A UX Case Study on Navigation, AI Discoverability, and Financial Decision-Making

Project Overview

Financial Aura is an AI-powered investment research platform designed to help users analyze portfolios, evaluate stocks, and receive personalized financial insights through conversational AI tools.

Before launch, the Financial Aura team wanted to better understand how real users interacted with the platform. While the product offered advanced AI-driven functionality and a visually polished interface, there were concerns about whether users would fully understand the platform’s purpose, successfully navigate its features, and trust the insights being presented.

As part of a UX research team, I helped conduct a moderated usability study to evaluate the platform’s usability, information hierarchy, AI discoverability, and onboarding clarity.

This case study documents our research process, findings, and recommendations for what Financial Aura can implement in the future. This case study reflects on what I learned and contributed throughout the project.

The Challenge

Financial Aura sits at the intersection of fintech and AI. Unlike traditional investment apps, the platform focuses on research and portfolio analysis over stock trading itself.

The knowledge of that distinction became the central challenge of the project.

In addition, the client wanted to answer several important questions from user insights:

Would users understand what Financial Aura actually does?
Could users easily navigate the platform’s core workflows?
Would users trust and understand AI-generated investment insights?
Were the platform’s most valuable features easy to discover?
Could intermediate investors interpret the financial metrics confidently?

The platform already looked polished and modern, but our purpose was to test its usability. Our goal was to evaluate whether the experience actually aligned with user expectations and mental models.

My Role

I worked as a UX Researcher within a collaborative usability testing team. Michael Persson, Zahra Hakani, Vincent Allport and I sectioned responsibilities equitably based on our personal strengths.

My responsibilities included:

Helping design the research methodology
Writing and refining usability tasks
Participating in pilot testing
Moderating live testing sessions
Observing user behavior and documenting friction points
Synthesizing qualitative findings
Contributing to the final presentation and recommendations

One of the most valuable parts of this role was seeing participants in real time. Watching users hesitate, backtrack, or verbalize confusion revealed usability issues that would have been difficult to identify through analytics alone (or in unmoderated user sessions).

Building the Research Plan

Starting with the Client Kickoff

Our project began with a kickoff discussion focused on understanding the product, the business goals, and the target audience.

During this meeting, we learned that Financial Aura wanted to position itself as an AI-first financial research platform rather than a brokerage app. However, the team suspected users might confuse the platform with traditional trading applications because of its portfolio-management features and investment terminology.

From the kickoff meeting, we made sure to establish four, measurable research goals:

Evaluate navigation clarity
Test discoverability of AI-powered features
Assess comprehension of financial insights
Identify onboarding and expectation gaps

Designing the Study

Why We Chose Moderated Testing

We selected moderated remote usability testing because we wanted to capture more than task completion rates, and as I mentioned before, there is a lot of value in seeing users move through tasks in real time. Each session was conducted live over Zoom with a moderator and note-taker present.

We wanted to understand:

Why users made certain decisions
Where hesitation occurred
How users emotionally reacted to the interface
What users expected versus what they experienced

Participants completed five core tasks:

Locate portfolio holdings
Add a stock to holdings
Use Full Portfolio Analysis
Use the Screener tool
Interact with AI personas

Pilot Testing & Refining the Experience

Before launching official sessions, our team conducted pilot tests to validate the study structure and task wording. During pilot testing, we discovered that some task instructions unintentionally guided participants toward the correct answer. We revised the language to make the tasks feel more natural and unbiased.

After this informative session, we made sure to adjust:

Session pacing
Financial terminology
Moderator intervention guidelines
Follow-up questioning techniques

Pilot testing improved the consistency of the study and helped us create a smoother experience for participants.

Meeting the Users

We recruited 10 participants between the ages of 18 and 35 who reflected Financial Aura’s target demographic: tech-savvy users with varying levels of investing experience.

Participants included:

Intermediate investors
Experienced traders
Users familiar with Robinhood and Webull
Professionals across technology, finance, healthcare, and sales industries

Most participants were located in major metropolitan areas such as Atlanta, New York City, Dallas, and Chicago.

This participant mix gave us insight into how both casual and experienced investors interpreted the platform.

What We Discovered

Insight #1 — Users Mistook Financial Aura for a Brokerage

The most consistent usability issue emerged within the first few minutes of testing.

Many users assumed Financial Aura was a trading platform rather than a research and analysis tool.

Because users expected brokerage functionality, they developed incorrect assumptions about how the platform should behave.

This created confusion around:

Portfolio management
Search behavior
Investment actions
AI-generated recommendations

Why It Matters

Users form mental models almost immediately. When a product’s purpose is unclear, even well-designed features can feel confusing.

Recommendation

We recommended stronger onboarding and homepage messaging that clearly positions Financial Aura as an AI-powered investment research platform.

Insight #2 — The AI Features Created the Most Excitement

The platform’s strongest differentiator was also its most successful feature set.

Participants consistently praised:

AI-generated portfolio summaries
“What’s Good” insights
“Watch Out For” risk indicators
Personalized AI personas

Several users described the platform as feeling more intelligent and personalized than traditional investing apps.

One participant compared it to “having a financial analyst built directly into the platform.”

Recommendation

Because the AI tools generated such strong engagement, we recommended increasing their visibility throughout the platform experience rather than hiding them within secondary navigation layers (see bottom right text icon).

Insight #3 — Important Information Was Buried

While users appreciated the analysis tools, they often struggled with the platform’s information hierarchy.

“The Bottom Line” summary repeatedly emerged as the most valuable section — yet it appeared at the bottom of the analysis pages.

Users wanted immediate visibility into:

Buy/Sell/Hold recommendations
Risk evaluations
Portfolio summaries
Key investment takeaways

Recommendation

We suggested restructuring the analysis pages so that actionable insights appear first, followed by supporting metrics and detailed breakdowns (more visualizations, less textual explaining).

Insight #4 — Financial Metrics Needed More Context

Many users struggled to interpret scores such as:

Risk Factor
Diversity Rating
Portfolio Health indicators

For example, participants saw “Risk Factor 60” but had no context for whether the number represented high or low risk.

Recommendation

We recommended adding:

Tooltips
Benchmark scales
Color-coded indicators
Plain-language explanations

These additions would improve accessibility without reducing analytical depth.

Final Deliverables

At the conclusion of the project, our team delivered:

A formal usability report
A stakeholder presentation
Prioritized design recommendations

Rather than simply listing usability problems, we focused on translating user behavior into actionable product improvements.

Our recommendations centered on:

Clarifying onboarding
Improving AI discoverability
Simplifying navigation
Reorganizing information hierarchy
Adding contextual guidance for financial metrics

Reflection & Takeaways

This project reinforced how critical user testing is for validating assumptions before launch.

Financial Aura already had a polished visual design and impressive AI capabilities, but observing real users revealed several disconnects between the product’s intentions and users’ expectations.

The experience also taught me several important UX research lessons:

Users form product assumptions almost immediately
Strong visual design does not guarantee usability
AI features only succeed if users can easily discover and understand them
Information hierarchy strongly impacts perceived usefulness
Moderated testing uncovers emotional reactions and behaviors that analytics alone cannot capture

Most importantly, I learned how usability testing transforms abstract assumptions into concrete, evidence-based design decisions.

If I continued working on this project, I would next explore:

Iterative testing on revised prototypes
Mobile responsiveness studies
First-time onboarding optimization
Comparative testing against brokerage competitors

Ultimately, this project demonstrated how thoughtful UX research can help shape the entire way users understand and trust a product.