Define and validate an airline profitability metric
Company: Capital One
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: hard
Interview Round: Onsite
You are designing a profitability metric for airline routes to support a PowerDay-style case presentation. Propose a single primary metric and 2–3 guardrails that incorporate both revenue and operational quality. The metric must be decomposable by route and month and robust to irregular operations. Available fields include: route_id, flight_date, seats_sold, fare_usd, ancillaries_usd, fuel_cost_usd, crew_cost_usd, airport_fees_usd, block_minutes, delay_minutes, cancellations, refunds_usd, rebooking_cost_usd. Tasks:
(a) Define your primary metric formula (e.g., Adjusted Route Profit per Block Minute) and each guardrail (e.g., cancellation rate, on-time arrival rate, NPS proxy if available). State all assumptions (e.g., how to allocate refunds and rebooking costs) and justify why the metric makes business sense.
(b) Outline how you would validate the metric historically: backtest against past route openings/closures; perform sensitivity analyses to demand shocks and fuel spikes; and check correlation with long-run cash contribution.
(c) Suppose management trials a new policy (e.g., dynamic overbooking). Design an experiment or quasi-experiment to detect lift in your primary metric while controlling for seasonality, competitor moves, and weather. Include unit of randomization, power analysis inputs, guardrails, and a plan for interpreting heterogeneous effects across routes.
Quick Answer: This question evaluates competency in metric design, business analytics, causal inference, and experimental design by requiring a decomposable airline route profitability metric, operational guardrails, validation via backtests and sensitivity analyses, and a policy experiment plan.
