[Case 01]
Accenture Ai Navigator Platform
GEN Ai
25% More Bookings Through Simplified Navigation
Boosting Conversion Rates for E-commerce Checkout
[Project Overview]
[Problem Statement]
[Industry]
GEN Ai
[My Role]
Design Lead
[Platforms]
Desktop and Android
[Timeline]
December 2022- March 2024
[Persona]
Rhyan Fransis
Marketing Manager
Age: 29
Location: New York City
Tech Proficiency: Moderate
Gender: Male
[Goal]
Quickly complete purchases without interruptions.
Trust the platform to handle her payment securely.
Access a seamless mobile shopping experience.
[Frustrations]
Long or confusing checkout processes.
Error messages that don’t explain the issue.
Poor mobile optimization that slows her down.
[Process]
[01] User Research
Conducted interviews with 15 frequent users to uncover frustrations and preferences.
Analyzed behavioral data to identify bottlenecks in the current flow.
Benchmarked against competitors to identify best practices for checkout flows.
[01] User Research
Conducted interviews with 15 frequent users to uncover frustrations and preferences.
Analyzed behavioral data to identify bottlenecks in the current flow.
Benchmarked against competitors to identify best practices for checkout flows.
[01] User Research
Conducted interviews with 15 frequent users to uncover frustrations and preferences.
Analyzed behavioral data to identify bottlenecks in the current flow.
Benchmarked against competitors to identify best practices for checkout flows.
[01] User Research
Conducted interviews with 15 frequent users to uncover frustrations and preferences.
Analyzed behavioral data to identify bottlenecks in the current flow.
Benchmarked against competitors to identify best practices for checkout flows.
[Outcome]
25% increase in checkout completion rates.
30% reduction in cart abandonment on mobile devices.
40% improvement in perceived ease of use, as measured by post-launch surveys.
[Key Learnings]
Access constraints are a research brief, not a blocker.
When I couldn't interview users, I observed them instead contextual inquiry surfaced the dual-screen behaviour that no interview would have, because users weren't even conscious of it themselves.
In ambiguous technical environments, design with assumptions, not after certainty.
Waiting for legal and tech teams to resolve LLM constraints would have stalled the project indefinitely. I designed with explicit, stated assumptions and validated iteratively instead.
The best enterprise AI interface stays out of the way.
The most impactful decision wasn't a feature we added it was what we removed. Restraint and trust signals outperformed every dashboard we explored.