视频游戏中玩家流失和转换的时间序列方法
A Time Series Approach To Player Churn and Conversion in Videogames
摘要：
免费游戏的玩家分为三个主要群体：非付费活跃用户，付费活跃用户和非活跃用户。然后使用状态空间时间序列方法来对不同组之间的每日转换率进行建模，即，从一个群体过渡到另一个群体的可能性。这不仅可以预测这些速率将如何演变，还可以更深入地了解游戏内规划和日历效应的影响。在这项工作中，它还用于检测没有信息的营销和促销活动。特别是，两种不同的状态空间配方被认为是和比较：自回归综合移动平均过程和未观察到的成分的方法，在这两种情况下，与线性回归的解释变量。这两种方法对协变量参数的估计非常接近，对大多数转换率的预测具有相似的性能。虽然未观察组件方法更强大，并且在模型定义方面需要更少的人为干预，但它对非付费用户放弃概率的预测明显较差。更关键的是，它也没有发现一个合理的营销和促销活动的情况。
英文摘要：
Players of a free-to-play game are divided into three main groups: non-paying active users, paying active users and inactive users. A State Space time series approach is then used to model the daily conversion rates between the different groups, i.e., the probability of transitioning from one group to another. This allows, not only for predictions on how these rates are to evolve, but also for a deeper understanding of the impact that in-game planning and calendar effects have. It is also used in this work for the detection of marketing and promotion campaigns about which no information is available. In particular, two different State Space formulations are considered and compared: an Autoregressive Integrated Moving Average process and an Unobserved Components approach, in both cases with a linear regression to explanatory variables. Both yield very close estimations for covariate parameters, producing forecasts with similar performances for most transition rates. While the Unobserved Components approach is more robust and needs less human intervention in regards to model definition, it produces significantly worse forecasts for non-paying user abandonment probability. More critically, it also fails to detect a plausible marketing and promotion campaign scenario.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
