ReelSavvy is a web application that offers mood-based movie recommendations, ensuring users find the perfect film every time.
Gametrix is a cloud-based SaaS growth platform for video games. It allows gaming companies, game developers, and all game publishers to see in-depth proprietary data and metrics of their PC, console, and Web3 games.
7 weeks
Lead & Sole: UX Researcher, Product Designer
Figma, Figjam, Miro, Adobe Illustrator, and Adobe Photoshop
Entertainment Industry
As a movie lover, I noticed a recurring problem...
I was constantly having a hard time finding good recommendations for movies.
Then it hit me. If, as a watcher, I have a hard time finding a game, doesn't that mean entertainment companies have a hard time keeping their subscribers?
The film industry is constantly evolving, and in turn, production companies are successful businesses that generate billions of dollars into the industry annually.
It's been a bittersweet transition for movie enthusiasts in the digital age. Most, if not all, of the streaming service platforms are subscription-based. We have access to a vast range of movies online, and that can be overwhelming.
This has led to a whole demographic of film lovers being left unsatisfied with the current methods of navigating the journey—actually finding movies to watch.
Me and my team saw the opportunity to create a product that helps movie lovers find movies personalized to how they feel. And with that goal in mind, the research for ReelSavvy began.
The research goals were:
The research investigates the pain points and needs of users when looking for movies on streaming service platforms.
Market research was carried out on ReelSavvy's direct competitors to optimize the user experience.
Direct competitors:
Some strengths seen from competitors that were implemented were:
Direct competitors:
Some weaknesses seen in competitors that were avoided were:
Direct competitors:
A series of in-depth interviews were then conducted to further identify pain points, frustrations, needs, and desires with existing tools in the streaming service platforms and determine how we could improve our users' experience of finding movies that relate to how they feel.
To understand, we interviewed users who:
78.50% of users mentioned frequently receiving suggestions that do not align with what they feel like watching, leading to dissatisfaction and frustration.
70% of users reported experiencing decision fatigue while attempting to select a movie, often resulting in abandoning the search completely.
70.83% of users cited issues related to poor movie recommendations from streaming services.
70% of users expressed that personalized movie recommendations were either "extremely important" or "very important" to them when searching for movies.
Initially, we assumed that providing users with movie recommendations based on their favourite actors and actresses could be a potential solution. However, after conducting research, we discovered that this preliminary solution did not address the actual pain points of users.
Users were especially frustrated with conventional streaming platforms focusing solely on watch history for movie recommendations.
78.50% of users mentioned frequently receiving suggestions that do not align with what they feel like watching, leading to dissatisfaction and frustration.
After gathering insights about ReelSavvy's users' characteristics, goals, needs, and pain points directly from the source, their needs were refined into point-of-view reflections and "how might we" questions to better understand how to solve them.
As a tech-savvy individual with limited time, I want to receive high-quality movie recommendations based on my current mood to quickly find a movie that suits how I feel and maximize my leisure time.
How might we ensure our users that they're getting the best recommendations?
Answer: Incorporate trending movies that update regularly and also give them various options to personalize recommendations.
As a digital entertainment enthusiast, I want different mood options so that I can explore movie options based on how I feel throughout the day.
How might we make sure our users have a wide range of moods to select from?
Answer: ReelSavvy has 19 moods to choose from, once a mood is selected, you are prompted with movie recommendations based on the genres linked to the mood selected.
As a movie connoisseur, I want access to detailed information about a movie so that I can decide if I want to watch it.
How might we provide information about our movies that users actually want to see?
Answer: Based on our user research, users deemed genre, reviews and ratings, plot synopsis, and trailers as the top factors that influence them to watch movies. These factors are included in the movie detail pages.
Based on research from the market analysis, these were the features that would bring the best user experience.
By prompting you with tailored questions about your current mood, ReelSavvy provides movie recommendations that resonate with your emotions.
Add and remove movies from your watchlist to watch later.
See relevant movies based on your search.
ReelSavvy offers movie recommendations based on how you're feeling and some of your favourite genres.
The main user persona emerged out of user research and interviews that would best benefit from using ReelSavvy.
This is the sitemap created for ReelSavvy's prototyping process.
Now that ReelSavvy has its target users, I drafted up a user flow.
This is the user flow created for ReelSavvy's prototyping process. It includes users' abilities to search for a movie, use our mood quiz feature, and add/remove movies from a watchlist.
These are the low-fidelity wireframes created from the user flow.
The goal of ReelSavvy's design system was consistency with uniquely crafted iconography, reusable components, a balanced colour theme, and seamless typography.
Gathering insights I got seven users to test the high-fidelity prototypes via remote video call to receive feedback.
All seven users completed all three tasks, achieving a 100% success rate. ReelSavvy received an overall usefulness score of 8.5/10, with all users expressing their willingness to use the service.
The overall feedback was positive, as users required little to no guidance on the tasks, resulting in minimal changes.
After feedback from ReelSavvy users, the following iterations were made for a better user experience.
BEFORE ITERATIONS
This is the home page before iterations.
AFTER ITERATIONS
I applied changes to the landing page’s hierarchy, and the use of whitespace was structured so that the mood feature was emphasized more. I also added titling to the movie cards for accessibility.
BEFORE ITERATIONS
This is the influencer search page before iterations.
AFTER ITERATIONS
I applied more titling to different page sections.
These are key features for ReelSavvy.
Depending on your current mood, choose from 19 options to begin your movie search.
Explore popular and trending movies updated regularly.
See the information you want to see about the movies.
As I completed ReelSavvy, these are the things I've learned.
To summarize, this MVP gave me the experience I needed to work with a cross-functional team, along with the experience that comes with working with a team. ReelSavvy was picked out of 5 other teams to present our shipped product in front of Co.Lab.
I was used to working on projects by myself, from ideation to conceptualization. It was very fun to work with a product manager on the user research side of things and with frontend and backend developers when it came to my user interface side of things. I learned more about agile practices and that consistent communication is very important, especially as a product designer.
INDUSTRY COMPLEXITIES
In an industry as complex as entertainment, a lot of success relies heavily on the satisfaction of the viewers. Movie enthusiasts are very noble and can take your film into their communities and blow them up or make sure it tanks. Because of such complexities, user research was extremely highlighted.
The next steps, with the limited time for this project, would've been a lot more user interviews with more diverse age groups, widening the lens of the community.
NEXT DESIGN
My team will not be continuing this minimum viable product, but the next flows that I would design based on our user research to implement would be the login, community features, making moods functional in our search, and curated mood movie playlists.
The login feature would enable us to save user data to their accounts, allowing for personalized homepages based on their most frequently viewed moods.
The community feature would allow users to give and receive movie recommendations from their network, a valuable future feature given that 74.52% of survey respondents feel more inclined to watch recommendations from friends and family. I would have loved to design this if given more time on this project.
.webp)
