Cancellation rate on Uber is currently at 25%. What would you do to bring this down to 5%?

Clarifying questions

1. When we talk about Uber, are we talking about Uber Eats or Uber ride share? Uber Rideshare

2. Cancellation rate is down from the end of drivers or riders? Drivers 

3. Are we focusing on a specific geography? Yes, NYC, USA

Uber - Uber offer ride share services where riders can book a car to take them from A to B.

Are goal here is to bring the cancellation rate down from 25% to 5% ( 100 rides book during the day, 25 of them gets canceled)  

List the user groups and select yours

1. As I said earlier - There are two users here for the product - Drivers and Riders and since we made an assumption that we are focusing on drivers, will move forward with that. 

List and prioritize the use cases / pain points

Drivers signups,  Get ride request > Sees information about the ride> Money, Pickup and Dropoff, User rating > Driver either accpts the riode or cancels it 

Driver Accpets the ride> picups up the rider> Get another request fro ride> Drivers accepts or cancels the ride 

Paint Point. 

1. Money - The drovers only sees the money for the total ride and not how much he/she is going to make. 

2. The location is to far for him to serve.

3. The rider has bad reviews, and is worried about safety and cleaniness. 

4. In between rides, the driver gets a new ride requests - which could be far, or doesn't have all the information.

5. The drivers are near the end of the shift and doesn't want to accept new rides. 

6. The wait time gets too long when driver is trying to pick up riders and hence they have to cancel

Based on the above lets solve for Money Incentive, Location and Wiat time 

Proposed Solutions

Money Incentive:

• Suggested Solution: Create a pricing model where ride income increases progressively.
• Enhancement: Provide real-time earnings visibility for each ride upfront to improve transparency.

• Suggested Solution: Incentivize drivers for longer distance rides.
• Enhancement: Implement dynamic pricing based on distance to ensure fair compensation for longer trips. 

Matching Closest Driver to Far Locations

• Suggested Solution: Ensure the closest driver is offered rides for distant locations.
• Enhancement: Optimize the algorithm for rider-driver matching to prioritize proximity alongside incentives.

Implementing Time Limits and Penalties

• Suggested Solution: Introduce a time limit for drivers to arrive at pickup spots, with penalties for delays
• Enhancement: Include rider compensation options in case of driver delays to balance accountability.

 Evaluate solutions

Metrics for Success: Reduction in cancellation rate and driver satisfaction scores to track the impact of each solution.

I'll build for Implementing Time Limits and Penalties and location first

Summary 

I believe that implementing two strategic solutions will significantly reduce the cancellation rate among Uber drivers in NYC. One solution focuses on convenience by optimizing location matching, ensuring drivers are offered rides closer to their current position. The other solution involves providing better compensation and incentives to drivers, enhancing transparency and satisfaction. Together, these measures aim to improve driver experience and reduce cancellations effectively.