ESTIMATING APP DEMAND FROM PUBLICLY AVAILABLE DATA
With the abundance in variety of products available online, many online retailers provide sales rankings for available products to make it easier for the consumer to find popular products. Successful implementation of product rankings on online platform was done a decade ago by Amazon and more recently by Apple’s App store. However, none of these market providers provide actual downloads data, a very useful statistics for both practitioners and researchers. To address similar issues, researchers in the past developed strategies to estimate sales from product rank. Almost all of that work is based on either doing some experiments to shift sales or partnering with a vendor to get actual sales data. In this research, we present an innovative method to use purely public data to infer this relationship for Apple’s iTunes App store. We provide various validations to show our method provides highly accurate estimates on downloads if rank data in available for a given app.