Rights statement: This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in WWW'19, https://doi.org/10.1145/3308558.3313428
Accepted author manuscript, 1.65 MB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Tortoise or Hare?
T2 - Quantifying the Effects of Performance on Mobile App Retention
AU - Zuniga, Agustin
AU - Flores, Huber
AU - Lagerspetz, Eemil
AU - Nurmi, Petteri Tapio
AU - Tarkoma, Sasu
AU - Hui, Pan
AU - Manner, Jukka
N1 - This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in WWW'19, https://doi.org/10.1145/3308558.3313428
PY - 2019/5/13
Y1 - 2019/5/13
N2 - 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.
AB - 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.
U2 - 10.1145/3308558.3313428
DO - 10.1145/3308558.3313428
M3 - Conference contribution/Paper
SN - 9781450366748
SP - 2517
EP - 2528
BT - WWW '19 The World Wide Web Conference
PB - ACM
CY - New York
ER -