Giving Rideshare Experiences a Lyft: Using Bayesian Network Modeling to Improve Rides for Drivers and Passengers
Kourosh Arianejad, Associate Vice President, Client Partner, Material
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.