Ride-share Profit Plan (Next Quarter)

Context

You are the data scientist for a ride-share marketplace (two-sided: riders and drivers). Your goal is to increase contribution profit next quarter while clearly differentiating from street‑hail taxis (e.g., transparency, reliability, safety, loyalty). You may make minimal, explicit assumptions to size impacts.

Tasks

1. Prioritize 5 profit levers (e.g., pricing, driver incentives, matching/ETAs, acquisition/retention, cost control). For each, quantify expected impact with back‑of‑the‑envelope math and state key assumptions.
2. Define success metrics and guardrails for the quarter (e.g., contribution margin per ride, driver online utilization, cancellation rate, wait time, supply fill rate) and give target ranges or “no‑worse‑than” thresholds.
3. Explain how supply–demand elasticity and surge pricing interact. Outline how you would estimate price elasticity from historical data versus via an experiment.
4. Design one A/B test to validate a pricing or incentive change. Specify: hypothesis, target segments, randomization unit, key metrics, minimal detectable effect (MDE) with assumptions, and risk mitigations for marketplace interference/imbalance.
5. Provide two recommendations you would ship first, with risks and how you’d monitor for cannibalization of non‑surge hours.