Target Drive Up Redesign
2025
The Challenge: Employee Frustration in Drive Up
Drive Up is chaotic. Employees including fulfillment associates, pickers, and Drive Up specialists navigate a restrictive, cognitively demanding app to coordinate orders across fragmented systems. The interface increases stress, slows experienced employees down, and fails to accommodate real-world problem-solving. When things go wrong (missing orders, inventory discrepancies), employees have no transparency into what happened or how to fix it.
Multi-Method Research Approach
Autoethnography
Andrew documented his own experiences as a Target employee, capturing pain points, error handling, and daily workflow friction.
Semi-Structured Interview
Conducted in-depth interview with active Target employee, exploring teamwork, time management, cognitive load, and exception handling.
Reddit Thematic Analysis
Analyzed ~200 Reddit comments from Target employees and customers to identify recurring frustrations, feature requests, and systemic issues.
Competitive Analysis
Evaluated Walmart and Instacart drive-up systems to understand workflow differences and employee sentiment across retailers.
Five Critical Findings
High Cognitive Load
The app is visually complex and restrictive, forcing employees to work against their natural workflow rather than supporting it. Particularly challenging during rush hours.
Lack of Employee Agency
The system restricts decision-making freedom. While guidance is helpful for new employees, it slows down experts. Employees cannot adapt workflows to store-specific needs.
Poor Order Categorization
Orders are frequently miscategorized, making them impossible to find during rush-hour stress. This creates cascading failures and manual recovery workflows.
No Transparency in Failure
When things go wrong (missing bags, inventory discrepancies), employees have no visibility into what happened or where. This forces them to call managers instead of solving problems independently.
Friction in Handoffs
Coordination between picking, staging, and Drive Up specialistsrequires multiple taps and unclear feedback. Quick physical handoffs are slowed by app UI.
Design Philosophy
- Focus on personal agency by giving employees control and transparency to solve their own problems rather than forcing all decisions through the system.
- Reduce cognitive load: Minimize unnecessary visual complexity and clicks, especially important in high-stress retail environments.
- Support real workflows: Design for how employees actually work, not how the system thinks they should work.
- Respect expertise: Provide helpful guidance for new employees without slowing down experienced staff.
Solution #1: Bag History (Transparency & Personal Control)
When a bag goes missing, employees typically spend 15 or more minutes searching, calling managers, and manually re-picking items. Bag History gives them immediate visibility: where the bag went, who touched it, and what was in it. This enables faster problem-solving and reduces manager dependency.
Solution #2: Take from Hand (Streamlined Handoffs)
Physical handoffs between pickers and Drive Up specialists happen in seconds, but the app requires multiple taps and unclear feedback. Take from Hand is a single-tap handoff confirmation that respects how teams actually coordinate, providing one button, instant feedback, and no friction.
Key Design Decisions
Followed Target's design system and Zebra device constraints. Implemented stakeholder feedback from mid-fidelity testing: reduced screen count from 4 to 1 for Bag History; added confirmation dialogs to prevent accidental handoffs. Prioritized usability testing with actual employees over perfect visual polish.
Iteration Process
- Low-fidelity storyboards to validate problem framing and solution direction
- Mid-fidelity prototypes tested with stakeholders, and feedback led to major simplifications
- High-fidelity prototypes implemented Target design system and real Zebra device frame
- Usability testing revealed that reducing screens and clicks was critical. Employees don't have time for complex workflows.
How AI Supported Development
We used AI to organize and summarize large volumes of qualitative input, especially employee comments and workflow notes, so we could identify repeated pain points faster. That improved the pace of synthesis and made the research more actionable.
By surfacing patterns in errors, handoffs, and cognitive load more quickly, AI helped steer the redesign toward fewer taps, clearer recovery states, and more control during busy shifts.
Impact
Drive Up associates, pickers, and specialists got clearer handoffs, less friction, and faster recovery during stressful shifts.
Limitations & Next Steps
- Single interview: We only interviewed one employee. More interviews would validate findings across store types and locations.
- High-performing stores: Both the interviewee and Andrew worked at high-performing stores. Issues may be more severe elsewhere.
- System constraints: Access to Target design documentation and backend systems would enable more comprehensive redesigns.
- Next phase: Conduct additional interviews, implement formal usability testing, explore Item Audit workflow (currently out of scope).
The Team
Andrew Petersen
Research Lead, Auto-ethnography, Interview
Eesha Gupta
Data Analysis, Synthesis
Isha Chury
Design Systems, Prototyping
Lilian Mathis
Interview, Stakeholder Coordination
Nuti Mody
Literature Review, Storyboards