Analyze why Freight usage is growing but revenue is flat 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 is seeing increasing usage globally, but revenue is not growing accordingly. You are asked to investigate this as a root-cause analysis problem for a freight marketplace serving merchants, shippers, and carriers across regions, shipment types, and contract models.

Assume “usage” could refer to marketplace activity such as active shippers, shipment requests, booked loads, completed loads, carrier engagement, or freight volume, while “revenue” may depend on gross booking value, take rate, fees, pricing, discounts, cancellations, contract terms, and settlement outcomes. Your task is to clarify the metric definitions, isolate where the usage-to-revenue conversion is breaking down, and structure a rigorous investigation.

The problem should be approached at global scale, accounting for differences by geography, merchant segment, lane density, shipment type, pricing model, carrier supply, seasonality, and operational reliability. You should also consider whether the anomaly reflects a real business issue, a mix-shift effect, or a measurement/instrumentation problem.

The experience should consider:

- How to define “usage” and “revenue,” including numerator, denominator, time window, and whether revenue is gross, net, recognized, or contribution-based.

- Segmentation by region, country, lane, merchant size, industry vertical, shipment type, carrier type, and new versus existing customers.

- Funnel checks from quote request to booking, pickup, delivery, invoicing, payment, and revenue recognition.

- Marketplace dynamics such as pricing pressure, carrier costs, load matching quality, cancellations, discounts, and take-rate changes.

- Instrumentation and data-quality checks across booking systems, finance systems, billing, FX conversion, refunds, and cross-border reporting.

- Hypotheses that distinguish volume growth with lower monetization from operational leakage, pricing changes, customer mix shift, or delayed recognition.

- Evidence needed to prioritize causes, including cohort trends, regional comparisons, unit economics, margin trends, and merchant-level behavior.

- Mitigation and prevention considerations, including alerting, ownership, monitoring dashboards, and decision gates for business or product changes.

The goal is to demonstrate how you would structure the RCA, validate or eliminate plausible causes, and identify the most decision-useful evidence before recommending any corrective action.