The goal of this project is to help new members find something to watch quickly by improving recommendations. Through collecting data on what they have previously watched, it will help us better predict what they will likely watch.
Netflix is famous for its algorithm to provide personalized recommendations. However, the challenge with a new member experience is since we don't know what they previously liked, we can't recommend the right content for them. Secondarily, a lot of Netflix users didn't know that their homepage's content is personalized to them, and some ended up frustrated when they see their friend's Netflix has a newer title on their homepage but they can't find it on theirs.
The goal of this UI is twofolds -- to gather data on what the user previously watched and liked and to communicate that we are using that data to generate personalized recommendations for them.
RESEARCH & TESTING
This project used both qualitative and quantitative methods to gather user feedback. We tested a variety of designs and isolated variables to determine what type of layout is more effective in gathering user data (e.g., scrollable page vs. one-page gallery). We've also tested different ways of communicating the benefit of them providing this data. I used Keynote to create prototypes for this research.
This test was launched in the US, Canada, UK, Ireland, and LATAM.