Netflix Case Study

  • Kehinde O

Design Challenge: How might we create a more personalised viewing experience that allows users to discover, choose & watch Netflix content more efficiently? Disclaimer: This is a passion project conducted for the development of my storytelling, UX and UI skills. Project Date: April 2021

Defining the Problem:

Netflix’s wide range of content creates a paradox of choice as users are provided with too many film and TV show options; this results in problematic, stressful and longer decision-making processes. The catalyst for my research stemmed from a disgruntled Netflix user review: ’I’m a 27 year old man and my choices reflect what most men my age generally watch. But somehow I get a 96% match on a Princess Diana docuseries???’.
Although Netflix provides users with personalised content, the current algorithm creates generic match recommendations based on popularity and genres. However, it does not go into depth in identifying other factors that would in fine-tuning recommendations for each user.

DISCOVER -


Competitor analysis:

Both Spotify and TikTok are very successful platforms, that personalise content through their algorithms. Both Spotify’s ‘Discover Weekly’ and TikTok’s ‘For You Page’ have contributed to the companies’ success and increased brand engagement. This inspired me to find a solution for Netflix’ seemingly recurring issue: Users have difficulty in deciding what to watch because there is too much content to choose from.

Research:


Surveys:

To ensure that the right algorithms are chosen I surveyed 22 Netflix users (14 of which were Gen Z) to understand how they find and choose films and shows to watch.


Interviews:

I also conducted semi-structured interviews with each of the 22  Netflix users so that I could gain greater insight into how they use Netflix. A part of the task was for users to share their screens and identify what categories users tend to pick content from.


Research Conclusions:

From the user research I was able to identify the following insights:

1. Users have trouble picking what to watch quickly and easily
2.The content recommendations aren’t personalised & specific enough
3. Social-based recommendations (friend recommendations) are of high interest
4. Grouping content, e.g. based on genre, are useful

DEFINE (SYNTHESISE) -


After discovery, I created user personas based on the user interviews & other research. Here is an example of one I created.
I also created ‘How Might We’ questions, to refer back to when designing:
1. How might we make recommendations more personalised for Netflix users?
2. How might we allow users to see recommendations from friends on the platform?
3. How might we help users find content a lot quicker?

DEVELOP (IDEATION) -

As users have made it clear they would like more personalised recommendations. I believe the following key areas of the platform are important to improve: user interactions, programme information and account settings.
Proposed features to add:
• Daily & Timely Recommendations - user tastes and interests are likely to change depending on the time of day, or day itself. For instance, some users may be more interested in thrillers & horror on Friday evenings, but not on Sunday mornings
• Friend Recommendations - the user interviews clearly showed that a balance of human emotion and options are needed, in order to make decisions

Other factors to consider:
• Minimum and maximum amount of time spent watching content on specific days - some users may spend less time on weekdays than on weekends
• Watch history - often times the platform may give recommendations based on something that was watched once for 10 minutes out of the total 60
• Language preferences - many users voiced their trouble with finding content that has been dubbed over and is in different languages

Sketches & Wireframes:

I created quick sketches of the platform implementing the new features. After a few iterations I combined the sketches and created mid-fidelity wireframes to test them with users.


User Testing:

I asked 5 participants to provide feedback based on the wireframes.

Results:

1. The content recommendation at the top gets overlooked because often times it is irrelevant
2. Filter bubble problem - users raised concern about Netflix recommending the same content time and time again, completely ruling out other interests
3. User were interested in the ‘Day & Time’ feature
4. Friends recommendations - users would also like to see ratings by each friend

As a result, I added a final wireframe to solve the problem in no. 4:

DELIVER (IMPLEMENTATION) -

The user testing enabled me to make changes to my design based on the concerns raised. I used Jordan’s persona in order to create the final design:

1. Recommendation image at the top -
In order to reduce the filter bubble problem, the content recommendation image at the top is important when it comes to introducing new content. Users will have the opportunity to remove this recommendation so it does not appear again

2. Friends recommendations -
Users will be able to click the '+' button in order to see each rating given by all friends
Below is the link to the working prototype:
Thank you for reading!
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