Project Overview

With the rise of data-driven decision-making in e-commerce, businesses require robust analytics tools to monitor performance, optimize sales, and improve customer experience. The goal was to design an eCommerce data analytics dashboard similar to Oracle’s but with a more intuitive, user-friendly interface.

My Role:

As the Product Designer, I was responsible for conducting research, creating wireframes, developing high-fidelity prototypes, and testing the designs with users.

Design Process

Research & Discovery

I analyzed existing enterprise analytics tools, including Oracle, to understand their strengths and pain points. Key insights included:

  • Many dashboards were too complex for non-technical users.

  • Users preferred customizable reports and real-time data visualization.

  • Clarity in information hierarchy was crucial for quick decision-making.

Wireframing & Information Architecture

Based on research, I structured the dashboard to:

  • Prioritize key metrics like sales performance, customer insights, and inventory trends.

  • Include customizable widgets for different business needs.

  • Offer drill-down features for in-depth analysis.

Prototyping & Visual Design

I designed an interactive prototype focusing on:

  • A clean, minimalistic UI for easy navigation.

  • Data visualization using interactive charts and graphs.

  • Customizable layouts tailored to user needs.

Usability Testing & Iteration

Given the tight timeline, testing was conducted in quick iterations. Users highlighted:

  • The need for clearer labeling on complex metrics.

  • Interest in additional filtering options for reports.

These insights led to refined designs with improved usability.

Log in

Orders

Add New Product

Calendar

Products

Dashboard

Challenge: Time Constraints

  • Solution: Lean UX approach—prioritized core functionalities first, conducted rapid iterations, and leveraged existing design patterns to accelerate development.

Outcome & Next Steps

The project is still evolving, with ongoing refinements based on user feedback. Next steps include:

  1. Implementing AI-driven insights for predictive analytics.

  2. Enhancing customization options for enterprise clients.

Previous
Previous

Aura Salon

Next
Next

HAUS