Netflix discovery experience — a UX/UI case study
Introduction
DISCLAIMER: This project was done by me and my classmates for a school project and is not made, owned, or affiliated directly to Accedo.
What if Netflix knew what you wanted to watch, before you?
Introducing Netflix Discovery — A new discovery experience
This UX study will mainly focus on how to make the user experience more pleasant for the everyday user. We based the research process through various methods and built our visual hierarchy and UI based on results from interviewing and testing our target group.
The average amount of choices we make every single day is around 35 000. From minor decisions like which clothes to put on in the morning, or what you will eat for breakfast to more groundbreaking choices like what idea to pursue at work.
A study made by professor and author Barry Schwartz at Swarthmore College showed that too many options can lead to stress, anxiety and discontent. That’s the most important piece of fact that we would gather as we started the research phase of our journey through this case.
It turned out that, through a survey we did, that 77% had a hard time choosing a movie. The survey also showed us that 75% wanted a more personalised interface, 71% wanted to see what their friends are watching and 63% are trusting their friend’s movie recommendations.
The point is, nobody gets happier with maximum freedom of choice.
Which insights could we gather?
What prevents our users from spending less time on choosing and more time on enjoying? We decided to explore this further by doing deep interviews.
This is what we found out:
1.Firstly, one of the major interference is not finding relevant content fast enough. Meaning that we are spending a lot of time on scrolling through irrelevant content that we won’t engage with.
2. Secondly, and most interesting discovery was that we don’t consume the same content every time we log in. It differs between parameters such as, time, day of week, location, device, etc.
Which lead us to our main insight:
How can Netflix, recommend something that the user wants to see, before they even know they want to see it?
Netflix need to figure out which content is the most relevant content for the specific user. Every time he or she logs in.
We started by looking at existing data points that we could use such as, geolocation, time, weather-data, device, voice recognition etc. Netflix could recommend the best and most relevant recommendation based on these data points, giving the user a better experience.
Visual Hierarchy
The visual hierarchy is built on results based from our interviews and testing sessions.
We gave each feature in Netflix points, where we ranked the highest and most liked feature at the top, and then the second most liked feature, and so on and so fourth.
Landing
The new discovery experience will get to know you better and can then give you recommendations on the different parameters.
Based on how Facebook works, Netflix will work in the same way. Meaning that it will recommend you content that you’re most likely to engage with.
Let’s talk context. You wake up in the morning, making coffee and are ready for some mindfulness. Netflix would then recommend you and give you two options, based on what you like to watch in the morning.
In this case: Planet Earth.
When you’re done with your morning coffee, it’s time to make your way for work. When you’re sitting on the train and open Netflix, you will get this view.
The most relevant content for you at the top, which you are most likely to engage with. And as you are commuting, it will fit that timeframe.
Under the most relevant content, you will find what you’re friends has watched this week, month or year. It will only choose the friends you are integrating on e.g Facebook or Instagram.
News
Scrolling through the feed to news.
You’re most likely to be on the road and giving you a recommendation that is a 2 hour movie is nothing you would be interesting in.
So it will only give you relevant content for your timeframe based through geolocation.
If you’re commuting to work and it takes 45 minutes. You’ll get recommendations that are 40 minutes.
Continue Watching
Pick easily up where you left off. Nothing new here.
Popular
You have no inspiration. And want to save some movies for the night. Let’s hit the one and only, popular list.
My List
Everything you save will be saved to “My List”. Nothing new here.
Dinner View
As you come home from work, it’s time for dinner. Let’s open Netflix and get some inspiration.
Netflix knows that you love to be inspired by Chef’s Table and Jamie Oliver. So those are the recommendations you will get at the top. Easy to access and easy to choose. No choice overload.
Blockbuster View
When you’ve cooked dinner and it’s time to choose a movie to end the night with.
One thing we discovered on our path, which is something I think many of us is familiar with, is the infinite scrolling when choosing a movie for dinner.
The food get’s cold, before we can find anything to watch
The solution is to give the one and best recommendation of all movies in the service to avoid scrolling until the food get’s cold.
Wireframes
We wanted to get an idea of how to design the visual hierarchy, so we tested out different type of layouts with our target group. Using eye tracking and letting the users speak out loud, we managed to get a good idea of the visual hierarchy early in the process.
So, how would Netflix look like if it was driven by algorithms based on parameters such as, time, location, day of week and device? When reading the UX and UI trends that is getting more recognition in 2019. I’m convinced that services like Netflix, Spotify and other streaming services will head towards a more and more user experience-friendly interface, which is much more personalised.
Thank You!
This is my first article on Medium and I’m currently studying Digital Creative at Berghs School of Communications in Stockholm, Sweden. We have recently finished courses in UX and had the amazing opportunity to work with briefs like this. I hope you liked it. I’m Faraz. Thanks!
Team:
Andreas Iagoridcov (Digital Creative)
Faraz Ali (Digital Creative)
Linus Stevrin (Digital Creative)
