My Role
Design strategist, product design, and visual design
Team
Product Manager
Content Designer
UX Researcher
Timeline & Status
4 weeks
Overview
Moto drivers operate in far more demanding environments than car drivers, navigating traffic, noise, heat and sunlight while managing a passenger behind them.
Problem Statement
At the same time, they need to evaluate incoming trip offers while riding, causing them to frequently miss offers they would have preferred.
Outcome
Quick Accept let drivers set their preferences and automatically accept matching offers, while preserving their ability to review and reject them.
In the first month, 27% drivers adopted, driving a 2.46% lift in completed trips and an estimated $25.5M in incremental revenue.
HIGHLIGHTS
Helping earners automtically accept offers that match their preferences without losing control.
Setting up preferences
Video
Auto accepted offers
Video
CONTEXT - BUSINESS
Uber Moto is growing fast, but with a problem
21X
Revenue growth in 4 years
9%
of global mobility trips
1M+
daily active earners
15M+
daily active riders
Moto business is under pressure
Local competitors such as Rapido in India and DiDi in Latin America were gaining ground, putting pressure on Moto's position in some of its most important markets.
Drivers were barely accepting Uber trips
Many Moto drivers were running multiple apps simultaneously and choosing between competing offers. As a result, Uber trips were frequently ignored, leading to longer rider wait times and limiting marketplace growth.
PROBLEM
Why were drivers missing so many offers?
Every offer demanded a decision in 24 seconds
Uber's offer card gives drivers the information they need to choose their next trip—price, distance, destination and other key details.
But the offer disappears after roughly 24 seconds, leaving little time to evaluate it.
Current Product experience
Image
Current offer card experience
Image
Moto earners operate in difficult environments
Drivers make these decisions while navigating dense traffic, horns, heat, sunlight and a passenger behind them, all competing for their attention.
Evaluating one offer might be manageable.
No of offers drivers evaluate per minute during peak times
Reality of driving condition for Moto drivers
Videos
PROBLEM STATEMENT
Drivers find offer evaluation as cognitively demanding, but constantly do it in fear of missing out on good offers.
USER RESEARCH
Drivers were already paying other apps
Evaluating in a glance with pre-set preferences
More than a million drivers had downloaded third-party tools that charge roughly $10–12/month to help evaluate offers. Drivers set preferences, and the apps translate each incoming offer into simple green, yellow or red signals, making it easier to judge at a glance.
Different third party apps
Images
IDEA
What if Uber can act on those preferences?
Making offers easier to evaluate still wasn't enough
Even with better signals, drivers still had to notice, evaluate and act on every offer while riding. The real opportunity wasn't to make the decision faster, it was to remove unnecessary decisions altogether.
A SMALL IDEA iN MY MIND
What if Uber automatically accepted the right offers based on their preferences?
Testing the idea with drivers
We tested the concept with four focus groups in Brazil, covering 32 drivers across different experience levels and working patterns, from new to tenured and part-time to full-time drivers.
Design one pager & early mocks
GIF
TRADEOFFS
Balancing autonomy with marketplace health
Drivers liked automation, but not giving up autonomy
Our initial concept imagined a future where drivers could rely heavily on automation.But research revealed a clear boundary: drivers did not want Uber accepting trips they hadn't explicitly agreed to.
Instead we allowed drivers to review and reject automatically accepted offers, just as they could with any other trip.
More control created a marketplace risk
If drivers could precisely automate which trips they accepted, they could potentially optimize only for the highest-paying offers while ignoring everything else—hurting marketplace balance.
Leadership backed the concept & research findings, judging the potential driver experience gains worth the risk of testing it through an XP.
FLOW
Turning preferences into automatically accepted trips
Designing the end-to-end UX
Drivers first define what a worthwhile trip looks like to them using criteria such as earnings per kilometre, total distance and rider rating.
When an offer matches all of those preferences, Uber automatically accepts it and surfaces it to the driver, who still retains the ability to reject it.
End-to-end design flow
Image
SOLUTION
Making automation predictable, noticeable and trustworthy
Drivers set their own preferences
Drivers choose their own criteria and define the minimum or maximum values they are comfortable with.
As they adjust those preferences, the product shows how many offers would qualify, helping drivers understand the consequences of their choices before turning Quick Accept on.
Different states while setting preferences
Video
Make accepted trips hard to miss
Quick Accept only works if drivers immediately know when Uber has accepted a trip for them.
We designed clear visual, sound and haptics feedback so drivers could recognise an accepted offer without needing to inspect the app closely.
Auto-accept offers
Video
Navigation
Video
Explain every automated decision
Research showed that drivers didn't automatically trust Uber to apply their preferences correctly. For every Quick Accept trip, we therefore showed why the offer qualified, letting drivers verify that the system was behaving exactly as they expected.
Establishing trust
Image
IMPACT
Early adoption translated into more completed trips
Business impact
The experiment is still running when this case study is documented, but the first month showed strong early signals.
27%
of drivers in the treatment adopted Quick Accept.
2.46%
Increase in completed trips represents $25M revenue
