Hi-fi prototype

Onboarding for every level

Tradeoffs

1. Gamification. Streaks and rewards can increase engagement, but in investing they can reward activity over quality. Engagement had to be tied to learning, not just completion.

2. Removing Exchange and Market sections. Reduced beginner overload at the cost of less functionality for power users.

3. Personalization vs. complexity. Three levels require more design + engineering, but a one-size-fits-all experience becomes irrelevant for everyone.

Finding 1: Intermediate and advanced users often did not know where to start when they entered free exploration.
Solution 1: Suggest a career path and personalized content recommendations for all levels as the final step of onboarding, and highlight recommended content directly on the labs page.

Finding 2: Not all beginners started the career path after completing the assessment, even when that was the clearest next step for them.
Solution 2: Apply the Endowment Effect by redirecting beginners straight to their recommended career path after onboarding instead of asking them to choose again.

Finding 3: The intro test was not always a reliable signal of user knowledge or intent. Some participants moved through it too quickly or answered inconsistently, which created a risk of inaccurate personalization.
Solution 3: Make content adaptive to user behavior and progress in real time, rather than relying only on a single onboarding assessment.

Constraint and compromise

The biggest unresolved tradeoff was personalization quality versus feasibility. A truly adaptive onboarding system would require instrumentation, content tagging, recommendation logic, and ongoing tuning based on real usage data. In this concept, I kept the MVP more conservative: redirect beginners into a clear default path and use lightweight recommendations, accepting that some users would still receive imperfect routing until behavioral signals could be collected and iterated on.

Results

Usability testing was conducted with 54 participants across beginner, intermediate, and advanced skill levels via moderated Zoom sessions. After completing onboarding, participants were given no specific task - just open access to the prototype - and I observed their natural navigation: what they explored first, where they hesitated, and where they lost momentum or dropped off.

What would I do differently?

- Instrument the MVP before launch. Hotjar showed where users dropped, but not enough about why. With proper event tracking from day one, the data would have been richer and the think-aloud sessions could have started with sharper hypotheses.

- Run a copy audit before finalising the IA. One unfamiliar term can end a session, so labels, headings, and actions should have been reviewed for beginner clarity before the structure was locked.

- Design one shared system, not three parallel experiences. Most of the difference between beginner and advanced users is in content and guidance, not in completely separate interface patterns.

What will I test next

- Run user tests for advanced and intermediate users (simulations and labs) adapted for different type of content (situational task, advanced concepts exploration etc.)

- The core differentiator of this platform is feedback that distinguishes luck from skill. I'd track whether users who receive reasoning-coherence scores make structurally different decisions after 10 labs vs 1 lab - not just whether they feel more confident.

- I'd run a structured interview with 5-8 financial advisors or analysts to test whether the experiment log and reasoning-quality feedback genuinely replace their external spreadsheets - or whether the professional context requires features not yet designed.

What is next for Investure?

- Develop the adaptive content system - the mechanism that adjusts difficulty based on real-time performance rather than the initial test. This is the architectural prerequisite for long-term retention and for the personalization promise made throughout the design.

- Evaluate every step of user journey against engagement metrics - including the habit loop, streak mechanic, and dashboard design.

What did this teach me about UX

- Feedback quality matters more than feedback quantity. "Order added" is technically feedback, but it doesn't help users understand whether their reasoning was sound, whether the result was skill or luck, or what to do next.

- Gamification without understanding can do harm. In high-stakes products, motivation mechanics need to support learning, not just drive activity.

- One size for everyone is a choice to serve no one. Personalization is expensive, but the alternative is a product that is slightly wrong for every user it tries to help.