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Tortoise or Hare? Quantifying the Effects of Performance on Mobile App Retention
handle: 10138/307559
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.
- University of Hong Kong China (People's Republic of)
- University of Hong Kong China (People's Republic of)
- Aalto University Finland
- Lancaster University United Kingdom
- Hong Kong Polytechnic University China (People's Republic of)
Apps retention, mobile networks, mobile computing, HOT DECK IMPUTATION, Crowdsensing, Mobile networks, energy consumption, QUALITY, data fusion, Mobile computing, Computer and information sciences, crowdsensing, ta213, apps retention, Data fusion, 004, performance evaluation, Energy consumption, Performance evaluation, EXPERIENCE
Apps retention, mobile networks, mobile computing, HOT DECK IMPUTATION, Crowdsensing, Mobile networks, energy consumption, QUALITY, data fusion, Mobile computing, Computer and information sciences, crowdsensing, ta213, apps retention, Data fusion, 004, performance evaluation, Energy consumption, Performance evaluation, EXPERIENCE
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).21 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
