Pearson New International Edition International pcl tp indd 1

MANAGING FORECAST DATA
By considering the historical data of an event, FOMs can help maximize their hotel’s
RevPar.
FIGURE 6
Actual room usage at the Altoona Hotel—Saturday analysis (total rooms 
⫽
300).
Report Run Date: 
Monday, 7:00 a.m.
Time period
Actual rooms sold
Occupancy (%)
Actual last Saturday
195
65%
Average last four Saturdays
205
68%
Average Saturdays in last quarter
188
63%
Average Saturday 
YTD
199
66%
Saturday one year ago
202
67%
Saturday two years ago
185
62%
On-the-books
(current)
260
87%
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223

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FRONT OFFICE SEMANTICS
YTD (year to date):
Numbers that include all relevant data for the current year. For example, a YTD ADR
of $95 indicates a cumulative average ADR of $95 to this point in the current year.
On-the-books:
Hotel term for cumulative current data. The term is used most often in reference to reser-
vation data. For example, a 300-room hotel with reservations for 200 rooms on a given (future) date is
said to have 200 reservations 
on-the-books. (The term originated when hotel reservation data were
stored in a bound reservation book rather than in a software program.)
Note, however, that the number of rooms already on-the-books for this Saturday
(260) suggests demand that is stronger than that indicated by the historical data.
Therefore, FOMs must always monitor current data as well as historical data before
assembling demand forecasts.
Figure 7 illustrates that FOMs also need future data to accurately forecast occu-
pancy (room demand). In fact, an FOM’s ability to accurately assess future data is the
most critical determinant of an accurate demand forecast. The importance of future
data in forecasting can be demonstrated by the following real-life situations routine-
ly faced by FOMs:
•
An airport hotel in the U.S. Northeast normally discounts heavily (sometimes
as much as 50 percent off rack rate) on Sunday night, because historically it sells
an average of only 100 rooms on that night. At noon on Sunday, January 1, the
400-room property has 105 reservations on-the-books. However, Internet users
can go online and see rates for the hotel offered at up to a 50 percent discount.
The weather forecast is for heavy snowstorms, and in the past, such storms have
significantly delayed flights or closed the airport. On these nights, the hotel usu-
ally experiences a sellout as airlines seek housing for their distressed passengers
and accommodations for delayed flight crews. Additional rooms are also sold to
stranded travelers (those who missed connections) and to travelers with tickets
on outbound flights originating in the city (but delayed until the next day).
In this case, should the FOM eliminate heavy discounts on the rooms avail-
able on Sunday night? Most FOMs would answers “yes” and would monitor
the weather to make future data predictions based on their own experience and
intuition. Discounts would be reduced or eliminated to take advantage of
anticipated future data.