REBECCA TORVIK
UX/UI Bootcamp
The George Washington University
Mobile App Invention
Case Study: TV Show App
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Project Description
Team
Rebecca and Liz
Duration
3 weeks (2020)
Tools Used
Sketch, InVision, AdobeXD, Draw.io
Summary
We were tasked to find a solution to a modern-day problem and created an app that offers users a new way to find TV shows. The App, Mestow, is a TV recommendation app that provides users with cross-platform TV show suggestions tailored to the user's preferences. It also adds a social component to receive recommendations based on what their friends have watched.
Our Roles
As a team, we collaborated during the research, definition, and ideation phases. However, prototypes were designed and tested individually.
The Process
Research
Definition
Ideation
Prototyping
Testing
UX Interviews
We conducted interviews with 6 participants. Notable quotes included:
"There are so many choices and new ones coming out every day."
With limited time and so much content (old and new), they struggle to find a new show to genuinely enjoy.
"I rarely go into it completely blind."
They use multiple resources to find a new show. Top methods included: Google searches, friend/family recommendations, online reviews, and social media.
"Online reviewers are all just so different."
Their trust in online reviews is correlated to the volume of reviews and if there is consistency in perspective.
"I get this empty feeling when I watch a series finale."
They get emotionally invested and attached to shows that truly resonate with them. After a beloved show has ended, they hope to find a replacement that is just as fulfilling.
UX Survey
We sent out a survey and recieved 28 responses. Our data showed that:
50%
Enjoy family/friend show recommendations most
of the time
64.2%
Are somewhat too adventurous when trying new shows
57.1%
Sometimes to frequently start watching a show and not liking it.
64.3%
Found genre categories within streaming platforms very helpful
Affinity Diagram
To better organize our interview and survey data, we grouped similar responses in an affinity diagram.
User Persona
Adam Jenkins
Age: 37
Status: Married with 2 kids
Profession: Art Teacher
Adam loves how much quality programming exists today compared to even five years ago. He just wishes there was a more efficient way to find shows that match his specific taste.
KNOWN HABITS
“Overhearing” students talk about the “cool” music they’re listening too, then go listen to it himself.
Getting into crazy debates with friends, trying to outwit them on his knowledge of shows and music he loves.
PAIN POINTS
New things must readily make an impact. There’s no patience waiting for things to grow on him.
Family-friendly activities can honestly leave him wanting some adult-time.
Research
Problem Statement
Our research showed that TV show viewers find it both challenging and time-consuming to select new programs. There are too many options to choose from across the various streaming platforms and researching multiple review sites is time-consuming. Word-of-mouth recommendations are high in volume and difficult to remember when you need them. How might we streamline the process for TV show viewers, so they can access relevant recommendations tailored to their viewing habits, all in one place?
Value Proposition
Mestow was developed to unite the resources that TV show viewers utilize to efficiently find relevant recommendations. Mestow sets itself apart from competitors by creating a more accurate approach to matching shows with viewers, a personal “watch list”. The social feature not only helps users get recommendations from their friends, but could serve a marketing tool that will help the business attract new users.
Storyboard
Definition
"I Like, I Wish, What If" Diagram
To begin the process of prioritizing the app's features, we organized our user data from the affinity diagram into "I Like, I Wish, What If" statements and had our classmates dot vote on their favorites.
Feature Prioritization Matrix
To help narrow down features and give our product more focus, we created a feature prioritization matrix from our "I like, I wish, What If" diagram.
HIGH PRIORITY, HIGH IMPACT FEATURES
-
"I like recommendations from multiple sources (friends, family, online reviews)"
-
"I like the trending content on Netflix to see what is new"
-
"What if you could easily find similar shows"
From these statements, we decided our app should focus on providing personalized recommendations that are based on users' preferences from shows they've seen. It will have a social component that allows users to follow friends and family, sharing their favorite shows. Users can also see what shows are trending and are most popular.
Competitive Analysis
Stardust
FEATURES
-
Add shows to a watch list
-
Rate shows/movies
-
Film reactions
-
Follow/connect with influencers
USABILITY
-
TV & movies merged together
-
Home screen cluttered
-
Detailed & clean follower section
Yidio
FEATURES
-
Recommendations based on what you add to watch list
-
All streaming services aggregated
-
IMDB and Metascore reviews
-
Watch show/movie within app
USABILITY
-
Clean layout
-
Efficient, detailed filtering
-
Not very engaging or personalized
Ideation
User Flow
This user flow depicts the onboarding experience for a new user with the end goal of having a user add a show to their watchlist.
Sketches
Low-Fidelity Prototype
Prototyping
Goals/Objectives
-
New user steps are straightforward in purpose and usability
-
User successfully saves a show to the watch list
-
User enjoys the overall experience
Test Results
Thought they were at the home screen
Having title below header is confusing
Did not know you can skip presets
Interaction when closing is inconsistent
Design is inconsistent with an opened show from the homepage
Confusing lang, different design but content opens the same
Not clear this is the homepage
No follow button on home
Guerrilla User Testing
The video of the prototype depicts how a user would sign up for the app by choosing their favorite genres and rating shows they've seen. Guerrilla testing of our LoFi wireframes revealed that users could not easily differentiate between the signup process and the homepage. The final prototype solves this issues by adding distinct onboarding screens and a preloader. Moving "Rate 20 Shows" to the header also help to divert homepage confusion.
After their recommendations are calculated, users can view a personalized feed where they can filter shows based on platform availability and different genres. For both these filters, users have the ability to select one or multiple genres and platforms. Users experience on current streaming platforms, which solves a pain point users experience on current streaming platforms.
Since our user research found that many users get recommendations from both online and word-of-mouth, a social component was an important feature to include. Users can follow their friends through Facebook or their contacts. They are able to see a custom feed of shows their friends have watched. On individual shows, users can see which friends watched the show and sort the reviews by their friend's comments.
A feed for trending and popular shows was important to have because our user research suggested that is a popular show searching method.
Overall, the app gives users a unique experience by offering cross-platform, customized, and peer created show recommendations.
High-Fidelity Prototype
Final Thoughts
Next Steps
-
Test new features
-
Create new user flow
-
Design screens for other menu items
-
Add a notification menu option
What I Learned
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It is possible to revolutionize the way we search for new shows
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With so many viewing options out there and growing exponentially, the demand for ease of TV searching will only continue to increase.
Other Work
