Adding a Feature

Enhancing the popular streaming platform with a social feature

Adding a Feature

Enhancing the popular streaming platform with a social feature

Netflix Friend Activity Feed
Netflix Friend Activity Feed

Role

UX Designer

Timeline

January 2024 - March 2024

Introduction

Helping a Top Streaming Platform Stay at the Top

Netflix is an app that pioneered the streaming entertainment industry. With many competitors now fighting for the attention of users, Netflix needs to find a way to keep users engaged and seeing the value of the service. I sought to add a feature that would engage users and leverage a social aspect of the platform to aid in a user’s content selection process.

Research

Proving the Hypothesis

Research was first performed on Netflix’s deprecated feature, "Play Me Something", to understand why this feature, which was implemented to randomly play content for a user based on the Netflix algorithm, did not work for the product. It was relevant to understand why the feature failed because its purpose was helping a user choose content, something I hypothesized users struggled with when watching in groups.

Additionally, I performed competitive analysis on Hulu, Prime Video, HBO Max, and Spotify to inform myself of the streaming industry landscape.

User Interviews

User interviews were then conducted over Zoom with 5 participants who use or have used Netflix and other streaming services.

User interview headshot
User interview headshot

Insights

Group Viewing Choices

In group settings, users often choose familiar, crowd-pleasing shows like "The Office" or something pre-agreed upon, as the focus is on socializing rather than the content itself.

Mood-Based Decisions

When users don't want to spend energy deciding, they turn to quick, familiar shows for background noise, especially when nothing on Netflix fits their current mood.

Trust in Word of Mouth

Recommendations from friends are preferred over Netflix’s suggestions, as users trust their friends' taste and enjoy discussing mutually watched shows.

Analyze

User Persona

The key insights from research were then used to develop a user persona.

Visualizing the User Journey

Based off of the user persona, Rachel, a customer journey map was used to illustrate a user's thoughts and emotions, highlight pain points, and opportunities for improvement along the Netflix customer journey.

The Two How Might We’s

How might we leverage word of mouth recommendations users receive to increase the quality of recommendations when users are searching for something new to watch?

How might we make it easier for a user to find content that matches their current mood?

Ideation and Prioritization

Putting On My Thinking Cap

With my ‘How Might We?’ statements top of mind, I held a brainstorming session using FigJam.

After reviewing the ideas I had come up with, I decided to build on the feature of recommending content with friends within the Netflix app to solve for the first statement, which states:


“How might we leverage word of mouth recommendations users receive to increase the quality of recommendations when users are searching for something new to watch?”


I decided to deprioritize the solution for solving for the second HMW statement for MVP. The best idea for matching content to the user’s current mood involved the user taking a survey, which would require more thought around the questions asked, length of the survey, and whether users would even want a feature like this since they typically don’t like Netflix’s recommendations anyway. Due to time constraints, expansion of this idea can come at a later date since it would require more time for researching and brainstorming to develop a stronger solution to solve for the problem.

Feature Prioritization

Next, I prioritized feature development based on user testing insights, ultimately focusing on enhancing content recommendation and social interaction within the app.

Must Have

  • Search for friends

  • Add, remove, or block friends

  • Friend activity feed

  • Recommend content to friends

  • Privacy setting

  • Share recommendations with non-Netflix users

Should Have

  • Collaborate on watchlists

Could Have

  • Add profile information

  • See friend's series or movie progress

  • Friend content ratings

Will Not Have

  • Match My Mood

User Flow: Recommend Content to a Friend

Before designing screens, I diagrammed what the user flow might look like when a user recommends content to a friend.

Design

Putting Pencil to Paper

Once I decided on the features I was going to focus on, I started sketching to visualize the screens I would need to create. I solicited feedback through group critiques, thinking through interaction flows and interface designs in this phase before spending time digitizing these screens.

Activity Feed/Add Friends - Version 1

Activity Feed/Add Friends - Version 2

Recommend Content to Friends - Version 1

Recommend Content to Friends - Version 2

Digitizing and Testing My Designs

Then, I translated my sketches into mid-fidelity designs. I went through four iterations, incorporating feedback from user testing and perfecting prototype interactions. I put in the time on my mid-fidelity screens so that I can go into usability testing gaining more in depth feedback to reduce the amount of iteration needed on my high-fidelity screens. After refining my mid-fidelity prototype, I conducted moderated usability testing sessions. I led sessions with my usability test plan while my users navigated my prototype in Figma.


  1. Add a friend

  2. Recommend a show to a friend

  3. Accept recommendation

Mid-Fidelity Iteration Based on User Feedback

Streamline the navigation bar and establish an intuitive task flow for accepting a recommendation

3 users expected recommendations and friend requests to appear in Netflix’s existing notification bell dropdown.

Before

After

1

Removed inbox icon and nested all notifications under the existing bell icon

2

Added and reorganized the notification dropdown to include "Friend Requests & Recommendations"

Separate Section For Friend Recommended Shows

Users expressed that once the recommendation request was accepted, they would want a separate section where they could specifically see the shows that a friend recommended in addition to having it added to the "My List" section.

Before

After

1

Added a separate watchlist specifically for content recommended by friends

Updated the Friend Activity feed with Timestamp Information

Adding timestamp information to the friend cards should enhance the social feature and make the activity feed more relevant.

Before

After

1

Added last activity timestamp to friend cards and ordered them by the most current person online

Iterations

Taking My Designs to a Higher Fidelity

Next, I created my high-fidelity screens, polishing my designs based on user feedback from my mid-fi usability testing to ensure seamless interaction and intuitive navigation.

View Friend Activity

Add a Friend

View Friend Requests a Recommendations

Recommend a Show to a Friend

Usability Testing

Usability testing was conducted through Maze. 4 were moderated while 1 was unmoderated. Participants were asked to complete the following tasks.


  1. Add a friend

  2. Recommend a show to a friend

  3. Accept recommendation

Insights

Adding a Friend From the Search Bar

1 user attempted to use the Netflix search bar to add a friend, while all others clicked on the friends icon in the navigation bar.

Sharing a Show Using the Friend Activity Panel

2 of 5 users initially chose the Friend Activity panel to locate a friend’s page, whereas the other 3 went the expected path by first accessing the show.

Accepting a Recommendation is Intuitive

All users followed the expected path with no variation.

Minor Changes Made Based on Usability Testing

After analyzing insights from usability testing, I decided not to make a majority of the changes since the majority of users completed the tasks using the expected path. The final iteration did address one minor improvement based on feedback from users. The prototype was then perfected for consistency and to prepare for development and real-world deployment. This iteration was ready for development and to be released for users to use this new feature in reality and provide real feedback.

Visually Show Friend's Active Status

Some users made a comment that it would be interesting to know who is currently online. This was included in the prototype with the word ‘Now’ under the user’s name but it was not obvious to users based on their feedback.

Before

After

1

Added a green dot to the friend profile picture in the activity feed to visually indicate that your friends are online

The Resulting Prototype

The Resulting Prototype

The Resulting Prototype

Learnings

Next Steps

Add a way for users to select a friend from the Friend Activity panel and search for a show from there to recommend to the friend. A minority of testers (2 of 5) intuitively tried to go through this user flow.

Key Insights

You can’t always solve for every user problem in one development sprint. As much as I would have loved to solved both ‘How Might We'?’ problems I stated, I had to evaluate my constraints and prioritize what features were most useful and realistic for MVP.

Spending the time in lower fidelity iterations pays off. I didn’t have to worry about the images for my mid-fidelity screens and was able to focus on fine tuning the interactions of the feature. It laid the groundwork for me needing to spend less time perfecting my high-fidelity screens and final prototype later on.

Adding a Feature

Enhancing the popular streaming platform with a social feature