Predicting Online Video Engagement Using Clickstreams

TL;DR

This paper presents methods to analyze clickstream data from web portals to understand user interests and predict engagement with online videos, aiding companies in gaining a competitive edge in content delivery.

Contribution

The paper introduces novel techniques for leveraging clickstream data to predict online video engagement, enhancing understanding of user behavior beyond traditional metrics.

Findings

Clickstream data effectively predicts user engagement with videos.

Methods provide insights into visitor interests and preferences.

Approach improves content personalization and targeting.

Abstract

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.