A fun bright colored news experience for people to stay connected with their favorite stars.
Creating Starstruck
When I met my client, Bruce, he had an idea for a curated news service that targeted super fans. To validate this market, we began with some initial user interviews.
We created a feeder survey to make sure the interviewees were a part of our target audience. Then we scheduled 15 minute interviews with people asking about how they currently follow people, characteristics about the stars they follow, and other aspects of their behavior.
Following 10 user interviews, we created three personas, one empathy map, and affinity mapped to begin to narrow in on the problem we want to solve with our app.
Deep Research
Empathy Map for Our Users
We found that different user behaviors around who they follow. Some users are only passive followers.
While others are active fans that receptively searched across platforms for information regarding their star.
Affinity map made from user interviews. Comments about “Motives for Following”
Ideation
We understood from our initial research that there was an overwhelming need people felt to be connected. Some followed starts to better themselves, others because they saw themselves in the star, and others because they felt connected with the star’s story.
Our value proposition:
Starstruck is a news aggregation app for super fans to know it all about their favorite star.
We then utilized our user personas to ideate features through user stories.
We narrowed in on ideas for filtering capabilities, notification techniques, like/dislike features, and determined that our main feature is news aggregation.
Rapid Prototyping: Designing a Solution
I met with my client in person. I scribed, and together we created our first design solution. We iterated several versions just sitting at the drawing table. We took valuable notes and spoke through all of our goals with each design feature.
We were working in the form of sprints, and this wireframe completed our second week (week one being research and insights). We then threw this design quickly into a prototype version, so that we could get right to user testing our initial design.
Initial Iteration
A/B Testing and Prioritization
Through user testing, we verified design decisions and narrowed our app from five features to one essential one. We did 5 user tests for each iteration. We always tested the new iteration as an A/B campaign with the previous version.
Our method for testing was to measure the user’s ability to complete 10 tasks on a scale of 1-3 and then we paid the user with a Starbucks gift card.
Pivots
While our initial design solved every problem we heard in our original user interviews, we wanted to narrow in our app on one feature for our beta launch.
We simplified from having extensive features such as chat, events near me, deep analytics tools, and more to being very centered around the user experience of news aggregation.
We created a system of easily selecting stars in the onboarding in order to hopefully increase retention. We utilized a bright fun color, that sets the tone for our app, and reaches our target audience.
Our user tests have gone from people not understanding what the app is at all, to having an in depth understanding of it from the get go, and being able to intuitively navigate the features.
Our Team
Until this point, I have been the only active member of the team other than my client. I have now determined our full stack developer, our web scraper, and a data scientist.
In order to put this team together we had to determine our tech needs. We decided to use web scraping technology to curate the news. We have a web scraping writing live scripts that bring real time news from across platforms into our app. He also manages our AWS account
Our full stack developer is awesome. We wanted someone who had experience launching apps to the app store, and this will be her 54th app. She is driven, detail oriented, and awesome.
This project is now in the development stage, stay tuned!
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