SHA542: Price Sensitivity and Pricing Decisions...Customers' buying decisions reflect their price...

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SHA542: Price Sensitivity and Pricing Decisions


This course includes

• Four self-check quizzes

• Two discussions

• Four tools to download and use

on the job

• One final action plan

assignment

• One video transcript file

Completing all of the coursework should take

about five to seven hours.

What you'll learn

Employ a strategic, proactive

approach in pricing decisions

Evaluate the importance of price

elasticity in pricing decisions

Estimate price sensitivity and use the

results in pricing decisions

Use mathematical modeling and

analysis to understand the

relationship between variables (for

example, price and demand)

Course Description

Pricing has become an increasingly important mechanism in maximizing a firm's profits. The ease with which consumers

comparison-shop has enticed firms to be more active pricers. Unfortunately, if you lack a proper understanding of the

impact of price on demand (and contribution), changing prices can quickly erode your firm's profits. This course, produced

in partnership with the , describes the impact of changing prices in a competitiveCornell School of Hotel Administration

environment and then describes several methods for measuring demand sensitivity to price changes (or price elasticity).

https://s3.amazonaws.com/ecornell/content/SHA/SHA542/SHA542_Transcripts.pdf

http://www.hotelschool.cornell.edu/


The course begins with a strategic look at pricing and discusses the impact of price changes and the anticipated reaction

of your competitors. We illustrate these impacts with a discussion of recent price changes during economic declines as

well as a well-documented airline price war. After this strategic discussion, we describe a set of tactical tools you can use

to evaluate the effect of a price action on demand and, ultimately, on profitability.

Chris Anderson Associate Professor, School of Hotel Administration, Cornell University

is an associate professor at the Cornell School of Hotel Administration. Prior to his appointment in 2006, heChris Anderson

was on faculty at the Ivey School of Business in London, Ontario Canada. His main research focus is on revenue

management and service pricing. He actively works with industry, across numerous industry types, in the application and

development of RM, having worked with a variety of hotels, airlines, rental car and tour companies as well as numerous

consumer packaged goods and financial services firms. Anderson's research has been funded by numerous governmental

agencies and industrial partners and he serves on the editorial board of the Journal of Revenue and Pricing Management

and is the regional editor for the International Journal of Revenue Management . At the School of Hotel Administration, he

teaches courses in revenue management and service operations management.

Start Your Course


Module Introduction: Price Sensitivity and Its Impact on Pricing Decisions

Think about pricing strategy, which is the central component in your overall profit performance. Should you price high,

hoping to generate a large margin, or low, aiming to increase demand? Which tactic will be more profitable? A key to

answering these questions is knowing how customers will respond. Customers' buying decisions reflect their price

sensitivity and, in turn, should influence your pricing decisions.

Estimates of customer price sensitivity and willingness to pay can sometimes substantially improve both price setting and

segmentation. Numerous procedures can be used to measure and estimate price sensitivity.

After completing this module, you will be able to:

Explain the implications of and competitive responses to price changes

List characteristics of an industry that make it especially susceptible to competition on the basis of price alone

Use break-even analysis to evaluate pricing actions

Evaluate price changes in a competitive market


Watch: Prisoners' Dilemma

Hoteliers face an ongoing challenge: trying to make sound pricing decisions without knowing what their competitors' prices

will be. If your rival cuts prices, it will be in your best interests to cut prices to remain competitive. In this video, you will

examine this pricing dilemma with a classic case study frequently taught in university classes, known as the "prisoners'

dilemma." How does one person make a decision without knowing what the other will do?


Read: Airfare Price Wars

When American Airlines, Northwest Airlines, and other U.S. carriers began competing to match and exceed one another's

price reductions, the was a record level of air travel-and record losses. One estimate suggested that the fare warsresult

reduced overall industry profits in 1992 by $1.53 billion. 1

The price war began when American Airlines determined that complex fare structures, which had contributed to the growth

in air travel in the 1980s, were leading to a sudden drop in travel in the early 1990s. American Airlines believed its

complex pricing system was driving away potential customers.

American introduced what it labeled "value pricing," which eliminated most discount pricing but, at the same time,

substantially reduced standard prices for coach, business, and first class. Within a few days, competitors Delta and United

Airlines reduced the complexity of their fares. TWA dropped its rates. Northwest Airlines followed suit and offered

"two-for-ones"-buy one ticket and get one free. American, which had begun the pricing move, noted its competitors'

actions and promptly introduced a 50% price cut.

In a very short time, American's rational move to simplify its fare structure led to a race to the bottom. The price war was a

huge bonus for customers; capacity utilization climbed by 20%. But the result for the companies was grim. Some

estimates suggest that losses exceeded the combined profits for the entire industry from its inception. The price war

ended with American Airlines, as it had started. It announced it was basically dropping its value fares, and it went back to

its old fare structure. Over time, all the other airlines followed American's lead and dropped their deals. The industry

recovered.

An article by David Besanko for the Kellogg School of Management traced the sequence of events in this price war.

Students who wish to read the entire Besanko article should visit the site for purchase.Harvard Business Publishing

Steven Morrison and Clifford Winston. 1996. Causes and consequences of airline fare wars. Brookings Papers:1

Microeconomics 1996: 85-123.

http://hbr.org/product/the-mother-of-all-pricing-battles-the-1992-airline/an/KEL006-PDF-ENG?Ntt=david+besanko&Nao=10


Read: Post-9/11 Hotel Price Wars

Key Points

Hoteliers should avoid following competitors' actions out of panic

Rate reductions do not necessarily lead to increased demand

Use statistical tools to make pricing decisions

When you are working to gain market share, there are no perfect solutions-but there are options far less damaging than

fighting the battle with price alone.

As the previous airline price war example shows, it's important not to panic and follow the actions of your competitors

reflexively. Promptly matching rate reductions, for example, can have consequences from which it may take years to

recover.

Using historical data, we can see how reacting to the pricing decisions of your competitors rather than taking a myopic

approach affects revenue. This chart, created by Smith Travel Research, shows changes in average daily revenue1

(ADR) and demand in the hospitality industry over a period of 20 years.

Beginning in 2001, there are two distinct dips in both ADR and demand. The first downturn followed the attack of

September 11, 2001. In the months that followed, people cut back their travel, and demand fell by close to 10%. Over the

next year, hotels reacted by reducing their rates - but with little response in demand (i.e. price cuts did not increase

).demand

Over time, demand began to improve, and by 2003 hotels began to raise their rates. However, it took a long time to move

the rates back up to their pre-2001 levels. It was almost six years before demand and rates showed a substantial


increase. Over the intervening years, the hotel industry had sacrificed an enormous amount of revenue.

As demand growth started to slow in 2004, hotels continued to increase (versus decrease) rates, with very strong ADR

growth through 2007. Hotels reached a new rate peak in 2007, but in 2008 the collapse of Wall Street precipitated a

recession. This time the race to the bottom was steeper and deeper. Demand dropped by close to 15%, ADR dropped

even further than it had in 2002, and RevPar plummeted. Operators should have learned after 9/11 that rate reductions do

not necessarily lead to increased demand. Sometimes a return of demand takes time, and making extensive changes in

rate structure may not quicken the pace.

Although pricing can be a great strategic lever, simply decreasing prices is bound to encourage competitors to respond in

kind. It's a classic illustration of the prisoners' dilemma, in which acting in one's own apparent best interest may produce

greater harm than taking the collective interest into account. A better response is to think tactically. We suggest the best

approach is to use statistical tools to determine whether it's wise to raise or lower your prices, either on your own or in

response to changes by your competitors.

In addition to statistical analysis, another adaptation is to think like a marketer, in terms of market segments. Rather than

change your prices universally, target your changes at specific segments of the market. When you lower all your rates,

you lower them not just for those who care about the lower rate, but also for those who would continue to pay a higher

rate. A family visiting their son at college for the weekend might be quite sensitive to price and shop around for the best

bargain. Corporate travelers, on the other hand, may care more about the convenience of a familiar location than about

saving 10% on their hotel bill. Why lose revenue from both groups when you may only have to lose it from one? If pricing

actions are not properly segmented or targeted, they can dilute profits instead of creating incremental demand.

Each month, Smith Travel Research (STR) collects performance data on over 22,000 hotels representing more than 2.71

million rooms. This data comes from chain headquarters, management companies, owners, and directly from independent

hotels. The data is audited for accuracy and checked for adherence to the STR reporting guidelines. STR collects three

pieces of data each month: rooms available for occupancy, rooms sold, and room revenue.


Read: Industry Characteristics and Price Wars

Are hotels unusually prone to competitive pricing problems? What industry characteristics make them so? On both the

supply and the demand sides, certain aspects of the hotel business increase its susceptibility to frequent price wars.

Examine the chart below, which shows the price war risk factor as it relates to a particular industry characteristic.

Supply

Industry Characteristic High Risk Low Risk

Cost High fixed costs Low fixed costs

Capacity utilization Relatively low capacity utilization Relatively high capacity utilization

Product perishability Perishable Nonperishable

Product differentiation Little differentiation among competitors Strong differentiation among competitors

Demand

Price sensitivity of demand Customers very price sensitive Customers not price sensitive

Efficiency of shopping Very easy to find competitors' prices Relatively difficult find competitors' prices

Brand loyalty Low brand loyalty High brand loyalty

Growth rate Low growth in demand High growth in demand


Read: Impact of Price Changes

You need to ask certain questions before changing pricing at your establishment, as illustrated here. Will a price reduction

help you fill rooms? It may; decreasing room rates may increase your occupancy, but it needs to increase enough to offset

the lower REVPAR. Increasing prices will increase your REVPAR, but what will it do to occupancy?

Increasing prices may lower occupancy but result in a higher REVPAR. How many rooms can your hotel afford to leave

empty before the price increase results in a profit decrease? Once again, if you do decide to increase prices you must

consider the relative merits of making a unilateral move or reacting to the competitors' price increase.

Decreasing room rates may sell more rooms if sufficient demand exists. This will result in lower REVPAR and therefore

occupancy must increase sufficiently to compensate for the lower REVPAR. Will lowering the price by 10% bring in

enough customers? Will lowering the price by 20% bring in enough?

You must also consider your price action in relation to the competition. The competitive hotels in the area may be

considering price changes of their own. Should you take the lead in lowering prices, or should you wait and follow the

Break-even analysis is a good tool to evaluate the impact of a price change (the minimum change in sales volume or

occupancy to offset a price change). The analysis can be performed with and without considering variable costs.


Watch: Break-even Calculation

It's important for hoteliers to be able to anticipate the effects of a price change. This isn't guesswork; the key is to base

decisions on data and analysis rather than your intuition or competitive instincts alone. Aggressive pricing wars, in which

companies reflexively try to match or undercut their competitors, can end up benefiting no one and sometimes harming an

entire industry. But when used wisely, can play an important role in a competitive strategy. Usingprice adjustments

information from your company and your competitors, along with some basic statistical tools, you can determine the price

point at which a particular adjustment will yield the most revenue. If, for instance, you are considering lowering prices in

your hotel, you can use these tools to determine how many additional guests you must attract to generate a profit. This

tactical procedure is called a break-even analysis. The break-even point is a benchmark that helps you determine how

much you need to earn to make a profit. In this section, use break-even analysis to examine pricing in isolation and in

relation to competitors.

In this video, Professor Chris Anderson leads you through an examination of a break-even calculation.


Watch: Break-even with Variable Costs

When pricing changes result in increased demand, that will change your staffing costs and overhead. You need to predict

the variable costs that are going to change as a result of your demand increase, and as Professor Anderson explains

here, you need to include those changes in variable cost in our break-even calculation.


Read: Break-even Examples

The Rest-a-While Hotel currently has a room price of €100 with variable costs of €15. It is considering a price decrease of

€10 and wants to calculate the percent break-even point. In this example we will work through the break-even analysis

first without considering variable costs and then factoring variable costs into the analysis.

Break-even without Variable Costs

This is the percent break-even formula we will use.

P - Price

CM - Contribution margin

VC - Variable costs

P - Change in price

Begin by calculating the contribution margin (CM).

CM = P - VC

= €100 - €15€85

Insert the CM into the break-even formula and calculate the results. Contribution margin is €85 and price

change (or P) is €10.

The Rest-a-While's break-even point is 13.3%. Occupancy must increase by 13.3% for the hotel to break

even with a €10 price decrease.

With the decrease in room rate, Rest-a-While expects its occupancy to increase, and as a result, its variable costs to

increase by €5. Now we can calculate the percent break-even and factor in the change in variable costs.

Break-even with Variable Costs

This is the percent break-even formula we will use in this calculation.


In this example, the price change P is €10 and the change in variable costs VC is €5. The contribution

margin CM is €85.

When we consider the variable costs, the break-even is 21.4%.

If the Rest-a-While hotel decreases rates by €10 and its variable costs increase by €5, its occupancy needs to increase by

21.4% to break even. This number is more than the one calculated without considering variable costs.


Read: Break-even to Evaluate Competitors

The Regal Suites, one of Hotel Ithaca's major competitors, has decided to raise its prices. Should Pascale, the

rooms-division manager at Hotel Ithaca, follow and raise her rates as well? You may think it is in her best interest not to

follow-in the short term, Hotel Ithaca's lower price will surely attract some guests from their competitor, and they will make

more money. But Pascale does not want to act with only short-term results in mind. She wants to take a pragmatic

approach to the problem using a break-even analysis. Will this analysis be easier or more difficult than a break-even

analysis in isolation?

It actually is easier because price (P) is a constant and quantity (Q) is variable. When we perform break-even in isolation,

both P and Q are variables. In this case you are determining the increase or decrease in quantity (variable) based on a

given price (constant).

If your competitors are dropping rates, odds are your business will feel the effects. You can either choose not to follow and

lose a little bit of market share, or you can choose to follow and not lose any share. If you choose the latter, you will earn

less money because you're selling the same inventory for less-the classic prisoner's dilemma, with Bill and Ted both

confessing.

If your competitor drops price by some amount P, you can assume you will lose some volume. The questions are, how

much volume will you lose and are you better off losing volume or losing margin? If you follow the competitor's price move,

your percent drop in contribution is P over your current margin (CM). If you don't follow, you will lose some sales volume,

Q. In this case, the break-even point is the percent change in sales as a function of the percent change in margin.

Pascale knows that Regal Suites and Hotel Ithaca currently sell rooms for €250 and Regal has decided to raise its rates

by €10 to €260. Hotel Ithaca's variable costs are €25. Its current margin (CM) is then €225.

Using the following break-even equation, she can determine the break-even amount and then the break-even percentage.

This shows that if Hotel Ithaca expects sales to rise by more than 4%, then it should hold its price where it is-not match the

price increase. If it expects its sales to increase by less than 4%, then it should match Regal's price increase.


Module Introduction: Measuring Price Sensitivity

Consumers of all products have some degree of price sensitivity, and these sensitivities vary by market segment.

Business owners must understand the sensitivity of their various customer segments and use that information when

making pricing decisions. Economists measure price sensitivity using elasticities-the percentage change in consumption of

a good caused by a 1% change in its price.

After completing this module, you will be able to:

Calculate price elasticity and use the result in a pricing decision

Evaluate the relationship between elasticity and break-even analysis

Measure price elasticity when demand is linear and when it is curvilinear

Differentiate between the uses of correlation and regression

Apply regression analysis to pricing decisions

Estimate relationships using linear regression

Calculate and use the fair share and average daily rate indexes as a guide for pricing

Use regression analysis to help determine your fair share of the relevant market

Outline an experiment to estimate price sensitivity


Read: Economics of Elasticity

Key Points

Demand for its product will change in response to a price change

Distinguish among buyers who are willing/unwilling to pay more

A natural extension of break-even analysis is price elasticity, the relative responsiveness of demand for a product or

service when prices change. A precise measurement of price elasticity gives the revenue manager a better idea of

expected demand at different price points and for different customer segments.

For example, if you decrease your price by 5% you may your revenue by 10%. Another hotel, however, mayincrease

increase its price 10% and its revenue by 5%. A price cut increases revenue only if demand is and a pricedecrease elastic,

increase only raises total revenue if demand is . Price elasticity of demand (or simply price elasticity) is a measureinelastic

of the responsiveness of buyers to price changes-the relative change in the quantity of a product demanded relative to the

change in its price.

When elasticity is small (the absolute value is less than 1), we consider the relationship to be inelastic. The quantity of an

item demanded is not very sensitive to price. Many of the stable requirements of daily life are inelastic. For instance, a

price increase of 1% for gasoline may lead to a fall in demand of only 0.2%. If gas increases from $4 a gallon to $4.04, the

change isn't large enough to keep people from filling their tanks.

We consider elasticity to be large when its absolute value is greater than 1. Luxury goods are typically more elastic than

necessities. When the price of gold jewelry increases by 1%, demand may fall by 2.6%, so the elasticity is 2.6 (the

absolute value of - 2.6 is 2.6).

In pricing, the challenge for the company is to be able to distinguish between buyers who are willing to pay a high price

and those who are not. Enterprises must be careful not to mischaracterize consumer groups or the elasticity of demand.

Factors influencing price elasticity:

Availability of substitutes

The greater the number of substitute products, the greater the elasticity

Degree of necessity or luxury

Luxury products tend to have greater elasticity than necessities

Proportion of income required

Products requiring a larger portion of the consumer's income tend to have greater elasticity

Time period considered

Elasticity tends to be greater over a long period of time because consumers have more time to adjust their behavior

to the price changes

Permanent or temporary price change

A one-day sale will result in a different response than a permanent price reduction of the same amount

Price perception


Decreasing the price for a meal by 5% from €30 to €28.50 will probably produce a greater increase in quantity

demanded than decreasing a room rate by 5% from €250 to €237.50

, the proportionateWhen demand is relatively elastic

change in demand (quantity) is greater than the

proportionate change in price. Henc e, when the price is

raised, the total revenue falls, and when the price is

lowered, total revenue increases. For example, res

taurant meals tend to be elastic. If your restaurant

increases its prices by 8%, demand for meals may

decrease by 12%.

, any increase inWhen demand for a product is very elastic

the price, even a small one, causes demand for the

good to drop. Hence, when the price is raised, the total

revenue can fall to near zero.

, the proportionateWhen demand is relatively inelastic

change in demand is less than the proportionate change

in price. Hence, raising the price raises total revenue,

and lowering price decreases total revenue. For

example if the price of bread increases by 10%,

consumer demand may decrease by only 2%.

, changes in price have aWhen demand is very inelastic

very small effect on demand for the good-the quantity

demanded is almost independent of price. Raising

prices will cause total revenue to increase because

demand stays the about the same but the customer

pays more for each good.


Watch: Calculating Price Elasticity

In the context of learning to make decisions about hotel pricing, calculating price elasticity is critical. Price elasticity is

trying to describe the relationship between demand changes and price changes, as Professor Anderson explains.


Read: Elasticity and Linear Demand

A linear demand function expresses the quantity demanded (Q) as a linear function of the unit price (P). Think about this

as Q being room nights and P being price or ADR. Linear demand can be graphed with a line with a constant slope.

Elasticity of demand, on the other hand, changes continuously as one moves up or down the demand curve because the

ratio of price to quantity continuously changes.

At one point on the demand curve, elasticity equals one. Above this point is the elastic range of the demand curve

(meaning that the elasticity is greater than one). Below this point is the inelastic range of the demand curve (meaning that

the elasticity is less than one). The decline in elasticity as one moves down the curve is due to the falling P/Q ratio. (Recall

that the slope is constant.)

How does this help with pricing decisions?

This means that if you have a constant slope and want to evaluate the elasticity, you need to evaluate it at a particular

price-quantity point along the line. The slope (P/Q) is a constant a smooth line. But when it is multiplied by a non-constant-

(P/Q), it becomes non-constant. If demand is linear or downward facing, we have non-constant elasticity. The elasticity will

depend upon where on the demand curve you pick the P and Q.

Slope measures the of change of one variable (P) in terms of another (Q).rate

Elasticity measures the change of one variable (Q) in terms of another (P).percentage

The figure to the right is room nights (Q) plotted as a function of room rate (P). By definition a straight line has a constant

slope. In the figure, point A has a room demand of 120 at a price of €225 and B has a demand of 200 at a price of €150.

The slope is -0.94.

Below are examples of the elasticity calculation at points A and B:

At A


Absolute value of the elasticity = 2.0

At B

Absolute value of the elasticity = 0.8

In this case, room demand is elastic when you consider a price change at €225 but inelastic if you change your price at

€150.

It may help to think of elasticity in terms of market segments. At higher room rates, there may be sufficient unmet

customer demand to offset price increase (demand is elastic), whereas at lower prices, there may not be sufficient

consumers still in the market (inelastic). Even though part of your demand is inelastic, part of it may be elastic. With a

linear constant slope, we end up with ranges of elasticity.


Watch: Elasticity and Break-even

In this video lecture, Professor Anderson will discuss the interplay between price elasticity and break-even analysis, as

well as how critical one is to the other.


Read: Constant Elastic Demand

When a demand curve is a straight line, the slope is constant, and absolute demand changes are identical for each

segment on the curve. For example, the slope of the red line in the chart is 0.66. In the scenario it represents, every price

increase of one euro results in a drop in demand of 0.66 units. Regardless of where the price change is-whether it's from

€20 to €21 or from €50 to €51-there is always a constant absolute change in demand of 0.66 units.

The straight-line demand curve has a constant slope, which means that there is a constant relationship between changes

in price and changes in demand. However, elasticity for this the straight-line demand curve is always changing. On one

portion of the line, the demand may be inelastic, while on a different portion, it may be elastic.

A straight-line demand curve is applicable in many situations, but in the hospitality industry, demand changes are often not

constant. They vary depending on the price change. For example, the absolute change in demand resulting from a price

increase from €20 to €21 will be different than the absolute change in demand resulting from a price increase of €50 to

€51.

As we have seen, a straight-line demand curve indicates changing elasticity. What does a demand curve with constant

elasticity look like? The slope of this curve changes in a particular manner. That is, when prices are low, the unit change in

demand is greater than the unit change in price, resulting in a steep slope. And when prices are high, the unit change in

demand is smaller relative to price, resulting in a flat slope. As such, the demand curve goes from steep to flat as the price

increases and the demand decreases. (See the blue line in the graph.)

Price

Linear

demand

Curvilinear

demand

Linear

elasticity

Curvilinear

elasticity

€ 5 46.67 44.7

€ 9 44.00 33.3 0.136 -0.573

€ 13 41.33 27.7 -0.210 -0.532

€ 17 38.67 24.3 -0.293 -0.518

€ 21 36.00 21.8 -0.389 -0.512


€ 25 33.33 20.0 -0.500 -0.508

€ 29 30.67 18.6 -0.630 -0.506

€ 33 28.00 17.4 -0.786 -0.505

€ 37 25.33 16.4 -0.974 -0.504

€ 41 22.67 15.6 -1.206 -0.503

€ 45 20.00 14.9

When demand is curvilinear, the slope is not constant. That is, the relationship between changes in P and changes in Q is

not constant. You can estimate slope at various points along the curve by drawing a line tangent to the curve.

If you perform the same elasticity calculations for the curvilinear demand curve, you will find that the elasticity is

approximately -0.5 over the entire range of the curve.

For example, over the price range of €21 to €29, the percent change in (quantity) is 18.6 - 21.8/20 = -0.145. Thedemand

corresponding percent change in is 29 - 21/25 = 0.32. This results in curvilinear elasticity of -0.5. Regardless ofprice

where the curve is evaluated, the elasticity will be approximately -0.5. Again, the slope of this demand curve is not

constant, but the elasticity is constant.


Read: Correlation Scatter Graphs

Key Points

Price changes influence consumer behavior

Correlation is not the same as causation

When we perceive two elements that covary, what do we see? We might see, for example, that when gas prices increase,

people tend to drive less, or that when airline prices decrease, customers tend to travel more. There is a relationship

between the two events. can be used to evaluate this relationship, first to determine if, in fact, there is aCorrelation analysis

relationship and then to assess the strength and direction of the relationship.

Correlation indicates the degree of relationship between two data sets, such as price and demand. A correlation

coefficient ( ) is similar to standard deviation-it is a measure of the strength of the linear relationship between twor

variables. Correlation coefficients vary between 1 (perfect positive correlation: as one element goes up, the other goes up

by a perfectly proportional amount) and 1 (perfect negative correlation: as one element goes up, the other goes down by

exactly the same proportion).

A good way to get a sense of the relationship between two data sets is to plot each point on a graph in which the two axes

represent the two data sets and see what type of pattern they make. This type of graph is called a scatter graph. If there is

a correlation, can help estimate . But we must be careful in interpreting correlation-it is not the same as causality.x y

Correlation does not indicate a change in causes a change in .x y

Let's look at an example. Suppose last month Peter Carter at Ideal Rental Car increased the rental price of his luxury car

from €32 to €37. He has historical data for the past 60 days-30 days before the price increase and 30 days after. He wants

to determine if the price increase is related to demand. Keep in mind that demand is a function of many things. One is

Peter's price; others might be the price charged by the rental agency across the street and the season of the year. In this

case Peter wants to assess the price-demand relationship. What scatter plot might he develop?

Suppose, from his data, he develops Graph 1, showing no

evidence of a pattern. Points on the y axis (Demand) aren't

related to points on the x axis (Price) in any systematic way.

In this case, the correlation coefficient r is 0-there is no

relationship.

r= 0

Graph 1


Although unlikely, Peter may find that as he increases price,

demand increases. He may find that there is a positive

correlation-the variables tend to move in the same direction. If

there is a weak positive relationship, Peter may arrive at

Graph 2

r= .6

Graph 2

In Graph 3 there is a strong relationship. This relationship is

similar to a standard deviation of zero. The ratio of to isx y

constant, and the points form a single straight line. This graph

indicates when Peter increases price, demand tends to go

down-there is a negative correlation between price and

demand. Graph 3 shows a strong negative correlation.

r= -.8

Graph 3

In summary a correlation of zero ( =0) indicates no relationship between the variables-as one goes up, the other may gor

down (or up). A weak correlation (.2 to .6), either positive or negative, indicates that as one goes up, the other usually

goes up, or as one goes down, the other usually goes down, but not always. Whereas a stronger correlation indicates that

when one goes either up or down, the other does the same.


Read: Correlation Versus Regression

Correlation describes whether two variables are related and if so, how strong their relationship is. It might tell you, for

example, that car rental prices and demand tend to vary together, or that the color of the cars available and demand do

not vary together. It doesn't indicate which factor causes the other-just how strongly they do or do not tend to move

together. Regression not only describes the relationship between two variables, it shows how you can use changes in an

independent variable, such as price, to predict changes in another, such as demand. By creating the "best fit" line for all

the data points in a two-variable system, you can predict values of y based on known values of x ; you can predict how

changing the price of renting midsize cars will influence how many you rent. In this section, examine how to use linear

regression in business to predict events and how to analyze a variety of data types for decision-making.

Although correlation and regression are related, they provide different information about data. We use them for different

purposes. Correlation is simply a measure of association between two variables - it shows that variable and variable A B

tend to move together, and it estimates the degree of association between them.

Regression goes a step further than correlation-it not only indicates the degree of association but also describes the

relationship between two variables. As long as two variables are correlated, you can use regression to help with

predictions.

Before attempting to fit a model (regression) to data, you should first determine whether or not there is a relationship

between the variables. A scatter graph is a helpful tool in determining the strength of the relationship between two

variables. If there appears to be an association between the variables (that is, the scatter graph indicates any increasing

or decreasing trend), then fitting a linear regression model to the data will probably be useful.

The best way to appreciate this difference is by example. Pascale from Hotel Ithaca has been reviewing data from the

hotel's previous year. A study shows that as revenue at the hotel's famous five-star restaurant increases, so does

occupancy. They are highly positively correlated.

Pascale can use regression analysis to help understand the relationship between restaurant demand and room demand.

Using restaurant customers as the independent variable, she can predict that when the restaurant has revenue, she willX

have occupancy in the hotel. Note that this does not assume that restaurant demand drives rooms demand-in fact itY

may be the opposite. It is key to remember that regression (and correlation) measure association not causation.


Watch: Use Linear Regression

As hoteliers, we want a way to predict what the effect of our price changes will be. If we decrease price, what will happen

to demand? If we increase price, what will happen to demand? We want to look at variable factors to gain insight. Linear

regression is one of that tools that helps us make informed decisions, as we'll find out from Professor Anderson.


Tool: Use Excel for Linear Regression

Download the Tool

Regression Spreadsheet

Excel is used to automate linear regression

calculations

Linear regression has many practical uses in the hospitality industry, but it is difficult to calculate by hand. In this lesson

we demonstrate how to use Excel to automate the calculations. The attached spreadsheet on the right (images also

shown below) lists sales and revenue data for a 24-day period. We can use this data set to create a model and then use

the model to predict the value of for any value of . If we use only correlation, we may arrive at a curvilinear relationshipy x

that is difficult to use in predictions. But we can use linear regression to fit a straight line, = mx + b, to data that gives the

best prediction of for any value of .y x

There are a number of different ways to compute regression. We will demonstrate using a scatter plot. You will have a

chance to practice using the exercise on the next page in the course.

Open the attached spreadsheet from the link above.

Select all the data in columns B and C. Using your mouse, select cell B1 through C26.

Insert a scatter plot in the spreadsheet by selecting the Scatter option with "Markers" or "Marked Scatter".

https://d31owemgwclw9w.cloudfront.net/content/SHA/SHA542/sha542_325_regression.xls


Excel will create an XY scatter graph showing Restaurant Revenue as a function of Room demand.

Right click on the plotted data (on the dots themselves) to open a menu.

Select Add Trendline.

Select the Linear Line option.

Display the equation on the chart. In some versions of Excel, the equation feature is found under Options rather

than on this menu.


You now should have the line of best fit and the equation of that line.

This indicates Restaurant Revenue = 29.481 x Rooms + 376.85, or for each additional room occupied at the hotel, they

can expect €29.48 in additional restaurant revenue.

For example, if 100 rooms were occupied, the restaurant could expect approximately €3,325 in restaurant revenue.

29.481x100 + 376.85 = 3,324.95

If ten more rooms were occupied (110 rooms total), revenue would increase by €294.81 (10 x 29.481), resulting in a total

of €3,619.81.

29.481x110 + 376.85 = €3,619.81

By adding 10 more rooms to the previous example of 100 total rooms, you could use this calculation:

(10 x 29.481) + 3,324.95 = €3,619.81


Tool: Linear Regression Review

Download the Tool

Review this completed to check your work.spreadsheet

On the previous page, you calculated data for Grand Sky Airlines. On this page, you may download the spreadsheet that

contains the graph and answers. You may try the quiz again after reviewing the completed spreadsheet.

https://d31owemgwclw9w.cloudfront.net/content/SHA/SHA542/sha542_326_evaluatereservationsanswers.xls


Read: Fair Share

When you attempt to evaluate your hotel's performance from historic data, you see what looks like a counter-intuitive

relationship. During August on the coast of the Mediterranean, for example, demand for hotel rooms is high and so are

prices. If you look at the relationship statistically, you see that higher prices are associated with higher demand and lower

prices with lower demand. That association could seem to indicate that higher prices demand-if only this were so!increase

But it isn't, of course-it is increased demand that allows you to raise prices, not raising prices that increases demand.

To correct this counter-intuitive impression, we use the concept of . A firm's fair share is the percentage of fair share

demand you should capture if customers in the market choose your hotel in proportion to your firm's market share. It is an

index of volume.

Assume that the information in the table is data collected from your hotel and comparable hotels. You have 159 rooms,

and there are 762 rooms in the market. On March 1, the total rooms sold was 321, and your portion of the total was 135

rooms.

A B C D E F G H

1Date My Capacity My Sales My Revenue Market Supply Market Demand Market Revenue fair share

2 3/1 159 135 € 8,291 762 321 € 17,243 67

3 3/2 159 118 € 8,271 762 319 € 15,639 67

4 3/3 159 102 € 7,133 762 333 € 16,896 69

5 3/4 159 71 € 4,971 762 231 € 12,666 48

6 3/5 159 103 € 7,038 762 268 € 15,421 5

To find your fair share, divide your capacity (159) by market supply (762) and multiply by market demand (321).

Your fair share of the market is 67 rooms.


Now you can calculate your fair share index and determine if you are selling more or less than your fair share.

Your fair share index is 2.016.

If capacity were distributed evenly among the hotels, you would receive 20% of the market.

or

But it turns out you are getting 40% of the market-twice your fair share.

or

The fair share index is a good proxy for estimating the relationship between price and quantity. If you simply look at the

relationship between what you sell and how you price, you would find no relationship. We mentioned the counter-intuitive

view of the raw data, in which high prices seem to cause high demand. You want to remove this distortion from your

statistics to understand the true relationship between price and demand. You do this by incorporating a factor that

expresses price and demand relative to the market, and that is what the fair share index allows you to do.


Tool: Use Fair Share to Estimate Elasticity

Download the Tool

Elasticity Spreadsheet

Use STR data to estimate the ADR and RevPar indexes

Use ADR and RevPar indexes to estimate elasticity

Smith Travel Research, Inc. (STR), is a company that tracks supply and demand data for the hotel industry and provides

market share analysis for hotels. The data it provides are typically used to calculate indexes such as Average Daily Rate

(ADR) and RevPar indexes. The ADR index is the ADR divided by the competitive-set ADR. This basically indicates

whether or not you have a price premium to the market. The RevPar index is your RevPar divided by the competitive-set

RevPar.

This is typically where most companies end their use of STR data. Unfortunately, it's not quite enough for you to evaluate

elasticities. In this example, you learn how to use STR data to estimate the ADR and RevPar indexes and how to use

those indexes to estimate elasticity.

Rest-a-While Hotel is a 159-room, three-star hotel located near several competing three-star hotels, some nice 2.5-star

hotels, and a few dated 3.5-star hotels that need renovation. There is a total supply of 762 comparable rooms nearby. The

attached Fair Share spreadsheet (above) displays 100 days of data from the Rest-a-While Hotel and comparable

establishments. The My Rooms column lists the total rooms at the Rest-a-While and the Market Supply column lists the

762 rooms in their competitive market. Each row lists the data for a different day.

The second row of the table below shows the computation used to arrive at the indexes we need.

A B C D E F G H I J K L

1 DateMy

Rooms

My

Sales

My

Revenue

Market

Supply

Market

Sales

Market

Revenue

Fair

Share

(FS)

ADRMarket

ADRFS Index ADR Index

2

159/762*

Market

Sales

My

Revenue/

My Sales

Market

Revenue/

Market

Sales

My Sales/

[159/762

*Market

Sales]

[My Revenue/ My

Sales]/ [ Market

Revenue/Market

Sales]

3 3/1 159 135 € 8,291 762 321 € 17,243 67 € 61 € 54 2.016 1.143

4 3/2 159 118 € 8,271 762 319 € 15,639 67 € 70 € 49 1.773 1.430

5 3/3 159 102 € 7,133 762 333 € 16,896 69 € 70 € 51 1.468 1.378

6 3/4 159 71 € 4,971 762 231 € 12,666 48 € 70 € 55 1.473 1.277

7 3/5 159 103 € 7,038 762 268 € 15,421 56 € 68 € 58 1.842 1.188

8 3/6 159 101 € 6,796 762 281 € 16,950 59 € 67 € 60 1.723 1.115

https://d31owemgwclw9w.cloudfront.net/content/SHA/SHA542/sha542_332_fairsharerestawhile.xls


After we run the computations, we can use the ADR index and the fair share index to create a scatter plot that displays the

elasticity of the relationship. The graph indicates that elasticity of price and demand is curvilinear. In other words, there is

constant elasticity and the hotel will find that total revenue does not change when price changes.


Watch: Logarithm to Evaluate Curvilinear Demand

Over time, as you expend or invest resources into your efforts, you may see that the incremental impact of subsequent

changes is less than the prior. That also happens with pricing, as Professor Anderson explains.


Tool: Price Elasticity and Fair Share Review

Download the Tool

Review this to check your work.spreadsheet

On the previous page, you estimated price elasticity for the Hotel Ithaca. On this page, you may download the

spreadsheet that contains the graph and answers. Use this spreadsheet to compare your answers to the correct data.

https://d31owemgwclw9w.cloudfront.net/content/SHA/SHA542/sha542_334_elasticityathotelithacaaanswers.xls


Tool: Use Multiple Regression

Download the Tool

Multiple Regression Spreadsheet

Instructions

Use regression to help with predictions

As we've seen, regression indicates the degree of association and also describes the relationship between two variables.

As long as two variables are correlated, you can use regression to help with predictions.

Evaluating regression by adding a trendline to a scatter graph works well if you want to describe the relationship between

two variables ( and or ADR and Fair Share). But you can also perform multiple regression using Excel functions. Usingx y

Excel you can estimate the impact of more than one variable upon your outcome variable. Multiple regression analysis is a

statistical technique that uses more than one predictor, or independent variable, to examine the effects on a single

outcome, or dependent variable. For example, a multiple regression model might examine demand (dependent variable)

as a function of customer ratings and season of the year (independent variables). Multiple regression calculates

coefficients for each independent variable. The coefficient estimates the effect of a particular variable while holding

constant the effects of other variables.

Download the multiple regression spreadsheet (above) and the instruction document (above) to practice using Excel to

estimate elasticity at Hotel Ithaca. We also extend this model to include review scores (i.e. feedback scores from prior

guests), simultaneously estimating the impact of ADR and review scores on demand (Fair Share).

https://d31owemgwclw9w.cloudfront.net/content/SHA/SHA542/sha542_336_multipleregression.xls

https://d31owemgwclw9w.cloudfront.net/content/SHA/SHA542/sha542_336_multipleregressioninstructions.pdf


Read: Controlled Experiments

Key Points

Controlled experiments help you with pricing decisions

Compare discount weeks to control weeks

Sometimes you may not have access to market-level data, or you may not have much variance in prices-that is, your

prices may tend to be relatively fixed. When this is the case, you can use very simple experiments to help estimate

elasticity.

Penny Frugal Auto Group (PFAG) focuses on renting cars to the value-conscious leisure customer through its brands

Penny Rent-a-Car and Frugal Car Rental. Together they have more than 1,550 corporate and franchised locations

worldwide, including approximately 600 in the United States and Canada. PFAG rents cars mostly at airports, and

although the two divisions have the same owner and share the same inventory, at the consumer level they operate as

separate companies, each with its own counter at the airport, its own Web site, and so on.

This example of pricing at the San Francisco airport shows that rates for Penny and Frugal are the same for all car types.

Company Frugal Penny Hurts Avits Nationete

Type of Car Price

Economy €34 €34 €39 €82 €38

Compact €35 €35 €39 €83 €38

Midsize €36 €36 €40 €84 €39

Luxury €38 €38 €42 €86 €41

Full Size €38 €38 €42 €87 €41

Like many companies, PFAG has felt the impact of the recent economic downturn. After noticing sluggish sales in

consumer travel early in the year, management decided to conduct a two-month experiment. Over the eight-week test

period, prices were manipulated for alternating weeks.


- Penny and Frugal had the same prices.Week 1

- Frugal's prices were lowered below Penny's prices.Week 2

- The prices were the same.Week 3

- Frugal's prices were again lowered below Penny's.Week 4

And so on. Conducting the experiment for eight weeks and oscillating lower prices on and off allowed a natural control for

the seasonality of the booking cycle. Here are the results of the experiment.

The bar heights represent Frugal's total demand over the eight weeks. The purple bars represent sales when Frugal and

Penny had the same price (the control). and the green bars represent Frugal's demand at a discounted price.

The point at which the line crosses the green bars provides the number of cars Frugal would have rented if prices had

been equal. For example, if prices had been equal for weeks one and two, Frugal would have rented 21 cars during week

two.

The new demand at Frugal (the demand created by the discount) is A B = 28 21 = 7.

Calculated as a percent, it is (A B) / B = (28 21) / 21 = 33%.

If rental prices during week one were $50 and during week two were $45, then the change, or P, is 10%.

The elasticity is [(AB) / B] / [(P2P1) / P1] = [(2821) / 21] / [(45-50)/(50)] = (0.33/0.1) = 3.3.

The net result shows that demand is very elastic for Frugal. This new demand probably comes at the expense of Penny.

We could do a similar analysis for Penny and for total demand at both Penny and Frugal to determine if the price reduction

would improve contribution. This example illustrates how the proper design of price tests ensures that we account for

factors such as seasonality.


Read: Thank You and Farewell

Congratulations on completing the Price Sensitivity and Pricing Decisions course. The course provided a strategic look at

pricing, the impact of price changes, and the anticipated reaction of your competitors. We illustrated these impacts with a

look at real-life examples. We also described tactical tools you can use to evaluate the effect of a price action on demand

and, ultimately, on profitability. I hope to see everyone in the remaining courses in the series.

Thank you.

Chris Anderson.


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Additional Resources

The provides focused whitepapers and reports based on cutting-edge research.Center for Hospitality Research

http://www.hotelschool.cornell.edu/research/chr/

SHA542: Price Sensitivity and Pricing Decisions...Customers' buying decisions reflect their price...

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