This project can be long to read because I add all the details, so here is an overview if you're in a hurry:
As part of a selection process for a Senior Product Designer position, I was challenged to solve a problem for a digital product, showing how my design process would work in that scenario.
Making an adjustment in a Design Sprint method, I aimed to use the same approach by dividing the work into 1 evening, 1 morning, and 2 whole days. 
I used desk research, user interviewing, and survey to identify users' pain, then brainstorming and crazy eights to ideate, and finally prioritized what to prototype using an effort x impact matrix, resulting in high-fidelity mockups that were tested with a few users.
I conduct the research and design process all by myself.
Considering this was a fake product, success was defined by how easy it was to use the product during usability tests and by the correct application of design tools and frameworks. Usability tests were a success, since users had little to no difficult while executing main tasks.
Detailed project timeline
Getting context
With the rise of short videos and a growth of 140% per year on the social network TikTok, Netflix launches a new feature in a possible attempt to recover users who now spend more time on short videos than series and movies.
In March 2021, "Fast laughs" appeared, an area of short funny videos, which gained a prominent position in the mobile app and soon appeared on TVs.
About Netflix
Netflix's mission is to entertain the world. Up until march/22, they:
→ Were present in 190 countries
→ Had 222 million users
→ Had media in +30 languages
Films and series dominate the catalog, but they are already looking at another market: games.
They launched a mobile platform within the app itself in Nov/2021, with the intention of expanding the game catalog in 2022.
All that to say: Netflix is expanding. Here, this is important information to keep in account further in the design process.
About Social Media
To better understand the context, it was also necessary to dive into the types of social networks and what content is on the rise.
Here, they are divided by type of content and social media, so it was possible to understand what was more common.
Desk research
When we look across the internet, several people had already described features they wish Netflix had, but looking at the social side one of the most requested is Watch Party (similar to what Amazon does).
Others mentioned were:
. Know what your friends are watching
. Recommend movies to friends
. Create and share playlists
. Have better reviews given by users

You can check the complete feature lists in: 1, 2, 3, 4, 5 and 6

Amazon's Watch Party feature advertising

In order to understand media consumption and sharing patterns, as well as the interaction models that users are used to, a quick survey of 10 questions was conducted to identify:
. What are the most frequent interactions?
. What is the recurrence of contact with Netflix?
. What are the most common forms of sharing?
. What other platforms are they on?
You can check the whole survey and answers here (Portuguese).

Since this was a case study and by the time available, statistics were based on 19 answers.

On the other hand, to understand how Netflix and social networks are part of users' daily lives and what they freely associate with the two contexts, interviews were carried out to identify:
. When do these platforms appear
. How they consume movies and series
. What is the first association with Netflix and social networks
. What is the relationship with the social network
Check the full interview script and notes here (Portuguese).
To start working on the solution, it was time to summarize everything that was learned and discovered.
For that, it was made a brainstorm focused on two things:
. Keywords discovered during the process
. Possible solutions that could be used to solve all the pain points

On the left, are all the words that I associated with the project so far. On the right, are all feature-related ideas.

Effort x Impact Matrix
To decide which functionality to proceed with, the impact and effort of each proposal were evaluated.
Here, were considered aspects such as:
. Creation of something completely new or not
. Consumption of existing functions and data
. Correlation and/or change between possible databases within the Netflix system 
. If they were noticed as an idea in the discovery stage
Technical solutions were evaluated assuming information about Netflix's architecture that was not available, in this case, personal previous experience with tech teams was taken into account.
Finally, the feature chosen was the one that brought the most impact and direct interaction within the app, even if its effort was a little higher.
The idea is that the interaction of extracting and sharing GIFs can be used while the user watches, and create a connection with the action of "telling" to another person what was seen.
During this exercise, a few patterns naturally appeared:
. Cut a title scene
. Preview previously cropped scenes
. Screen size ratio
It was possible to notice a relationship between the steps and the beginning of a flow.
Then, it was time to start structuring what path the user would take to make and share those GIFs. Here, are some attention points arrived:
. GIFs would need to have a 15-second limit (so its easier to share on social media)
. Auto spoiler warning should be added to custom material
. The team would need to evaluate copyrights from other studios
. Automatic still captures may need AI to filter out spoiler and/or sensitive content
. GIFs could be defined by editing and/or captioning process
Ideally, at this stage, adequate interface components would be used - preferably from Hawkins, the platform's official design system - but no material minimally similar to this was found.
The decision, then, to keep the project agile, was to work with screenshots, drawing over what was 100% new within the application.
To bring more realism during the prototype's use, GIFs were applied to simulate interaction with videos.
Since the end of the flow happens in other applications, they have also been included to analyze user interaction up to that point.
Check it out the whole prototype and features in the video below:
Usability test
A moderated testing approach was chosen to ensure that the results were more targeted toward the problem at hand. The testing was conducted in person due to the difficulty of adjusting the prototype, which was composed of prints so it would work better in a phone with the same ratio. Besides, the short recruitment time also influenced the decision to conduct in-person testing with individuals I already knew.
The scenarios simulated that the user was a fan of the series Stranger Things, and should do these tasks:
. Watch an episode
. Share the current scene
. Share a scene from a different moment
. Find general scenes from the series
. Share a general scene.
The whole test planning and notes are in Portuguese, here.
Three individuals did the test in two different locations. Each person had a different frequency of using the app, and that was chosen to understand if familiarity would influence their understanding of the feature. The profiles were:
. Active - they use the app every week
. Occasional - they use the app a few times per year
. Inactive - opened the app only once in their lifetime
It's important to note that all of them were familiar with using Netflix in the browser or in a TV.
Overall, the functionality was easy to understand, especially the navigability within the video player.
Some users believe they would indeed use the tool in their daily lives, whether to convince someone to watch a series or simply to share scenes.
The frequency of usage did influence the understanding, not only of the flow but also of the concept of "Scenes" itself: the more used to the app, the more easy it was to understand.
Improvement points
The presence of the current "Collections" menu that Netflix already has interfered with the recognition of "Scenes," which requires repositioning and/or title changes. The interaction for scene cropping, its duration, and its location was not entirely clear. Introducing a tool walkthrough or adding help text could address this issue. A more in-depth analysis of the target audience is needed to align the concept of the scenes itself better.
Tracking data
Each shared scene would have its own link, in that way it's possible to track where and how often it is shared. Would be important to keep track of:
. Total number and type of shared scenes (if custom or stock scene) - looking closely, this could indicate adoption as well as using pattern
. Plays and registrations from the link - to see if this could be an attraction strategy for Netflix
. Comparison of sharing from the title versus the player - to understand what's the most natural path for a user
Next steps
So far, this is a suitable solution for the proposed problem within the available time. However, if this were to be built as a real product, several steps would need to be revised, such as:
Also, in a real product, it would be more beneficial to implement it over the course of several stages, where each one of them should answer to specific questions and hypotheses.
Kew takeaways
In this project, the main thing that I learned was that it is possible to do a deep research phase, even with fewer resources. Even though the user samples weren't enough to justify a product decision, it was enough to bring up insights and clarify which path would be best, all of that in a really short amount of time. This mindset is something I've been applying in everything that I work on since then.

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