Project Challenges
It was the first project I conducted on my own during the General Assembly UX Research course.
I love movies, and who doesn't? The modern library of movies and TV shows is bursting at the seams, and video platforms offer only a tiny fraction of that. We all have particular preferences, and streaming services provide expensive and complex algorithms to fit our needs. However, the platform is limited by its catalog and can't scan the user's mood at the moment of search.
I came up with the idea of a platform where everyone can create their unique library of movies, divide them into categories, and share them with others. But just an idea is insufficient, so I need to validate it.
Research
Because this is a personal project, I had some limitations on resources, so my research is based more on qualitative data than quantitative.
I explored the landscape and trends that drive people's choice of movies and TV shows. I studied our direct competitors in detail, including their strengths and weaknesses. I interviewed potential users to empathize with their needs, pains, behaviors, motivations, and influences. Then, I narrowed the scope using affinity mapping to filter the insights.
Competitor analysis
I've looked at products with a similar idea, such as “Listy”, “Must”, and “ Letterboxd” The “Listy” app is a great library creator. Still, there is no user interaction in the app, and the search process is frustrating because there are no suggestions, unlike the "Must" and "Letterboxd" apps, which give you a vast library of recommendations and readymade lists.
The "Must" app has a simple, modern design but does not allow users to create multiple unique lists and share them with friends.
The "Letterboxd" is closest to the idea. However, it still has a frustrating movie search for lists to add, no personalized suggestions, and a confusing interface.
User Personas
I've created three user personas.
Mr. Otto loves to create nerdy lists with many added preferences and doesn't know where to keep them. He is frustrated by the limited video platform catalog and wants to see movie ratings. He trusts his friends with movie suggestions and likes to discuss them. He dreams about the app with perfect recommendations based on his mood.
Mrs. Puchi Has a massive movie catalog in her head that she constantly rewatches but doesn't want to share. She doesn't like unexpected endings. She likes to experience exact emotions at the moment when she needs them. Her choice always depends on her mood, and she categorizes movies by the atmosphere.
Mr. Zeegy has a quirky taste in movies. He is busy and prefers short films or series, so he has specific time frame preferences. He usually depends on friends' suggestions and likes to discuss movies with friends.
The main difference between Mr. Zeegy and others is that he is more of an explorer, not a creator. But he would like to share movies and see what friends are watching. Also, he has the most peculiar taste and Preferences.
The differences between Mrs. Puchi and Mr. Otto are:
Mr. Otto creates lists and wants to share them with other users and friends; he trusts his friends' choices and ratings.
When Mrs. Puchi makes these lists for herself and a tiny circle of close people, she doesn't depend on ratings or friends' tastes. But she wants to see readymade thematic lists and lists of critics.
Affinity Mapping
Sorting all of the points into 12 categories helps to identify a list of features for the personas. The colors represent the type of persona.
The most exciting categories for me were Unique Preferences and Platforms. These categories contain many valuable insights that help identify features. For example, short-duration series that do not connect with a plot that does not bind and does not require much time.
Journey Map
I've conducted a journey map with a user that fits Mr. Otto's persona to understand which phase of his journey could be improved. I asked Mr. Otto to find a movie he would like to watch and why. Mr. Otto searched through 3 video platforms: Netflix, Peacock, and Amazon Prime Video.
The main pain points:
The search process is exhausting because of the limited catalog content, the unsorted catalog, confusion about which content is free, and the missing rating.
Ideate
And now is the time to step into the "Define" process.
The next step was to create features using insights from affinity mapping. Next, feature prioritization using a 2by2 matrix.
Again, I didn't have a developers team to help me on this one, and I can't be sure about every feature's impact. But I've tried to gather as much information as Google and my experience can provide.
I decided to create a site map, which would be a starting point for my design idea. This helped me avoid skipping any step. Feature prioritization helped me pick two most important features that stand out from competitors and create value for the product: creating a list of movies and sharing them with friends. So, I created a user flow of these features before jumping into sketching.
Low Fidelity Wireframes
High fidelity prototype
