Streamlining Lease Management for Walmart’s Global Real Estate Expansion
Overview.
Context
Walmart’s real estate agents are responsible for managing complex lease agreements across international markets. When leasing new store spaces, Walmart signs contracts with landlords and subleases in-store retail space to vendors. The existing workflow required agents to manually interpret lease PDFs, input large volumes of data, and manage relationships between master and child leases—leading to inefficiencies and a high risk of human error.
As the UX Designer, I led the end-to-end design effort to simplify lease creation, master agreement tracking, and tenant management, while reducing data entry errors and improving collaboration.
Problem
The legacy lease management system required Walmart agents to manually extract data from scanned lease agreements and input them into the system. This process was time-consuming, error-prone, and lacked standardized workflows for creating master and child leases. There was also no structured review process between junior and senior agents before final submission.
Impact.
- Increased Efficiency: Lease creation time was reduced by 40%, enabling agents to handle more agreements with less effort.
- Improved Accuracy: Streamlined workflows and guided inputs significantly reduced data entry errors and lease linkage issues.
- Faster Approvals: Senior reviewers reported a 25% improvement in task prioritization and response time due to the new review dashboard.
- Boosted Satisfaction: Post-implementation surveys showed an 85% satisfaction rate among users, citing easier navigation and clearer task ownership.
Objectives / Goals.
- Reduce manual data entry and associated human errors.
- Simplify the lease creation process (Master–Child structure).
- Improve document review and submission workflows.
- Enable seamless collaboration between agents and senior reviewers.
- Ensure scalability for global lease operations.
Discovery & Research.
Research
- Stakeholder interviews with real estate agents and managers
- Workflow shadowing sessions to understand the current process
- Workflow observation sessions
- Heuristic evaluation of legacy tool
Key Insights
- Agents often re-enter the same data across multiple fields and forms
- Lack of standardization in how lease hierarchies (master-child) were recorded
- Time-consuming approval processes due to unclear handoff steps
- High reliance on offline communication and document sharing
Personas
We began by crafting personas that represent our primary user types, grounded in both qualitative and quantitative research. These personas helped us capture user goals, behaviors, and pain points, guiding design decisions with real-world context.

Empathy maps
Empathy maps were developed to explore each persona’s mindset—capturing what they think, feel, see, hear, say, and do. This exercise deepened our understanding of user behavior and aligned the team around a shared user-centric perspective.

Define.
- How might we reduce manual data entry for lease agents?
- How might we clearly define and link master-child lease structures?
- How might we simplify internal lease review and approval?
Ideation.
- Auto-extract data fields from uploaded PDFs using AI-assisted tools
- Introduce lease templates for recurring scenarios
- isual hierarchy for lease relationships (tree structure)
IA & User Flows.
- Clear sections for Master Lease, Child Lease, Tenant Info, Attachments
- Role-based navigation for Agent vs. Reviewer
- Streamlined flow: Upload → Auto-fill → Edit → Review → Submit
IA

User Flow
Following a deep dive into the user journey, we mapped out clear user flows for critical tasks. These flows define the step-by-step interactions users take to accomplish their goals within the system, ensuring intuitive navigation and task efficiency.

Wireframes & Prototypes.
Wireframes
Designed low-fidelity wireframes for initial feedback.
Usability Testing.
Methods: Moderated remote testing with agents
Tasks: Creating new master lease, adding child leases, submitting for review
Findings
- Users preferred collapsible sections for complex lease data
- Needed autosave and error warnings for incomplete fields
- Reviewers requested better filtering/sorting of pending leases
Final Design Highlights.
- Visual lease relationship mapping (Master → Child → Tenant)
- Smart form validation and autofill based on templates
- Review queue with status tracking and annotations
- Accessibility-compliant design for global users
Final Design
Impact.
- Reduced average lease entry time by 35%
- Minimized errors through form validation and data reuse
- Streamlined submission and approval process, reducing turnaround time by 2 days
- Positive feedback from real estate teams across regions
Reflection & Next Steps.
What Went Well
- Early stakeholder involvement ensured relevant features
- Validated with real-world lease examples for accuracy
Future Improvements
- Integrate AI to extract structured data from scanned leases
- Add audit trail for compliance in high-risk regions
- Enable multi-language support for regional lease agreements