This stage was focused on user research and generating insights. To better understand user needs, behaviors, and frustrations, I conducted surveys, built personas, and created empathy maps.
User personas and empathy maps.
The online survey revealed how people usually discover and consume music. Most users rely on curated playlists, social media, and recommendations from friends. The most common listening scenarios included working, commuting, and exercising. Participants highlighted frustrations such as too many ads, repetitive recommendations, and poor mood detection. At the same time, personalization was rated as highly important, with strong demand for smart mood detection and seamless transitions between tracks.
Based on these insights, I defined two main personas. Emily, a 26-year-old fitness and wellness coach, uses music throughout her day for workouts, meditation, and relaxation. Her biggest struggle is wasting time choosing the right tracks for each activity. Jordan, a 22-year-old student and barista, is always on the go and listens to music while commuting, studying, or working out. He doesn’t have the time to build playlists and feels frustrated with repetitive, generic recommendations.
To deepen understanding, I developed empathy maps for both personas, capturing what they say, think, do, and feel. Emily values smooth transitions between activities and music that adapts to her pace, while Jordan is motivated by discovering fresh tracks that fit his energy but gets frustrated when music feels repetitive or mismatched.
This stage clarified the core usage scenarios, key pain points, and user expectations, forming a solid foundation for the design of the Muzi platform.