Outbrain serves 150 billion content recommendations to more than 500 million users every month. Masses of data tell us what’s driving the mindset of the crowed at each point in time. But how do you analyze if the individual user finds real value in recommendations? And why being satisfied with click-focused-metrics is dangerous for long term growth?
This lecture outlines a Data Scientist’s experience and challenges when analyzing post-click-engagement, in the context of content discovery. We will show examples of how relying on click-focused-metrics might be misleading you in the long run. We will share data of how crowed preferences of consuming content differ from individual user preferences. Finally, we will suggest a 3-layer framework for Data Scientists to measure and analyze post-click-engagement, while considering the perspectives of host publishers, marketers and recommendation providers.
Roy Sasson is Outbrain’s Chief Data Scientist. His research focuses on the construction of predictive models and metrics for Outbrain’s recommendation system. Roy’s academic background includes a PhD in Applied Econometrics at Tel-Aviv University, where he has been teaching Econometrics since 2008. His academic research focused on gaining insights and predictions from data concerning the behavior of individuals, firms and managers. Prior to joining Outbrain, Roy served as the R&D group lead (Asia Branch) at Visual Domains.
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