Cancellations & Wash Forecasting
Bookings on the books are not guests in the house - wash forecasting turns reservations into reality.
show_chartThe Cancellation Curve
Cancellations cluster rather than spread evenly. A share of bookings cancels immediately after booking, most cancel inside the final 72 hours, and the rest simply no-show on the day.
Every hotel builds a characteristic curve by booking window - the pattern of when its own bookings cancel between the reservation date and the arrival date.
Comparing same-time-last-year on-the-books against today reveals whether future dates will wash more or less - and by how much the current book should be discounted or sold deeper.
person_removeSources of Wash
Six behaviors drain rooms from the book. Each one needs its own expected rate before the house can forecast honestly:
Individual Cancellations
Transient bookings cancelled as plans change - the largest single source of wash in most houses.
No-Shows
Arrivals that never materialize - the room was held all along and spoils on the night.
Early Departures
Checked out before the booked departure - tonight's room is now unsold unless it is resold same-day.
Late Arrivals
Arriving next day instead of tonight - tonight is spoiled unless the room is resold late.
Group Wash
Blocks that pick up below contract, releasing rooms back to the house late in the game.
Downgrades
Guests moving to cheaper room types at check-in - the room survives, but the revenue washes.
insightsForecasting Wash Forward
Applying last year's wash rate to this year's book is a choice - and often the wrong one. Two philosophies compete:
descriptionStatic Forecast
Assume last year's wash percentage applies to this year's book - simple to compute, but blind to how the book itself has changed.
insightsDynamic Forecast
Adjust for booking mix, pace, rate paid, cancel policy changes, and market events - the book's composition changed, so its wash changed.
The bottom line: the occupancy you forecast is the book times its expected survival. Revenue managers who model wash accurately can safely sell deeper and capture the rooms others let spoil.
translatePenjelasan dalam Bahasa Indonesiaexpand_more
Prakiraan pembatalan dan wash (wash forecasting) adalah praktik memperkirakan berapa reservasi di buku yang benar-benar bertahan menjadi tamu. Pembatalan menggerombol, bukan menyebar rata: sebagian langsung setelah booking, mayoritas di 72 jam terakhir. Enam sumber wash, dari pembatalan individual hingga downgrade, masing-masing perlu laju harapan sendiri sebelum hotel berani menjual lebih dalam.
- check_circleKurva pembatalan khas tiap hotel: kapan booking batal antara tanggal reservasi dan kedatangan; mayoritas di final 72 hours.
- check_circleEnam sumber wash: pembatalan individual, no-show, early departure, late arrival, group wash, dan downgrade tipe kamar.
- check_circleBandingkan on-the-books tahun lalu pada titik waktu sama untuk menilai apakah tanggal depan akan wash lebih banyak.
- check_circleForecast statis memakai persentase wash tahun lalu mentah; forecast dinamis menyesuaikan mix booking, pace, tarif, dan kebijakan pembatalan.
- check_circleOccupancy yang diprakirakan = book dikalikan expected survival-nya.
- check_circleMemodelkan wash secara akurat membuat hotel aman menjual lebih dalam dan merebut kamar yang dibiarkan hangus pesaing.
helpPertanyaan Umum (FAQ)
Apa itu wash forecasting hotel?
Wash forecasting memperkirakan berapa banyak reservasi di buku yang benar-benar bertahan hingga menjadi tamu, dengan memperhitungkan pembatalan, no-show, dan keberangkatan awal. Prakiraan ini mengubah daftar reservasi menjadi estimasi occupancy yang jujur.
Apa saja sumber wash di hotel?
Enam sumbernya adalah pembatalan individual sebagai yang terbesar, no-show, early departure, late arrival, group wash yang pickup di bawah kontrak, dan downgrade ke tipe lebih murah. Tiap sumber butuh laju harapan tersendiri.
Apa perbedaan forecast statis dan dinamis untuk wash?
Forecast statis menerapkan persentase wash tahun lalu pada buku tahun ini, sederhana tetapi buta terhadap perubahan komposisi. Forecast dinamis menyesuaikan mix booking, pace, tarif, kebijakan pembatalan, dan event pasar sehingga lebih mencerminkan buku sekarang.