A Large-Scale Constrained Joint Modeling Approach for Predicting User Activity, Engagement, and Churn With Application to Freemium Mobile Games
A Large-Scale Constrained Joint Modeling Approach for Predicting User Activity, Engagement, and Churn With Application to Freemium Mobile Games
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DOI：
10.1080/01621459.2019.1611584
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发表时间：
2019-06
影响因子：
3.7
通讯作者：
Trambak Banerjee;Gourab Mukherjee;S. Dutta;Pulak Ghosh
中科院分区：
文献类型：
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作者：
Trambak Banerjee;Gourab Mukherjee;S. Dutta;Pulak Ghosh
摘要我们开发了一个受约束的极度零膨胀关节（CEZIJ）建模框架，用于同时分析基于应用程序的移动的免费增值游戏中的玩家活动，参与和退出（流失）。我们提出的框架解决了玩家决定使用免费增值产品，她直接和间接参与产品的程度以及她决定永久放弃其使用之间复杂的相互依赖关系。CEZIJ以多种方式扩展了纵向和生存数据的现有关节模型类别。它不仅适应联合模型设置中的极端零膨胀响应，而且还对模型参数引入了特定于域的凸结构约束。来自基于应用的手机游戏的纵向数据通常表现出大量的潜在预测因子，并且出于包括提高可预测性的各种目的，选择相关的预测因子集是非常期望的。为了实现这一目标，CEZIJ在高维惩罚广义线性混合模型中同时协调选择固定效应和随机效应。为了分析这样的大规模数据集，变量选择和估计是通过基于分布式计算的分裂和征服方法进行的，该方法大大提高了可扩展性，并提供了比竞争预测方法更好的预测性能。我们的研究结果揭示了促进玩家活动和参与的不同玩家特征之间的相互依赖关系。此外，预测的流失概率随着时间的推移表现出玩家简档的特质集群，营销人员和游戏管理人员可以基于此对玩家群体进行细分，以提高基于应用程序的免费增值游戏的货币化。本文的补充材料，包括可用于复制作品的材料的标准化描述，可作为在线补充。
Abstract We develop a constrained extremely zero inflated joint (CEZIJ) modeling framework for simultaneously analyzing player activity, engagement, and dropouts (churns) in app-based mobile freemium games. Our proposed framework addresses the complex interdependencies between a player’s decision to use a freemium product, the extent of her direct and indirect engagement with the product and her decision to permanently drop its usage. CEZIJ extends the existing class of joint models for longitudinal and survival data in several ways. It not only accommodates extremely zero-inflated responses in a joint model setting but also incorporates domain-specific, convex structural constraints on the model parameters. Longitudinal data from app-based mobile games usually exhibit a large set of potential predictors and choosing the relevant set of predictors is highly desirable for various purposes including improved predictability. To achieve this goal, CEZIJ conducts simultaneous, coordinated selection of fixed and random effects in high-dimensional penalized generalized linear mixed models. For analyzing such large-scale datasets, variable selection and estimation are conducted via a distributed computing based split-and-conquer approach that massively increases scalability and provides better predictive performance over competing predictive methods. Our results reveal codependencies between varied player characteristics that promote player activity and engagement. Furthermore, the predicted churn probabilities exhibit idiosyncratic clusters of player profiles over time based on which marketers and game managers can segment the playing population for improved monetization of app-based freemium games. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.