VUE

A content tracking and social network app that unifies streaming platforms, as well as improving content suggestions. Ideal for users with access to multiple streaming services.

Problem Statement

Due to an oversaturated market of streaming services, browsing and keeping track of streaming content has become overly complex, time consuming, often inaccurate and lacks meaningful social influence.

Hypothesis

I believe that unifying streaming platforms and incorporating a social aspect will improve choices and the viewing experience.

Background

Why

As of 2021 there are over 200 streaming services worldwide. Non-exclusive content is often expiring and switching between streaming platforms making it near impossible to keep track of where to find specific shows or movies. As a result of the over saturation of streaming platforms, algorithm based content suggestions can be inaccurate as streaming services only take into account what the user has watched on that specific service rather than what the user has watched across all streaming platforms.

Business Opportunities

Not only is this valuable to people who use multiple streaming services, I believe it can be beneficial to the streaming services themselves and the film & TV industry:

Firstly, I looked at products and services that incorporated a social element into media as well as services that acted as a link to third party platforms.

I then looked up global demographics and statistics in the video streaming space to give me an idea on the general landscape and who to interview for the qualitative research phase.

Empathy Map

Concept

Touchpoints

Information Architecture

I thought it was vital for the structure to be minimal and comparable to existing products that users already know, making VUE suitable to use for a wide age demographic.

Wireframes

I prioritised tasks that implemented features I believed users would use the most such as; 'finding a show to watch based on a friends recommendation' and 'recommending a movie to a friend'. Testing common features found in similar apps such as 'adding a movie to a watchlist' were also included, to make sure the users found familiar features in obvious places.

Key Learnings

Users found the interface familiar and easy to use.

Everyone was able to fulfill scenarios that required users to utilize commonly found features, showing that the UI shared familiarity with existing apps and ensured people knew how to use features just from muscle memory and intuition.

The importance of hierarchy.

It was suggested that users want to see what their friends are watching significantly more than what they themselves are currently watching, meaning rearranging the content featured on the home screen would be required.

Adding personality.

Because users profiles consist of what that person is watching/watched, the idea to personalise user profiles was raised. Displaying a user's top ten recommended movies and shows on their profile was a valuable suggestion, as well as the option to include a personalised message when sending a recommendation since no chat functionality is included.

Colours

Typography

Work Sans was decided as the sole font family due to its modern look while also having character, and also for the versatility its many styles create. The type colours were finalised ensuring that all colours received at least an AA contrast standard.

Spacing

When designing the VUE app I used an 8pt grid system, the advantage being that it is easy for devices to render even numbers instead of odd, due to the resulting half pixel offset. Additionally, most of the common screen sizes are divisible by 8.

Buttons

Based on the scenarios in the user testing phase, I created high fidelity flows of some of the key features found in the app. Further research and testing will most likely reveal further uses and features.

Outcome

Deliverables

Designed and prototyped screens illustrating the concept of improving content recommendations through the use of a social network.

Summary

This project shows that users tend to neglect suggestions provided by algorithms, as the data they use is quantitative rather than qualitative.

People surround themselves with those who share the same tastes and interests, meaning users respond more to suggestions when it’s provided by a name and a face that they know.

Takeaways

Keeping the problem and hypothesis in mind during the duration of this project was instrumental in designing a minimal viable product. Although the app may be light on features, keeping focus on the main goal is important especially with time constraints. From there the product can be further expanded and perfected.

Future Roadmap

Future Research

The main functionality of improving streaming content recommendations was the primary focus for this phase of the project. As important as it is to nail down the core functionality, there are some concepts and features I would like to research / revise and explore for a later update.