Clipboard Health Case Study

Arush Sharma
2 min readApr 1, 2024


Following is the case study submission for the Senior Product Manager Role at Clipboard Health.

Case Study —

The world of ride-sharing is all about balancing supply and demand. Lyft, a major player in this space, constantly strives to optimize its pricing to maximize revenue while keeping both riders and drivers happy. But what exactly goes into this pricing strategy?

This blog post dives into a simplified case study, exploring how Lyft might approach optimizing its pricing structure in a specific city, let’s say Toledo, Ohio.

The Data Pointing the Way

Let’s assume we have some key data points:

  • Match Rate: The percentage of times a rider request is successfully matched with a driver.
  • Churn Rate: The rate at which riders or drivers stop using the platform.
  • Driver Pay: The average amount Lyft pays drivers per ride.
  • Customer Acquisition Cost (CAC): The cost of acquiring a new rider or driver.

The Quest for Net Revenue

Our goal? Uncover the pricing sweet spot that maximizes Lyft’s net revenue in Toledo. Here’s where it gets interesting:

  • Maximizing net revenue involves a delicate dance. We want to attract riders with competitive prices, but not at the expense of driver satisfaction. Happy drivers lead to a higher match rate, which in turn, keeps riders happy.
  • Customer acquisition cost (CAC) plays a crucial role. While low fares might entice riders initially, if CAC is too high, it can eat into profits.

Beyond the Obvious: A Look at Additional Factors

While factors like match rate and driver pay undoubtedly influence net revenue, there’s more to the story:

  • Competition: Toledo’s ride-sharing landscape might involve competitors offering different pricing structures. Lyft’s pricing needs to be strategically positioned relative to them.
  • Long-Term Impact: Pricing strategies should consider long-term effects. While a low fare might lead to a surge in ridership initially, it could negatively impact driver supply in the long run.

The Road Ahead: Refining the Model

This case study presents a simplified model. Here’s how we can add more layers of sophistication:

  • Non-Linear Relationships: The relationship between pricing and factors like churn rate might not be perfectly linear. Exploring more complex models can provide a more accurate picture.
  • Sensitivity Analysis: What happens if CAC fluctuates? How do driver changes pay impact net revenue? Sensitivity analysis helps us understand how the model reacts to variations in these factors.

The Final Word: Data is King

By leveraging data and building robust models, ride-sharing companies like Lyft can create pricing strategies that optimize net revenue while ensuring a positive experience for both riders and drivers. This data-driven approach paves the way for a sustainable and successful future in the ever-evolving world of ride-sharing.

PS: I got through the first round basis the assignment submission. However, I had other better offers to pursue.