Study on user quitting in the Puffer live TV video streaming service

Video streaming is an important application on the Internet. To ensure user satisfaction, video streaming service providers monitor their services in terms of quality and engagement. This enables them to ensure high quality services and grow their platform and incomes. However, studying user engagement is difficult as the decision of a user to quit or continue watching videos is jointly affected by many factors such as interest towards content, time available, and service quality. Therefore, the reason for quitting is difficult to identify. To address this, this study is based on usage data of a real-world TV service called Puffer and aims to study the relationship between service quality and quitting actions. Data from December 2020 were collected and correspond to 230,880 distinct viewing sessions. On the basis of these data, performing analysis at different scales (hour, day, month) enables the identification of different reasons for users to quit videos. By using this analysis, quality-related quitting events are identified and put into relation with quality-related parameters as well as state-of-the-art video quality and user quitting prediction models. Results show that quitting prediction models can be used to identify such events. Finally, by the means of logistic regression, this work describes the first steps towards mapping quitting prediction on the basis of models trained using data from laboratory experiments to real-world scenarios and shows a classification accuracy of 75.9%.