Introducing Earbot: Blending AI in Music Discovery
Users wanted more app features without relying on friends, requiring quick and budget-friendly implementation.
Overview
We had been working towards expanding our user base through social groups, however faced with resource limitations, project feasibility concerns, and time constraints, the decision was made to pivot away from social group features and explore new avenues for user retention.
Through extensive user research, we uncovered a growing demand for standalone features, and with AI moving to the forefront of technology, we seized the opportunity to innovate and embarked on the creation of Earbot—an AI chatbot seamlessly integrated into our app.
Objectives & Goals
- Pioneer AI integration with music to enhance user experience.
- Develop a standalone AI feature to drive app engagement.
- Increase monthly active users through innovative AI implementation.
- Deliver a feasible MVP within a tight timeframe.
- Enhance user retention through personalized AI interaction
- Improve app stickiness through AI-driven music discovery
Constraints
- Limited time – 2 weeks timeframe for new feature implementation.
- Venturing into unfamiliar territory: Integrating AI into a music app posed technical and conceptual challenges.
- Team disagreements on feature prioritization and implementation strategies.
- Technical constraints with AI implementation
Designing Earbot
In charge of Earbot’s visual design, I consulted my co-designer to help solidify the bot’s personality, aligning it with our brand guidelines. Opting for a fun, conversational, and approachable persona, I swiftly generated design concepts to allow ample time for iteration and implementation.I
Brainstorming & Sketching
After the initial discussion of Earbot, I dove straight into a brainstorming session where I sketched out a variety of concepts, ranging from abstract representations to music-themed robot designs, ensuring versatility in potential iterations.
Vectorization
Selecting the most promising sketches, I digitized them through vectorization, maintaining flexibility for further refinement.
Feedback & Revision
Presenting designs to the product manager and co-designer, through their feedback, I quickly refined the logos that resonated most with my team, ensuring that we captured the essence of Earbot.
Iteration and Finalize
With the limited time, we quickly, but carefully selected the character that we felt best embodied what we wanted Earbot to be, aligning seamlessly with our brand identity.
Animation
The implementation of the loading animation came after the initial design phase of Earbot, prompted by loading issues encountered during AI experiences. Users faced prolonged playlist loading times, exceeding a minute in some cases. To address this, I created a loading animation featuring Earbot, aiming to enhance the user experience during playlist loading periods while our team worked on optimizing load times.
This project presented a learning curve as I ventured into animation for the first time. Despite the initial challenge, I quickly acquired the necessary skills and knowledge to create the loading animation. It was a rewarding experience, adding an exciting dimension to the project and showcasing the adaptability and creativity essential in a dynamic project environment.
/Fin
The MVP launch of Earbot marked a significant milestone, enabling users to engage in music-related conversations and receive song recommendations directly within the app. This integration of AI led to a notable increase in active user rates by 5.6%, underscoring the success of our cross-functional platform.
The Earbot project highlighted the importance of agile adaptation and creative problem-solving within tight constraints. Collaborative decision-making and quick iteration were key in delivering a successful MVP within the designated timeframe. Additionally, the experience of designing and implementing Earbot’s animation served as a valuable learning opportunity, emphasizing the benefits and excitement of acquiring new skills in a dynamic project environment.
