Design an experimentation dashboard for Freight at global scale

Problem Statement Description

Product context: Uber is a mobility and delivery platform; its products include rides, Uber Eats, grocery and retail delivery, freight, driver and courier tools, and marketplace pricing.

Uber Freight runs a global logistics marketplace connecting shippers, carriers, facilities, and internal operations teams across many markets. Product teams may experiment with pricing, load matching, booking flows, carrier incentives, shipper tools, support workflows, and marketplace policies. Your task is to define what an experimentation dashboard should measure and expose so teams can evaluate experiments consistently and make confident launch decisions.

This is a metrics design question, not a visual design exercise. Focus on the metric framework, data cuts, instrumentation, and decision usefulness of the dashboard. The dashboard must support complex freight workflows where outcomes may span multiple steps: quote, tender, carrier match, pickup, in-transit execution, delivery, payment, and post-delivery issue resolution.

Because Freight operates globally, the dashboard should account for market-level differences, heterogeneous user segments, operational constraints, and long feedback loops. It should help teams understand whether an experiment improves shipper and carrier outcomes without harming marketplace reliability, safety, service quality, or unit economics.

The experience should consider:

- Clear experiment-level metric definitions, including primary success metrics, secondary diagnostics, and guardrails.

- Appropriate denominators and units of analysis, such as shipper, carrier, lane, load, quote, booking, facility, market, or shipment lifecycle event.

- Instrumentation needed across product surfaces, logistics operations systems, pricing systems, dispatch workflows, and support channels.

- Cohort and segmentation needs by geography, shipper size, carrier type, lane density, shipment type, tenure, contract type, and operational maturity.

- Handling of delayed outcomes, attribution windows, marketplace interference, seasonality, and cross-market spillover effects.

- Data quality checks, experiment health indicators, sample-ratio issues, missing event detection, and exposure logging.

- Guardrail metrics for reliability, cancellation, on-time pickup/delivery, claims, support burden, safety, margin, and marketplace liquidity.

- How different stakeholders—PMs, data scientists, operations, sales, finance, and leadership—would use the dashboard to make ship, iterate, rollback, or expand decisions.

The goal is to describe a robust experimentation measurement system for Uber Freight at global scale: one that enables trustworthy interpretation of tests, highlights trade-offs across marketplace participants, and supports repeatable product decision-making across regions and freight workflows.