a large potential to contribute to scientific game analytics and anticipate further research on this topic.