Case Study

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Relocation Workflow

Enterprise tool for triaging relocation requests

The Brief

Vacasa manages 1.5 million reservations annually—45,000 experience disruptions requiring guest relocations. The existing process was unsustainable: 190+ manual steps across 10+ tools, with urgent cases taking 20+ hours to resolve. As sole Product Designer, I redesigned the workflow from a fragmented spreadsheet operation into a CRM-inspired, queue-based system that automated case creation, embedded urgency logic, and preserved context through handoffs. Result: urgent relocations dropped to 6.5 hours (68% reduction), and agents reported significantly less stress and better focus on guest care.

The Process

Research

Interviews described a linear workflow, but shadowing live cases revealed the reality: constant stalls, lost context during handoffs, and supervisors manually prioritizing by gut feeling. I documented the full process for the first time (190+ steps, 10-15 tools), exposing how fragmented the operation truly was.

Design ideation

Early prototypes explored a rigid, step-by-step flow—but feedback showed cases were too start-stop for that model. A 6-month project pause provided perspective: CRM tools like Salesforce handle this perfectly. That insight sparked a pivot to a queue-based, case-detail system that let work pause and resume without losing context.

Design Strategy

I restructured the workflow into three phases: automated creation (eliminating manual data entry), initiation + handoff (preserving context and embedding urgency logic), and structured wrap-up (automating financials and giving Leadership visibility). This three-phase model became the system's foundation and later, a reusable pattern for other operational workflows.

Testing & Rollout

I iterated prototypes with beta users and refined based on real-world feedback. I also navigated three Product Manager transitions across 18 months through clear documentation and design rationale, ensuring momentum held through leadership changes and stakeholder pressure to launch prematurely.

The Results

Efficiency Gains

Within 6 weeks of rollout, urgent cases showed measurable improvement:


  • Mid-Stay Relocation (guest is in-home and must move): 20 hrs → 6.5 hrs (68% reduction)


  • Today Check-In (guest checking in by 4pm): 9.7 hrs → 3.6 hrs (63% reduction)


  • 24-72 Hour Check-In: 10.5 hrs → 8.5 hrs (22% reduction)

Human Impact & Longitudinal Evidence

Time savings only mattered because they freed agents to focus on guest care. I ran a pre/post rollout survey with 83% and 93% response rates respectively:


"The current relocation process enables me to do my job efficiently."

  • Pre-rollout: Only 4 agents strongly agreed

  • Post-rollout: 24 agents strongly agreed


"I can focus my attention on guest well-being."

  • Pre-rollout: 5 agents strongly agreed

  • Post-rollout: 20 agents strongly agreed


"How often do you experience work-related stress?"

  • Pre-rollout: 14 agents nearly every day

  • Post-rollout: 8 agents nearly every day


See full case study here

2024

Built by Dan Rattigan

2024

Built by Dan Rattigan

2024

Built by Dan Rattigan

2024

Built by Dan Rattigan