Inferring App Demand from Publicly Available Data

Abstract

With an abundance of products available online, many online retailers provide sales rankings to make it easier for the consumer to find the bestselling products. Successfully implementing product rankings online was done a decade ago by Amazon and more recently by Apple’s App store. However, neither market provides actual download data, a very useful statistic for both practitioners and researchers. In the past, to estimate sales from product rankings, researchers developed strategies that allowed them to estimate demand. Almost all of that work is based on either experiments that shift sales or collaboration with a vendor to get actual sales data. In this research, we present an innovative method to use purely public data to infer rank-demand relationship for Apple’s iTunes App store. We find that the top ranked paid app for iPhone generates 150 times more downloads compared to the 200th ranked app. Similarly, the top paid app on iPad generates 120 times more downloads compared to the app ranked at 200. We conclude with a discussion validating our findings, and an extension of this framework to the Android platform.

Keywords: Mobile Apps, App Store, Sales-Rank Calibration, App Downloads, Pareto Distribution, Android, Apple iTunes, In-App Purchase

JEL Classification: D40, D49, L10, L86, M20