Airbnb’s Core Stays marketplace serves ~6M active listings and ~150M monthly site/app visitors globally. The Finance team uses a weekly GMV forecast (gross booking value) to set marketing budgets, staffing for customer support, and liquidity planning for host payouts. Over the last 8 weeks, forecast accuracy has deteriorated: the median absolute percentage error (MdAPE) increased from 6% to 14%, with several weeks missing by >20%. This is now a board-level issue because a 10% GMV miss can translate into ~$80–$120M variance in quarterly revenue recognition and marketing ROI.
You own the analytics for forecasting and KPI instrumentation. Stakeholders are debating whether the issue is:
You have access to historical forecasts and actuals, plus event-level product logs and booking lifecycle data. The VP of Finance asks: “What strategies would you use to improve the accuracy of your forecasts, and how would you prove they work?”
| Source | Description | Grain |
|---|---|---|
forecast_runs | Each forecast run with model version, run timestamp, forecast horizon, predicted GMV | model_run × week × region |
bookings | Booking lifecycle: created, confirmed, canceled, check-in/out, booking_value, fees, currency | booking_id |
search_events | Search sessions: query, dates, guests, results_count, latency, filters | search_session_id |
listing_availability_daily | Listing-night availability, min-stay rules, price, occupancy | listing_id × date |
funnel_events | View listing, start checkout, payment attempt, confirmation, errors | user_id × event |
marketing_spend | Channel spend, impressions, clicks, attributed sessions | channel × day × region |
exogenous_factors | Holidays, major events, travel advisories, FX rates | region × day |
Constraints: