Overview
FlyBird is an AI-powered feature within YouTube Music that discovers trending songs from around the world, segmenting them by genre, mood, and user personality. Acting as a digital explorer, FlyBird delivers real-time trending songs tailored to users’ listening habits and music preferences, allowing them to effortlessly discover new music beyond their usual scope.
Problem Statement
Users often struggle to discover trending songs, especially those that align with their personal tastes and are outside of mainstream music. Current discovery mechanisms rely heavily on algorithms based on past behavior, limiting exposure to diverse music. FlyBird addresses this by introducing a global, AI-powered discovery tool that broadens music exploration while keeping recommendations highly relevant to the user.
Goals & Non-Goals
User Goals:
- Enable users to discover trending songs worldwide based on personal moods and tastes.
- Find music that matches their current mood and personality.
- Provide an effortless way to stay on top of global music trends.
- Offer a dynamic experience that adapts to users' evolving preferences.
Business Goals:
- Increase daily active users (DAUs) by encouraging frequent music discovery.
- Improve user retention and session duration on YouTube Music.
- Strengthen YouTube Music’s brand as a leading platform for music discovery by differentiating it from competitors through unique AI-powered discovery.
Non-Goals:
- Competing directly with social media music trends.
- Replacing existing recommendation algorithms.
- Offering in-depth artist analytics for industry professionals.
- Development of offline music storage capabilities.
- Immediate expansion of the AI features to non-music content like podcasts or videos.
- Customization of user interface themes.
User Personas & User Stories
1. The Student - Trend Enthusiast
- User Story: As a student, I want to stay on top of global music trends so that I can engage in conversations with friends and always know the latest hits.
2. The Music Lover - Explorer
- User Story: As a passionate music lover, I want to discover unique tracks in my favorite genres, including mainstream and underground artists, so that I can always find fresh music.
3. The Everyday Listener - Casual Explorer
- User Story: As a casual music listener, I want to discover trending songs that match my personality and mood so that I can enjoy music that resonates with my emotions throughout the day or week.
Impact
Customer Impact:
- Expands musical horizons by providing a unique and engaging discovery experience.
- Reduces time spent searching for new music.
- Creates a personalized music experience with minimal effort.
Business Impact:
- Boosts user engagement, session duration, and DAU growth.
- Enhances YouTube Music’s competitive edge by differentiating it from other streaming services.
- Encourages user-generated content and organic sharing of discovered songs.
Solution Development
- AI-driven curation: FlyBird will analyze global music trends and cross-reference them with user preferences.
- Mood & Personality segmentation: Users can select mood-based or personality-driven recommendations.
- Interactiveness: A dedicated FlyBird Trending section within the Explore tab will feature trending songs categorized by genre and location, with the ability for users to engage through comments and shares.
Alternative Solutions
- Community-based curation to allow users to vote on trending songs
- Mood-based playlists curated by popular influencers
- AI-Powered listening rooms to introduce real-time group music discovery sessions
Requirements Table
Priority
Requirement
High
AI integration for trend detection
High
Mood & personality-based detection
Medium
UI integration within the YouTube Music Explore tab
Medium
Playlist auto-generation
Low
Social sharing functionality
Experience 1: Curious User
Scenario: A new user stumbles upon FlyBird organically while exploring YouTube Music
User Flow:
- Opening YouTube Music – The user launches the app, looking for something fresh to listen to.
- Exploring the Home Page – They scroll through recently played tracks, curated playlists, and trending charts.
- Noticing FlyBird – A dynamic, eye-catching banner labeled “FlyBird: Global Trending Picks” catches their attention.
- Engaging with FlyBird – The user taps on FlyBird and sees an engaging intro animation that briefly explains FlyBird’s AI-powered global music curation.
- Hooking the User – A prompt appears: “Want fresh global music tailored for you? Try FlyBird now!” with a one-tap “Start Exploring” button.
- Selecting a Category – They explore sections such as:
- Trending by Genre (Pop, R&B, Hip-Hop, etc.)
- Popular by Mood (Chill, Energetic, Nostalgic, etc.)
- Emerging Artists Worldwide
- Discovering a Song – A song labeled “Trending in Latin America” grabs their attention; they play the first 30 seconds.
- Deep-Dive Information – They swipe up to read an AI-generated summary about why the song is trending.
- Engagement Action:
- Add to “Weekend Vibes” playlist.
- Share with a friend via YouTube Music chat.
- Follow the artist to receive updates.
- Re-engagement Hook – FlyBird prompts: “Love discovering new music? Enable weekly trend alerts.”
Experience 2: Returning User
Scenario: A frequent user is prompted by the app to engage with FlyBird based on past activity
User Flow:
- Receiving a Notification – The user gets a push notification during their commute:
- “FlyBird has found trending Indie Folk tracks perfect for your late-night study sessions!”
- Opening the Notification – They land on FlyBird’s personalized recommendation page.
- Mood & Activity-Based Filtering – FlyBird asks: “Are you studying, relaxing, or working out?” The user selects “Studying”.
- AI-Curated Playlist Appears – A tailored “Focus Mode” playlist is generated with Lo-Fi and Indie Folk tracks.
- Previewing Song Details – The user hovers over a track to see AI insights, such as “Trending in quiet coffee shops.”
- Listening & Engagement:
- Plays a track and taps “Save to My Library.”
- Adds the song to a personalized playlist.
- New Artist Discovery:
- The app suggests: “Want to explore more like this? Meet our Spotlight Artist.”
- Subscription Prompt:
- The user is encouraged to unlock deeper FlyBird insights with a premium subscription.
- Engagement Hook:
- “Get notified when similar tracks trend worldwide. Enable notifications?”
Experience 3: AI-Driven Personalized Discovery (Moods, Events & Personality)
Scenario: The user wants recommendations based on their mood, activity, or personality
User Flow:
- Entering FlyBird’s Personalized Mode – The user taps “Find music tailored to you”.
- Selection Options Appear:
- Mood: Joyful, Sad, Melancholic, Playful
- Activity/Event: Gym, Dinner Party, Road Trip
- Personality: Creative, Activist, Tech Enthusiast
- User Chooses a Combination – They select “Melancholic”, “Solo Night Drive”, and “Curious”.
- AI-Generated Playlist Appears – A real-time, dynamic playlist of trending and undiscovered tracks is presented.
- Contextual Track Descriptions:
- Each song has a backstory explaining its recent popularity in a region.
- Engagement Action:
- Listens, saves multiple songs, and follows recommended artists.
- Re-engagement Hook:
- “Want FlyBird to refine your taste over time? Enable adaptive recommendations.”
Experience 4: Active Seeker
Scenario: The user is actively searching for new music and interacts with FlyBird
User Flow:
- Initiating a Search – The user types “new underground R&B artists” into the search bar.
- FlyBird’s Smart Results – A highlighted section labeled “FlyBird Top Picks” appears with AI-powered trending lists.
- Exploring a Song – The user taps on a track labeled “Most Streamed R&B Track in Berlin.”
- Contextual Insights Appear:
- A 30-second preview with a note: “This song has gained traction in niche clubs.”
- Expanding the Discovery:
- User clicks on “More like this” and discovers emerging R&B artists in Europe.
- Engagement Action:
- Follows the artist, adds the song to a playlist, and shares it in a group chat.
- FlyBird Recommends More:
- “Would you like more trending R&B from around the world?”
- Re-engagement Hook:
- “Set up weekly trend updates for your favorite genres?”
Experience 5: Community Engagement
Scenario: The user engages with FlyBird’s social features by interacting with other music lovers
User Flow:
- Discovering a Trending Song – The user finds a FlyBird-recommended song labeled “Viral in Nigeria.”
- Checking the Comments Section:
- A conversation thread appears discussing the song’s rise in popularity.
- Joining the Discussion:
- User adds a comment: “This beat is fire! Didn’t expect Afrobeats to trend this much in Europe.”
- Reacting to Other Comments:
- Likes and replies to other users’ insights.
- Sharing the Track:
- Sends the song to a friend via YouTube Music chat.
- Following Community Playlists:
- User subscribes to a FlyBird-curated playlist made by the community.
- Engagement Hook:
- FlyBird suggests: “Want to join a global music discussion? Try FlyBird Community.”
- The user flows for the user #1 can be found below
- The mock up for the user#2 can be found here
- The final version of the mock up is here
FAQs
Q: How does FlyBird determine global music trends?
A: FlyBird uses AI to analyze music consumption data from various regions, considering factors like play counts, user engagement, and social media mentions.
Q: Can I customize the regions I want to discover music from?
A: Yes, users can select specific regions or opt for a global discovery experience.
Q: How does FlyBird assess my mood and personality?
A: The AI analyzes your listening history, app usage patterns, and optional user inputs to infer mood and personality traits. Users can also manually select their mood.
Q: How does FlyBird support upcoming artists?
A: The AI considers various factors beyond just play counts, allowing for the discovery of promising new artists.
Q: Will FlyBird replace my current "Recommended" section?
A: No, FlyBird will be an additional feature within the Explore tab, ensuring users remain engaged without disrupting their existing recommendations.
Q: What markets are prioritized for FlyBird’s launch?
A: FlyBird will initially launch in North America, Latin America, and Europe. These regions were chosen due to their high engagement with streaming services and diverse music consumption habits.
Q: How often is the trending music updated?
A: Trending music is updated daily to ensure fresh discoveries.
Q: Is my personal data used for anything other than recommendations?
A: No, all personal data is used solely for improving your YouTube Music experience and is handled in accordance with Google's privacy policy.
Q: How much does FlyBird cost?
A: FlyBird is included in YouTube Music for free. However, premium users get deeper AI insights and ad-free discovery experiences.
Q: Will FlyBird affect my data usage?
A: FlyBird uses minimal additional data for its AI processing, primarily relying on your normal music streaming data.
Success Metrics & Timeframe
- Increase in daily active users (DAUs) by 15% within 6 months.
- 25% increase in average time spent on the app per session within 6 months.
- 30% increase in the number of new artists discovered per user per month within 6 months.
- 20% increase in user-created playlists featuring FlyBird discoveries within 6 months.
- 10% increase in premium subscription conversions attributed to FlyBird within 6 months.
Milestones & Timelines
Milestone
Timeframe
Research and completion of prototype
4 weeks
AI model testing + beta user analytics
2-3 weeks
Beta testing with select users
3 weeks
Feedback interviews
2 weeks
Feature rollout
3 weeks
Ongoing error fixes & UI enhancements
3 weeks (post-launch)
FlyBird is a game-changing AI-driven discovery tool, reinforcing YouTube Music as the leading platform for personalized global music trends.
Learnings
Product Manager Learnings:
Belinda Handou
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.