Towards Release Strategy Optimization for Apps in Google Play
Sheng Shen, Xuan Lü, Ziniu Hu, Xuanzhe Liu · 2017
In the appstore-centric ecosystem, app developers have an urgent requirement to optimize their release strategy to maximize user adoption of their apps. To address this problem, we introduce an approach to assisting developers to select the proper release opportunity based on the purpose of the update and current condition of the app. Before that, we propose the update interval to characterize release patterns of apps, and find significance of the updates through empirical analysis. We mined the release-history data of 17,820 apps from 33 categories in Google Play, over a period of 105 days. With 41,028 releases identified from these apps, we reveal important characteristics of update intervals and how these factors can influence update effects. We suggest developers to synthetically consider app ranking, rating trend, and update purpose in addition to the timing of releasing an app version. We propose a Multinomial Naive Bayes model to help decide an optimal release opportunity to gain better user adoption.