AI MUSIC EXPERIENCES
Achieving Higher User Retention by Introducing Innovative AI Music Experiences
Company
Earbuds
Role
Product Designer
Responsibilities
UI/UX design, user research, bug testing, prompt engineering
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Summary
Process
Solution
Impact
Conclusion
Next Steps
Summary
Daily Active Users
↑ 0 %
Session Duration
↑ 0 %
Playlist Creation
↑ 0 %
User Retention
↑ 0 %
We faced a significant challenge with low user retention, as many users only interacted with the app when others shared songs or messages. Our goal was to create standalone features that would encourage users to spend more time in the app. To address this, we introduced AI-powered experiences that generated music playlists. These new features were branded under our AI Music Chat Bot, Earbot, and integrated into a new home tab called Discover.
Problem
Users lacked intrinsic motivation to stay engaged with the app beyond interacting with shared content. As a startup, we also needed to attract funding, with VCs showing a strong interest in AI projects.
Goals
- Increase User Retention: Develop features that would encourage users to return to the app more frequently.
- Improve Session Duration: Create engaging experiences that would lead users to spend more time in the app.
- Enhance User Retention: Ensure that users who tried the new features would continue to use the app regularly.
- Leverage AI for Innovation: Integrate AI to create unique and compelling user experiences, aligning with current market trends and investor interests.
- Attract VC Funding: Develop innovative AI-driven features that would make the app more attractive to potential investors.
Hurdles
- Limited Timeline: Only a few months of runway left
- Small Team: Few resources to handle multiple new features
- User Interviews: Many interviews with the target audience fell through
- AI Limitations: Learning and adapting to AI constraints
- Technical Issues: Slow load times for AI playlists, differences in iOS and Android implementations
Process
We started by brainstorming a variety of mini-games and experiences, scoring them based on usability, feasibility, time constraints, value, internal motivation, shareability, virality, and confidence in the build. We also incorporated user feedback to understand their needs and interest in AI. This process led to the development of three key AI experiences: Swipey, Nostalgia, and Dreamer, all branded under Earbot.
steps taken
- User Interviews: The initial step was to see how our target audience felt about incorporating AI
- Kickoff: A brainstorming session with the entire team to establish a list of experiences
- Feasibility Check: Engineers and designers discussed the scope and limitations of the initial experiences
- User Flows: Designed flows for each experience
- Design: Created sketches, wireframes, and HiFis
- Review: Conducted multiple rounds of critiques, comments, questions, concerns, and threats
- Prompt Engineering: Dedicated space to learning how to improve our playlist recommendations
- Usability Testing: I conducted the first test with users to discuss their initial thoughts on experiences and what they wanted
- Launch: Made the app’s first push of new experiences and conducted extensive bug testing
- Follow-up Survey: A study was sent out to analyze and gather data on the initial success of AI experiences
Solution
We developed three AI experiences, focusing on playlist creation. These experiences were integrated into a new Discover tab (our new home tab), making it easier for users to explore and engage with the app.
Swipey
Swipe through songs to create a playlist with Swipey. It’s like Tinder for music.
Nostalgia
Select a year and genre to relive hits from years past with Nostalgia. Making a throwback playlist just got a whole lot easier.
Dreamer
Want a playlist to fit your vibe? Set a vibe, pick an activity, and craft the ambiance to create a playlist.
Impact
To measure the effectiveness of the new AI experiences, we conducted a small-scale user testing phase over a month. The results indicated modest but promising improvements in user engagement and retention. These initial results were encouraging, showcasing the potential of our AI-driven features to enhance user engagement and retention. While the improvements were small, they provided a strong foundation for further development and optimization of the AI experiences.
Daily Active Users (DAU)
We observed a 15% increase in the number of daily active users engaging with the new AI experiences. This showed that users were coming back to the app more frequently to interact with the new features.
Session Duration
The average session duration increased by 10%, suggesting that users were spending more time exploring and using the AI experiences compared to the previous app version.
User Retention
We saw a 12% increase in user retention rates, indicating that users who tried the new features were more likely to return to the app within a week.
POsitive Feedback
Over 75% of users who participated in our feedback survey reported that they found the AI experiences enjoyable and engaging. Many highlighted the Swipey and Nostalgia features as particularly fun and unique.
Playlist Creation
There was a 20% increase in the number of playlists created through the app, driven by the new AI experiences. This metric was a key indicator of increased user activity and interaction with the app’s core functionality.
Conclusion
In conclusion, this project was incredibly rewarding as a designer because it allowed us to build a feature at a fast pace while growing our skill set in AI. I firmly believe AI will play a huge role in our future, and being one of the first music apps to explore its potential was both exciting and fulfilling. I learned a lot about the give and take involved in this process, as the MVP is nowhere near what I initially envisioned. However, I’m thrilled with the positive initial feedback and the new opportunities it presents. This experience has deepened my passion for creating innovative user experiences and reinforced my commitment to leveraging cutting-edge technology in design.
Reflection
- Manage Expectations for MVPs: Setting clear expectations for what an MVP can achieve helped manage both team and stakeholder expectations, ensuring a focus on continuous improvement.
- Streamline Communication: Improved communication channels within the team could have mitigated some of the challenges faced during the rapid development phase.
- Balance Innovation and Practicality: While innovative AI features were exciting, ensuring they were practical and user-friendly was essential for adoption.
- Embrace Flexibility: Adapting quickly to user feedback and technological constraints was crucial in delivering a functional and engaging product.
Next Steps
To build on our initial success, we planned to introduce a web version of the experiences to make them more accessible and encourage app downloads. Additionally, we aimed to add social features to promote sharing and further increase user engagement.
future features
- Web app version of each AI experience
- Social buttons
- leaderboards
- multiplayer
- loading animations
