Choosing a north star, marketplace metrics and guardrails before Uber Eats goes live

You are the product manager for Uber Eats, which has not launched yet. Set the goals for the launch and the metrics you would use to judge it: what the business should achieve first, the primary metric, the supporting metrics for each side of the marketplace (people ordering food, restaurants and delivery couriers), and the guardrails you would watch. ```hint Separate readiness from success Before launch there is no data to optimize. Ask what must be true on each side of the marketplace for the first orders to go well, and how your goals should change between the run-up to launch, the first weeks live and later growth. ``` ### Clarifying Questions - Will Uber Eats launch inside the existing Uber rides app, as a separate app, or both, and only in cities where Uber rides already operates? - Is the first priority winning market share, proving that orders arrive reliably, or reaching profitable unit economics? - Will couriers be the same drivers who give rides, a separate fleet that includes bikes and scooters, or a mix? - Should the answer include readiness criteria for the go-live decision itself, or only goals for after launch? ### What a Strong Answer Covers - A mission-level goal for Uber Eats and how it connects to Uber's wider business. - A primary metric that reflects value for all three sides, with reasons it beats alternatives such as sign-ups or gross bookings. - Supporting metrics for eaters, restaurants and couriers, including marketplace liquidity and delivery reliability. - Guardrails and counter-metrics, including effects on the rides business. - How goals change from pre-launch readiness to early launch to scale, and how targets are set without historical data. ### Follow-up Questions - Two weeks after launch, orders are ahead of plan but the share of orders arriving late keeps rising. What do you do? - How would you set a target for your primary metric when there is no history to forecast from? - Drivers are switching from giving rides to delivering food at dinner time and rider wait times are rising. How do you weigh this?