SmartSaver:
Bank Calculator Tool

My discovery process began with a key insight: despite the abundance of financial comparison websites, none effectively addressed the multi-variable qualification criteria that determine actual interest returns.

Through structured interviews with 15 potential users across different financial profiles, I uncovered three critical user pain points:

• Calculation Complexity: "I can't manually calculate all these conditional tiers across different banks."
• Optimization Challenge: "Should I put all my money in one bank or split it? I have no way to figure this out."
• Information Asymmetry: "Banks make their interest structures deliberately complicated to prevent easy comparison."

Competitive analysis revealed most existing calculators only calculate single bank returns without comparative analysis, ignore multi-variable qualification conditions, present raw numbers without actionable insights, and lack fund distribution optimization capabilities.

Through research, I identified four primary user personas:

Based on user research, I developed a clear product vision: "To empower users to make optimal banking decisions through transparent analysis of complex interest structures."

This vision guided four strategic pillars:

• Comprehensive Analytics Engine: Account for all conditional factors impacting interest rates
• Insight Transformation: Convert complex calculations into clear comparative insights
• Optimization Intelligence: Develop algorithms to determine optimal fund distribution
• Continuous Learning: Collect anonymous usage data to enhance calculations

For feature prioritization, I developed a comprehensive feature set and prioritized it using a value-effort framework, focusing first on high-value, lower-effort features for the MVP, such as the multi-bank interest calculator, side-by-side comparison table, mobile-responsive interface, and basic feedback collection system.

Unlike typical calculators that simply present data, I took an analytics-first design approach focused on transforming raw calculations into actionable insights. This included creating a logical flow from input variables to insight presentation, developing progressive disclosure hierarchy for complex information, and designing a comparative visualization framework highlighting key differences.

The technical architecture was designed to support complex calculations while maintaining performance and scalability:

• Modular Calculation Engine: Separated core calculation logic from presentation layer
• Data Management: Used Supabase for structured storage of rates and criteria
• Performance Optimization: Implemented calculation memoization to improve response time
• Analytics Implementation: Designed event tracking for feature usage patterns

I established a comprehensive measurement framework to evaluate product success, focusing on these primary metrics:

• User Confidence Score: Measured through post-calculation survey
• Decision Time: Time from initial visit to completion of comparison
• Optimization Value: Percentage improvement through fund distribution optimization
• Satisfaction Rating: Post-usage satisfaction survey results

I implemented a robust analytics architecture to measure both usage and impact, with an event tracking framework aligned with the user journey, a feedback collection system, and performance monitoring for calculation response time.

I implemented a comprehensive security strategy to protect user data and prevent common web vulnerabilities:

This multi-layered security approach addresses key vulnerabilities including cross-site scripting (XSS), cross-site request forgery (CSRF), injection attacks, and authentication security, providing a robust security posture for the application.

Early results demonstrate significant impact on user financial decision-making:

• 76% of users report increased confidence in their banking decisions
• Average optimization value of 21% through the fund distribution algorithm
• Decision time reduced by 89% compared to manual comparison methods
• 4.7/5 average satisfaction rating from post-usage surveys

User feedback revealed several key insights: transparency drives trust, with users particularly valuing understanding exactly how interest is calculated; the fund distribution optimization feature receives the highest satisfaction ratings; and mobile usage exceeds expectations, with 68% of users accessing the tool via mobile devices and spending 24% more time exploring different scenarios.

While developing this product independently, I applied enterprise product management methodologies throughout. This included structured stakeholder communication, agile project management with structured sprints, and comprehensive risk management.

I established a structured decision framework to guide product development with a Four-Factor Evaluation Model:

• User Value: Does this enhance the user's ability to make better decisions?
• Technical Feasibility: Can this be implemented reliably with available resources?
• Maintenance Impact: How will this affect ongoing maintenance requirements?
• Strategic Alignment: Does this advance the core product vision?

Based on usage data and feedback, I've evolved the product roadmap to focus on highest-impact enhancements, including enhanced scenario comparison functionality, personal financial goal integration, rate change alert system, and historical rate tracking visualization.

SmartSaver has evolved significantly from its initial concept, with each release adding valuable functionality based on user feedback and strategic priorities:

The roadmap is continuously refined based on user feedback, market trends, and strategic priorities, ensuring SmartSaver remains focused on delivering maximum value to users.