modeling the number of active software users

More and more software applications are developed within a software ecosystem (SECO), such as the Facebook ecosystem and the iPhone AppStore. A core asset of a software ecosystem is its users, and the behavior of the users strongly affects the decisions of software vendors. The number of active users reflects user satisfaction and quality of the applications in a SECO. However, we can hardly find any literature about the number of active software users. Because software users are one of the most important assets of a software business, this information is very sensitive. In this paper, we analyzed the traces of software application users within a large scale software ecosystem with millions of active users. We identified useful patterns of user behavior, and proposed models that help to understand the number of active application users. The model we proposed better predicts the number of active users than just looking at the traditional retention rate. It also provides a fast way to monitor user satisfaction of online software applications. We have therefore provided an alternative way for SECO platform vendors to identify rising or falling applications, and for third party application vendors to identify risks and opportunity of their products. © 2011 IEEE.Microsoft Research; Alberta Innovates; Siemens; University of Calgary; RIMMore and more software applications are developed within a software ecosystem (SECO), such as the Facebook ecosystem and the iPhone AppStore. A core asset of a software ecosystem is its users, and the behavior of the users strongly affects the decisions of software vendors. The number of active users reflects user satisfaction and quality of the applications in a SECO. However, we can hardly find any literature about the number of active software users. Because software users are one of the most important assets of a software business, this information is very sensitive. In this paper, we analyzed the traces of software application users within a large scale software ecosystem with millions of active users. We identified useful patterns of user behavior, and proposed models that help to understand the number of active application users. The model we proposed better predicts the number of active users than just looking at the traditional retention rate. It also provides a fast way to monitor user satisfaction of online software applications. We have therefore provided an alternative way for SECO platform vendors to identify rising or falling applications, and for third party application vendors to identify risks and opportunity of their products. © 2011 IEEE