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Featured Case Study

MyG2 Performance Analytics

Transforming complex data into actionable insights. A complete redesign of G2's analytics platform that empowered customers to make data-driven decisions.

Client G2
Role Lead Product Designer
Duration 6 Months
Team 4 Designers, 6 Engineers
Year 2024
500+
Active Users
35%
Adoption Rate
60%
Faster Insights
85%
User Satisfaction
MyG2 Dashboard

The Story

Understanding the context and setting the stage for transformation

The Problem

Data Overload

G2 customers were drowning in data. The existing analytics interface was cluttered and overwhelming, making it nearly impossible to extract actionable insights quickly.

The Goal

Clarity & Action

Transform the analytics experience into an intuitive, insight-driven platform that empowers customers to make proactive, data-driven decisions with confidence.

The Solution

Smart Analytics

A redesigned dashboard with AI-powered insights, progressive disclosure, and customizable views that surface critical information at the right moment.

My Role

Lead Product Designer

As the lead designer, I owned the entire UX process from discovery to delivery. This included conducting user research, defining information architecture, creating wireframes and prototypes, establishing the visual design system, and collaborating closely with engineering to ensure pixel-perfect implementation. I also facilitated design reviews, stakeholder presentations, and worked directly with the PM to define feature priorities and success metrics.

The Challenge

Deep diving into user pain points and business constraints

The Process

A structured approach to solving complex problems

Phase 01

Discovery & Research

Conducted 24 user interviews and analyzed usage data to identify pain points. Created journey maps and identified key moments where users struggled to find insights.

User Interviews Journey Mapping Data Analysis
Phase 02

Ideation & Wireframing

Explored multiple approaches through rapid sketching and wireframing. Tested information architecture with card sorting exercises to validate navigation patterns.

Wireframes Card Sorting IA Design
Phase 03

Prototyping & Testing

Created high-fidelity prototypes with realistic data. Conducted 12 moderated usability tests achieving a 95% task completion rate.

Hi-Fi Prototypes Usability Testing Iteration
Phase 04

Implementation & Launch

Worked closely with engineering to ensure pixel-perfect implementation. Conducted QA reviews and iterated based on beta user feedback before full launch.

Dev Handoff QA Review Beta Testing

Iterations

The journey from concept to final design

Iteration 1

Information Architecture Overhaul

Started by completely restructuring how information was organized. Created a three-tiered hierarchy: Priority Insights, Deep Analytics, and Custom Reports.

  • Reduced navigation depth from 5 levels to 2
  • Grouped related metrics into logical clusters
  • Introduced persistent navigation sidebar
Iteration 1
Iteration 2

Smart Dashboard Concept

Introduced AI-powered insights that automatically surface the most critical information. The dashboard now adapts to user behavior and priorities.

  • AI-curated "Priority Insights" section
  • Customizable widget-based layout
  • Proactive churn risk alerts
Iteration 2
Iteration 3

Data Visualization Refinement

Refined chart types and visual hierarchy based on user feedback. Introduced interactive tooltips and drill-down capabilities for deeper exploration.

  • Simplified chart legends and labels
  • Added comparison view for benchmarking
  • Implemented export functionality
Iteration 3
Final Design

Polish & Micro-interactions

Added finishing touches including smooth animations, loading states, and empty states. Ensured accessibility compliance and responsive behavior across devices.

  • Smooth page transitions and animations
  • WCAG 2.1 AA accessibility compliance
  • Mobile-responsive design system
Final Design

The Results

Quantifiable impact on user experience and business metrics

60%

Faster Time-to-Insight

Users find critical data points 60% faster than before

85%

User Satisfaction

Up from 30% with the previous interface

35%

Feature Adoption

Monthly active users engaging with analytics

45%

Reduced Churn

Early detection of at-risk accounts improved retention

Key Learnings

What I learned from this project

01

Progressive Disclosure is Key

Showing too much data upfront overwhelmed users. By layering information and letting users drill down on demand, we dramatically improved comprehension and task completion.

02

Trust Through Transparency

AI-powered insights needed clear confidence indicators to build trust. Users wanted to understand why certain alerts were surfaced and how recommendations were generated.

03

Customization Matters

Different users have different priorities. Giving them control over their dashboard layout and which metrics to prioritize increased engagement and satisfaction significantly.