AI-Powered Prediction Platform
Refined the fintech dashboard and subscription process, ensuring accessibility for beginners and depth for professionals.
Overview
Indicator Lab is an AI-powered fintech product for analysts to make predictions. To expand the market, the startup developed new features targeting amateur investors. However, the new pages retained patterns for professional users, causing challenges.
Through in-depth research and multiple iterations with cross-functional teams, I successfully delivered the desired outcomes, securing funding and attracting many retail investors.
Responsibility
Product design Designer - Low to high-fi prototype, Competitor analysis, Usability test, Data visualization
Timeline
July - Oct, 2022 4 months
Collaboration
Designers, 1 PM, 2 Engineers, Finance
Impact
+300% ↑
Achieved an increase in user growth
+ $$$↑
Succeed funding
-70% ↓
Reduce user first time exploration time
Overview
Business focus shift
Indicator Lab, originally for professional analysts, now caters to amateur investors as well, offering profit insights to analysts and stock predictions to amateurs.
Problem
Over 70% of users leave after their first visit, reflecting trust and comprehension issues that impact engagement and commercial success.
Goal and Metric
Our goal is to reduce cognitive load, ensuring clarity and trust for both analysts and investors. Success will be measured by retention, engagement, and funding milestones.
design Highlight
Before
The platform prioritized creators, forcing all users to navigate the "create" section to access models, adding unnecessary complexity for investors.
Final design
The redesign separated model creation for analysts and provided investors with a simple, intuitive workflow for exploring, subscribing to, and using models.
Agile Research
Two Persona
We conducted 12 interviews to identify 2 user groups and their challenges, amateur investors (Kevin) and professional investors (Alex).
Define problems
Why over 70% of users abandoned the platform after their first visit?
Through tests, we identified three key pain points:
How might we
Create clear user flows for both user groups while building trust in the product and ensuring a clear understanding of the forecast results?
Competitive Analysis for Model Exploration & Usability
I studied competitors to uncover strategies for navigation, trust-building, and actionable insights. These findings guided design enhancements to improve user flows, establish credibility, and link data to decision-making.
Drawing from these insights, I proposed solutions:
Two rounds of tests
Solution
Solution 1:
Simplify Navigation and Clarify Product Structure for All Users
By separating the creator and investor views, I create a seamless subscription-to-use flow. This reduced information overload and streamlined user navigation, improving accessibility and clarity.
Challenge 1: Balancing Layout for Diverse Users
When designing model operations, I faced challenges in balancing information density and workflow efficiency. To address these, I adopted a Dynamic Layout solution, meeting the needs of amateur users for simplicity, advanced users for complex comparisons, and business goals.
Idea 1: Make Charts and Model Panel Collapsible
Direct suggestions is easy to access
Limited space for large charts.
Idea 3: Three-Section Dynamic Layout
Chosen
Enhances comparisons for advanced users.
Maintained clear space for results.
Challenge 2: Reduce Information Density to Enhance Space Efficiency
User testing showed users prioritized trends over detailed values. I redesigned charts by reducing detailed information and emphasizing trend shapes, improving readability and reducing visual clutter.
Solution 2:
Build Credibility Through Clear and Trusted Data for All Users
User interviews showed that credibility is critical. I redesigned the model cards to highlight usage, accuracy, and historical performance, building user trust and confidence.
Final flow
Model Context Display
Key metrics like usage, accuracy, and adoption rates are displayed clearly in the subscription flow for better decision-making.
Solution 3:
Enhance Data Interaction and Connect Data to Action
To improve usability, I introduced features like dynamic feedback, synchronized colors, and tooltips to clarify chart relationships and provide actionable insights.
Challenge: Strengthening Chart Connections
“I didn’t get any hints that the results are changed by my input”
Users struggled to link inputs with chart results. By adding dynamic feedback and tooltips, I ensured users could easily interpret changes and understand data relationships.
Before
After
Final flow
AI summary for Amateur User
Provides direct summaries for quick understanding of key data points.
Cross-Reference, Tooltips, and Color-coding for Deeper Understanding
Dynamic visuals like color-coded charts, tooltips, and cross-references help users connect related data for deeper insights.
User feedback
“
User quote
The information displayed on the cards feels friendly and easily accessible. It helps me know which model I need.
Jason Chen, amateur investor
“
User quote
Your newly designed charts and legends are incredibly helpful—I can actually try to understand them now.
Kai Tan, stock trader
Reflection
01. Leveraging Research Data and User Insights
Research findings and user quotes are compelling tools to align teams and build consensus, transforming evidence into impactful design direction.
02. Enhancing Data Visualization for Accessibility
Clear, accessible visuals balance information density with thoughtful design—leveraging color-coding, tooltips, and inclusive practices for maximum impact.