Giving Rideshare Experiences a Lyft: Using Bayesian Network Modeling to Improve Rides for Drivers and Passengers
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
Kourosh is a Client Partner at Material, bringing deep expertise in brand measurement and strategic research, helping clients translate data into decisions that build stronger customer relationships and drive business growth. With over a decade of experience leading high-impact studies across a wide array of industries, his work is rooted in methodological rigor, data quality and a sharp focus on actionability.