Composite Social Network for Predicting Mobile Apps Installation

TL;DR

This study demonstrates that combining multiple social networks from smartphones can effectively predict app installations, providing valuable insights for app marketing strategies.

Contribution

The paper introduces a novel composite social network model that improves prediction accuracy of app installations by integrating various sensed social networks and accounting for individual differences.

Findings

Predicts 45% of app installations with 45% precision.

Composite network approach outperforms generic methods by four times.

App installation is shown to be significantly predictable.

Abstract

We have carefully instrumented a large portion of the population living in a university graduate dormitory by giving participants Android smart phones running our sensing software. In this paper, we propose the novel problem of predicting mobile application (known as "apps") installation using social networks and explain its challenge. Modern smart phones, like the ones used in our study, are able to collect different social networks using built-in sensors. (e.g. Bluetooth proximity network, call log network, etc) While this information is accessible to app market makers such as the iPhone AppStore, it has not yet been studied how app market makers can use these information for marketing research and strategy development. We develop a simple computational model to better predict app installation by using a composite network computed from the different networks sensed by phones. Our model also captures individual variance and exogenous factors in app adoption. We show the importance of considering all these factors in predicting app installations, and we observe the surprising result that app installation is indeed predictable. We also show that our model achieves the best results compared with generic approaches: our results are four times better than random guess, and predict almost 45% of all apps users install with almost 45% precision (F1 score= 0.43).