Product design
Leveraging research insights to unlock opportunities
As Pegboard was a design-led project, I was not funded for usability research on our developing solutions. However, we did benefit from foundational research insights.
Early on, I initiated a discussion with a senior researcher regarding Pegboard, its problem space, and the kinds of understanding that would most benefit our work. I was most interested in the predominant mental models that viewers shared regarding creator-driven interactions, and any common problems viewers encounter with existing interactions. As it happened, there were two arcs of research that might shed light on these questions, and by late July we had our insights.
- Viewers have distinct concepts of “on-stage” entertainment (via the creator and the video stream) versus“off-stage” entertainment (via the community).
- Viewers consider creator-driven interactions, such as those available in Pegboard, to be “on-stage” entertainment.
- The relevance of an interactive opportunity to a given viewer is relative to the viewer’s relationship to the channel.
- Low-ask interactions are broadly relevant, while high-ask interactions are narrowly relevant. Relevance is additive as a viewer grows their relationship to the channel.
- Timely creator-driven interactions, such as Polls, act as metadata for viewers to help them understand “what’s happening now.” But, their relevance as interactions still depends on the viewer’s relationship.
These insights seemed to validate of many of our prior design decisions. The choice to ground Pegboard in the context of the live video aligned with viewer mental models of creator-driven interactions as "on-stage" entertainment. Furthermore, the utility of timely creator-driven interactions as stream metadata ("what's happening now") gave us further reason to not filter interaction availability or visibility.
Meanwhile, the way that interaction relevance was dependent on a viewer's relationship to channel revealed a potential opportunity. Instead of filtering interactions, we could use common markers of channel-relationship strength as a predictor of relevance, and weight the way we displayed interactions on a per-viewer basis — essentially personalization via prioritization.
My next round of explorations reflected these learnings, separating interactions by persistence and timeliness and experimenting with personalized weighting.