Uber users can now rate their trip mid-ride with categorized and written feedback, instead of having to wait for the end of the trip
“Last year was pretty hard, I'm not gonna lie” says Peter Deng, Uber's head of rider experience. But as part of new CEO Dara Khosrowshahi's push …
Context & Ripple Effects
Mid-ride rating is the latest step in a multi-year rebuild of Uber's feedback system. The company first softened the channel with Compliments, private stickers and thank-you notes for drivers, then made rider scores more visible in-app to nudge behavior. Under new CEO Dara Khosrowshahi, whose rider-experience chief Peter Deng admits last year was rough, the system is being made both faster and more granular.
The timing matters because ratings cut both ways: Uber had just overhauled driver support to protect drivers from unfair bad ratings, so giving riders a live channel creates new volume that those protections have to absorb.
First-order effects
- Riders can now flag problems with categorized and written feedback while the trip is still happening, instead of only at its end — shifting complaint resolution from post-trip review to in-ride intervention.
- Drivers receive feedback in real time during fares, raising the stakes of the unfair-rating protections Uber committed to in its recent driver-support overhaul.
Second-order effects
- Richer, time-stamped feedback gives Uber the evidence base for acting on the rider side too — a path it followed by later moving to ban significantly below-average riders after warnings.
- Categorized mid-ride data eventually becomes something Uber can surface back to users, as it did when it began showing riders a breakdown of their own average ratings alongside city-level comparisons.
Third-order effects
- If the pattern holds, ratings stop being an end-of-trip scorecard and become a continuous governance layer over both sides of the marketplace — the mechanism through which the platform polices driver quality and rider behavior alike.
The trend: Ride-hailing platforms are converting ratings from static end-of-trip scores into always-on feedback infrastructure used to govern drivers and riders symmetrically.