No more wasting time trying to decide what to watch, just get the popcorn and press play.
Areas of Responsibility
UX Research
Mobile App Design
Wireframing
Prototyping
UX/UI Design
Watchibrary is the largest movies and TV shows library.
Users just need to answer three simple questions and Watchibrary will recommend something to watch based on their current state of mind.
Users can create different watchlists and share them with friends so next time they meet they won’t spend hours trying to find something to watch.
They can also add friends to their profile and build a community of movie and TV lovers, creating a net of personal and quality recommendations.
About Watchibrary
Create an interactive online space where members of the art community can connect, collaborate, and engage in discussions globally.
Provide intuitive tools for managing and sharing art collections or portfolios and showcasing work to a broader audience.
Implement a broad range of filters and an intuitive search function to get to accurate results.
About Watchibrary
Pain Points
Create an interactive online space where members of the art community can connect, collaborate, and engage in discussions globally.
Provide intuitive tools for managing and sharing art collections or portfolios and showcasing work to a broader audience.
Implement a broad range of filters and an intuitive search function to get to accurate results.
The Problems
Movie and TV libraries' recommendations are based on previous watching activity, overlooking the user's current mood.
They mostly have popular movies and TV shows in their database, resulting in incomplete watchlists for users who may be looking for a broader range of content.
Their search options offer a limited selection of filters.
Lack of important information in movie/TV shows pages.
Not enough community activities in these apps.
Project Definition
The Solutions
Improve rating method to maximize results.
Add option to send personal recommendations to friends.
Develop a questionnaire centered on the user's present mood for more personalized results.
Provide essential information in each page.
User Research
Questionnaires Results
75%
Base their choice on what to watch on the genre of the movie/TV show and how it matches their current mood.
89%
The likelihood of them watching something increases when it's recommended by a friend.
78%
Find out about new movies/TV shows thanks to friends recommendations.
61%
The content on their watchlist comes from recommendations by friends.
90%
Watch movies at home rather than at the cinema.
Special Conclusions
Friends recommendations are very important for the user.
The user’s mood is a main factor in their watching choices.
Watchlist should be readily accessible within the app.
Favorite Genres
Comedy
Musical
Sci-Fi
Action
Thriller
BIO
Paul and Hannah are a couple from NY who love watching movies in their spare time. They’re always looking for something new and different from what is offered in VOD streaming services, but when it's time to choose a film - they get lost in generic and repetitive lists found in the internet: “Top 25 Comedy Movies of All Time”, “50 Movies that Every Musical Lover Has to Watch”... they want personal recommendations based on their current mood.
Frustrations
All the recommendation lists online are based on the same movies.
The titles found in those lists are mostly Hollywood movies.
Their taste in movies is very different from their friends’ tastes.
Goals
Not waste so much time figuring out what to watch.
Get personal recommendations based on their current mood.
Find out about indie and under the radar films.
Paul Bailey and Hannah Alderson
34 years old, Project Manager | 32 years old, Data Analyst
32 years old, Data Analyst
34 years old, Project Manager
User Personas
Benchmark
Spotflik
Strengths
Redirects you to the user's VOD streaming subscription to watch the movie.
Several options in the movie screen: watch trailer, tags for the movie, rating, recommendation quotes, links to the actor/director’s google search results and recommendations of similar movies.
There is a movie trivia in the movie page.
Weaknesses
There are no TV shows, only movies.
The recommendations are not based on personal preferences or friends recommendations.
There is an "Emotions Map" where movies are grouped into emotions, but the map ends up being incomprehensible and thus useless.
Very limited tags to select from so the recommendations aren’t accurate.
Cluttered user interface.
JustWatch
Strengths
Option to connect app to TV.
Option to watch the trailer in the movie/TV show page.
Clean user interface.
It shows where every actor played with links to the movies/TV shows.
Weaknesses
No division between movies and TV shows in the homepage.
Lack of several items in library.
Search filters can only be used on saved movies/TV shows.
Watcha Pedia
Strengths
Great variety of movies in database - even less popular movies can be found.
Users can write comments on the movie and share them on Twitter (but only Twitter), and mark if there’s a spoiler in the comment.
Users can follow other people’s tastes as in Instagram.
Weaknesses
No filter options when searching.
Recommendations are mostly based on trends and general rating, with few personal recommendations.
Not clear how to use the community activities feature.
The bright color palette doesn't match the usual expectations for this type of app.
Conclusions
None of these apps help the users find something to watch according to their current mood.
Recommendations are based only on previous watchings.
Recommendations
Display the results according to mood questionnaire.
Show friends' recommendations in the movie/tv show page.
Display in search results friends' rates and personal recommendations.
Architecture Information
User Scenario
Finding a Movie
High Fidelity Wireframes
Final Designed Screens
Homepage & Search Process
Search Results, Rate Process & Movie Page
Key Features
Custom Search
No more searching lists like “Top 100 Romcoms” or “Best Tarantino’s Movies” - this feature provides customized results.
Users can choose if they want the results to include movies or TV show they’ve already watched.
In only three easy steps the app finds something to watch that matches the user’s current mood.
Rate Method
Once the user marks a movie or a TV show as watched, the app automatically asks them if they want to rate it.
The part of the process where the user is asked about the mindset that resonates with the film is used to feed the search algorithm, allowing the app to refine and improve its recommendations.
Once the user finishes the process, they can recommend the movie or TV show to a friend either within the app or outside of it.
Recommend to a Friend
After rating a film/TV show or after marking it as watched, the app asks the user if they want to recommend it to a friend.
Recommendations can be sent within the app or outside of it.
Friends' recommendations and comments on the movie/tv show can be found in the respective pages in a section designated for it.
The Recommendations
If a movie was recommended by a friend, a label will be shown in the movie card with the amount of recommendations.
Friend's comments on the film/TV show can be found in the item's page.
UI Kit
Color Palette
I chose to use a Dark Mode palette since films and tv are better watched in the dark.
0F0F0F
1A1A1A
242424
737373
E5E5E5
(90%)
F7F7F7 (98%)
ED903B
ED903B (50%)
ED523B
(95%)
Dubai
Type Scale
Heading 3 (20px)
Heading 2 (22px)
Heading 1 (26px)
Body 2 (14px)
Body 1 (16px)
Button (16px)
Caption (12px)
Tag (16px)
Components
Logo
Buttons
Tags
Navigation Menu
Movie Cards
Pop-Ups
Graphic Elements
Rate Scale
Searching Animation
Illustrations
