At Prizma we are always trying to understand the “why” of what people are watching. It’s one thing to know that people are watching a lot of videos about politics, but it’s another to know whether what they’re watching is coming from a desire for information, or driven by outrage around the news, or the desire to empathize with other people. Understanding that “why” is part of how Prizma is able to drive consistently high performance while surprising and delighting users.
In order to do this, we use machine learning to generate a “psychographic” feature space that covers some of the underlying reasons why users might be engaging with content. This enhanced feature space informs our entire approach to both recommendations and optimization and allows us to pinpoint not only which videos are doing well, but why.
For example, when we think about people’s preferences we often think about topics, or perhaps their favorite celebrities (including the ones that they love to hate) as what drives their engagement. However, these kinds of tags are often incapable of capturing the emotions or driving forces behind that engagement. When we looked at data from the last several weeks on one of our partner’s sites we found increased performance from videos that covered politics and the incoming President, both did a little shy of 50% better than videos that didn’t cover those subjects, but when we considered our tag of “outrage” we were able to identify the videos that drove nearly 3× the engagement of other videos. It is clear that while people have a renewed interest in politics, “outrage” is one of the key emotions that gets users to watch videos in the current climate.
In practice, our ability to infer these more general features translates into tangible value for our customers. In one A/B test we compared the performance of our recommendations pipeline with and without our extended feature space. We found that by utilizing psychographic features we were able to increase the number of initiated views by 10% and the number of completed views by >30%, indicating that while these features are important to the “click”, they are even more important for generating sustained user attention and engagement.
We have calibrated and honed our psychographic dimensions based on how well they describe and distinguish our partners’ content; with the aid of intuitive psychological models that use some of the language used by users and creators alike to describe content. This human-centered approach has the capacity to provide actionable insights for our partners. We train these models on highly diverse data and utilize these dimensions throughout our pipeline. Using our psychographic dimensions reduces the resources and data points required to generate more abstract representations of both videos, and user preferences - allowing us to create high-quality video discovery experiences for any publisher and environment.
We believe that this human-centered approach to understanding content is the key to driving deeper video engagement. As we expand our offering, we hope to go beyond using these psychographics to inform our own optimizations, to provide deeper insights to content creators and advertisers helping them to understand their users better, and create the most engaging content for their audience.