Predicting Online Video Engagement Using Clickstreams
This work addresses the need for better consumer understanding in competitive e-content markets, though it appears incremental by applying existing methods to new data.
The paper tackled the problem of predicting user engagement with online video streams by leveraging clickstream data from web portals, achieving effective prediction results.
In the nascent days of e-content delivery, having a superior product was enough to give companies an edge against the competition. With today's fiercely competitive market, one needs to be multiple steps ahead, especially when it comes to understanding consumers. Focusing on a large set of web portals owned and managed by a private communications company, we propose methods by which these sites' clickstream data can be used to provide a deep understanding of their visitors, as well as their interests and preferences. We further expand the use of this data to show that it can be effectively used to predict user engagement to video streams.