A content sharing platform that connects people to content and creators through user defined interests NOT real world relationships and personal data.
An inspiring and fun content first experience that respects user’s data privacy and puts them in control of their content.
The creative who is concerned about their data privacy, but wants to be connected to a larger community to be inspired and entertained.
Internet platforms like YouTube use AI that deliver personalized recommendations based on thousands of data points they collect about us. Social platforms like Facebook, Twitter, YouTube, and other platforms have pushed content on their users that their algorithms tell them those users will want to see, based on the data they have about their users. Users have no control or say about the content that they are presented with, how their personal data can influence what they view, and who they are connected to.
The target user for Scrip is a person who is concerned about their data privacy, but wants to be connected to a larger community to be inspired and entertained. This larger group can be split into two subgroups of users; “The Fan” and “The Creative“. Both groups have three main goals: to connect, to inspire/be inspired, and to control their own experience.
User scenarios were used to depict how the users would use the application in a particular context and situation. For these I focused on the user’s goals, what actions were taken, what touch points and channels are involved, and what outcomes occurred. The goal was to create realistic and captivating stories that would demonstrate how the users would interact with the app and what value they receive from it.
Four main task flows were defined based on the insights made through creating user scenarios and benchmarking apps that were working and some that were not. The tasks flows were pulled from the information architecture and were refined through the wireframe process. The task flows defined are:
Through the wireframe process revisions were made to make the design more cohesive, fill gaps, and make the content more understandable. Several rounds were needed in order to account for the new insights gained from user testing and to refine the system as a whole.
User research and testing was preformed for each flow in order to determine likes, dislikes, and understandability of the content as a whole. The talk-a-loud method was used and participants were recorded as they navigated each flow.