As VP of Design, I led the complete design strategy and execution for the Trading Recommendation feature.

Design Strategy & Vision: Defined the overall UX direction for the recommendation engine.

Team Leadership: Managed a 12-member design team, ensuring cross-functional collaboration.

User Research & Analysis: Conducting deep research to understand traders' behavior and pain points.

UX & UI Design: Creating wireframes, prototypes, and high-fidelity designs.

Stakeholder Collaboration: Aligning the design roadmap with product managers, engineers, and executives.

Quality & Usability Oversight: Ensuring design consistency, accessibility, and intuitive interactions.

Users were overwhelmed by the volume of recommendations and complex trading data.

Information Overload: Users were overwhelmed by the volume of recommendations (50+).

Complex Trading Data: Presenting Equity, Futures, and Options insights in a simplified way.

Personalization Challenge: Providing tailored recommendations for traders with different expertise levels.

Trust & Transparency: Users needed confidence in recommendation accuracy and reliability.

Seamless Integration: Ensuring the recommendation feature fit naturally within the existing Samco 3.0 app.

Create a clear, intuitive, and trustworthy recommendation system.

Support Multiple Recommendations: Support over 50 recommendations without overwhelming the user.

Provide Actionable Insights: Provide actionable insights for Equity, Futures, and Options traders.

Ensure Personalization: Ensure personalization based on trading preferences and history.

Maintain Consistency: Maintain consistent design language across the entire Samco 3.0 app.

We defined four key user personas:

Novice Traders: Seek guidance and clear recommendations.

Intermediate Traders: Desire actionable insights to improve decision-making.

Expert Traders: Require advanced analytics with fast, precise information.

Institutional Traders: Need a comprehensive view of market trends across Equity, Futures, and Options.

User Persona Example: Name: Rajesh, 32
Occupation: Software Professional & Part-time Trader
Goals: Wants to invest in Equity & Futures efficiently
Pain Points: Overwhelmed by too much data, lacks time to analyze every stock

We defined the key process:

1. Discovery & Research: Market analysis of top trading apps.
Interviews and surveys with 100+ traders.
Identified key behaviors and pain points.

2. Ideation & Wireframing: Sketches and low-fidelity wireframes of recommendation flows.
Created multiple layouts for data visualization of Equity, Futures, and Options.

3. Prototyping & User Testing: Developed interactive prototypes.
Conducted usability testing with 30 users across all expertise levels.

4. High-Fidelity Design: Created polished UI for dashboards, recommendation cards, and filters.
Focused on clarity, hierarchy, and accessibility.

5. Implementation & Iteration: Collaborated with developers to ensure pixel-perfect implementation.
Iterated based on internal stakeholder feedback and user testing results.

The final design focused on clarity and user experience:

Recommendation Dashboard: Separate tabs for Equity, Futures, and Options.

Interactive Filters: Filter by risk, return, or sector.

Recommendation Cards: Clear data, insights, and suggested actions.

Portfolio Integration: Users can directly act on recommendations from the dashboard.

We defined the key challenges:

Balancing complexity and simplicity for all trader levels.

Presenting over 50 recommendations in a digestible format.

Aligning multiple stakeholders' expectations with user-centric design.

Integrating real-time market data without slowing the app.

Maintaining brand consistency while innovating.

We defined the key learnings:

Deep user research is critical for trust-based financial products.

Iterative testing ensures clarity in complex trading features.

Data visualization needs to prioritize user comprehension over aesthetics.

Cross-functional collaboration accelerates product quality and delivery.

Personalized insights increase user engagement and adoption.

We defined the key Improvements:

AI-driven predictive recommendations based on portfolio and market trends.

Gamified experience to teach novice traders using recommendations.

Enhanced analytics dashboards for professional traders.

Integration with voice commands and chatbots for faster action.

Expand recommendations to global markets and derivatives.

Successfully launched Samco 3.0 Trading Recommendation as the first in the Indian market.

High engagement: Over 70% of active users interacted with recommendations.

Strengthened Samco's position as an innovative leader in fintech.

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