Cha-Ching
Lowering the Barrier to First-Time Investing
Role:
Product Designer
Timeline:
Sep 2025 - Dec 2025
Team:
Student Team Project
Overview
Cha-Ching was built as part of a product management course taught by Mr. Zafar Razzacki — where we took a product from early idea discovery to a validated MVP. The focus was on research-driven decisions, iterative design, and testing assumptions before building.
Practice First. Trade Later.
Cha-Ching helps Gen Z learn, practice, and invest — with confidence. It combines clear market explanations, risk-free simulation with live data, and guided investing in one place. So users can understand what's happening, try strategies safely, and feel prepared before real money is ever on the line.
The Market is Growing. Confidence Isn’t.
The financial education industry is valued at $340.76 billion and projected to hit $1.5 trillion by 2034. Gen Z is entering adulthood hungry for financial independence — around 30% start investing in college. But confidence hasn't kept up. Gen Z reports the lowest financial literacy confidence of any generation, often feeling lost on basic investing concepts.
The tools exist. The support doesn't.
Investing Apps
Apps like Robinhood prioritize execution over understanding. They assume prior knowledge, highlight performance metrics, and surface incentives — but offer limited guidance before users commit real money.
Educational Platforms
Platforms like YouTube explain concepts but leave users to translate theory into action. Advice is often unverified, static, and disconnected from real decision-making.
What this told Us
Access ≠ Understanding
Investing tools make action easy, but don’t help beginners build confidence.
Education ≠ Guidance
Content explains concepts, but rarely shows how to apply them safely.
The Missing Middle
No product helps users practice before committing real money.
Before we designed anything, we set three rules
Financial learning had to feel approachable, not intimidating.
It had to match how Gen Z already consumes content — short, contextual, mobile-first.
And it couldn't push anyone toward real money before they felt ready.
With those guardrails in place, we explored multiple early concepts — onboarding flows, dashboard structures, an AI coach, and a subscription model — before narrowing direction.
Testing Early Concepts
We validated early assumptions through three methods:
Here's what the testing told us
And behind the numbers, three moments that changed the product.
The Pivot
Practice before you learn
Educational content wasn't building confidence. We introduced simulation as the entry point — $100K of virtual money, real market data, no real risk.
Your portfolio first, not ours.
The home screen was showing recommended stocks before the user's own holdings. We flipped the hierarchy — your portfolio leads, market insights follow.
Less clutter, more clarity
Profile was trying to do too much — goals, subscription, spending limits all at once. We simplified it to one thing: your investment progress.
A coach, not an advisor.
Trust dropped when the AI felt prescriptive. We repositioned it as a market explainer — responses now show verified sources, risk reminders, and adapt to your learning level.
The final product
Onboarding
We wanted users to feel like the app was built for them from the first screen.
Onboarding collects experience level, goals, and risk comfort upfront — so everything they see after is relevant to where they actually are.
Dashboard
The hardest call was deciding what a first-time user should see versus someone already investing.
We designed the same screen to behave differently depending on where you are — beginners land on simulation and learning, returning investors see their portfolio and market context.
Learn and Simulation
Combined learning and simulation into one experience
This was our biggest bet. We wanted simulation to feel like the real market, not a demo.
So we built insights into every step — when a stock moves, you see why. When you make a trade, you learn something. The goal was to make practice indistinguishable from the real thing.
AI Coach
The debate was around how much the coach should reveal about what it knows about you.
We didn't want it to feel like it was making assumptions. So we added a learning level tag on every chat — transparent enough that users know it's personalised, subtle enough that it never feels intrusive.
How we would make money
Freemium subscription
Testing killed content gating early.
We reframed premium around tools & advanced simulation $4.99/month.
Simulation-led conversion
100% of testers preferred paying for practice over content.
Free tier gets you in, simulation makes you upgrade.
Partner-driven revenue
A future hypothesis — commissions from partner platforms once users feel confident enough to invest for real.
Key Learnings
The right problem is half the solution.
We could have built a general financial education app. We almost did. Narrowing to one specific gap is what made every other decision cleaner. The research didn't just validate our direction, it gave us one.
Product thinking goes beyond the screens.
This project pushed us to think about market whitespace, regulatory constraints, and long-term viability. The screens were the output, not the work.
