Projecting Your Video Content Library Performance Through Monthly Cohorts
Tim Jablonski, Little Monster Media Co
Monthly Cohorts represent a new way of looking at monthly performance and how we can project the performance and revenue of videos in a channel’s library. Many digital media brands live off the long tail of vast content libraries, but there’s been little work done to find accurate ways to predict the performance of videos uploaded months or years ago.
In this presentation, we’ll go through a monthly cohort model example, how it works, and provide actionable takeaways for programming meaningful content in its wake. You’ll better understand how to determine whether your content is influenced by time of year or whether or not some of your content is evergreen. At the end of the session, you’ll have a more robust ability to understand and predict the behavior of content in a brand’s past library.