Apple Podcasts' New Feature


This case study explores a feature enhancement for the Apple Podcasts app, focused on improving the search experience.
The goal was to make podcast discovery more intuitive and emotionally engaging. By adding filter and sort options to the search interface and introducing a "Browse by Mood" section, the redesign empowers users to find content that fits both their preferences and their current mindset.

View Slides โ†—๏ธ

Project

Graduate Studio Project

My Role

UI/UX Design, Website Prototype, Branding

Timeline

Fall 2024

Tools Used

Figma, Illustrator, 

Photoshop

Overview

Background


As podcast platforms grow in content volume, users are increasingly overwhelmed when trying to find shows that match their interests or mood. While Apple Podcasts offers basic search functionality, it lacks more nuanced tools for discovery. Through a personal audit and review of user feedback, I identified an opportunity to improve how users explore content — not just through keywords, but also through emotional context and customizable filters. This led to a redesign concept aimed at enhancing the search experience with both functional and mood-based discovery.


Project Goals


  • Improve the search experience in Apple Podcasts by adding intuitive filtering and sorting options.

  • Introduce mood-based discovery to help users find podcasts that align with how they feel or what they need.

  • Make podcast discovery feel more personal, flexible, and enjoyable, even for first-time users.


Design Process

Preliminary Concepts


Target Users

Research Methods
                                                 

- App Audit: Conducted a thorough evaluation of Apple Podcasts' current search and discovery flow,
identifying usability gaps and missed opportunities for personalization.

- Competitor Analysis: Studied how competing platforms (such as Spotify and YouTube Podcasts)
handle podcast discovery, filters, and emotional categorization.

- User Interviews: Spoke with 3–5 regular podcast listeners to understand how they search for new content
and what makes a good discovery experience.

User Research

People I Interviewed

Key Research Questions

User Insights

1๏ธโƒฃ
Users mostly use the search feature to find topics they like. But it takes a lot of time to find shows they want to keep listening to.

2๏ธโƒฃ
Users often look at top show lists and would like to see how many people are listening or following specific shows.

3๏ธโƒฃ
Users would like podcasts grouped by mood or occasion,
like Spotify playlists.

Challenge

How Might We


How might we make it easier for users to find podcast episodes that match their available time or energy level?

Ideation and Testing

User Testing Feedback

๐Ÿ˜€

“I like filter and sorting feature and categorization by mood.”

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“I don’t think ‘upload date’ options in filter seems useful. ”

๐Ÿ˜ฐ

“The filter and sorting menu
UI is too small to click”

Final Design

Filter & Sorting Buttons
To improve search usability, I added filter and sorting options directly beneath the search bar. Users can now sort results by release date, popularity, or episode duration, and filter them based on specific preferences like episode length or content type. This helps users quickly narrow down relevant podcasts without having to scroll through long, unstructured lists.

Browse by Mood

To make discovery more intuitive and emotionally engaging, I introduced a "Browse by Mood" section within the search interface. Instead of searching by topic or genre alone, users can explore curated podcast categories based on how they feel — such as Feel-good, Grim, Exciting, Funny or Inspiring. This human-centered approach encourages exploration and helps users find content that resonates with their current mindset or activity.

Design System

Reflection

Reflection & Takeaways


  • Discovered that many users prefer music over podcasts during short commutes, highlighting the importance of understanding when and why people choose podcasts.

  • Realized the value of interviewing frequent podcast listeners to gain deeper insights into listening habits and feature needs.

  • Identified an opportunity for an AI-powered suggestion feature that recommends episodes based on mood, time of day, or listening patterns to enhance personalization.

More Projects



© 2025 Irene Ahn

jahn6122@gmail.com

© 2025 Irene Ahn

jahn6122@gmail.com