AI and machine learning
Product design and data science worked seamlessly to meet user needs and hit KPIs. If a driver gets a price within the first minute they engage more, which increase revenue and increases KPIs.
My Role: Lead Product Designer
Team: Data scientist, product manager, copy writer, marketing manager
- Quote rate increased by +145%
- Acceptance rate increased by +36%
Empathise with the users
Interviews and surveys
Do drivers expect the price to be final? How would they feel about us creating a price for them as opposed to a bespoke quote coming from the garage.
MVP for feedback
We released an MVP with 20 volunteer garages that we could then gather feedback on:
- How accurate was the price
- What factors affect prices
Concerned that the price we generate is wrong, worried about drivers accepting a wrong price and bad customer relationships leading from this.Key comments from garages
“I dont expect the price to be final, I expect this to go up or down when they see the car”Key comments from drivers
Group all the feedback and findings to notice larger patterns and fuel conversation and planning within the team.
Brain storm and feedback
With all the user feedback, flows, wireframes and UI I arranged a brainstorming session to discuss findings and gather feedback from the team.
The garage will want to view and manage all the quotes we provide, and we need to differentiate the quote types for drivers.Key findings
Interaction design problem
Garages want to be able to update a price in real time, we then need to show the change to the driver.
How will we update a price in real time on the comparison page whilst keeping the user in full control at all times.Interation problem statment
"What if a driver accepts a quote that's wrong? We need to be able to update and monitor prices in live time"Garage concern
"The AI model needs to be kept up to date to learn"Data science feedback
- Release on two job types first
- On-boarding to allow mechanics to authorise us to start quoting on their behalf
- Set up hot-jar to monitor users live
- Set up GA dashboard
- Data team daily feedback reports
- Release on a category by category basis and slowly introduce more features and improvements based of further feedback.
- Working with a local Usability Lab and research team to test the full journey from the driver side.
- Base changes on this feedback.
- Release next interaction of the management tool.