Spotify - moodlist

Spotify - moodlist

A new feature for Spotify that tracks a user’s current emotional state with an interactive tool and immediately creates a more hyper-personalized playlist that matches the user’s specified mood of the moment.

My Role

Product Design, Prototyping

Product Design, Prototyping

Product Design,

Prototyping

Timeline

5 weeks / Oct - Nov 2023

Tools

Figma, Illustrator, After Effects

Background

Spotify currently offers a feature called 'daylist,' which creates personalized playlists based on your past listening patterns, adapting to different times of the day. This feature aims to hyper-personalize user preferences by understanding individual music habits and tastes throughout the day.

Spotify currently offers a feature called ‘daylist,’ which creates personalized playlists based on your past listening patterns, adapting to different times of the day. This feature aims to hyper-personalize user preferences by understanding individual music habits and tastes throughout the day.

Spotify currently offers a feature called ‘daylist,’ which creates personalized playlists based on your past listening patterns, adapting to different times of the day. This feature aims to hyper-personalize user preferences by understanding individual music habits and tastes throughout the day. It only mirrors user's past listening habits/history.

Limitations

  1. Lacks real-time personalization that
    aligns with the dynamic mood
    changes of the user throughout the day.

  1. Lacks real-time personalization that aligns
    with the dynamic mood changes of
    the user throughout the day.

  1. A repetitive playlist cycle restricted by
    their past listening habits/history.

User persona

Target Audience -

1. Age 18-35

2. Tech-savvy individuals who use streaming services daily.

3. People who experience frequent mood changes and seek music that aligns with their emotional state.

Target Audience -

1. Age 18-35

2. Tech-savvy individuals who use streaming services daily.

3. People who experience frequent mood changes and seek music that aligns with their emotional state.

Solution

Spotify moodlist -
An interactive tool to track the user’s current emotional state and immediately
receive a personalized playlist that matches their specified mood of the moment.

How it works?

An interactive dynamic gradient mood tracker. 

An interactive dynamic gradient mood tracker. 

Search or share
your current mood.

Search or share
your current mood.

Change your mood using the icon at the top at daylist.

Change your mood using the icon at the top at daylist.

Final product

Prototype - Drag the cursor to interact!

Design process

Data research

User Survey

To support my solution with data, I conducted a user survey targeting
the following demographics, involving 20 participants:

- Individuals aged 18-35
- Individuals with experience with Spotify 'Daylist'
- Tech-savvy individuals who use music streaming services daily. 
- People who experience frequent mood changes and seek music that aligns with their emotional state.

Competitor Analysis

To identify market gaps and user preferences, enabling the creation of a unique, user-centric service
that stands out in the competitive music streaming industry.

Key Findings


  • High Demand for Mood-Based Music Selection:
    A significant majority (75%) of users reported difficulty in selecting music that matches their mood, highlighting a gap in the current music selection process. This underlines the need for a more intuitive, mood-responsive playlist feature that can adapt to users' emotional states.

  • Agreement on Mood's Influence on Music Choice:
    All participants (100%) agreed that their mood affects their music choices, emphasizing the importance of emotional context in the listening experience.

  • Strong Interest in Mood Identification and Personalization Features:
    Users are seeking a deeper level of personalization and support in recognizing their emotional states.

Key Findings


  • High Demand for Mood-Based Music Selection:
    A significant majority (75%) of users reported difficulty in selecting music that matches their mood, highlighting a gap in the current music selection process. This underlines the need for a more intuitive, mood-responsive playlist feature that can adapt to users' emotional states.


  • Agreement on Mood's Influence on Music Choice:
    All participants (100%) agreed that their mood affects their music choices, emphasizing the importance of emotional context in the listening experience.


  • Strong Interest in Mood Identification and Personalization Features:
    Users are seeking a deeper level of personalization and support in recognizing their emotional states.

User flow

Key screens

Design iterations

Design Iteration 01.

1. Instead of a pop-up, create a separate page for the mood list, providing more space for users.
2. To create a less generic and more mood-enhancing gradient mood map.
3. To create larger and more prominent mood guidelines for the mood map.
4. Adding a feature called 'Mood Intensity' to enable users to have more personalization and control over their current mood.

Design Iteration 02.

1. No noise effect for better and clear user engagement.
2. Adding a feature which users can preview songs matching their mood through the mood map.

Final Design Analysis

Design system

What I've learned…

Understanding User Comfort and Familiarity

Reflecting on the insights gained from my project, I've come to understand that users are not fully accustomed to interactive tools that closely monitor their emotional states. This realization has underscored for me the critical importance of considering user comfort when introducing innovative functionalities into digital products.

Understanding User Comfort and Familiarity

Reflecting on the insights gained from my project, I've come to understand that users are not fully accustomed to interactive tools that closely monitor their emotional states. This realization has underscored for me the critical importance of considering user comfort when introducing innovative functionalities into digital products.


Next Project →