This paper argues that App Store Analysis can be used to understand the rich interplay between app customers and their developers. We use data mining to extract price and popularity information and natural language processing and data mining to elicit each app’s claimed features from the Blackberry App Store, revealing strong correlations between customer rating and popularity (rank of app downloads). We found evidence for a mild correlation between price and the number of features claimed for an app and also found that higher priced features tended to be lower rated by their users. We also found that free apps have significantly (p-value < 0.001) higher rating than non-free apps, with a moderately high effect size (Â12 = 0.68). We also provide initial evidence that extracted claimed features are meaningful to developers (precision = 0.71, recall = 0.77). All data from our experiments and analysis are made available on-line to support further analysis.

Introduction to platform ecosystems Ecosystem governance and partnership models Research question Microsoft Office365