Towards Automated Capturing and Processing of User Feedback for Optimizing Mobile Apps

Mobile devices are nowadays ubiquitous and are heavily used for business and private purposes. Millions of apps exist that support users in multiple ways, e.g., for car navigation, fitness purposes, or messaging in our private lives, but also for business planning purposes and even for controlling whole business processes. Failures in mobile business apps can lead to dramatic consequences in terms of lost revenue, but also in terms of lost trust or even threats for human beings, and thus quality plays a crucial role. On the other hand, as software is nowadays one of the main drivers for innovation, fast delivery of new apps, respectively new functionality, is necessary, i.e., the time to market must often be short. However, in order to understand whether the quality is sufficient, and whether the functionality of the app serves the needs of the users, lean development approaches are emerging and propose the deployment of apps as a minimal viable product (MVP). Here, the app is provided with acceptable quality, but not with every feature, just with the main functionality. Based on such an MVP, early feedback from users is to be collected, which may be related to the quality of the app, but also include wishes and requests for new functionality. In order to analyze and draw conclusions from user feedback, we first have to understand what kind of feedback exists and how it can be interpreted, respectively how valid such feedback is. In this publication, we provide a classification of user feedback for mobile apps, derive feedback channels, and sketch how this can be used in a continuous process to improve mobile apps.