Do you ever spend hours scrolling through streaming apps looking for something to watch only to be disappointed and settle?
What if there was another way?
Movieland
Find a movie to watch within minutes
With the abundance of content available across various streaming apps, many users find themselves overwhelmed. They often spend a significant amount of time navigating through each app, only to either settle for trending content or watch something they’ve already seen. Despite the wealth of great content out there, it often gets overshadowed by the sheer volume of lesser-quality options.
Moreover, the recommendations offered by these streaming platforms tend to miss the mark. Services like Netflix often base their suggestions solely on a user’s viewing history. This approach fails to truly understand individual preferences and likes, leaving users feeling like they’re at the mercy of the platform’s algorithm rather than receiving personalized recommendations.
But what if there was something that knew what I should watch without all that searching hassle?
How Do People Watch Movies?
To understand movie-watching habits, I asked individuals aged 22 to 55, representing a diverse mix of genders, races, and socioeconomic statuses, about how they watch their movies.
Here are some key insights:
Film Choice: Users typically select films based on trends, recommendations, social media influence, personal interests, mood, favorite actors or directors, preferred genres, and availability of free content.
Watching Habits: Movies are commonly watched in the evening or on weekends. Action and sci-fi films are usually enjoyed in theaters, while drama, explicit content, and documentaries are watched at home. Binge-watching, often done alone or with family, predominantly occurs on weekends.
Time Spent Searching: Users spend anywhere from less than 10 minutes to up to an hour finding something to watch.
Streaming Services: Popular platforms include Netflix, Crave, Amazon Prime, Disney+, CBC Gem, and YouTube. The reasons for choosing these apps include their categorization features, availability of preferred and Canadian content, gifts with purchases, shared access with friends or family, and ease of use.
Frustrations: Common issues include excessive time spent searching for movies, interruptions to find new content, lengthy trailer viewing, confusion between paid and free content, and irrelevant trending suggestions.
Wishes: Users desire better recommendations, more personalized suggestions based on favorite artists, improved genre filtering, easier navigation based on personalized parameters, and clearer separation between free and paid content. They also hope for better algorithms, options to follow users with similar tastes, and reduced irrelevant content on screens.
USER PERSONAS
There are several ways people decide what to watch. Some base their choices on their mood for the day, while others rely on data, such as ratings or their favorite actor or director. Many use a combination of these factors. However, there's growing frustration with the abundance of low-quality content being promoted first, as well as the overwhelming amount of unregulated content available. Users also wish the personalization algorithms were more intuitive and that there was clearer distinction between what’s included in the subscription and what requires an additional payment.
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As I had these date, I was able to develop three different user personas based on similar ways people like to or have to watch film or TV: The Rational Viewer, The Emotional Viewer and The Inbetweener.
Let's take a closer look.
The Rational Viewer
This user values thoughtful, data-driven film recommendations and prefers movies that are well-crafted, featuring complex characters, interesting storylines, and strong performances. They enjoy comedy, cult films, and stories with depth, often leaning toward movies with notable casts and directors. Frustrated by the gap between trending films and actual quality, they feel that popular recommendations often overshadow their true preferences. The user seeks suggestions that align with their specific tastes, such as films starring their favorite actors, directed by favored filmmakers, or featuring similar musical compositions to those they enjoy. They want recommendations grounded in reliable sources, rather than just what’s trending.
The Emotional Viewer
The
Inbetweener
This user is highly engaged with the film world and takes a discerning, mixed approach to selecting what to watch. Their favorite genres include surrealist and stylized films, like those of Tim Burton, Guillermo Del Toro, and Wes Anderson, as well as stop-motion, animation, action-adventure, and romantic comedies. They are frustrated by the overwhelming amount of content spread across numerous streaming platforms, feeling that the sheer quantity often comes at the expense of quality. The user wishes for a smarter, more effective algorithm that helps uncover unique and interesting films they might otherwise miss, without having to sift through endless irrelevant titles. They are keen to discover hidden gems but feel that the current system doesn't do enough to surface these types of films, often leaving them buried under a sea of less intriguing options.
Inspiration
While doing user interviews and developing personas, I kept thinking about three things:
IMDb, emotions wheel and Letterboxd.
IMDb
IMDb, or the Internet Movie Database,
is a website that provides information about movies, TV shows, and other media. IMDb includes information about cast and crew, release dates, box office information, plot summaries, trailers, biographies, trivia, ratings, and reviews. It’s perfect for those who need the hard data in regards to the films.
Emotion Wheel
An emotion wheel is a visual tool that helps people identify and label their emotions. It organizes emotions into categories, with primary emotions in the center and more nuanced emotions on the outside. The range of the emotions potentially can be matched to the movies and the feelings of the user.
Letterboxd
Letterboxd is a social media platform and app for tracking movies, writing reviews, and creating lists. While it is similar to IMDb, it also provides a social media feature, so that way you can see what your friends are watching.
So, what if I could combine all those things within the app, Movieland, the combination of database, emotions matching and social media?
Movieland will try to understand who the user is as a person, their likes and dislikes, their daily mood and current activity. That way recommendations become more tailored, quality prevails over the quantity and the user can learn about variety of films that they would normally never hear of. Additionally, Movieland will aim to consolidate all the film library within one app and direct the user to the content that they have already available on the subscribed streaming services to save time.
Journey Mapping
I wanted to understand what a journey could look like for each of these personas once they started using app. Let's map out those.
The Rational Viewer
The rational viewer likes data. When searching for a movie, it is important to provide sorting and filtering options, so the viewer can sift through bunch of data to find exactly the parameters they need.
The Emotional Viewer
The emotional viewer needs more intuitive approach, specifically, related to their mood. That's where the Emotions Wheel comes in handy and Daily Mood tool can be utilized. What would be very useful for this viewer is to determine what they feel on that day and match that specifically to the the movie that would compliment their emotional state.
The Inbetweener
The inbetweener knows what's happening in the world of cinema and they are pretty good to find matching film to their emotional state, so what they need is access to some unique and interesting movies that they potentially never heard of. That's why I chose to create Daily Suggestion tool that can suggest new films for the user. In addition, by adding friends on this platform, the user can explore what their friends are watching for better suggestions.
Task Flows
The following Task Flows have been developed to reflect the journey maps.
SORTING TASK
FLOW
DAILY MOOD
TASK FLOW
DAILY SUGGESTION
AND PROFILE
TASK FLOW
Movieland Sketches
The sketch below shows some preliminary ideas on how to transform the user journeys and task flows into viable screens.
Lo-Fi Wireframes
Here are some lo-fi screens developed based on the sketches.
 Home |  Search |  Film Info |
|---|---|---|
 Profile Questions |  Daily Suggestion |  Daily Mood 2 |
 Daily Mood 1 |
High Fidelity Prototype
And here is the video of the prototype.
The complete prototype can be found here.