Booking Curve
Booking Curve is the cumulative shape of demand for a single future arrival date, plotted against lead time. Where a pace report tells a revenue manager how many rooms are on the books today, the booking curve describes the pattern by which those rooms accumulated — steep and early for a conference date, flat until the final fortnight for a leisure weekend, near-vertical in the last 72 hours for an urban date driven by mobile last-minute demand. It is the reference shape against which every pace reading is interpreted.
How it is built
Each curve is constructed from historical on-the-books snapshots for comparable dates, expressed as the percentage of final occupancy achieved at each lead time:
Curve point (day D) = OTB at D days out ÷ Final rooms sold × 100
Curves are usually segmented — by day of week, season, market segment, and channel — because a single blended curve averages away exactly the differences that make it useful.
Example
A hotel's Saturday leisure curve historically reaches 30% of final occupancy at 60 days out, 55% at 30 days, 80% at 7 days, and 100% at arrival. Today, 30 days from a given Saturday, the hotel holds 44% of its typical final occupancy. Read against the curve rather than against a flat target, the date is not simply "56% empty" — it is pacing roughly 11 points behind its normal shape, with about 45% of its demand still expected to arrive. The appropriate response is a modest rate adjustment, not a fire sale.
Why it matters
The booking curve is what turns an occupancy number into a decision. Without it, a revenue manager cannot distinguish a date that is genuinely soft from one that simply books late, and the most common pricing error in hospitality — discounting a late-booking date too early and giving away rate the market would have paid — follows directly from reading pace without a curve. Curves also underpin forecasting: multiplying current OTB by the inverse of the expected curve position gives a fast, defensible remaining-demand estimate that most revenue management systems use as a baseline.
Caveats
Curves are historical artefacts and shift when behaviour shifts. The booking window compressed sharply after 2020 and has never fully returned to its previous shape, which invalidated many pre-pandemic curves and forced properties to rebuild them on shorter reference histories. Channel mix changes distort curves too: growth in OTA mobile share tends to flatten the early portion and steepen the tail. Curves for dates affected by a citywide event or a compression night should be modelled separately rather than blended into the standard pattern.
Related
- Pace Report — the report the curve gives meaning to
- Booking Window / Lead Time — the horizontal axis of the curve
- Pace Variance — the deviation measured against the curve or STLY
- Unconstrained Demand — the true demand a completed curve only partially reveals