LXRInsights Dashboard Redesign
Improving the UX of an AI-powered eCommerce Analytics Platform
Project Information
Client: NetElixir
Duration: 12 weeks
Team: 7 multidisciplinary students
Role: UX Designer
Tools: Figma, UX Research, Prototyping, Usability Testing
Overview
LXRInsights is an AI-powered marketing analytics platform used by eCommerce businesses to analyze customer behavior and optimize marketing strategies. During a 12-week Rutgers MBS Externship, our team partnered with NetElixir to improve the platform’s usability by redesigning complex workflows, simplifying navigation, and validating solutions through user testing.
The Challenge
How might we simplify a complex analytics dashboard so marketers can find insights faster without relying on manual guidance?
These issues resulted in friction, anxiety, and low user satisfaction, undermining the convenience value that scooter-sharing is supposed to offer.
confusing navigation
Final Design & Prototype
Map / Home Screen: shows real-time locations of available scooters, battery level, and parking zones. Includes search bar + filter (availability, distance, battery).
Ride Flow: “Scan to unlock” → ride tracking (time, speed, battery, route) → ride end & summary screen (time, cost, distance, carbon saved)
Profile & Dashboard: user ride history, favorite routes, payment methods, sustainability stats (e.g., CO₂ saved, miles ridden)
Support & Feedback: in-app help center, report issue button, feedback form, emergency contact option
Safety Alerts: push notifications for campus events, weather hazards, restricted zones – proactively warn users
Usability Testing & Iteration
We conducted a usability test with 4 participants. Feedback included:
Positive: Users appreciated simplicity, clarity of ride flow, and the sustainability dashboard.
Key suggestions: wanted clearer destination arrival indication (walking vs riding), estimated travel time before unlocking, and stronger security/verification for ride start.
Post-Test Improvements
Added estimated time/distance preview before unlocking
Enhanced destination screen to indicate arrival status and walking vs ride estimates
Added optional user verification (e.g., confirm phone or student ID) to improve trust
Results & Impact (What This Case Study Shows)
Though this was an academic project and not a live product, the design exercise demonstrates:
Ability to conduct user research, draw real insights, and translate them into design requirements
Strength in designing a full end-to-end user journey: onboarding → ride flow → post-ride feedback/impact
Thoughtful inclusion of safety, sustainability, and user trust — criteria often overlooked in scooter apps
Clear documentation of process and design thinking, which increases credibility as a designer
Learnings
Contextual inquiry + direct observation yields stronger, more realistic user pain points than surveys alone
Importance of building trust and transparency when designing mobility / shared-transportation solutions
The value of iterative prototyping + user feedback — even small interface tweaks (timing, confirmations) significantly change perceived user confidence
Next Steps
Expand testing to more users, including first-time riders, and track long-term usage patterns
Add scooter maintenance/reporting features (photos, status, geo-tagging)
Integrate push-notifications for campus-wide alerts (weather, events, maintenance)
Collaborate with campus/scooter providers for real data and pilot implementation