Introduction
RockIQ is a stock aggregator using machine learning to provide intelligent stock picks for the everyday investor. Their mission is to shorten the learning curve and provide a clear financial analysis to make smart investments easier. Since they are an early-stage startup, the request was to build an MVP for their dashboard that highlights sentiment score and market trends to inform them of the best which is used to raise capital and customer development.
Process
Discovery
The team worked closely with the founders to understand their business model and key differentiators.
Needs Finding
The team set out to validate the following:
- If the sentimental value is important to users in making buying stock decisions?
- What data should be on the dashboard?
- What are the triggers for investment?
Competitive Analysis
We audited both direct and indirect competitive landscape, e.g. Robinhood, Yahoo finance wanted to know the data shown to users, strengths and weakness and where we stand and how we can improve our product.
User Interviews
We interviewed 12 users and using affinity mapping key insights were derived:
- The biggest takeaway was that RockIQ's initial unique value prop did not match users goals and needs
- Users were overwhelmed with the plethora of information
- Users rely on multiple resources to validate their understanding
- Some didn't know where to begin
- Identified more than one user type -- beginners and Intermediate
Ideation
The team used crazy 8's to go broad and eventually narrow to few best options based on dot voting. Based on the top 5 concepts, the team diverged to sketch low-fi dashboard concepts. From here the team built upon one another's widget concepts, to come up with a final design.
Prototype + Test
These provocations were tested with additional users and iterated further and later shared with the same and the findings with the founders.
Hi-fi
After multiple rounds of validation, and iterations final design was wrapped.