Inferring App Demand from Publicly Available Data
INFERRING APP DEMAND FROM PUBLICLY AVAILABLE DATA
Rajiv Garg, Rahul Telang {rgarg, rtelang}@andrew.cmu.edu School of Information Systems & Management, Heinz College Carnegie Mellon University, Pittsburgh, PA August 2012
ABSTRACT
With an abundance of products available online, many online retailers provide sales rankings to make it easier for consumers 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, researchers developed various strategies that allowed them to infer demand from rank data. Almost all of that work is based on experiment that shifts sales or collaboration with a vendor to get actual sales data. In this research, we present an innovative method to use public data to infer rank‐demand relationship for the paid apps on 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 paid app. Similarly, the top paid app on iPad generates 120 times more downloads compared to the paid app ranked at 200. We conclude with a discussion on extension of this framework to the Android platform, in‐app purchases, and free apps. Keywords: Mobile Apps, App Store, Sales‐Rank Calibration, App Downloads, Pareto Distribution, Android, Apple iTunes, In‐App Purchase
Electronic copy available at: http://ssrn.com/abstract=1924044
1 INTRODUCTION
The growth of mobile phones and smartphones over the last few years has been phenomenal. Based on recently published reports, there are about 106 million users of smartphones1 in the US. Globally, there are 1.1 billion active mobile subscriptions2 with over 100,000 new smartphones being sold every quarter3. As more
Rajiv Garg, Rahul Telang {rgarg, rtelang}@andrew.cmu.edu School of Information Systems & Management, Heinz College Carnegie Mellon University, Pittsburgh, PA August 2012
ABSTRACT
With an abundance of products available online, many online retailers provide sales rankings to make it easier for consumers 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, researchers developed various strategies that allowed them to infer demand from rank data. Almost all of that work is based on experiment that shifts sales or collaboration with a vendor to get actual sales data. In this research, we present an innovative method to use public data to infer rank‐demand relationship for the paid apps on 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 paid app. Similarly, the top paid app on iPad generates 120 times more downloads compared to the paid app ranked at 200. We conclude with a discussion on extension of this framework to the Android platform, in‐app purchases, and free apps. Keywords: Mobile Apps, App Store, Sales‐Rank Calibration, App Downloads, Pareto Distribution, Android, Apple iTunes, In‐App Purchase
Electronic copy available at: http://ssrn.com/abstract=1924044
1 INTRODUCTION
The growth of mobile phones and smartphones over the last few years has been phenomenal. Based on recently published reports, there are about 106 million users of smartphones1 in the US. Globally, there are 1.1 billion active mobile subscriptions2 with over 100,000 new smartphones being sold every quarter3. As more