About
FITRADIO is a top performing mobile fitness app with millions of daily users, they offer innovative DJ-curated workout mixes, audio-guided workouts, tempo-matching technology, and much more.
Role & Collaboration
I partnered with FITRADIO to design their first AI feature, enhancing user engagement by allowing personalized workout mixes based on activity, mood, and genre preferences. By combining FITRADIO’s DJ expertise with AI, we deliver a dynamic and tailored fitness experience.
View Prototype
Role
Lead Product Designer
Services
Stakeholder Management
UI Design
UX Research and testing
Interactive Prototypes
Usability Testing
Design System
Dev Support & Collaboration
UI Design
UX Research and testing
Interactive Prototypes
Usability Testing
Design System
Dev Support & Collaboration
Status
In Development
Length
2 weeks
Platforms
Mobile (iOS, Android)
Overview
Problem
FITRADIO users struggle with navigating an overwhelming number of workout categories and types, making it difficult to quickly find a mix that matches their current mood and activity. This challenge reduces user satisfaction and engagement.
Solution
The AI Mood Mix feature was designed to address this challenge by streamlining the process and providing personalized workout mixes. This feature allows users to quickly find the perfect mix based on their current mood , enhancing their overall workout experience and increasing their satisfaction and engagement.
Research
I researched over 12+ competitors like Playlistable and Chosic to compare their AI generation features with FITRADIO’s AI MIX. I looked at user customization, playlist creation, and overall experience. This helped highlight FITRADIO’s unique strengths and inspired future improvements.
Optimizing user flows
Integrating AI Mood into the existing player
I designed several exploratory concepts to integrate the new AI features into the existing player screen. The challenge was to enhance functionality without straying too far from the current experience, ensuring it remained intuitive for users.
Hi fidelity designs
A/B Testing the entry point
I conducted an A/B test to determine the best placement for the entry point, ensuring users could easily discover the feature. After testing, the navigation bar achieved a 12% adoption rate, demonstrating the highest level of user engagement.
Outcome & Impact
25
Increase in User Satisfaction
20
Increase in User Engagement
We are currently developing the feature and will roll it out to 5% of our user base incrementally to gather user feedback and provide engineering support. Initial user tests show a promising 25% increase in user satisfaction and a 20% rise in engagement rates.