Giving Rideshare Experiences a Lyft: Using Bayesian Network Modeling to Improve Rides for Drivers and Passengers
Sally Sadosky, Group Manager, Research & Insights, Lyft
Abstract
What does it take to ensure rideshare experiences are positive, safe and worthy of five-star ratings for both passengers and drivers? We brought Bayesian network modeling to bear on the complex mix of factors that make up Lyft’s rideshare experiences. In two distinct studies – one focused on riders, the other on drivers – we mapped the attributes of ride experiences and uncovered relationships between them to reveal key areas for strategic intervention that traditional analytical methods might have missed.
Attendees will learn
- How to use Bayesian network modeling to structure and make sense of the complex, interconnected components of experiences
- How this data-guided approach helped Lyft prioritize improvements for both riders and drivers
- How to apply Bayesian methods to strategic roadmap development beyond traditional correlation or regression techniques
About the Presenter
Sally Sadosky leads the Consumer and Market Insights team at Lyft. She built her career in Survey Research and Insights for technology companies—including Intel, Adobe, and LinkedIn—after earning her MBA and Masters in Applied Social Research from the University of Michigan. Sally has developed a strong discipline for both qualitative and quantitative research, allowing her to investigate and answer business questions with agility. She is well-versed in B2C and B2B audiences. Among her peers, she is recognized for being a compelling storyteller, trustworthy, and passionate about her subject matter. Outside of work, Sally enjoys golf in Palm Beach Gardens, Florida.