About Rebooking Credit Experiment

At Turo, we are experiencing a low rebooking rate for last-minute cancellations due to trip fee increases, vehicle price hikes, and changes in protection plans. Guests (renters) often have to pay more to rebook at the last minute. In this project, I am supporting our data science team in developing an automated rebooking credit system to improve customer self-serve conversion for last-minute bookings.

Role

Project Type:

Mobile App Design, Email Copy

Product Designer

Team

Tools

Figma, Usertesting.com

Data Science Team, Engineering Team, PM

Insight

69% of rebooked trips have a higher daily price than the original trips

  • Due to urgent turnaround time, the host usually price bumps for last-minute bookings

Guest need to pay extra money for the similar car if they get cancel within 72 hours (daily price, trip fee, and insurance increase).

67% 0f host cancellation occur within 72 hours of trip starts

  • Bad experience and low retention rate

32% of guests don’t rebook when a host cancels on them

  • ~15M annual loss net revenue

35% of Segment A guests don’t rebook after cancellation

  • Segment A: Most quality guest in Turo rating (Low accident risk + Good review)

Last-minute cancellations create

  • Customer loss: Bad experience and low retention rate

  • Company loss: Revenue and quality customers

Find the optimal rebooking amount to increase the rebooking rate and enable customers to self-serve after last-minute cancellations

How might we

Before we allow guest to self-serve, we need to find out the right amount for rebooking credit

Project Planning

Goals

Post cancellation language - Product design and copy writer

  • Communicate rebooking credit to guests

  • Make sure guests understand the next step post-cancellation

⬇️ Bring more user to rebooking flow and pursue rebooking

Experiment - Data team

  • Determine the optimal rebooking credit based on experiment result

Unmoderated usability test

Email title/subject line

Email title/subject line - pt2

Email content

Final design - All notification touch points

Outcome and next step

This is a very recent project we launched 2 weeks ago. Based on the first week data:

  • Increase ~39% of people entered rebooking flow

  • ~74% of rebooking credit used in the past week (fully or partially)

Starting research and design on recommend vehicle list

  • What information does the guest need to know when looking at a replacement vehicle in the rebooking flow?

  • HMW shows more vehicles in the rebooking flow when having short inventory in a specific area.

Product AB test for language after experiment

Thanks!!!

Please contact me for more detail about this project!