Tortoise or Hare? Quantifying the Effects of Performance on Mobile App Retention

Agustin Zuniga, Huber Flores, Eemil Lagerspetz, P. Nurmi, S. Tarkoma, P. Hui, J. Manner
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引用次数: 19

Abstract

We contribute by quantifying the effect of network latency and battery consumption on mobile app performance and retention, i.e., user's decisions to continue or stop using apps. We perform our analysis by fusing two large-scale crowdsensed datasets collected by piggybacking on information captured by mobile apps. We find that app performance has an impact in its retention rate. Our results demonstrate that high energy consumption and high latency decrease the likelihood of retaining an app. Conversely, we show that reducing latency or energy consumption does not guarantee higher likelihood of retention as long as they are within reasonable standards of performance. However, we also demonstrate that what is considered reasonable depends on what users have been accustomed to, with device and network characteristics, and app category playing a role. As our second contribution, we develop a model for predicting retention based on performance metrics. We demonstrate the benefits of our model through empirical benchmarks which show that our model not only predicts retention accurately, but generalizes well across application categories, locations and other factors moderating the effect of performance.
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乌龟还是兔子?量化性能对手机应用留存率的影响
我们通过量化网络延迟和电池消耗对移动应用性能和留存率的影响来做出贡献,即用户继续或停止使用应用的决定。我们通过融合两个大规模的众感数据集来进行分析,这些数据集是通过移动应用程序捕获的信息收集而来的。我们发现应用性能会影响其留存率。我们的研究结果表明,高能量消耗和高延迟降低了保留应用程序的可能性。相反,我们表明,只要在合理的性能标准内,减少延迟或能量消耗并不能保证更高的保留可能性。然而,我们也证明了什么是合理的取决于用户已经习惯了什么,设备和网络特征,以及应用类别发挥作用。作为我们的第二个贡献,我们开发了一个基于绩效指标预测留存率的模型。我们通过经验基准证明了我们模型的优势,这表明我们的模型不仅可以准确地预测留存率,而且可以很好地概括应用类别、位置和其他调节性能影响的因素。
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