A B2B SaaS tool that improved website phishing detection to 96% using computer vision.
This software was made to help enterprises detect phishing websites before their customers.
Semi-structured user interviews were designed to understand the end to end process an enterprise takes from detecting a phishing source to taking it down, and note the pain points.
In our ideation phase, we focused on integrating our design guidelines into the first version of our user dashboard. We started with multiple sessions of brainstorming to figure out the features we can include. Given the strict timeframe, we started adjusting the scope of our features through user testing creating multiple iterations.
The feedback from this accordion was.. pretty bad. Even though it felt like a dashboard, there were not many elements (visualizations, tables, filters) that reflected the same. The label names were confusing to 7 out of the 10 people we talked to, and the whole dashboard was "hard to maneuver".
So we took a step back and started from scratch. We dove deep into the feedback we received by re-examining some of the interview notes. We also looked at 100s of B2B dashboards fro inspiration so as to make sure we could identify some key components that were easy to understand.
This time around, we decided to narrow down to the goals of this project. Phishing detection and increasing proactive action items to fight phishing were evaluated as central themes to the design solution. After finalizing this new direction, an information architecture was made that detailed how the functions were spread across the dashboard. This helped bring the team on the same page for our design solution would entail.
At par with some of the suggestions we received, the next step includes introducing an onboarding experience for users. Having a modal pop up or a help chat box would help the users navigate the dashboard comfortably and reach its full potential.