Pearson New International Edition International pcl tp indd 1

MANAGING FORECAST DATA
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performance. Assume, for example, that an FOM is reviewing historical data to fore-
cast no-shows. By selecting a specific PMS no-show report, the FOM can identify
the property’s historical no-show averages for last night, last week, last month, the
average of the last 10 similar days (e.g., Saturdays, Wednesdays, or Mondays), the
same date in the prior year, and an average day on the same month in the prior year.
The types of reports selected by the FOM will influence no-show projections.
Depending on the type of hotel, factors such as day of week, season, and room type
(to name just a few) may affect the no-show rate. The challenge for an FOM is to
select and evaluate the “right” historical data rather than just “any” historical data.
Determination of precisely how to best track no-show data for a specific hotel
(i.e., to assess which specific historical trends to monitor) depends on the hotel’s
unique characteristics. To generalize that the best way to forecast no-shows in all
hotels is to use the average no-show number for the last two weeks (or other time
period) trivializes the distinctive insight that an FOM can bring to the process of
estimating no-shows. Instead, an accurate estimate of no-shows requires an FOM’s
talent, intuition, and experience, along with accurate data from the PMS.