Definition
A measure of how closely your demand predictions match actual results, typically reported as a percentage error (e.g., an average error of 5%) or as an accuracy percentage. High forecast accuracy enables better pricing decisions, optimal staffing, and reduced operational waste. Track accuracy at multiple lead times (7, 14, 30 days out) to identify where predictions need improvement.
Also related to Demand Forecasting
Related Terms
Booking Pace
The speed at which reservations accumulate for a specific future date compared to the same point in time for similar historical dates. Tracking pace helps identify whether demand is building faster or slower than expected, enabling proactive rate adjustments. A date pacing ahead of last year may warrant a rate increase; pacing behind suggests promotional action.
Learn more →Booking WindowBooking Window / Lead Time
The number of days between when a reservation is made and the guest's arrival date. Understanding booking windows by segment is crucial—business travelers typically book 7-14 days out, while leisure guests may book 30-90 days ahead. This knowledge helps optimise when to release inventory and adjust pricing as the arrival date approaches.
Learn more →Constrained Demand
The actual number of rooms you can sell given real-world limitations—your physical inventory, existing bookings, and any restrictions you've applied (closed dates, minimum stays). Constrained demand represents what will actually happen, as opposed to unconstrained demand which shows total market interest. The gap between them reveals missed revenue opportunities.
Learn more →OTBOn-the-Books
All confirmed reservations and their associated revenue for future dates—your guaranteed baseline before any new bookings come in. OTB is the starting point for all forecasting: you add expected future pickup to OTB to predict final occupancy. Monitoring OTB daily reveals whether you're building toward budget or falling behind.
Learn more →Related Articles
A Beginner’s Guide to Hotel Demand Forecasting
The signals that matter (pickup, lead time, seasonality) and how to turn them into better pricing decisions—especially when using dynamic pricing.
Learn more →AI in Hotel Revenue Management: What It Can (and Can’t) Do for Pricing
A practical guide to using AI for forecasting and dynamic pricing: what inputs matter, how to set guardrails, and how to validate recommendations.
Learn more →How to Read Booking Pace (Pickup) and Use It for Better Pricing
A step-by-step workflow to turn pace reports into pricing actions: build a baseline, validate signals, and apply dynamic pricing with confidence.
Learn more →Want to improve your Forecast Accuracy?
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