Data; we all talk about it – and continuously generate it – but it turns out hardly any of us knows what to do with it. Even after a deep-dive into the space, I’m honestly still scratching my head. Monicat Data is on a mission to help creatives leverage their data and came to us to help prototype a SaaS that would automate and aggregate data generated by creative economists and entrepreneurs. As an entrepreneur, I have access to data from Google Analytics, Instagram, Pinterest, QuickBooks, and Squarespace Analytics but sifting through these data sets takes time, and without the knowledge to leverage insights the data does not tell me much. Our team interviewed a number of other creatives that had the same story.
RESEARCH AND METHODS
Monicat provided us with some preliminary wireframes, which I converted to a low-fidelity prototype using Sketch and InVision. Our group utilized Interviews, Usability Testing, Comparative Research, Kano Analysis, and Interactive Prototyping in our inquiry process.
We meet multiple times a day for three weeks during this project. Sydney, Jon, and Brandon lead our research efforts and Rebecca and I were the visual team. I primarily generated testing assets such as prototypes, a design system/library, and production elements.
After a round of informal interviews and testing some low-fidelity wireframes we quickly decided that Monicat Data would benefit from a landing page that would help creatives understand the value proposition of insights gleaned from data.
Rebecca crafted this lovely landing page, which helped our participants talk about the potential value of data for creatives.
The landing page piqued some interest for our participants. Some got excited about data visualization, where some were excited about the dashboard – and its tool set – as well as having their data all in one place.
After the first round of research and interviews we worked up a guiding set of values:
Make data approachable.
Creativity meets tech.
Meet users where they are.
Provide help along the way.
Show what is possible.
Build trust with users.
These values would inform our subsequent work. We then moved to raise the fidelity of the dashboard of the actual SaaS and a create clickable InVision prototypes which would allow users to experience some of our features that were received the most favorably in our Kano Analysis, like the Project Builder, Goals, and Recipes (working title).
After presenting our work we delivered a set of InVision prototypes, Sketch files, and a folder of research to Monicat. Our team is excited to see how Monicat Data develops their SaaS platform going forward.