Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 1 Managing Forecast Data

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 2 The FOM as Forecaster Global Distribution System (GDS): Referred to as the GDS for short, this system consists of the companies (SABRE, Galileo, Apollo, Amadeus, and Worldspan) that connect hotels offering rooms for sale with individuals and travel professionals worldwide who will potentially purchase them.

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 3 The FOM as Forecaster Occupancy Forecast: An estimate of future occupancy stated as a percentage of rooms available. Forecast of Room Demand = Occupancy Forecast % Rooms Available Rooms Sold = Actual Occupancy % Rooms Available Availability Forecast: An estimate of the number of rooms that remain to be sold.

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 4 Tracking Room Demand Historical Data: Data related to events that have already occurred. Sometimes referred to as “actual data.” Current Data: Data related to events that are entered into the PMS but have yet to occur. Future Data: Data related to events that have yet to occur and will not be found in the PMS. While this data is unknown, it can be estimated.

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 5 Tracking Room Demand Stayover: A guest who is not scheduled to check out of the hotel on the day his or her room status is assessed. No-show: A guest who makes a room reservation but fails to cancel the reservation or arrive at the hotel on the date of the reservation. Early Departure: A guest who checks out of the hotel before his or her originally scheduled check-out date. Overstay: A guest who checks out of the hotel after his or her originally scheduled check-out date.

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 6 Tracking Room Demand YTD: Short for year-to-date; these numbers include all relevant data for the current year. On-the-Books: Hotel jargon for cumulative “current” data. The term is used most often in reference to reservations data.

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 7 Using Predictive Data Sources Group History: The number of rooms blocked for and ultimately used by a group during similar events held in the past. Trend Line: The documentation (usually displayed on a graph or chart) of changes in data values. Trend lines may show increases, decreases, or no change in comparative data values.

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 8 Using Predictive Data Sources Most FOMs evaluate transient guest internal trend lines relative to: –Occupancy % –Room count –Reservation/cancellation activity –No-shows –Arrivals –Early departures –Stayovers

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 9 Using Predictive Data Sources STR reports include: –Number of hotel rooms available to sell –Number of rooms sold –Total room revenue

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 10 Using Predictive Data Sources STR uses this information to compute: –Property occupancy percentage –Property ADR –Property RevPar –Property % change from prior period –Competitive set occupancy percentage –Competitive set ADR –Competitive set RevPar –Competitive set % change from prior period –Index scores –Index change scores

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 11 Using Predictive Data Sources STR publishes reports including: –Trend report –Monthly STAR –Daily detail by week –Daily detail by month –Weekday/weekend report

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 12 Using Predictive Data Sources Competitive Set: The group of competing hotels to which an individual hotel’s operating performance is compared. Often referred to as the “comp set.” Index (STR): The STR index is a comparative measure of a specific hotel’s operating performance. Performance of Subject Hotel =Index Performance of Competitive Set Hotels

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 13 Using Predictive Data Sources E-distribution Channel: A generic term used to indicate all “E” (electronic) methods of advertising and selling guest rooms. Also known as “E-commerce.” Feeder Market: A geographic location that includes a significant number of travelers using a hotel’s services.

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 14 Using Predictive Data Sources Peak Night: The night when the most guest rooms for a group are sold. Pick-up: The actual number of guest rooms reserved for (or by) individuals. Group pick-up is the number of guest rooms reserved for individuals in a group block. Host Hotel: A property that serves as the headquarters for a group when multiple hotels must be used to house all group members.

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 15 Occupancy Forecast Reports Four essential activities in producing an occupancy forecast: –Generating the demand forecast –Establishing an initial rate strategy –Monitoring pick-up reports –Modifying rate strategy (if warranted) Most common hotel reservation types: –Non-guaranteed –Guaranteed –Advance payment deposit

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 16 Occupancy Forecast Reports Pick-up Report: Any of a variety of PMS reports designed to summarize reservation activity. 10-day forecasts 30-day forecasts Extended forecasts

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 17 Occupancy Forecast Reports Total Rooms LessUnavailable Rooms Less Committed Rooms EqualsSellable (Available) Rooms

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 18 Other Forecasting Issues Impact on pricing GoPar: Short for “gross operating profit per available room.” The average gross profit (revenue less management-controllable expenses) generated by each guest room during a given time period. Revenue – Management-controllable expenses = GoPar Available Rooms for That Period

Woods et al., Professional Front Office Management © 2007 Pearson Education, Upper Saddle River, NJ All Rights Reserved. 19 Other Forecasting Issues Evaluating forecast effectiveness: –Consequences of: Unrealistically high forecasts Unrealistically low forecasts Low-Balling: Developing forecasts that are unrealistically conservative (low) for the express purpose of more easily achieving or exceeding them.