Decompose and optimize delivery operational costs

For a platform like DoorDash, decompose operational cost per order, and propose how to optimize it without harming customer experience. 1) Enumerate cost buckets with a driver tree (e.g., courier_base_pay, surge/incentives, batching_bonus, support_costs, refunds/chargebacks, payment_processing, insurance, fraud, idle-time, failed_delivery, packaging_subsidy) and define cost_per_order and contribution margins by city/restaurant cohort/hour. 2) Identify the top 3 levers that can reduce cost in high-cost markets while maintaining conversion and on-time rate; quantify expected impact using historical elasticities or prior experiments. 3) Propose an experiment to lower cost: e.g., dynamic batching thresholds, distance-based pricing tweaks, or incentive reshaping. Specify unit of randomization, stratification, power, guardrails (late_rate, cancellations, complaints), heterogeneity analysis, and how to prevent supply reallocation contamination. 4) Describe a monitoring plan for unintended effects (e.g., courier churn, order acceptance, reassignments) and a rollback criterion. 5) If the test saves $0.35/order but increases late deliveries by 0.6 percentage points, provide a framework to decide whether to ship or iterate.