Netflix: Adding a Feature
Netflix: Adding a Feature
Netflix: Adding a Feature
Project Overview
Role
UI/UX Designer
Team
Individual project mentored by Carolina de Bartolo
Timeline
4 weeks, November 2024
Tools Used
Figma, Figjam, Adobe Firefly, Gemini, Google Meets
Note
The purpose of this exercise is to add a feature to an existing service. For my project, I added a search feature to Netflix that enables users to browse streaming content by mood.
The Context
Problem
With so many streaming options, users can spend as much time looking for a movie as they do watching one. This can feel overwhelming and frustrating for people who want to jump straight into viewing content.
Solution
What users need is an intuitive and effective way to find the right movie for whatever mood they’re in. This will help them shorten the time between searching for content and enjoying it.
Project Goal
I want to produce a streamlined and satisfying experience for users. Searching by mood ideally creates a favorable atmosphere for users or amplifies the mood that they’re in.
Defining User Needs
User Interviews
I interviewed 5 people who frequently stream content across a wide variety of platforms. Their input guided us towards pain points and areas of opportunity.
User Interview Results
Incorporate mood-based content cards
Introduce fun and quirky categories (ex: cozy night with movies that feel like home)
Enable search for specific criteria (ex: comedy movies that lift my mood)
AI-generated key summaries and content tags
More accurate, personalized recommendations
Netflix Feature User Persona
Netflix Feature User Persona
Netflix Feature Lo-Fi Wireframe
Netflix Feature Lo-Fi Wireframe
User Feedback
Revising and Iterating
Based off direct user feedback, this is what I changed about the Netflix prototype:
updating emoji pictures
changing movies popcorn pic
adding scroll option
adding back button
adjusting gradient
even black background for the search categories
change Moods picture to little emojis
browsing hierarchy
“New” feature banners for moods
Recommended for you closer to the top
add a way to get back to home screen
add screens for more movies
Netflix Feature Revision Mapping
Netflix Feature Revision Mapping
Iterations Based on Feedback
Iterations Based on Feedback
Netflix Hi-Fi Wireframe Screenshots
Netflix Hi-Fi Wireframe Screenshots
Netflix Feature Prototype
Netflix Feature User Persona
Key Takeaways
Reflection
Working on the mood-based search feature for Netflix was a deep dive into understanding how people choose what to watch. It made me think about decision fatigue and how offering a more intuitive way to browse could make searching feel less overwhelming.
Finding the right balance between giving users enough options without making things cluttered was a fun challenge.
Future Notes
One thing I want to focus on moving forward is sharpening the connection between design choices and user emotions.
For this project, I spent a lot of time refining the interface, but I realized that understanding why people pick certain moods or what they expect from a “feel-good” category could have led to even more tailored design solutions.
Each project keeps stretching the way I think, and I look forward to carrying these lessons into the next one!
Netflix Feature Prototype
Netflix Feature Prototype
Project Overview
Role
UI/UX Designer
Team
Individual project mentored by Carolina de Bartolo
Timeline
4 weeks, November 2024
Tools Used
Figma, Figjam, Adobe Firefly, Gemini, Google Meets
Note
The purpose of this exercise is to add a feature to an existing service. For my project, I added a search feature to Netflix that enables users to browse streaming content by mood.
Defining User Needs
User Interviews
I interviewed 5 people who frequently stream content across a wide variety of platforms. Their input guided us towards pain points and areas of opportunity.
User Interview Results
Incorporate mood-based content cards
Introduce fun and quirky categories (ex: cozy night with movies that feel like home)
Enable search for specific criteria (ex: comedy movies that lift my mood)
AI-generated key summaries and content tags
More accurate, personalized recommendations
The Context
Problem
With so many streaming options, users can spend as much time looking for a movie as they do watching one. This can feel overwhelming and frustrating for people who want to jump straight into viewing content.
Solution
What users need is an intuitive and effective way to find the right movie for whatever mood they’re in. This will help them shorten the time between searching for content and enjoying it.
Project Goal
I want to produce a streamlined and satisfying experience for users. Searching by mood ideally creates a favorable atmosphere for users or amplifies the mood that they’re in.
User Feedback
Revising and iterating
Based off direct user feedback, this is what I changed about the Netflix prototype:
updating emoji pictures
changing movies popcorn pic
adding scroll option
adding back button
adjusting gradient
even black background for the search categories
change Moods picture to little emojis
browsing hierarchy
“New” feature banners for moods
Recommended for you closer to the top
add a way to get back to home screen
add screens for more movies
Key Takeaways
Reflection
Working on the mood-based search feature for Netflix was a deep dive into understanding how people choose what to watch. It made me think about decision fatigue and how offering a more intuitive way to browse could make searching feel less overwhelming.
Finding the right balance between giving users enough options without making things cluttered was a fun challenge.
Future Notes
One thing I want to focus on moving forward is sharpening the connection between design choices and user emotions.
For this project, I spent a lot of time refining the interface, but I realized that understanding why people pick certain moods or what they expect from a “feel-good” category could have led to even more tailored design solutions.
Each project keeps stretching the way I think, and I look forward to carrying these lessons into the next one!
Project Overview
Role
UI/UX Designer
Team
Individual project mentored by Carolina de Bartolo
Timeline
4 weeks, November 2024
Tools Used
Figma, Figjam, Adobe Firefly, Gemini, Google Meets
Note
The purpose of this exercise is to add a feature to an existing service. For my project, I added a search feature to Netflix that enables users to browse streaming content by mood.
The Context
Problem
With so many streaming options, users can spend as much time looking for a movie as they do watching one. This can feel overwhelming and frustrating for people who want to jump straight into viewing content.
Solution
What users need is an intuitive and effective way to find the right movie for whatever mood they’re in. This will help them shorten the time between searching for content and enjoying it.
Project Goal
I want to produce a streamlined and satisfying experience for users. Searching by mood ideally creates a favorable atmosphere for users or amplifies the mood that they’re in.
The User
User Interviews
I interviewed 6 people who expressed interest in expanding their social life. We gathered their priorities and found areas where we could address common needs.
User Interview Results
Interest in hobbies
Challenges in forming new relationships
Desire for more social time
Openness to meeting new people
Use of technology for social connections
Potential for a platform to facilitate connections
Group vs one-on-one settings
User Feedback
Revising and Iterating
Based off direct user feedback, this is what I changed about the Netflix prototype:
updating emoji pictures
changing movies popcorn pic
adding scroll option
adding back button
adjusting gradient
even black background for the search categories
change Moods picture to little emojis
browsing hierarchy
“New” feature banners for moods
Recommended for you closer to the top
add a way to get back to home screen
add screens for more movies
Key Takeaways
Reflection
Working on the mood-based search feature for Netflix was a deep dive into understanding how people choose what to watch. It made me think about decision fatigue and how offering a more intuitive way to browse could make searching feel less overwhelming.
Finding the right balance between giving users enough options without making things cluttered was a fun challenge.
Future Notes
One thing I want to focus on moving forward is sharpening the connection between design choices and user emotions.
For this project, I spent a lot of time refining the interface, but I realized that understanding why people pick certain moods or what they expect from a “feel-good” category could have led to even more tailored design solutions.
Each project keeps stretching the way I think, and I look forward to carrying these lessons into the next one!
Defining User Needs
User Interviews
I interviewed 5 people who frequently stream content across a wide variety of platforms. Their input guided us towards pain points and areas of opportunity.
User Interview Results
Introduce fun and quirky categories (ex: cozy night with movies that feel like home)
Incorporate mood-based content cards
Enable search for specific criteria (ex: comedy movies that lift my mood)
AI-generated key summaries and content tags
More accurate, personalized recommendations
The User
User Interviews
I interviewed 6 people who expressed interest in expanding their social life. We gathered their priorities and found areas where we could address common needs.
User Interview Results
Interest in hobbies
Challenges in forming new relationships
Desire for more social time
Openness to meeting new people
Use of technology for social connections
Potential for a platform to facilitate connections
Group vs one-on-one settings