Abstract
Firms often choose with whom to compete and how similar or how different their products should be relative to those of their rivals. This paper investigates this issue in the app economy by studying the determinants of mobile app success. We leverage natural language processing and unsupervised machine learning to cluster apps using pairwise cosine similarity, which provides a measure of horizontal differentiation within app categories. We find asymmetric effects of rivalry from first-party apps: Apple’s entry into a cluster stimulates sales of third-party apps, whereas Google’s entry decreases them. We also find that apps in a cluster that more closely resemble the most popular apps have fewer downloads, whereas greater similarity to average competitors enhances downloads. We further find that more frequent updates increase app downloads. These findings yield important implications for developers in choosing market segments and designing effective differentiation strategies.