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Module 5: REVENUE MANAGEMENT

This module is for ASCOT HM S.Y. 2021-2022 third year students use only, hence not
for sale. No part of this module may be reproduced or transmitted in any form or by
any means—electronic or mechanical including photocopying, recording, or any form
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Used by:

Aurora State College of Technology

Brgy. Zabali, Baler, Aurora 3200

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Module 5: Forecasting Demand

At the end of the lesson, the learners should be able to:

1. Recognize the principles and practices applied by revenue managers working
in the lodging segment of the hospitality industry.
2. Explain why collecting and analyzing data about customer demand for lodging
products and services are essential when forecasting future sales.
3. Present tools that revenue managers use to track historical, current, and
future demand for their rooms inventory.
4. Examine how demand forecasts affect decisions on hotel room and services
pricing.

Input
Demand Forecasting is the process of making estimations about future customer
demand over a defined period, using historical data and other information. Proper
demand forecasting gives businesses valuable information about their potential in their
current market and other markets, so that managers can make informed decisions
about pricing, business growth strategies, and market potential (Trade Gecko, n.d.).
In the context of the hospitality industry, it is critical that data is collected and properly
assessed. Why? Because effective data analysis leads to an accurate assessment of
demand. Estimating the number of potential buyers allows for accurate sales forecasts.
Hayes and Miller (2011) states that for hoteliers, an accurate estimate of future room
demand is essential to the effective operation of their hotels because:
 Accurate revenue forecasts allow hotel department leaders to more efficiently
schedule the department staff needed to serve guests. Proper staffing is required
to ensure that guests are provided with the service levels intended. Of course,
customer-centric revenue managers are just as concerned with ensuring guests
satisfaction as they are with revenue optimization.
 Accurate revenue forecasts give those responsible for purchasing supplies the
information required to buy needed items in the correct quantities. The impact pm
guest satisfaction of ensuring the presence of necessary products and supplies
upon guest arrival is significant.
 Accurate revenue forecasts allow managers and owners to estimate the future
probability of their properties. Doing so provides the information needed to make
decisions about profitability and cash flow that directly affect decisions related to
capital improvements capital expenditures.
 Accurate demand forecasts allows revenue managers to make better decisions
about how to modify and manage prices of their products and services.

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There are three sources of data that revenue managers look at to create accurate and
ultimately useful demand forecasts (Hayes and Miller, 2011).

Type of Information What it describes

Historical data Events that have already occurred
Current data Events occurring now or in the very near
term
Future data Events that will occur in the future

Below is the Four Components of Effective Demand Forecasts from Hayes and Miller
(2011).

Each data type is important to accurate demand forecasting. Insight involves the skillful
analysis of what each data type reveals. To better understand each data type, they will
be discussed in the next paragraphs.

Historical Data
 These are data describing events that have already occurred. These data are also
known as actual data and result data.
 Every operating hotel generates historical data even if the data are not recorded
or analyzed.
 Understanding a hotel’s past performance is one of the best ways to make good
decisions about future performance. Study the past to define the future.
 The errors of the past provide wisdom for future decision making.

The following re specific historical data that revenue managers are interested in and are
collected are related to the following:
 Number of reservations/room nights booked per day
 Number of reservations/room nights denied per day
 Number of daily reservation cancellations
 Total number of room nights cancelled
 Number of check-ins (arrivals)
 Number of check-outs (departures)
 No-shows
 Walk-ins
 ADR achieved
 Occupancy percentage achieved

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 By the property
 By room type
 Average number of guests per room
 Average length of guest stay

Vocabulary:
Trailing period—a data collection method characterized by the act of discarding the
oldest piece of data in a data set when the newest data are added, thus updating the
set’s information while keeping the set size constant. Data contained in a trailing period
are often used in calculating a rolling average.
Denied reservation—the situation that occurs when a hotel is unable to accommodate a
guest’s reservation preference due to unavailability of the room or service at the price,
or on the date requested by the guest. This is also known as a denial.
Walk-in—a guest that arrives at the property seeking a room but without an advanced
reservation.
Track data—to continually monitor a set of dat. It can be on a daily, weekly, and/or
monthly basis.
Fixed average—an average calculated by using historical data generated during a
specific and unchanged time period.
Rolling average—an average calculated by using historical data generated during a
changing time period. The use of this is more complex and time consuming than using a
fixed average, but it is extremely useful in recording historical data that will help the
revenue manager make effective predictions about the sales levels, rooms sold, or other
data that one might expect in the future. Rolling data is more current and therefore more
relevant than those fixed historical averages.

Current Data
 Current data helps in understanding the present.
 Current data can be examined best when it is divided into its three main reporting
areas:
- Occupancy and Availability Reports
What is happening now is best communicated by the monitoring of four key
areas:
1. The number of rooms available to sell
2. The number of rooms reserved
3. The number of rooms held and blocked
4. The estimated ADR resulting from currently reserved or blocked rooms

There are some revenue mangers that prefer to analyze their current data
based on room’s availability rather than on reserved and blocked rooms. They
use this formula: Rooms available – Total held = Available for sale

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Another formula that revenue manager uses too provide information they
need for analyzing each future date for which rooms are available for sale is:
Total rooms available – Total unsellable rooms - Presold/blocked rooms =
Sellable (Available) rooms

What can purchased or stand-alone programs or directly interfaced with CRS

or PMS do? By utilizing information collected from the hotel’s historical and
current sales data, these programs can:
+ Recommend room rates that will optimize the number of rooms sold
+ Recommend specific room rates that will optimize sales revenue
+ Recommend special room restrictions (e.g. minimum length of stay
requirements) that serve to optimize the total revenue generated by the hotel
during a specific time period
+ Identify special high consumer demand dates that deserve special
management attention in pricing

Evaluating current data can help revenue managers estimate room demand
and serves to assist in pricing rooms. Effective revenue managers review
each future sales period by day; for every upcoming day.
- Group Rooms Pace Reporting
This is a summary report describing the amount of future demand for a lodging
property’s group rooms and the rate/s at which that group business has been
captured. This is also referred to as a group rooms booking pace report.

There is a common misconception that hotel room sales in a typical hotel are
made to individuals, in some cases it might be true. But, in many more cases,
the majority of room sales are made to groups of individuals or to individuals
reserving large numbers of rooms. In most cases, these rooms will be
reserved and held for purchase at a date in the future that can range from a
few weeks to several years in the future.

In hotels that host extremely large meetings and conferences group rooms
pace reports may indicate blocked rooms for dates that are up to ten or even
more years in the future. Identifying those days on which rooms have already
been reserved helps revenue managers better make their pricing and rooms
inventory-related management decisions regarding remaining rooms that will
be sold to transient guests.

Revenue managers assessing future room sales are interested in two issues:
1. How many rooms have been sold at this point?
2. How quickly are we selling our remaining rooms?

Revenue managers record the number of rooms or group rooms that have
currently been sold or held for a future date using a position report. A position
report is a simplified form of a pace report that summarizes rooms sold or
held for a future date or time period.

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A pace report can be prepared based on the number of rooms sol, the sales
value of the sales, or both. It can also be produced to include any period of
time in the future. Most revenue managers prefer that group cancellations are
reported separately and in the month the sales are canceled not when the
group was slated to arrive. A pace report help tell revenue managers where
future pricing opportunities or challenges may exist.

- Nonrooms Revenue Pace Reporting

Revenue managers in full-service hotels should also carefully monitor the
sale of other hotel products and services. This can be done by the use of pace
reports designed especially for nonrooms revenue areas such as sales and
catering, which generate significant food and beverage or meetings income
and this may impact guest room pricing decisions.

Revenue management in the past has been seen as “guest rooms only” issue.
But today, revenue management includes a hotel’s revenue stream also
includes income from restaurants, bars, conferences, banquets, spas, and
other operating departments in the hotel.
Effective revenue managers understand that answers to the following
questions are indeed revenue management essentials. These questions apply
not just to guest rooms but also to the hotel’s restaurant, bar, etc. (Hayes and
Miller, 20211).
+ Who are our key buyers of conferences?
+ What is their lead time for reserving space?
+ What is the role of price in converting a prospect to a customer?
+ What is the best utilization of each meeting room?
+ What is the most profitable configuration (set-up) of those rooms?
+ What meetings business has recently been lost, and why?
+ What meetings or conferences business has been denied, and why?

Vocabulary:
Block(ed)—rooms reserved or removed from a hotel’s sellable inventory exclusively for
members of a specific group. Such rooms are said to be “blocked” (from being sold to
alternative buyers) especially for that group. This is also referred to as a group block.
Out-of-Order (OOO)—a room that is unrentable for reasons other than normal cleaning.
On-the-books—an industry term referring to the number of currently reserved and/or
blocked rooms committed for any future date. The term originates in the days when hotel
reservation data was stored in a bound reservations book, rather than in software
program.
Minimum length of stay (MLOS)—a revenue management strategy that instructs
reservationists to decline any room reservation request that does not equal or exceed
the predetermined minimum number of nights allowed. Example: We need to put an
MLOS of two night on any room requests that is on April 14. This means that a guest that

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will only stay for one night will not be able to be accommodated. Only those that will stay
for two nights and more will be accommodated.
Tentative (sale)—the situation that occurs when a hotel blocks group rooms for a
specific date in anticipation of a future confirmed contract or sale.
Request for proposal (RFP)—an official request by a potential rooms or space buyer that
a hotel quote, in writing, its rates and contract terms in response to the buyer’s
specifically identified rooms or space needs.
Pick up—the proportion of previously reserved rooms that are ultimately occupied.
Example if 200 group rooms are reserved, and only 100 are ultimately occupied, the pick
up rate is 50% (100 occupied/200 originally held = 50% pick up).

Future Data
This is the data describing events that will occur in the future. Also, known as forecast
data.
Factors that will most affect the future demand for any single hotel’s guest rooms:
+ Demand generators
+ Demand drains
+ The strength or weakness of the local as well as the state of the national economy
+ The property’s addition or elimination of specific services
+ The opening or closing of competitive hotels
+ Predictable factors such as planned road construction or seasonality
+ Unpredictable factors such as unplanned events, road construction, or severe
weather.
+ The pricing decisions made by the property’s competitors
+ The pricing decisions made by the property

Basic Forecast Types (Hayes and Miller, 2011)

Forecast Type Characteristics/Purpose
Occupancy forecast  Forecast at least 1, 2, 7, 14, 21, and
30 days out
 Produces daily and weekly
occupancy percentage estimates
 Unlikely to exceed 100 percent
 Helps improve employees
scheduling
 Shows guest arrival and departure
patterns
Demand forecast  Identifies periods of 100 percent or
more occupancy demand for
rooms

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 Identifies periods of very low

demand
 Forecasts 30, 60, 90 days out
 Produces at least weekly
occupancy percentages
 Used to help establish room rate
selling strategies
Revenue forecast  Forecasts 30 days out of more
 Estimates RevPAR
 Matches revenue forecasts to pre-
established budgets
 Advanced versions help estimate
the hotel’s cash flows
 Should not exceed the revenue that
can be generated by selling 100
percent of available rooms

The most important rationale for the development of extended forecasts is the impact
such forecast will have on rooms pricing, a factor significantly affecting total room
revenue. Experienced revenue managers often produce extended demand forecasts of
one year or more, but then alter those forecasts monthly as new information, data, and
insight becomes available (Hayes and Miller, 2011).

Vocabulary:
Demand Generator—an entity or event that produces a significant increase in business.
Example: Vacation season, Holy week, Christmas Break can be demand generators.
Demand drain—a circumstance that produces a significant decrease in business.
Example: a hotel that caters to businesspersons, holidays typically represent demand
drains.
Convention and visitors bureau (CVB)—the local entity typically responsible for
promoting travel and tourism in a specifically designated geographic area.
Stayover—a guest room not scheduled to be vacated on the forecasted day. That is the
guest will be staying and using the rooms for at least one more day.
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.

The Misuse of Forecasts

Not all forecasts are accurate. In some cases, the inaccuracy is intentional. Why? It is
because there are often real pressures on revenue managers to produce inaccurate
forecasts. This pressure can take the form of encouraging the production of forecast

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that are of one of the following types: (1) forecast that are unrealistically low; and (2)
forecasts that are unrealistically high.

Unrealistically Low Forecasts

When a hotel’s general manager or the hotel’s executive committee makes an internal
decision to reward those individuals they feel were responsible for the hotel exceeding
planned, budgeted, or forecasted revenues. When such rewards are significant, the
internal pressure on revenue managers must create artificially low forecasts may also
be significant (Hayes and Miller, 2011).

Unrealistically High Forecasts

Those forecasts that are unrealistically high may be produced to achieve either external
or internal goals. In many cases, these overly optimistic high forecasts have been
produced to encourage investment in hotels by local governmental entities, many of
which are unfamiliar with the manner in which accurate lodging revenue forecasts
should be produced, and thus lack the sophistication needed to knowledgeably assess
the validity of the forecast (Hayes and Miller, 2011).

Demand Forecast and Strategic Pricing

1. Impact of demand on price
Revenue managers that are honest recognize that very few buyers are pleased
to pay a higher-than-normal price for the same product simply because it is
temporarily in short supply. This is especially true when buyers realizes that the
supply shortage has not resulted in an increase in seller’s cost (Hayes and Miller,
2011).

For revenue managers in the lodging industry the best practice is one of not
permitting demand to have a direct impact on price. Demand can and should be
permitted to affect discounts. To optimize ADR and RevPAR in periods of
temporarily heightened demand, revenue managers should seek to eliminate
discounts rather than increase their rack rates (Hayes and Miller, 2011).

2. Impact of price on demand

Although demand should not be allowed to dictate prices, it is certainly true that
change in price will often result in a shift in buyer demand. Buyer demand is
affected by price. Revenue managers must need to understand how buyer
demand is influenced by:
+ Reduction in price to be paid
- Will reducing the selling price of hotel rooms result in increased demand for
them? In most cases, the answer is no. Why is it no? It is because a hotel by
itself, is not a demand generator. In majority of cases, people decide to travel
to a specific area, then select their hotels, not the other way around.

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+ Increases in price to be paid

- An increase in room rates can have a significant and quite varied effects on
demand. Recall our example in eBay, as the price increase, some buyers stop
bidding. As the cost of securing a given hotel room increases, the number of
buyers for that room will decrease. This is true in times of low, medium, and
high demand for rooms.
- Below is the impacts of alternative revenue manager rate strategies
Revenue Effect on Effect on Effect on RevPAR
Manager’s Demand for Average Daily (Total Revenue)
Pricing Strategy Rooms Rate (ADR)
Reduction in Negligible effect Lowered Generally
average rate on increasing the Lowered
number of new
buyers
Increase in Reduces number Increased Generally
average rate in of potential increased
response to high buyers choosing because demand
demand the property exceeds supply
Increase in Reduces number Increased Generally
average rate in of potential lowered because
times of average buyers choosing supply exceeds
or low demand the property demand

When demand for rooms exceeds supply revenue managers should review their
revenue manager teams’ answers to ten key questions:
1. What type guest is generating the increase in demand?
2. Which of our products would these guests prefer to buy?
3. From which distribution channel do these guests typically buy, and how can
these channels best be managed?
4. How can changes in price best be communicated to these guests?
5. When do these guests typically book (reserve their rooms)?
6. Who, if anyone, is offering similar products to these guests? Why would guests
select our product rather than our competitor’s?
7. What inventory management strategies can be effectively employed during this
period?
8. What rate management strategies can be effectively employed during this high-
demand period?
9. How will our loyal (repeat) guests be treated during this period of high demand?
10. As a property committed to customer-centric revenue management, how can
all of our guests’ perceptions of value be maintained or increased during this
period of higher demand and selling price?

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3. Impact of demand forecasts on an revenue manager’s pricing strategy

Demand forecasts are the single most important piece of data revenue managers
will review and evaluate when seeking to optimize rooms revenue. Only by
creating an accurate forecast can revenue managers know when room demand
is strong (or weak) enough to dictate significant changes in the pricing strategies
designed to help their hotels achieve their unique RevPAR and RevPOR goals. If
no active and accurate forecasting program is in place, RMs will consistently
make misinformed decisions and continually lead their properties in directions
that are detrimental to both the property and its guests (Hayes and Miller).

RMs who want to be continually headed in the proper direction do the following:
_ Understand the unique property features that affect demand for their hotels
_ Know about special citywide and area-wide events that affect room demand
_ Understand the demand for competitive hotels in the area
_ Consider the pricing strategies of competitive hotels in the area
_ Include weather, road construction, season of the year, special occurrences,
and any other relevant factors when making demand assessments
_ Adjust forecasts quickly when confronted with significant demand-affecting
events
_ Keep the interest and reactions of guests foremost in all decisions related to
rooms pricing

Agreement

Read the module. We will have a quiz about

this on April 21, Thursday

References:
Hayes, D. and Miller, A. (2011) Revenue Management for the Hospitality Industry. John Wiley &
Sons, Hoboken, NJ.

Trade Gecko. (n.d.). What is demand forecasting? QuickBooks Commerce.

https://www.tradegecko.com/ebooks/demand-forecasting

Revenue Management Module – For ASCOT students use only.