1. Price and Revenue
Management
Assignment – Part A
Revenue Management in
Hospitality Industry
EPGP 2009-10 - Term V- Individual Submission
25-April-2009
Instructor: Prof. Vinaysingh Chawan
Submitted by:
Rajendra Inani - #27

2. Table of Contents
1 Introduction – Hospitality Industry..............................................................................................3
2 Hospitality Industry Revenue Management.................................................................................3
2.1 Segmented Market......................................................................................................................3
2.2 Fixed Capacity..............................................................................................................................4
2.3 Perishable Inventory....................................................................................................................4
2.4 Low Marginal Cost ......................................................................................................................4
2.5 Advanced Sales............................................................................................................................4
2.6 Demand Fluctuations...................................................................................................................4
3 How does it all work?...................................................................................................................5
3.1 Market Segment Identification....................................................................................................5
3.2 Demand Forecasting ...................................................................................................................5
3.2.1 Historical models..................................................................................................................6
3.2.2 Advanced booking models....................................................................................................6
3.2.3 Combined forecast methods.................................................................................................6
3.2.4 Allocation..............................................................................................................................6
3.2.5 Overbooking.........................................................................................................................7
4 Challenges....................................................................................................................................7
5 Conclusion...................................................................................................................................7
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3. 1 Introduction – Hospitality Industry
If you stay in a luxury hotel it is not sure what your neighbor paid for his / her room? If he / she were a
business visitor, you may well have paid Rs.5000 for the same space you paid for Rs. 3000. It is also
possible that he / she paid Rs. 2000, if they planned well in advance and managed to better deal
which was hard for you. So, why hotels charge different customers different prices for the same type
of room?
Such differences are the result of an increasingly common strategy to maximize revenues (and
profits) in the hospitality industry - one practice, known as revenue or yield management. Revenue
Management (RM) is a scientific engineering, operations research, statistics, and Customer
Relationship Management (CRM) customers combined and divided into price bands, on different
services. The statistical analysis of data from the past helps in forecasting demand and establishing
the right price bands. When properly used, helps hotels revenue management to increase market size
and increase revenues. Some industry practitioners also refer to RM as the art of selling the right
room to the right customer at the right time and at the right price.
Revenue management, as often practiced in hotels catering to help determine the most profitable mix
of transient business. The prognosis of transient is the main factor in the revenue management
system; no published research examines the accuracy of forecasting methods for hotel guests.
Accurate forecasts are crucial for the proper management of revenue. It is noted that a 10% increase
accuracy of forecasts in the airline industry revenue by 0.5 to 3.0% on the high demand for flights. A
recent Wall Street Journal wrote that Continental Airlines, a gain of $ 50 to $ 100,000,000 per year
from the use of revenue-management system. Detailed forecasts for the important input for most
revenue management systems, and no accurate predictions, the speed and availability of the
recommendations of the revenue management system very accurately made.
The data that is used for hotel forecasting has two dimensions to it: when the reservation was booked
and when the room was consumed. The booking information gives the manager additional detail
which can be used to update the forecast. Without this information, the manager would have to rely
solely on the historical information on the daily number of arrivals or rooms sold.
2 Hospitality Industry Revenue Management
2.1 Segmented Market
Hotels usually have their market segment (customers) in a number of categories on the price of each
category is based ready. Typical categories are business and leisure guest. Because the demand for
the behavior may vary significantly for each category you can find hotels difficult to simultaneously
meet all requirements. A good example is the comparison between the time-conscious businessman
and the holiday price-sensitive customers. The first was prepared for a higher price in exchange for
flexibility to pay for a room booking at the last moment, while the latter is willing to introduce some
flexibility for a more affordable room to maneuver. RM tried to income through the management of the
compromise between a low occupancy and higher room rate scenario (business), compared with a
high occupancy and lower room rate (allows customers to maximize). Such a strategy makes it
possible to fill rooms that otherwise would have been empty.
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4. 2.2 Fixed Capacity
A hotel capacity is relatively established - it is almost impossible to add or remove rooms to
fluctuations in demand. If all the capacity of the hotel were flexible, there would be no need for
capacity management.
2.3 Perishable Inventory
In the hotel industry, hotel rooms are the inventory. A hotel room that remains vacant for a night, it
loses all its value for that night. This inventory cannot be saved and is lost forever. Because RM
attempts to demand rather than supply management appears to be economically viable for the hotel.
2.4 Low Marginal Cost
The fixed cost for adding a room in a hotel is very capital intensive. However, if the hotel manages its
initial fixed costs, the cost of an additional customer are low enough to use the hotel room with a lower
margin sell if he so wishes. Such a strategy is obviously necessary, by such is the space (s) to sell at
higher margins. This is how the high fixed costs and low marginal cost nature of the transaction price
differentiation is a necessity - something that is possible through the application of RM.
2.5 Advanced Sales
More often than not, requests for bookings to start early. Therefore have the flexibility to adapt hotel
rates based on the differences between the realized bookings and the expected demand. When all
rooms are sold at the same time, the hotel does not have the flexibility to adjust prices when demand
comes later. The downside occurs when a manager the possibility of early adoption of a reservation
from a customer, a low price, or will wait to see if a higher paying customer will appear eventually
show up.
2.6 Demand Fluctuations
Demand for hotel rooms is characterized by crests and troughs, which factors in the hotel room in the
pricing process. In peak season the hotel to increase its revenue by increasing room rates, while in
lean years, they can increase utilization by lower prices. Data from past provides the manager a way
to predict when this may periods of high and low demand for action. Unfortunately it is very difficult to
establish the real question with a high degree of safety to predict.
Therefore, for the most critical challenges facing the hospitality industry is predicting the potential
capacity, and developing a pricing strategy, the maximum capacity and revenue enhanced. Revenue
management is the most effective technique to the challenge of aggregating similar and often
hierarchical production planning techniques to solve in the industry.
Revenue Management is based on complex optimization methodologies developed from advanced
statistical and analytical models. To come to a solution, managers need to evaluate millions of
decisions that a significant investment of skills, hardware and time. Many doctors prefer RM
breakdown of the actual business scenarios into four sub-problems and then identify an individual
solution for some or all of these sub-problems. This would significantly reduce the number of potential
non-optimal decisions thereby providing fewer choices, leading to quicker results. These four sub-
problems are: a) Market segment identification, b) Forecasting and Pricing, c) Inventory allocation,
and d) Overbooking.
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5. 3 How does it all work?
3.1 Market Segment Identification
The first and most important step in a hotel RM system is the identification of the different market
segments for the hotel room, followed by implementation of a tiered pricing scheme. The aim of the
hotel, the expansion of the market and motivate the customer more than he / she will pay for a room.
It is also noted that customers in the business class segment is less sensitive to higher prices than
others in the holiday segment. An RM system helps hotels create additional price points by building
physical and logical gates around the different market segments, as listed in the table below.
CHARACTERISTICS HIGHER PRICE LOWER PRICE
Physical Fences
View Pool view, ocean view, hill view Non-scenic view
Size Bigger room with more facilities and Smaller rooms with fewer facilities
gadgets
Temporal Weekday bookings Weekend bookings
Logical Fences
Length of Stay Short stay. Often one or two days Longer stay. One night revenue can
spoil three nights revenue when demand
is high
Flexibility Cancellations and rescheduling are High penalty for cancellation and
allowed at a low penalty schedule changes
Time of Purchase Bookings are made very close to date Bookings are made quite early
of check-in
Privileges Are rewarded loyalty privileges either No privileges
as free services or free stay vouchers
Size of Business Corporate business customers Self funding vacationers booking rarely
Provided booking frequently
Point of Sale Physical delivery and confirmations By email or phone
Figure 1 - Market Segment Identification
3.2 Demand Forecasting
The next step in a process RM forecasting demand and prices for different market segments. Prices
and demand are linked and must be coordinated. In the hotel industry, the demand for a room is
cyclical in nature (day of the week, months of a year) and follows a trend (growth in demand resulting
from economic growth). These estimates are rarely accurate, but make the decision makers with an
approximate quantity of materials used in the planning. RM models help structure the question as to
minimize the uncertainty and manufacture the best possible prognosis.
Revenue management forecasting methods fall into one of three types: historical booking models,
advanced booking models and combined models. Historical booking models only consider the final
number of rooms or arrivals on a particular stay night. Advanced booking models only include the
build-up of reservations over time for a particular stay night. Combined models use either regression
or a weighted average of historical and advanced booking models to develop forecasts.
1 Historical a. Same day, last year
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6. b. Moving average
c. Exponential smoothing
d. Other time series (ARIMA etc)
2 Advance Booking a. Additive
1. Classical pickup
2. Advance pickup
b. Multiplicative
1. Synthetic booking curve
c. Other time services
3 Combined a. Weighted average of historical and advanced booking
forecasts
b. Regression
c. Full information Model
Table 1 - Revenue management forecasting methods
3.2.1 Historical models
Traditional methods such as exponential smoothing expected in its various forms, moving average
methods (simple and weighted) and linear regression can be used to make predictions based solely
on historical Arrivals. Unpublished hotel industry research shows two methods used to estimate the
historical question. Some companies use the number of rooms or inputs for the same days last year
to the historic treasures predicted, while other companies use the Holt-Winters exponential smoothing
method to estimate long-term prognosis.
3.2.2 Advanced booking models
Presale additive and multiplicative models are models distributed models. Additive models assume
the number of reservations on hand on a certain day of arrival (or reading day) is sold, whatever the
final number of rooms, while multiplicative models, it is likely that the number of reservations are still
dependent on the current number reservations at hand.
3.2.3 Combined forecast methods
Combined methods can predict regression or a weighted average of historical and forecast predicted
pre-booked or a full information model. Unpublished, proprietary hotel business forecasting methods
argue for a weighted average of the historical and forecast it in advance. When the day of arrival in
the distant future, more emphasis on historical estimates, while the date of arrival is imminent, the
focus is on advance bookings forecast.
3.2.4 Allocation
The next important step in a process is the allocation of RM inventory (rooms) between the different
market segments. The ratio of the discount to full-priced room is not fixed by the booking deadline, but
it's "tweaked" more accurate than the date of stay approaches. the opportunity cost of selling a
reduced price instead of the full price must be measured with the best decision. So if a customer
approaches the hotel for a reduced price, the manager for this scenario, the expected revenue from
another customer who might come later, prepared a higher price to pay for the same room. Rate The
manager would accept the request only if the expected price now is more than the expected price at
which the space can be reserved through the second customer. The key word here is "expected". RM
systems use complex mathematical algorithms to this decision using techniques such as Littlewoods
and to maximize the expectation for the EM algorithm to reach.
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7. 3.2.5 Overbooking
Overbooking is the practice of deliberately selling more rooms than the effect of cancellations and no-
shows "offset estimate available. Studies estimate that while a hotel is fully booked, about 5-8% of the
rooms are quite anytime. poor overbooking decisions can be very expensive for the hotel. In the short
term, it is only a loss of income space, but in the long term may be reduced customer loyalty, loss of
reputation hotel, etc. American Airlines developed an optimization model that income with
overbooking maximizes the decisions related to the aviation industry. This model can directly to the
hotel industry as Applied. The driving force behind the model is to evaluate the tradeoff between
Additional revenue from the sale of a previously room booked versus Downside of accrued interest. It
was found that net sales increased by overbooking, to the point where the downside of overbooking
an area larger than the customer's income. Beyond that point, the negative effects of overbooking
increased rapidly with fewer customers appreciate, turned away.
4 Challenges
It is clear that, while RM-Systems revenues increased to ensure the plan is quite complicated and
requires a high degree of know-how for implementation. Some of the challenges that hotels
implement an accurate and robust RM system include:
• Measuring performance of an RM system is an important issue. The use and profitability
measures can be influenced by external competition. An ideal measurement, with the opportunity
model, where the hotel is compared to its maximum and it is pointed out.
• Differential pricing is here to stay – customers seem so resigned to the hotels at different prices
for the same room. But some customers do not like this practice and punish the hotel by not
becoming a patron. Therefore, in a highly competitive environment where quality of service is the
key to success, it will not RM. in assessing the effectiveness of an RM-system, a compromise
between the generation of short-term profits and creating long-term customer loyalty and
"Mindshare" should be carefully examined.
• From an operational standpoint, RM Impact of motivational level of employees. In many cases,
RM takes a lot of the guesswork out of workers, so their decision. Sometimes, people with
reservations are a percentage of revenues to pay them off, their motivation, group bookings,
which in turn may be contrary to the objectives of an RM system.
5 Conclusion
As part of the ongoing changes in the industry, companies during the entire spectrum of hospitality,
with special attention to the implementation of major operational changes. Beyond the recognition that
a meaningful cost savings are achieved without compromising safety, capacity and service levels
have, they are also looking at reducing costs through greater flexibility and better use of assets by a
RM strategy. . In doing so, they continue to reassess their true core competencies, and are looking to
outsource many of these processes, as they look to optimize business efficiencies and increase
profitability.
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