RevenueAdvanced
Create a Hotel Revenue Forecast Framework
Build a repeatable way to forecast occupancy, ADR and revenue so you can act early.
The Prompt
You are a hotel revenue analyst. Help me build a practical revenue forecasting framework I can maintain in a spreadsheet. Context: - Hotel: [HOTEL NAME] in [LOCATION], [NUMBER OF ROOMS] rooms - History I can reference: [DATA AVAILABLE, e.g. last year's monthly occupancy/ADR, on-the-books data] - Demand drivers: [EVENTS/SEASONS] - How far ahead I need to plan: [HORIZON, e.g. 30/60/90 days] Deliver: 1. A forecasting method suited to an independent hotel (top-down from last year + bottom-up from on-the-books pickup). 2. The inputs to track weekly (pickup, pace vs last year, pace vs forecast). 3. A simple spreadsheet structure (columns and formulas in plain terms) I can build myself. 4. How to turn the forecast into pricing and staffing decisions. 5. How to spot when reality is diverging from forecast early enough to react. Keep it doable without forecasting software — a spreadsheet and 30 minutes a week.
What to fill in
Replace these placeholders with your own details before you run the prompt.
[HOTEL NAME]- Your property's name.
[LOCATION]- City / area.
[NUMBER OF ROOMS]- Room count.
[DATA AVAILABLE]- What historical/on-the-books data you can use.
[EVENTS/SEASONS]- Local demand drivers to build in.
[HORIZON]- How far ahead you need to forecast.
How to use this prompt
- 1Tell the AI exactly what data you have — the method adapts to whether you have last year's numbers or not.
- 2Ask it to describe the spreadsheet columns and formulas so you can build it directly.
- 3Update pickup weekly; a forecast you don't refresh is just a guess.
Example input
Hotel: Darjeeling Hilltop Hotel, 26 rooms, have last year's monthly occupancy + ADR and current on-the-books. Drivers: tea season + Puja holidays. Horizon: 60 days.
What you'll get
A hybrid method (last-year baseline adjusted by on-the-books pace), a weekly input list (pickup, pace vs last year, pace vs forecast), a described spreadsheet with columns and formulas, guidance to raise rates when pace runs ahead and to trigger demand campaigns when it lags, plus early-warning thresholds.
Pro tips
- •A rough forecast you actually update beats a perfect model you build once and abandon.
- •Forecasting's real payoff is time: seeing a soft month 60 days out gives you room to fix it before it happens.