StreamTV streaming wireframes

Wireframing Design Scenario

Your design team at a fictional platform, Stream TV, is working on a new and improved mobile app for their video streaming service. Competitors would include Netflix and YouTube. They want to see a wireframe for a “related videos” page that takes a unique approach compared to their competition.

Design Task

Create a wireframe for the “related videos” page that takes a unique approach compared to existing streaming video apps. This can be as low-fi or high-fi as you think is suitable for the project. Explain the rationale behind your wireframes.

Proposal:

Netflix, Hulu, and in a way, YouTube, really lack some more personalized recommendations or ways to make “playlists”. For this service, almost immediately I thought of Spotify. Spotify has these amazing playlists based on my listening history that intelligently know my favorite genres, what I do on a particular day, my mood, and favorite artists. Spotify will make “Made for You” playlists, blends with other friends or followers, and “niche” mixes that may not fit your genre mixes or daily mixes, but are recommendations based on what you enjoy.

Our competitors in this space are really missing two features that could make for an even more tailored recommendation system: community features or “mood” filtering, similar to the “Daylist” from Spotify. Our Stream TV app can serve better related videos or recommendations based on what people watch every night after dinner or binge.

Rationale of Solution

I designed wireframes for StreamTV to prioritize a more personalized user experience, with the "MoodMatch" feature playing a pivotal role. By allowing users to input their current mood, the platform can offer tailored content suggestions, creating a unique and adaptive viewing experience that resonates with the users' current or recent emotional state. This not only enhances engagement by connecting content with mood but also refreshes the home page dynamically, ensuring a consistently relevant and empathetic user experience.

While I focused more on personalization features related to the user’s current emotional state, the integration of community features like "Friend Recommendations" complements the MoodMatch feature by adding a layer of social influence to the mix, further enriching the personalized experience. With this, you could also get recommendations that overlap with your friend's previous watch history or recommended videos.