Lyft | Driver Airport Foundational research
Lyft | Driver Airport Foundational research
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Foundational and evaluative research to reduce rider uncertainty around airport pickup. This work helped clarify how timing, expectations, and education influence rider confidence and informed iteration on the airport pre-order experience.
Foundational and evaluative research to reduce rider uncertainty around airport pickup. This work helped clarify how timing, expectations, and education influence rider confidence and informed iteration on the airport pre-order experience.
This work demonstrates how foundational research can reframe a complex marketplace problem, shifting teams from tactical fixes toward addressing the underlying uncertainty that shapes behavior.
This work demonstrates how foundational research can reframe a complex marketplace problem, shifting teams from tactical fixes toward addressing the underlying uncertainty that shapes behavior.
PROJECT SUMMARY
PROJECT SUMMARY
Role: Senior / Lead User Researcher
Timeline: ~8–10 weeks
Partners: Product, Design, Analytics, Airport Operations
Context
Context
In 2022, airports were a critical marketplace for Lyft, with high rider demand but inconsistent driver supply. While analytics highlighted undersupply and lapses at airports, teams lacked clarity on why many drivers did not accept Lyft rides and chose those from competitors.
As the XFN team dug into the challenge area, we recognized that, post-COVID, many aspects of the experience remained unclear.
Research Focus
Research Focus
Understand how drivers experience airport driving across the airport journey: waiting, matching, and pickup—and identify where uncertainty, friction, and perceived risk influenced their willingness to participate in accepting Lyft rides.
Approach
Approach
Designed a foundational study that included 60 -90 minute remote, semi-structured interviews.
Key Insight
Key Insight
Airport driving feels like a gamble.
Drivers lacked confidence in how long they would wait, where they would end up, how stressful pickup would be, and whether the trip would ultimately be worth the tradeoff. This uncertainty—not a single feature gap—was the primary deterrent to airport participation.
Impact
Impact
Research surfaced a strong signal that airport undersupply was driven by uncertainty rather than demand or incentives alone. This reframed the problem space, aligned Product, Design, Analytics, and Operations around a shared understanding, and informed both near-term product and data science experiments and longer-term exploration of airport strategy, including re-imagining Lyft's policies and rules around airport rides.
Next Steps
Next Steps
Findings were shared across cross-functional partners to guide prioritization and experimentation. Insights-informed follow-up work focused on improving visibility, setting expectations, and clarifying policy across the airport driver journey.
Want to learn more? See below.
Want to learn more? See below.
Other case studies
Other case studies
Lyft | Airport Rider Preorder 0-1 Product
Foundational and evaluative research to reduce rider uncertainty around airport pickup. This work helped clarify how timing, expectations, and education influence rider confidence and informed iteration on the airport pre-order experience.
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