Table of Contents
- Introduction
- What is Additional Demand?
- How the Window Works
- Example Calculations
- Why the Window Rolls
- Holiday Adjustments
- Case Studies
- Practical Tips
- Conclusion
Introduction
Hotels face the challenge of predicting how many rooms could have been sold if capacity or restrictions were not a limitation. Additional Demand is a powerful concept to uncover this missed opportunity. It shows both what was booked and what could have been booked. By using proven statistical methods like Projection–Detruncation Tau, revenue managers can optimize rates, manage inventory, and plan more effectively.
What is Additional Demand?
Additional Demand captures the full room requests for a given date, including sold rooms and the ones turned away due to restrictions or full occupancy.
Example: A hotel with 100 rooms sold out but still received 20 more inquiries. The Additional Demand = 120, signaling pricing or strategy changes. It helps identify missed revenue and informs future capacity decisions.
How the Window Works
The algorithm uses a rolling window to review booking patterns. A window is written as X × Y × Z:
- X = Number of arrival dates (often the same weekday)
- Y = Days-before-arrival to include for each
- Z = Total points (X × Y)
Example: A 3 × 5 × 15 window means 3 Tuesdays × 5 days-before each = 15 points. Suppose today is Aug 1 and you forecast Aug 27. It looks at Aug 13, Aug 20, and Aug 27 with bookings 23–27 days out. The window rolls daily for fresh data.
Example Calculations
By Aug 22, for arrival Aug 27, the window moves to 6–10 days before arrival. Updated points reflect the latest bookings and cancellations.
| Arrival Date | 23 days | 24 days | 25 days | 26 days | 27 days |
|---|---|---|---|---|---|
| Tues 8/13 | 8/07 | 8/06 | 8/05 | 8/04 | 8/03 |
| Tues 8/20 | 8/14 | 8/13 | 8/12 | 8/11 | 8/10 |
| Tues 8/27 | 8/21 | 8/20 | 8/19 | 8/18 | 8/17 |
7 × 3 × 21 Window
Some hotels book late. A 7 × 3 × 21 window looks at 7 Tuesdays × 3 days-before each.
| Arrival Date | Day 1 | Day 2 | Day 3 |
|---|---|---|---|
| Tues 7/16 | 6/26 | 6/27 | 6/28 |
| Tues 7/23 | 7/03 | 7/04 | 7/05 |
| Tues 7/30 | 7/10 | 7/11 | 7/12 |
| Tues 8/06 | 7/16 | 7/17 | 7/18 |
| Tues 8/13 | 7/23 | 7/24 | 7/25 |
| Tues 8/20 | 7/30 | 7/31 | 8/01 |
| Tues 8/27 | 8/06 | 8/07 | 8/08 |
Another Format
| Arrival Date | Date | Date | Date | Date | Date |
|---|---|---|---|---|---|
| Mon 5/16 | 5/11 | 5/10 | 5/09 | 5/08 | 5/07 |
| Mon 5/23 | 5/18 | 5/17 | 5/16 | 5/15 | 5/14 |
| Mon 6/6 | 6/1 | 5/31 | 5/30 | 5/29 | 5/28 |
| Days left | 5 | 6 | 7 | 8 | 9 |
Why the Window Rolls
Demand is fluid. Each day brings new bookings and cancellations. Rolling ensures forecasts always use the freshest data, avoiding outdated assumptions.
Holiday Adjustments
- Holiday demand is treated separately.
- Holiday points are excluded from normal days to prevent skew.
Case Studies
A beachfront resort noticed 15% unmet Saturday demand. By expanding its window and adjusting pricing, it captured more revenue. A downtown conference hotel used windows to manage last-minute cancellations. Each property found unique insights from Additional Demand.
Practical Tips
- Align windows with booking behavior (short or long lead).
- Maintain an updated holiday/event calendar.
- Pair with metrics like Pick-up, Pace, and Market Share.
- Visualize trends with BI tools or dashboards.
Conclusion
Additional Demand changes forecasting and pricing. It reveals hidden opportunities and helps build stronger revenue strategies. Whether boutique or resort, applying rolling windows and keeping data current is key to success.