ken villapando
As Mobile Service scaled, dealerships needed a way to manage growing fleets of service vans and define where each van could operate. Without a centralized, visual interface, assigning vans to coverage areas was manual, inconsistent, and prone to overlap or service gaps. Advisors also lacked visibility into where technicians were at any given moment — making routing and rescheduling a constant challenge.
As the senior product designer, I partnered closely with strategy, stakeholders, and our development team in India to guide the project end-to-end. I created and presented a vision deck that secured stakeholder buy-in for a net-new map feature, ultimately influencing its inclusion in the 2025 product roadmap.
Working within Pega’s platform limitations and cross-time-zone collaboration challenges, I focused on designing solutions and collaborated with dev to come up with hi-fidelity comps that were both technically feasible and impactful for dealers while meeting tight deadlines.
We introduced a net-new map view that allowed dealerships to visually draw geofences and assign them to specific service vans, helping organize territories with clarity and flexibility. Layered with real-time technician location tracking, this feature empowered advisors to make faster decisions, reduce overlap, and optimize technician coverage across their service areas.
Deliver
2.3m
experiences with 4,300 Mobile Service Units on the road
Launch
513
dealers by the end of the year
Above
2.15+
Mobile Service Reservations per Van per Day, exceeding the national average
Secured alignment that a map feature was critical to meeting KPIs and business goals. This included pushing back on initial pressure to jump straight into development, and instead convincing stakeholders that user testing was necessary to validate assumptions before investing engineering effort.
Conducted four dealer usability tests with lo-fi map views, drawing competitive benchmarking from Strava, Lyft, and BizzyCar. These sessions simulated real scheduling scenarios to uncover how advisors assign and manage mobile service vans.
Dealer feedback confirmed the need for clarity and flexibility when assigning technicians. These insights shaped high-fidelity designs, ensuring the geofence solution directly supported advisor decision-making.
For early testing, I created lo-fi map views that focused on layout and functionality while keeping the interface simple. I made the conscious decision to keep these comps in color, as it was essential for dealers to quickly read and comprehend the data on the map. Color coding helped differentiate technicians, routes, and coverage areas, making it easier to simulate real-world scheduling scenarios.
This approach ensured that feedback captured both usability and the effectiveness of visual cues, which informed the next stage of high-fidelity design. It also reinforced stakeholder confidence that our testing would generate actionable insights before committing engineering resources.
One of the challenges I faced as a designer was our Pega development system being inflexible when it came to designing custom components. This meant we couldn’t immediately deliver the route optimization feature dealers strongly requested during testing.
Collaborating with product and engineering, we prioritized releasing an MVP that focused on clarity and usability, while continuing to explore technical pathways for optimization. This feature remains a key item under consideration for future iterations, and I’ve documented dealer feedback to ensure their needs are represented as we evolve the solution.
Dealer feedback reinforced the importance of designing for real-world workflows, not assumptions. Keeping adoption top-of-mind helped guide design decisions throughout the project.
Platform limitations required pivoting features like route optimization. Collaborating closely with product and engineering allowed us to deliver an MVP while documenting enhancements for future iterations.
The GeoFence feature continues to evolve. Next steps include exploring technical solutions for optimization, expanding usability testing, and measuring adoption and efficiency once the feature is live.