Towards Release Strategy Optimization for Apps in Google Play

TL;DR

This paper analyzes app update patterns in Google Play to develop a model that helps developers choose optimal release times, aiming to maximize app success and user adoption.

Contribution

It introduces a data-driven approach and a Naive Bayes model for optimizing app release timing based on empirical analysis of update patterns.

Findings

Identified key characteristics of update intervals in Google Play apps.

Developed a model to predict optimal release opportunities.

Provided guidelines for considering app ranking and rating trends.

Abstract

In the appstore-centric ecosystem, app developers have an urgent requirement to optimize their release strategy to maximize the success opportunity 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 interval of an update to its previous update to characterize release patterns, and find significance of the release opportunity through empirical analysis. We mined the update-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 what to update in addition to the opportunity before releasing an app version. We propose a Multinomial Naive Bayes model to help decide an optimal release opportunity to gain better user adoption.