SHA542: Price Sensitivity and Pricing Decisions

Transcription

1 SHA542: Price Sensitivity and Pricing Decisions Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 1

2 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 Cornell School of Hotel Administration, describes the impact of changing prices in a competitive environment and then describes several methods for measuring demand sensitivity to price changes (or price elasticity). Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 2

3 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 Chris Anderson is an associate professor at the Cornell School of Hotel Administration. Prior to his appointment in 2006, he 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 Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 3

4 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 Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 4

5 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? Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 5

6 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 result was a record level of air travel-and record losses. One estimate suggested that the fare wars 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 Harvard Business Publishing site for purchase. 1 Steven Morrison and Clifford Winston Causes and consequences of airline fare wars. Brookings Papers: Microeconomics 1996: Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 6

7 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, 1 shows changes in average daily revenue (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, 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 Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 7

8 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 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. 1 Each month, Smith Travel Research (STR) collects performance data on over 22,000 hotels representing more than 2.7 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 8

9 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. Industry Characteristic High Risk Low Risk Cost High fixed costs Low fixed costs Supply Capacity utilization Relatively low capacity utilization Relatively high capacity utilization Product perishability Perishable Nonperishable Product differentiation Little differentiation among competitors Strong differentiation among competitors Price sensitivity of demand Customers very price sensitive Customers not price sensitive Demand 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 Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 9

10 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 10

11 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, price adjustments can play an important role in a competitive strategy. Using 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 11

12 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 12

13 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 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 13

14 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 14

15 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 15

16 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 Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 16

17 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 increase your revenue by 10%. Another hotel, however, may increase its price 10% and decrease its revenue by 5%. A price cut increases revenue only if demand is elastic, and a price increase only raises total revenue if demand is inelastic. Price elasticity of demand (or simply price elasticity) is a measure 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 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 Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 17

18 Decreasing the price for a meal by 5% from 30 to will probably produce a greater increase in quantity demanded than decreasing a room rate by 5% from 250 to When demand is relatively elastic, the proportionate 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%. When demand for a product is very elastic, any increase in 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. When demand is relatively inelastic, the proportionate 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%. When demand is very inelastic, changes in price have a 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 18

19 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 19

20 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 rate of change of one variable (P) in terms of another (Q). Elasticity measures the percentage change of one variable (Q) in terms of another (P). 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 Below are examples of the elasticity calculation at points A and B: At A Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 20

21 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 21

22 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 22

23 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 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.) Linear Curvilinear Linear Curvilinear Price demand demand elasticity elasticity Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 23

24 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 demand (quantity) is /20 = The corresponding percent change in price is 29-21/25 = This results in curvilinear elasticity of Regardless of where the curve is evaluated, the elasticity will be approximately Again, the slope of this demand curve is not constant, but the elasticity is constant. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 24

25 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. Correlation analysis can be used to evaluate this relationship, first to determine if, in fact, there is a 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 ( r) is similar to standard deviation-it is a measure of the strength of the linear relationship between two 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, x can help estimate y. But we must be careful in interpreting correlation-it is not the same as causality. Correlation does not indicate a change in x causes a change in 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 Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 25

26 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 x to y is 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 ( r=0) indicates no relationship between the variables-as one goes up, the other may go 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 26

27 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 A and variable 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 X revenue, she will have Y occupancy in the hotel. Note that this does not assume that restaurant demand drives rooms demand-in fact it may be the opposite. It is key to remember that regression (and correlation) measure association not causation. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 27

28 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 28

29 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 y for any value of x. If we use only correlation, we may arrive at a curvilinear relationship 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 y for any value of 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". Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 29

30 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 30

31 You now should have the line of best fit and the equation of that line. This indicates Restaurant Revenue = x Rooms , or for each additional room occupied at the hotel, they can expect in additional restaurant revenue. For example, if 100 rooms were occupied, the restaurant could expect approximately 3,325 in restaurant revenue x = 3, If ten more rooms were occupied (110 rooms total), revenue would increase by (10 x ), resulting in a total of 3, x = 3, By adding 10 more rooms to the previous example of 100 total rooms, you could use this calculation: (10 x ) + 3, = 3, Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 31

32 Tool: Linear Regression Review Download the Tool Review this completed spreadsheet to check your work. 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 32

33 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 increase demand-if only this were so! 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 fair share. A firm's fair share is the percentage of 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 1 Date My Capacity My Sales My Revenue Market Supply Market Demand Market Revenue fair share 2 3/ , , / , , / , , / , , / , ,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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 33

34 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 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 34

35 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 My My My Market Market Market 1 Date Rooms Sales Revenue Supply Sales Revenue Fair Share (FS) ADR Market ADR FS Index ADR Index 2 159/762* My Market Revenue/ Sales My Sales Market Revenue/ Market Sales My Sales/ [159/762 *Market Sales] [My Revenue/ My Sales]/ [ Market Revenue/Market Sales] 3 3/ , , / , , / , , / , , / , , / , , Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 35

36 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 36

37 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 37

38 Tool: Price Elasticity and Fair Share Review Download the Tool Review this spreadsheet to check your work. 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 38

39 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 ( x and y or ADR and Fair Share). But you can also perform multiple regression using Excel functions. Using 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). Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 39

40 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 Compact Midsize Luxury Full Size 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 40

41 Week 1 - Penny and Frugal had the same prices. Week 2 - Frugal's prices were lowered below Penny's prices. Week 3 - The prices were the same. Week 4 - Frugal's prices were again lowered below Penny's. 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 = = 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 41