THEORETICAL FOUNDATION

2.1 Definition of Price

In economics and business, the price is the assigned numerical monetary

value of a good, service or asset. The concept of price is central to

microeconomics where it is one of the most important variables in resource

allocation theory (also called price theory). Price is also central to

marketing where it is one of the four variables in the marketing mix that

business people use to develop a marketing plan

(en.wikipedia.org/wiki/Price).

2.1.1 Conventional definition

In ordinary usage, price is the quantity of payment or compensation for

something. People may say about a criminal that he has 'paid the price to

society' to imply that he has paid a penalty or compensation. They may say

that somebody paid for his folly to imply that he suffered the consequence.

Economists view price as an exchange ratio between goods that pay for

each other. In case of barter between two goods whose quantities are x and

y, the price of x is the ratio y/x, while the price of y is the ratio x/y. This

however has not been used consistently, so that old confusion regarding

value frequently reappears. The value of something is a quantity counted

in common units of value called numeraire, which may even be an

imaginary good. This is done to compare different goods. The unit of value

is frequently confused with price, because market value is calculated as the

quantity of some good multiplied by its nominal price

(en.wikipedia.org/wiki/Price).

2.1.2 Dynamic Pricing

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• Dynamic Pricing refers to a fluid pricing scheme between the

buyer and seller, rather than the more traditional (and more recent,

over the last 100 years) fixed pricing. Dynamic pricing is a legacy

from the past that has lost its significance with the advent of the

industrial revolution and mass marketing and mass communication.

Before the industrial revolution, most trade occurred in markets,

with many buyers and sellers bartering for goods. Current, and

evolving models for dynamic pricing include the auction pricing

system, group pricing system and reverse pricing system. Typically

these systems will better reflect the true market value of the

product involved, but also require additional work on the part of

the purchaser. These systems will evolve more rapidly when better

standards are established and when intelligent agents evolve. Not

all markets will be served well with a dynamic pricing model

(www.definethat.com/define/299.htm).

• Tolls that vary in response to changing congestion levels, as

opposed to variable pricing that follows a fixed schedule

(www.hhh.umn.edu/centers/slp/vp/vp_org/glossary.html).

• Prices can be updated in real time according to the type of

customer or current market conditions

(sensacom.com/web_glossary.html).

2.2 Revenue Management

Revenue management is a technique to optimize the revenue earned from a

fixed, perishable resource. The challenge is to sell the right resources to

the right customer at the right time for the right price. Revenue

management implements the basic principles of supply and demand

economics in a tactical way to generate incremental revenues. There are

three essential conditions for revenue management to be applicable:

1. That there is a fixed amount of resources available for sale.

2. That the resources sold are perishable. This means that there is a

time limit to selling the resources, after which they cease to be of

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value.

3. Those different customers are willing to pay a different price for

using the same amount of resources.

Revenue management is of especially high relevance in cases where the

constant costs are relatively high compared to the variable costs. The less

variable cost there is, the more the additional revenue earned will

contribute to the overall profit.

Revenue management has significantly altered the travel and hospitality

industry since its inception in the mid 1980s. It requires analysts with

detailed market knowledge and advanced computing systems that

implement sophisticated mathematical techniques to analyze market

behavior and capture revenue opportunities. It has evolved from Yield

Management which the airlines invented as a response to deregulation and

quickly spread to hotels, car rental firms, cruise lines, media, and energy to

name a few. Its effectiveness in generating incremental revenues from an

existing operation and customer base has made it particularly attractive to

business leaders that prefer to generate return from revenue growth and

enhanced capability rather than downsizing and cost cutting

(en.wikipedia.org/wiki/Revenue_management).

Some hoteliers mistakenly believe the terms room rate management and

revenue management are synonymous. They are not. Although in many

cases revenue managers actually establish room rates, in many other hotels

they do not. For example, the hotel’s owners, the GM, the director of sales

and marketing (DOSM), or front-office manager may dictate the room rate

to be charged at a specific time or on a specific date. The revenue

manager, however, must still “manage” that rate. In a properly managed

hotel, even with an established pricing structure, there is typically not one

single, inflexible “rate” for a guest room. For most revenue managers, the

correct reply to the common question, “How much are your rooms?” is

“Who are you, how many rooms do you want, and when do you want

them?” (Hayes and Ninemeier, 2007, p.166).

In a very large property the positions of revenue manager, front-office

manager, and the director of sales and marketing may be at the level of

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department head and may entail supervising staffs of a significant size. It is

helpful, therefore, for GMs to understand how, as shown in Figure 2.1,

room rate management tasks intermingle (because they do in significant

ways) and how they can be coordinated (Hayes and Ninemeier, 2007,

p.170).

RevenueManager Tasks

RoomRates

FOM Tasks DOSM Tasks

Figure 2.1 Shared Room Rate Management Responsibilities

• Revenue Manager and FOM. Many guests locate a hotel through

key word searches on Internet search engines. Others may locate

the hotel on an Internet travel site such as Travelocity or Expedia

and then access the hotel’s proprietary Web site. Still others may

call the hotel directly to book their reservation. Regardless of the

source, it is critical that the rates encountered by the guest be

identical or at least internally consistent. Rates on a Web site and

all other e-based delivery systems should be monitored (reviewed)

regularly by the hotel’s revenue manager and, if adjusted, the

information must be relayed to the hotel’s in-house reservation’s

staff. This is true of rates to be charged for dates far into the future

as well as for day of.

• Revenue Manager and DOSM. Ideally, the hotel’s plan for rate

positioning is developed as part of the overall business/marketing

plan developed by the DOSM and approved by the hotel’s GM.

The marketing plan should consider the hotel’s competitors, its

group bookings history, rate resistance as reported by the

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property’s reservation agents, and the demand forecasts that

include the period of time covered by the marketing plan.

• Revenue Manager, FOM, and DOSM. Every individual reservation

made through the hotel’s front office as well as every group room

sale made by a hotel’s sales department affects the property’s

revenue projections and, therefore, can affect rate management.

The methods used by the revenue manager, FOM, and DOSM to

share information and to coordinate their activities vary between

properties. However, collaboration can be achieved through

periodic updates involving all members of the revenue

management team.

Despite honest individual differences in philosophic approach, most

revenue managers would agree that:

• Revenue Management is a Daily Activity. Revenue managers

should monitor room demand daily (or hourly). To keep current

with market demand, monitor the competitors’ room rates daily. In

addition, many revenue managers check their competitors every

day (by doing a call-around) to determine these competitors’ walk-

in rates.

• Occupancy and ADR Indices Should Be Close. Ideally, the

occupancy index and ADR index should be tight. That is, the

percentages should be within a few percentage points of each other.

If the occupancy index is well over 100 percent (110 percent or

more), the hotel should drive rate but be prepared to lose some

occupancy index points. If the occupancy index is well below 90

percent, the hotel should drive rate very conservatively.

• It Is Necessary to Gamble at Times. An aggressive revenue

manager can make a significant difference in a hotel’s RevPar.

Overbook on high-demand nights but do so based on known no-

show data for similar dates. Minimize costly walks.

2.2.1 Room Rate Economics

Any serious exploration of hotel room rates and their management must

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include basic information about room rate economics. The study of

economics related to room rates examines the social science associated

with the making, marketing, and consumption of goods and services and

involves considering how the forces of supply and demand allocate scarce

resources (such as hotel rooms). Revenue managers must know and use

room rate economics to price rooms and to understand how consumers

react to the pricing strategies they employ. Interestingly, the fundamental

rules of economics of most importance to hoteliers differ based upon the

time frame examined. In the short-term, the law of demand is most

important (Hayes and Ninemeier, 2007, p.172).

• Economics: The social science associated with the making,

marketing, and consumption of goods and services and how the

forces of supply and demand allocate scarce resources.

• Supply: The total amount of a good or service available for sale.

• Demand: The total amount of a good or service consumers want to

buy at a specific price.

• Room Rate Economics: The processes by which revenue

manager’s price rooms while considering how consumers may

react to pricing strategies used.

• Law of demand: The concept of economics that recognizes, when

supply is held constant, an increase in demand results in an

increase in selling price. Conversely, with supply held constant, a

decrease in demand leads to a decreased selling price.

Understanding the law of demand is critical because, unlike managers in

other industries, hoteliers cannot increase their inventory levels (supply) in

response to known (or projected) increases in demand. The ability to

comprehend the impact of this fundamental concept is a salient

characteristic of outstanding GMs because they are ultimately responsible

for revenue management.

The price a hotel charges for its rooms is influenced by many factors. One

of the most important is the number of rooms (supply) available relative to

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the degree of demand for these rooms. The supply (number) of rooms in a

market area is relatively easy for revenue managers to assess. When they

have determined the amount of supply and have accurately estimated room

demand, effective pricing decisions can be made. If, however, revenue

managers significantly over- (or under-) estimate demand, critical errors

can be made as a hotel’s room rates are established and marketed. That is

why a hotel’s ability accurately forecast demand is so important to the

hotel’s financial success.

Since information about supply is readily known and forecast data helps

estimate demand, revenue managers can gauge the relationship between

guestroom supply and demand. Using this information, they can determine

the best rates to be assigned to each of their room types, because when

revenue managers consider their hotel’s room rates, they must generally

consider multiple rate types (Hayes and Ninemeier, 2007, p.174).

2.2.2 Margin Arithmetic

Firms that implement revenue management techniques generally report

revenue increases in the range of 3 to 7 percent with relatively little

additional capital investment. The importance of that incremental revenue

can be understood with the set of “margin arithmetic.” A firm’s net profit

equation is straightforward:

Profit = R x M – F = Net profit % x R

R = Revenue

M = Gross margin as a percentage of revenue

F = Fixed costs

Net profit % = Net profit as a percentage of revenue

A firm’s net profit as a percentage of its revenue (Net profit %) is

generally in the range of 1 to 10 percent. Now let’s suppose we implement

revenue management and increase revenue. Let revenue increase be the

percentage increase in revenue we experience, which, as has already been

mentioned, is typically in the 3 to 7 percent range. Thus, a 3 to 7 percent

increase in revenue can easily generate a 50 to 100 percent increase in

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profit, especially in a high-gross-margin setting; revenue management

indeed can be an important set of tools. Our percentage change in profit is

then

% change in profit = [(100% + Revenue increase) x R x M – F] – [R x M – F]

R x M – F

= Revenue increase x R x M

R x M – F

= Revenue increase x M

Net profit %

(The second line above cancels out terms in the numerator such as the

fixed costs. The third line replaces the denominator with Net profit % x R

and then cancels R from both the numerator and denominator.) Table 2.1

presents data evaluated with the above equation for various gross margins,

revenue increases, and net profits as a percentage of revenues. The table

dramatically illustrates that a seemingly small increase in revenue can have

a significant impact on profit, especially when the gross margin is large

(Cachon and Terwiesch, 2006, p.316).

Table 2.1 Percentage change in profit for different Gross Margins, Revenue

Increases, and Net Profits as a percentage of Revenue

Net Profit % = 2 % Net Profit % = 6%

Revenue Increase Revenue Increase

M 1% 2% 5% 8% M 1% 2% 5% 8%

100% 50% 100% 250% 400% 100% 17% 33% 83% 133%

90% 45 90 225 360 90% 15 30 75 120

75% 38 75 188 300 75% 13 25 63 100

50% 25 50 125 200 50% 8 17 42 67

25% 13 25 63 100 25% 4 8 21 33

15% 8 15 38 60 15% 3 5 13 20

2.3 Traditional Pricing Strategies

GMs reviewing older, or even the most current hotel accounting or front-

office management texts will likely encounter a description of the

“Hubbart” room rate formula. Known by hoteliers worldwide, this formula

for determining room rates was developed in the mid-1950s by two

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national accounting firms (Horwarth & Horwarth and Harris Kerr Forster).

The model was named in honor of Roy Hubbart, a Chicago hotelier and a

major advocate of a “Hubbart” formula-style approach to room pricing

(Hayes and Ninemeier, 2007, p.179).

Essentially, the formula is for determining what a hotel’s average daily

rate (ADR) should be to reach the hotel owner’s financial goals. To

compute the Hubbart formula, specific financial and operational

assumptions must be produced. These include amount of money for

property construction (or purchase), the total cost of operations, the

number of rooms to be sold, and the owner’s desired return on investment

(ROI) on the hotel’s land, building, and furniture, fixtures & equipment

(FF&E).

The steps required to compute the Hubbart formula are as follows:

1. Calculate the Hotel’s Target Profits. Multiply the required rate of

return (ROI) by the owner’s investment.

2. Calculate All Fixed Expenses. Include accurate estimates of all

fixed costs including leases, depreciation, interest expense,

property taxes, insurance, mortgages, and fixed management fees.

3. Calculate All Operational Costs. Include expenses directly

associated with selling and cleaning rooms and providing food

services. All costs incurred to operate the front office would be

included. Additional direct operating costs include housekeeping-

related expenses for labor, guest room supplies, and laundry, as

well as cleaning the hotel’s public spaces. Interestingly, the

expenses required to operate a food and beverage department are

also considered a direct expense of selling rooms. In addition to

direct operating expenses, indirect operating expenses that cannot

readily be assigned to the front office, housekeeping, or the food

and beverage department must be computed. These will include a

variety of costs such as those for administrative and general tasks,

data processing, human resources, marketing, property operation

and maintenance, franchise fees, and energy costs.

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4. Calculate Nonrooms Profits. Hotels can make profits from a food

and beverage department or from telephone toll charges as well as

from other minor sources unique to a specific hotel. If these

sources generate a loss, the Hubbart formula requires the amount of

the loss to be entered into the formula.

5. Determine the Total Room Revenue Required to Meet the Hotel’s

Goals and Obligations. Sum the owner’s desired ROI, hotel’s fixed

expenses, direct expenses, and all indirect operating costs. Then

subtract the amount of nonrooms profit anticipated by the hotel.

Note: if there was a loss from the nonrooms department, this loss

would be added to the total room revenue required to meet all of

the hotel’s goals and obligations.

6. Determine the Forecast of Rooms to be Sold. Multiply the number

of rooms available by the projected occupancy rate.

7. Calculate the Hotel’s Required ADR. Divide the required room

revenue (Step 5) by the number of rooms to be sold.

The Hubbart formula is useful because it requires the user to consider the

owner’s investment goals and the costs of operating the hotel before

determining the room rate. It has been criticized for relying on

assumptions about the reasonableness of an owner’s desired ROI (Step 1)

and the need to know operating costs (Step 3) when these costs are

affected by the quality of the hotel’s management. Another criticism is

also frequently voiced: the formula requires the room rate to compensate

for operating losses incurred by other areas (such as from food and

beverage operations).

The formula’s primary shortcoming, however, relates to the number of

rooms forecasted to be sold (Step 6). Based upon the room rate economics

principles we have examined, the number of rooms sold is typically

dependent on the rate charged for the rooms. However, the Hubbart

formula requires that the number of rooms sold be estimated prior to

knowing the rate at which they would sell (Hayes and Ninemeier, 2007,

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p.180).

Despite its limitations, for GMs the Hubbart formula remains an important

way to view the necessity of developing a room rate that:

• Provides an adequate return to the hotel’s owner(s)

• Recovers the hotel’s fixed costs

• Considers the hotel’s operating costs

• Accounts for all the hotel’s nonroom net income (or loss)

• Results in a definite and justifiable rate goal

Additional historical methods of rate determination include those based

upon the square footage of guest rooms (assuming that a hotel’s larger

rooms should sell for more than its smaller rooms) and rates determines by

various “ideal” sales levels of the bottom-up selling (selling the hotel’s

least expensive rooms first), top-down selling equal sale of higher- and

lower-priced rooms.

Modern GMs understand that properly pricing their rooms is critical to

attracting first-time and repeat business. However, close examination of

many tactics used by revenue managers would reveal that they often use

one or more of the following nontraditional methods to establish rates:

• Competitive Pricing. Charge what the competition charges.

• Follow-the-Leader Pricing. Charge what the dominant hotel in the

area charges.

• Prestige Pricing. Charge the highest rate in the area and justify it

with better product and/or service levels.

• Discount Pricing. Reduce rates below that of the likely competitors

without considering operating costs.

2.4 Managing ADR

Most revenue managers understand that the best way to maximize ADR is

to manage room rates in conjunction with anticipated demand. That is,

when total demand is forecasted to be strong, discounting room rates (and

thus reducing ADR) is not typically necessary to ensure the sale of rooms.

Similarly, when demand for a single room type is strong, discounting that

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specific room type is not generally advisable even if discounts will be

offered on other, less popular, room types. Philosophically, the ADR

management goal of all revenue managers is to achieve an ADR that is as

close as possible to the hotel’s rack (non-discounted) rate.

Table 2.2 Forecast Types

2.5 Yield Management

Yield management is a set of techniques and procedures used to

manipulate occupancy, ADR, or both for the purpose of maximizing the

revenue yield achieved by a hotel. This term, first coined by the airline

industry, is now used less commonly in the hotel industry than is the term

revenue management, but its grounding philosophy and the actual

techniques originally employed to implement it are all important concepts

for revenue managers to grasp.

Yield management can also be define as a demand forecasting systems

Management Forecast Purpose/Characteristics

Occupancy Forecast 1. Helps improve employee scheduling

2. Shows guest arrival and departure patterns

3. Forecasts at least 2/7/14/21/ and 30 days out

4. Produced daily

5. Never exceeds 100%

Demand Forecast 1. Identifies periods of 100% occupancy or more demand for rooms

2. Identifies periods of very low demand

3. Forecasts 30/60/90 days out

4. Produced at least weekly

5. Used to help establish room rate selling strategies

Revenue Forecast 1. Helps manage the hotel’s cash flows

2. Considers important tracking codes when evaluating estimated total revenues

3. Matches revenue forecast to pre-established budgets (forecasts 30 days out or more as determined by management)

4. Produced at least monthly

5. Estimates RevPar (occupancy and rate)

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designed to maximize revenue by holding rates high during times of high

room demand and by discounting room rates during times of lower guest

room demand. These systems may be applied manually or with programs

built into a hotel’s property management systems (PMS) (Hayes and

Ninemeier, 2007, p.193).

Yield management, also known as Revenue Management, is the process of

understanding, anticipating and reacting to consumer behavior in order to

maximize revenue or profits. Firms that engage in yield management

usually use computer yield management systems to do so. The Internet has

greatly facilitated this process. Other terms to describe this process are

revenue optimization and demand management.

Yield management can result in price discrimination, where a firm charges

customers consuming otherwise identical goods or services a different

price for doing so. Three industries where yield management is used most

heavily are passenger air transport, lodging and rental car. Airlines monitor

through the use of specialized software how seats are being reserved and

react accordingly, as for example by offering discounts when it appears as

if seats will otherwise be vacant. Hotels use Revenue Management in

largely the same way, to calculate the rates, rooms and restrictions on sales

in order to best maximize the return for the property. In the rental car

industry, Revenue Management deals with the sale of optional insurance,

damage waivers and vehicle upgrades. It accounts for a major portion of

the rental company's profitability, and is monitored on a daily basis

(en.wikipedia.org/wiki/Yield_management).

2.5.1 Realization

Yield management can be viewed as the application of specific tactics that

predict (forecast) consumer behavior and effectively price highly

perishable products to maximize revenue per available room (RevPar).

Retailers that can easily carry inventory to the next day such as carpet,

lumber, and computer suppliers have difficulty employing yield

management because customers do not readily accept price variation in

their products. Interestingly, retailers perceived by customers to be easily

able to increase inventory (think bread, milk, and restaurant meals) do not

generally use yield management even though they may sell a perishable

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commodity (Hayes and Ninemeier, 2007, p.194).

Because hotel rooms are highly perishable, the goal of yield management

is to consistently maintain the highest possible revenue from a given

amount of inventory. Remember that yield management techniques are

used during periods of high, as well as low, demand. Revenue managers

should be implementing yield management procedures at their hotels if:

• Demand for their rooms varies by day of week, time of month,

season, or in response to local special events.

• Their demand variance is predictable.

• They have ever turned away a customer willing to pay a higher

price for a room because available inventory had been previously

sold to another guest t a lower price.

• Their hotel serves guest who are value conscious as well as those

who can afford to spend more for the sake of convenience, status,

or another motivating factor.

• They have, or can create, clearly discernable differences in service

or product levels that can easily be explained to guests.

• Their property is willing to commit the resources necessary to

properly train staff prior to implementation of yield management.

• They seek to maximize RevPar.

2.5.2 Techniques

Although the actual yield management techniques used by a revenue

manager will vary by property, in their simplest form, all these techniques

are employed to:

• Forecast demand

• Eliminate discounts in high-demand periods.

• Increase discounts during low-demand periods.

• Implement “Special Event” rates during periods of extremely

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heavy demand.

Sophisticated mathematical programs that help hoteliers manage yield are

built into most PMSs used in the industry today. In the final analysis,

however, it is the revenue manager’s skill and experience in maximizing

yield that is more critical to the yield maximization process (Hayes and

Ninemeier, 2007, p.195).

2.6 Measures of Effectiveness

More recently, RevPar has been the major factor applied in the evaluation

of revenue managers. Currently, some industry observers and professionals

have suggested that gross operating profit per available room (GoPar) is a

more useful measure of selling effectiveness than is RevPar.

GoPar considers the “cost” of selling rooms (not simply the total revenue

achieved) when evaluating sales effectiveness. Using a term from the

Uniform System of Accounts for Hotels, it is computed as:

Total Revenue – (Direct Operating Expense + Indirect Operating Expense)

Total Rooms Available to Be Sold

In most hotels, however, GMs seeking to measure revenue management

effectiveness will evaluate their own property’s ADR, occupancy, and

RevPar performance (Hayes and Ninemeier, 2007, p.196).

2.6.1 Occupancy index

One of the questions hoteliers are frequently asked by those whose

knowledge of the industry is somewhat limited is, “How’s your

occupancy?” The question implies, of course, that a high level of

occupancy is “good” and a lower level of occupancy is “bad.” That may,

in fact, be true at times, but it is a tremendously limiting manner in which

to view occupancy management. It is difficult to separate a hotel’s

occupancy level from the rates it charges. However, assuming that a

hotel’s rates are in line with its competitive set, the occupancy index is the

industry’s standard for measuring the management of occupancy rates

(Hayes and Ninemeier, 2007, p.197).

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• Competitive set: The group of competing hotels against which an

individual hotel’s operating performance is compared.

• Occupancy index: A ratio measure computed as:

Occupancy Rate of a Selected Hotel

Occupancy Rate of That Hotel’s Competitive set

Table 2.3 Occupancy Index Evaluation

Occupancy Index Assessment/ Recommended Action

Far Below 100% Management is ineffective. ADR excessive for the market. Reduce rack rate.

Below 100% Management is less than effective. Evaluate weekday/weekend ADR index. Closely examine sales effort.

At (Near) 100% Management is effective. Consider eliminating discounts for most popular room types during high-demand periods to test the hotel’s ability to maintain index.

Above 100% Management is less effective. Immediately eliminate discounts for most popular room types during high-demand periods.

Far Above 100% Management is ineffective. ADR to low. Increase rack rates on all room types at all time.

2.7 Demand Curve

Each price the company might charge will lead to a different level of

demand. The demand curve shows the number of units the market will buy

in a given time period at different prices that might be charged. In the

normal case, demand and price are inversely related; that is, the higher the

price, the lower the demand. Thus, the company would sell less if it raised

its price from low price to high price. In short, consumers with limited

budgets probably will buy less of something if its price is too high (Kotler

and Armstrong, 2006, p.299).

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2.7.1 Fixed Pricing Demand Curve

Figure 2.2 Demand Curve

Figure 2.2 above explain the fixed pricing strategy to set the average daily

rate. This demand curve shows that the demand respond to price changes,

and this is used to set the highest revenue.

o RNA: Room Number Available

o QD: Quantity of demand to the price level

o OP: Optimal price for the highest revenue

o VC: Variable Cost

The fixed pricing strategy is to set the optimal price to achieve the highest

revenue. Each price the company charge will lead to a different level of

demand. This relationship between the price charged and the resulting

demand level is shown in Figure 2.2. The demand curve shows the number

of units market will buy in a given time period at a different prices that

might be charged.

2.7.2 Dynamic Pricing Demand Curve

Q Dynamic Pricing

RNA

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Q Fixed Pricing

RNA

QD

VC OP P

R = QD x OP

QL

QH

VC Low Rate High Rate P

Figure 2.3 Demand curve with two prices level

Figure 2.3 above explains the dynamic pricing strategy with two prices

level; low rate and high rate.

o RNA: Room Number Available

o QL: Quantity of the low rate

o QH: Quantity of the high rate

o VC: Variable Cost

o Low Rate: Price set lower than the average price to attract demand

o High Rate: Price set higher than the average price to increase

revenue

Price, in this model, is assumed to be elastic, which sales respond to price

changes. A change in demand is caused by a change in price. A product is

said to be inelastic if a higher price does not affect demand (Heizer and

Render, 2006, p.521).

The dynamic pricing strategy is a strategy to maximize the revenue by

setting two prices level. The low rate is set on lower demand period and

the high rate is set on a higher demand period where consider to be less

price sensitive customer. The total revenue is by adding the low rate

revenue and high rate revenue.

2.7.3 Comparison between fixed pricing and dynamic pricing

In hotel industries, pricing is the hotel major factor to survive in the

competitive industry. In Table 2.4, we can see the comparison of using

fixed pricing and dynamic pricing. The aspects include strategy, pricing

type, point in time where the price is set, and the objective of each

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strategy. The conclusion of this comparison is that by using dynamic

pricing is better used in hotel industry with perishable resources, for the

reason that the dynamic pricing strategy reaches the opportunity to attract

more customers with the different price.

Table 2.4 Comparison of Pricing strategies

Strategy (what) Fixed Pricing Dynamic Pricing

Pricing type (how) Flat rate Dynamic rate with two

price levelPoint in time (when) Same rate everyday Different rate to demand

fluctuationObjectives (why) To set an optimal price

to achieve maximum

revenue

To attract more demand

in low season period and

maximizing revenue

2.8 Poisson Distribution and the Poisson Process

Experiments yielding numerical values of a random variable x, the number

of outcomes occurring during a given time interval or in a specified region,

are called Poisson experiments. The given time interval may be of any

length, such as a minute, a day, a week, a month, or even a year. Hence a

Poisson experiment can generate observations for the random variable x

representing the number of telephone calls per hour received by an office,

the number of days school is closed due to snow during the winter, or the

number of postponed games due to rain during a baseball season. The

specified region could be a line segment, an area, a volume, or perhaps a

piece of material. In such instances x might represent the number of field

mice per acre, the number of bacteria in a given culture, or the number of

typing errors per page (Walpole, Myers, Myers, and Ye. 2002. p.136).

Properties of Poisson Process

o The number of outcomes occurring in one time interval or

specified region is independent of the number that occurs in any

other disjoint time interval or region of space. In this way we say

that the Poisson process has no memory.

o The probability that a single outcome will occur during a very short

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time interval or in a small region is proportional to the length of the

time interval or the size of the region and does not depend on the

number of outcomes occurring outside this time interval or region.

o The probability that more than one outcome will occur in such a

short time interval or fall in such a small region is negligible.

The number x of outcomes occurring during a Poisson experiment is called

a Poisson random variable, and its probability distribution is called the

Poisson distribution. The mean number of outcomes is computed from µ =

λ t, where t is the specific “time” or “region” of interest. Since its

probabilities depend on λ, the rate of occurrence of outcomes, we shall

denote them by the symbol P (x; λt).

P (x; λt) = e-λt (λt)x

x!

Where λ is the average number of outcomes per unit time or region, and e

= 2.71828

2.9 Protection Level and Booking Limit

This protection level for a fare is the number of rooms that are reserved for

that fare or higher. This means that at least there are number of rooms that

at all times must be available that could be reserved with the high fare.

Hotels usually offer rooms to both leisure and business travelers. Leisure

travelers are more prices sensitive and tend to reserve rooms well in

advance of their stay. Business travelers are generally willing to pay more

for a room, in part because they tend to book much closer to the time of

their trip, and in part because they wish to avoid the additional restrictions

associated with the discount fare (Cachon and Terwiesch, 2006, p.317).

The booking limit for a fare is the maximum number of reservations

allowed at that fare or lower. There is a relationship between the high fare

protection level and the low fare booking limit:

High fare protection level = Capacity – low fare booking limit

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To implement the model, we need a forecast of high fare demand and an

assessment of the underage and overage costs. This forecast could be

constructed using booking data from similar nights, similar times of the

year, and managerial intuition.

The underage cost is the cost per unit of setting the protection level too

low (i.e., “under” protecting). If we do not protect enough rooms for the

high fare, then we sell a room at the low fare that could have been sold at

the high fare. The lost revenue is the difference between the two fares, that

is, Cu = rh – rl.

The overage cost is the cost per unit of setting the protection level too high

(i.e., “over” protecting). If we set the protection level too high, it means

that we did not need to protect so many rooms for the high fare customers.

In other words, demand at the high fare is less than our protection level. If

protection level (Q) were lower, then we could have sold another room at

the low fare. Hence, the overage cost is the incremental revenue of selling

a room at the low fare: Co = rl. According to the newsvendor model, the

optimal protection level is the Q such that the probability the high fare

demand is less than or equal to Q equals the critical ratio, which is

Cu = rh - rl = rh - rl

Co + Cu rl + (rh – rl) rh

In words, we want to find the Q such that there is a percent probability of

high fare demand is Q or lower. When the critical ratio falls between two

values in the distribution function table, choose the entry that leads to the

higher decision variable (Cachon and Terwiesch, 2006, p.320).

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