You are a PM at Lyft. How would you define and measure success for the driver supply side of the marketplace?

Clarifying Assumptions

• I'll focus on the US market where Lyft competes head-to-head with Uber (~27% vs. 68% market share).
• Driver supply health impacts: rider wait times, surge pricing frequency, driver earnings, and Lyft's operational costs.
• I'll approach this from a two-sided marketplace lens — driver supply and rider demand must be balanced at the market/hour level.

Framework Structure

Product Context → Goals & Lifecycle → Ecosystem → User Journey + Metrics Funnel → North Star Metric → Counter Metrics.

Product Context

Why driver supply is existential

Lyft's product is fundamentally a real-time matching market. When a rider opens the app and sees '28-minute wait,' they switch to Uber. When they see surge pricing, they feel gouged. Both experiences trace back to driver supply inadequacy. Lyft loses riders every time its supply fails to meet demand — and unlike a SaaS product, each failed match is an irreversible churn event.

Driver dynamics

Lyft has ~1.4M active drivers in the US. Like DoorDash's Dashers, drivers are independent contractors who multi-app (most also drive for Uber). Lyft's driver utilization and earnings competitiveness directly determine how many hours drivers allocate to Lyft vs. Uber. This is a constant, real-time competition for driver time.

The supply-demand balance problem

Driver supply must be balanced at the market level (city), the time-of-day level (rush hour vs. 3am), and the zone level (airport vs. downtown). A city-level 'enough drivers' metric masks micro-imbalances that create the worst rider experiences.

Goals & Lifecycle Stage

Lifecycle stage: Mature competitor. Lyft cannot out-acquire Uber on driver supply — it must optimize efficiency and earnings to retain existing drivers.

Business objective: Reduce the frequency of surge pricing events and long rider wait times by improving the predictability and density of driver supply.

Prioritized goal: Improve driver utilization rate (time with a passenger / time online) while maintaining earnings per active hour.

User Journey & Metrics Funnel

Step 1: Driver goes online

Driver opens app and toggles to available in a zone.

Metric: Weekly active drivers (WAD) — distinct drivers who complete ≥1 trip per week.

Step 2: Driver receives a match

Algorithm matches driver to a nearby rider.

Metric: Match rate — % of time online that driver receives a trip request. High match rate = good utilization.

Step 3: Driver accepts

Driver accepts or declines the match.

Metric: Acceptance rate — % of offers accepted. <70% suggests earnings mismatch or poor ride quality expectations.

Step 4: Driver completes trip

Driver picks up rider and completes the trip.

Metric: Trip completion rate — % of accepted trips completed without cancellation.

Step 5: Driver earns

Driver receives payment (base + per-mile + per-minute + tip).

Metric: Earnings per active hour (EPAH) — primary driver satisfaction metric.

Step 6: Driver continues or logs off

Driver decides whether to continue driving or go offline.

Metric: Session duration — hours per online session. Longer = driver is satisfied with earnings/experience.

Step 7: Driver returns next week

Metric: Driver 7-day retention rate — core supply health indicator.

North Star Metric

Chosen NSM: Fulfilled demand percentage — % of rider trip requests that are matched with a driver within 8 minutes.

This captures driver supply quality from the rider's perspective (which is what creates Lyft's business outcome) while being 100% driven by driver supply health. A 95% fulfilled demand rate means nearly every rider gets a match; 85% means 15% of riders see unacceptable wait times or leave.

Supporting driver-side metrics:

• Earnings per active hour (EPAH) — driver satisfaction, retention predictor
• Driver 7-day retention rate — supply health trend
• Market-level supply coverage index — % of market zones with adequate coverage by hour of day
• Surge activation frequency — % of market-hours with active surge pricing (lower = better supply balance)

Counter Metrics

• Rider wait time: The downstream output of supply health — if fulfilled demand % rises but wait times don't fall, the algorithm is mismatching.
• Driver earnings per trip: If we increase EPAH by reducing match rate (drivers wait for higher-value rides), we solve driver satisfaction but hurt supply coverage.
• Cost per trip (Lyft unit economics): High driver retention bonuses must not eliminate contribution margin.