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2017-04-07
More Money on the Table: A Study of Potential
Profit Increasing Opportunities during
Alternative Sporting Events
Boyer, Chase
http://hdl.handle.net/10456/42894
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ECONOMICS 620
Allegheny College
Meadville, Pennsylvania
16335
More Money on the Table: A Study of Potential Profit Increasing
Opportunities during Alternative Sporting Events
Chase J. Boyer
April 7, 2017
1
More Money on the Table: A Study of Potential Profit Increasing Opportunities during
Alternative Sporting Events
by
Chase J. Boyer
Submitted to The Department of Economics
Project Advisor: Dr. Hoa Nguyen
Second Reader: Dr. Janine Sickafuse
Date: April 7, 2017
I hereby recognize and pledge to fulfill my responsibilities as defined in the Honor Code and to
maintain the integrity of both myself and the College as a whole.
Signature:
Name: Chase J. Boyer
Table of Contents
List of Tables……………………………………………………………………………….ii
Abstract ……………………………………………………………………………………iii
Introduction………………………………………………………………………………...1
Chapter 1: A Literature Review…………………………………………………………..3
I.
Economic Consequences of Advertising…………………………………....4
II.
Advertising Effect on Sponsored Events…………………………………....6
A. Specific Event: Super Bowl……………………………………………...8
III.
Brand Name Placement in a Differentiated Market………………………...9
Chapter 2: Theoretical Framework………………...……………….…………………...12
I.
Return on Commercial Investment (ROCI)……………………………......12
II.
Price Elasticity of Supply…………………………….………………...…..14
III.
Price Elasticity of Demand……………………………………………........16
IV.
How Firms Compete………………………………………………………..18
A. Bertrand vs. Cournot Model....................................................................18
B. Case Study: Ford vs. Chevrolet………………………………………...19
Chapter 3: Empirical Analysis…………………………………………………………...21
I.
The Data Set……………………………………………………………......21
A. Sources of Data………………………………………………………...21
B. Discussion of Data……………………………………………………..23
II.
The Regression Models & Interpretation…………………………………..27
A. Super Bowl Regression…………………………………………………27
B. World Series Regression………………..................................................29
C. NBA Finals Regression………………………………………………....31
III.
Potential Biases and Errors………………………………………………....32
A. Breusch-Pagan Test for Heteroskedasticity…………………………….32
B. Correlation Matrix for Heteroskedasticity……………………………...33
Chapter 4: Closing Remarks & Conclusions…………………………………………….35
I.
Brief Overview of Study……………………………………………...…….35
A. Findings from the Project……………………………………………….35
B. Why the Super Bowl?................................................................................36
II.
Possible Ideas for Future Research………………………………………….37
Appendix................................................................................................................................41
A. Summary Statistics………………………………………………………………41
B. Regression Results………………………………………………………………42
Acknowledgements…………………………………………………………………………44
ii
List of Figures & Tables
Figure 2.1: Total Fixed Costs Graph
Figure 2.2: Average Costs Interaction Graph
Figure 2.3: Price Elasticity of Supply
Figure 2.4: Price Elasticity of Demand
Table 3.1: Summary Statistics
Table 3.2: Super Bowl Regression
Table 3.3: World Series Regression
Table 3.4: NBA Finals Regression
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Abstract
The object of this research paper is to investigate and analyze advertisements throughout
the Super Bowl, World Series, and NBA Finals. Using both statistical and theoretical analysis, I
will determine which sporting event offers the largest return on commercial investment (ROCI)
for a set number of companies. The data is collected from the years 2009 to 2013 and will
primarily focus on each events advertising costs to companies and other important factors that
affect ROCI. The analysis will concentrate on the ROCI’s for each event and conclude whether
or not the Super Bowl is still the best source for advertising. Although previous studies have
indicated there could possibly never be a better event for advertising, this research aims to refute
these broad statements.
1
Introduction
For some time, advertising has been a critical part of business, by allowing sellers the
opportunity to compete for potential customers. Advertising generates the ability to boost
awareness, comparison, and retention of a business’s goods or services. This, in turn, allows
companies to promote their products to new and repeat customers. Over time, firms have spent
an endless amount of effort and resources trying to achieve their business goals with the help of
advertisements. One of the most important aspects of advertising is to inflate ones existing
products as well as any new product lines while trying to receive a maximum return on their
investments. The goal is to generate large amounts of revenue, which leads to higher profit
margins, and finally a lucrative return on investment which contributes to a company’s success.
Continually trying to obtain a competitive edge has created a path for the advertising
industry to research countless, but different target groups. Advertising executives and scholars
have pursued topics including the effects of brand name placement on products, the overall
economic impact of advertising, how advertising influences changes to the stock market, and the
effects of advertising during sponsored events. As previously mentioned, an important focus of
the advertising industry is investigating the effects of advertising during sponsored events. The
Super Bowl is one such event that scholars continuously study and view as the ultimate
advertising opportunity. This one evening event attracts over one hundred million viewers. The
focus of my paper is to expand on the Super Bowl mega advertising opportunity by introducing
other main events such as the World Series in Major League Baseball and the NBA Finals. Is
Super Bowl advertising worth the big investment or can dollars be better utilized during other
events to minimize costs and maximize profits?
Additional research of the World Series and NBA Finals is important in understanding
this concept in further detail. Since the World Series and NBA Finals have traditionally not been
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considered the mega event, supplemental research is needed to determine any benefit. The two
sporting venues may prove to display a significant benefit to a company or companies long term
goals. In order to formulate an argument for any benefit, a time frame must be established to
review a company’s return on investment from advertising. The information will be used for
graphs that help visualize any or all impacts, either positive or negative. The period of time for
this particular study is a various range of data from a five-year period (2009-2013). This fiveyear time frame was used in order to guide this paper to a more accurate conclusion of the
results. My hypothesis is that companies could potentially earn higher margins of profit, and
thus, greater returns on investment advertising during the World Series and NBA Finals
compared to the Super Bowl.
This research could prove to be important because many companies would essentially be
losing profit that is there to gain. Advertising during the Super Bowl has proven to generate high
levels of profit. However, producing and marketing these commercials to television providers
comes at a large cost. In business, a company will choose to limit their costs of production on an
existing product if they can substantially increase their profit. This research could progress the
advertising industry entirely. To investigate this correlation, the amount of money spent on
advertising during these events and their return on investment will be examined. I will be
singling out ten of the most popular companies and looking at their profits before and after the
events. Hopefully, a stronger positive relationship exists between the World Series and NBA
Finals the 2009-2013 seasons.
The advertising industry is currently flourishing with its same practices. Companies
compete all over to establish brand loyalty to their consumers which has also proven to lead to
better profits. This research could influence smaller companies to advertise more efficiently
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allowing more competition to occur. Prior to this paper, there has been little research completed
to indicate that other sports events could impact companies’ profits.
To establish a connection between my explanatory and dependent variables, I plan on
using Statista in my early stages of research to develop a more in depth analysis. Secondly, I will
be researching thirty companies, ten for each of the events. Most of these companies are publicly
traded and can be found on the New York Stock Exchange, however, some are not, but
information on them can be found in their respective annual reports. The majority of the data
being collected for this project is available to anyone and will be found on public databases that
compile financial reports for these companies.
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Chapter 1: The Literature Review
There has been a variety of research completed in the advertising industry. The constant
in depth research has allowed for this industry to be shaped differently, create new strategies for
profit, and even analyze which factors best help with establishing a brand value. Each of these
findings has added to the overarching theme of the industry that advertising has a significant
impact on all companies profit margins. Advertising will continue to be one of the strongest
forms of product marketing due to its capability of differentiation.
I. Economic Consequences of Advertising
In Nelson (1975) he observes the economic consequences of advertising factors such as
elasticity of demand, and monopoly power of advertisements. Nelson analyzed both
assumptions by relating research of other authors along with his own stance on the subject. Other
work before Nelson (1975) tended to argue that advertising increases the elastic portion of
demand and that the industry represents some monopolistic features. His argument, related to his
empirical section, concludes with an entirely opposite outcome. Most of the concluding
statements stem from the theory of consumer information. Consumer tastes were separated into
search and experience categories. A search indicates consumer choices before advertising and
experience relates to choices after advertising. He concluded that consumers are better off with
advertising which indicates that with more information on a product, the elasticity of demand
becomes inelastic. This leads into his second argument on the monopolistic features of this
industry. Nelson explained that there needed to be enough entrants with a large amount of capital
to enter the market. If this is the case, when the long run rate of return exceeds the competitive
rate, this will offer no barriers to entry. Nelson held utility per consumer constant throughout his
research which was his only downfall to the paper. In his appendices, he also described that his
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distinctions derived from consumers categorizing products based on the volume of
advertisements.
Farris and Albion’s article (1980) looks at whether advertising can increase the retail
price of products. The two elements of this article used in developing further analysis and theory
are that economists understand the basic model of competition by conducting rational consumer
choices, and how businesses use advertising to differentiate their products for consumers. Farris
and Albion divide their research into two theories; advertising equals market power and
advertising equals information. The market power theory states that creating a brandable product
leads to a better reputation which allows a company to set higher prices, thus retain a larger
profit. The information theory states that supplying consumers with more information increases
price elasticity of demand and reduces cost margins between its substitutes, thus the companies
retain a higher profit. Much of Farris and Albion’s research comes from prior works completed
in the advertising industry by Robert Steiner. Steiner was an American Economist who spent
most his career researching how marketing productivity affects consumers’ choices. Farris and
Albion extracted data from five of Steiner’s papers (1973, 1977, 1978a, 1978b, 1978c) and
created a model in his name known as the dual-stage model. This model showed the impact of
advertising on absolute consumer prices. Farris and Albion’s extrapolated version of this model
suggests that there is a certain degree of correlation between manufacturing price levels and
prices at the consumer level. However, none of the empirical evidence backs up the hypothesis
explicitly and leaves their question unresolved. Based on the data they had in 1980, the authors
could not create a concise conclusion without running further macroeconomic experiments.
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II. Advertising Effect on Sponsored Events
Gwinner and Eaton’s (1999) discussion on the advertising industry provides us with
information on the role sponsored events play. Brand imaging is a unique aspect used to link the
consumer to the product. This connection can become stronger when they transfer the image
through sponsored events. There had been research done prior to their paper that suggested a
strong positive correlation between celebrities, advertisements, and products. McDaniel (1999)
also concluded that there was a significant relationship between a high involving product and a
high rated sporting event. For example, McDaniel found that a product by an automobile is better
paired with an event such as the Olympics rather than a low-grade event such as a PBA bowling
tournament. There are also ways in which homogenous products can be differentiated; by using
image-based similarity and functional-based similarity to signify the key differences. Image
based similarity is when a certain sponsor is labeled at a sporting event such as a sign or
billboard. Functional based similarity occurs when athletes are supplied equipment for a
sponsored event. An example is if Under Armor supplies a youth travel baseball team with
apparel and equipment to compete in national tournaments. Under Armor’s logo and name will
be visually displayed on the merchandise so all consumers can see. The results of the Gwinner
and Eaton’s (1999) discussion were that previous companies wanted to participate under
functional based advertising, but are now switching to image-based advertising because there is
more opportunity. This paper was limited due to the timeframe of their study, basis of research
(they studied event to the brand rather than brand to event), and the magnitude of sponsors at in
event. Their study also concluded that it may be more beneficial for sporting events to use a
higher number of sponsors.
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Filbeck, Zhao, Tompkins, and Chong (2009) research on share price reactions to
advertising announcements wishes to evaluate the rate of return on management strategy to
advertise during the events. Advertisements have caused profits for companies to increase for
years and they have realized a direct correlation with specific venues. The author's hypothesis is
that since investors understand the large amounts of viewership for these events, they hoped that
advertising during these events would increase sales for the firm. If their hypothesis is actually
concluded, investors will then use this information to purchase the stocks of companies in which
sales are increasing. The authors looked at three events in their study, the Super Bowl, the
Academy Awards, and Sitcoms. The Sitcoms showed to have no significant returns on the
advertisements placed during the episodes. In other words, they were listed as a non-value
adding event. The Academy Awards yielded the same results as Sitcoms. The shareholders did
not feel inclined to purchase stocks after the commercials were aired. The Super Bowl, however,
was perceived to be statistically significant (negative) because of it being shown as a live event.
The events downfall is its retention rate, the rate that companies return to advertise, which is only
around sixty percent. The Super Bowl has repeat advertisers so after a while, they stop
advertising realizing there is no additional benefit or revenue to gain. This led to evidence being
presented in their conclusion that first-time advertisers profited more than the repeated
companies. Out of the three events, the Super Bowl was the least perceived. Their conclusion
helps supplement my research because I am examining the correlation of return on investment
throughout three media sponsored events. The author’s research had some limits such as the
sample size and controlling for the advertisers on the final episode dates. This could have caused
for the interpretation to be broader than expected.
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A. Specific Event: Super Bowl
Hartmann and Klapper (2015) explored the effects of advertising during the Super Bowl.
Approximately forty percent of households watch the Super Bowl and because of this,
consumers are more willing to watch the advertisements of established brands. The authors use
the Neilsen rating system in addition to store level data of beer/soda sales to test the market
variation in sales and ratings. They also research the mechanisms of ad effects and how an
association between brand and viewership is created. Instead of using data on a monthly scale,
Hartmann and Klapper chose to use a week-specific evaluation to analyze the effectiveness. This
allowed them to throw other important weeks of sports events into their regression such as the
college basketball tournament. They concluded it would make more sense for non-established
brands to advertise during the Super Bowl as it would be more effective. However, the
companies that have brand loyalty such as Pepsi still make efforts to air commercials during the
Super Bowl. Why? Because they are also making a better margin of profit by continuously
advertising. The research conducted by Hartmann and Klapper will help guide my research in the
correct direction. I plan on implementing a differentiation scale using the Bertrand model that
bases all a firms’ decisions on price setting.
Krueger and Kennedy (1990) examined the Super Bowl and the effect it had on the entire
stock market. More importantly, they wanted to look at the Super Bowl predictor which is
associated with the winner of the event and how that is utilized to forecast the direction of the
stock market. There are two results of the Super Bowl Stock Market Predictor (SB SMP); the
first being if the winner is from the National Football Conference (NFC), then the stock market
will finish higher than where it began that year (691). The second being if the winner comes
from the American Football Conference (AFC), then the stock market will finish lower than
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began the year (691). Krueger and Kennedy observed five stock market indexes from the years
1967-1988. The SB SMP accurately anticipated the direction of the stock market 20 out of the 22
times. They based the return of statistical significance on the annual changes in the index plus
the dividends which are reported throughout the Security Price Record. This research completed
by Krueger and Kennedy was among the first to empirically prove that the SB SMP has a
statistically significant result and that it is not a hoax. Many investors now rely on the SB SMP
to decide whether it is a good time to invest in certain products or not. This study can help
further my research because I will be analyzing a few aspects of the stock market in regards to
advertising. This could prove to be beneficial for my project because if I have a sense of the
direction the stock market will head, I need to look for additional peaks in revenue and expenses
to calculate the profit margins.
III. Brand Name Placement in a Differentiated Market
Baker, Honea, and Russell (2004) article studies the effect of brand name placement on
television advertising effectiveness. Previous work on this topic had researched that placing the
brand name of a product toward the end of an advertisement seemed to yield the most successful
results for companies. However, Baker, Honea, and Russell created their hypothesis based on the
brand name being more effective at the beginning of advertisements. Each of the different
hypotheses intends to prove which has a more positive reaction to consumers. There have been
several main conclusions from testing the previous hypotheses. The first is consumers can
remember the contents of the advertisement, but not the brand name. Secondly, but related to the
first, consumers will remember the advertisement and label the brand incorrectly (mix up the
brand that the advertisement represents). Lastly, consumers can have positive reactions to an
advertisement, but not remember its premise or the brand name associated with the product.
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These authors concluded that their hypothesis was positively significant. It showed that
companies should highly consider revealing the brand name at the beginning of advertisements
as it could help increase brand identity and therefore, could improve sales of the products.
Grossman and Shapiro (1984) studied the role of expenditures by sellers in a market
where heavy product differentiation occurs. For this work, the authors used the idea that
advertising offers full and accurate information of products leaving consumers tastes unchanged.
Instead of focusing on a perfectly competitive market, Grossman and Shapiro researched
heterogeneous products and the impact they have on total consumer information. They tried to
do this by examining the effects of the bias of market equilibria with using characteristics of an
oligopolistic structure (fixed number of firms, free entry). Some of the factors the authors wanted
to use for their regression included production technology, information structure, advertising
technology, and equilibrium concept. The two conclusions Grossman and Shapiro presented was
that for equilibrium in the oligopoly market, different forms of advertisements lead to a higher
result for companies. Also, an improved efficiency of advertising led to more competition within
the market and because of that, prices begin to fall. The biggest setback of their study was that it
could have broadly described the importance of these conditions. However, under the
circumstances they did research, it was found that heterogeneity of the market is typically
excessive when consumers are given complete information.
Stevik and von der Fehr (1998) have researched persuasive advertising, an opposite, but
more in depth approach considering the ideas that Grossman and Shapiro (1984) explored.
Before Stevik and von der Fehr (1998), it was assumed that advertisements offer an ordinary
utility function specifying that it simply shifts the demand for distinct products. Even though this
has proven to be useful information, Becker and Murphy (1993) concluded that persuasive
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advertising could determine more significant results. One of these results being a larger profit
margin when demand elasticities are higher. Stevik and von der Fehr (1998) observed three main
ways in which consumer preferences can be changed; 1) advertising could augment the value of
a product (willingness to pay), 2) they attempt to persuade consumers that the good shown in the
advertisements is the best one (changes ideal product variety), and, 3) advertisements can cause
consumers to connect the differences that exist to a greater importance (increased perceived
product difference). Overall, Stevik and von der Fehr were looking to see the relationship
between the degree of product differentiation and equilibrium levels of persuasive advertising.
Their results concluded that persuasive advertising is more effective when trying to distinguish
between products that are similar rather than differentiated. The authors also suggest that
advertising and differentiation must be substitutes at the larger level of decision making for
firms. Several of their limitations included their control for simultaneous pricing decisions
amongst firms and an exogenous aggregate demand because any endogenous features would
have complicated the analysis.
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Chapter 2: Theoretical Framework
I. Return on Commercial Investment (ROCI)
The focus behind any business endeavor is to make a profit. To make a profit, many
elements need to be maintained and structured in a way that it allows the business to succeed.
One of these important characteristics is managing your return on investment. A company will
use this formula to measure their profitability or overall efficiency of the investments. The
𝑁𝑒𝑡 𝑃𝑟𝑜𝑓𝑖𝑡 𝑜𝑓 𝐶𝑜𝑚𝑚.
formula for this management tool is 𝑅𝑂𝐶𝐼 = 𝐶𝑜𝑠𝑡 𝑜𝑓 𝐶𝑜𝑚𝑚.
𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡
𝑥 100.
Every term used for this equation can be found under a firms’ income statement. The
income statement reports a company’s financial performance. The financial performance is
assessed by giving a summary of how the business incurs its revenues and expenses through both
operating and non-operating activities. Moving forward with breaking down the formula, the first
term that needs to be examined is the revenue. More specifically, the revenue being researched
for my paper will include the money earned from the advertising/commercial investments.
Revenue is the amount of money that a company receives during a specific period, including
discounts and deductions for returned merchandise.
Next, the costs of the investment need to be observed. Each type of cost will be outlined
and discussed in further detail how they are relevant to advertising. The first type, fixed costs,
are those that do not vary with output and typically include rents, insurance, depreciation, set-up
costs, and normal profit. Another term widely used when explaining this is overhead costs.
Variable costs, the second type, are costs that do vary with output, and are sometimes referred to
as direct costs. The next few types of costs, total and average costs, are broken up and generated
using information provided by the fixed and variable sections. To better understand the meaning
behind the total fixed costs, total variable costs, and total costs, refer to figure 2.1. Total fixed
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costs are constant as output increases, the curve is a horizontal line on the cost graph. Total
variable costs have a curve that slopes up at an accelerating rate. This reflects the law of
diminishing marginal returns which basically states that there is a point in which the profit
earned is less than the money invested. Total costs can be calculated by adding together total
fixed and variable costs. To make it simpler, the average fixed, variable, and total costs are
essentially all calculated the same. By dividing the total amount of each costs by a set number of
output. More importantly, the meanings of each type of average cost is different. As output keeps
increasing, the amount of average fixed costs will decline and will slope downward from left to
right on a graph. AVC will at first slope down from left to right, then reach a minimum point,
and rise again. For this reason, it is graphically u-shaped because it begins with increasing
returns (average costs fall), followed by constant, and ends with diminishing returns (average
costs rise). Average total costs represent the per unit cost of production and is also u-shaped due
to the same logic of diminishing returns. ATC are good for understanding how well the resources
are being used. Refer to figure 2.2 to visually see how each of these curves interact with one
another. In this case, producing, marketing, and purchasing air time would all fall under the costs
category for this specific industry.
Finally, to calculate the profit of the advertisements, I must subtract the total costs of the
investment from the revenue. After these elements are calculated using the proper accounting
formats, I will be able to use this data to generate each company’s return on investment for the
three sporting events. An interesting idea to point out about the return on investment is that there
seems to be an appropriate ratio depending on the type of market these companies categorize
themselves under. An exceptional ROI percentage is around five percent which simply means
that they should earn at least five dollars on an investment per every dollar spent. Not all
14
companies can earn this type of exceptional ROI percentage. However, companies that earn an
average ROI percentage, which is typically around two percent, can still be successful. For the
types of companies that earn an average return on investment, their successfulness can heavily
rely on having a significant less amount of cost of goods sold. Return on Investment is an
important concept to understand for my paper because this is one of the main ideas I am testing
for my hypothesis. Again, my hypothesis is that I hope to expect a more positive correlation
between the World Series and NBA Finals than the Super Bowl by comparing it their profit
margins.
Figure 2.1
II. Price Elasticity of Supply: Figure 2.3
Figure 2.2
15
The second theory incorporated into the analysis of my paper is the price elasticity of
supply. Looking at this concept helps add depth to my research because it allows me to observe
products over various markets. The price elasticity of supply measures the responsiveness of
quantity supplied to a change in price. This is useful for companies because they should know
how quickly they can react to changes in the market. There are certain factors that influence the
price elasticity of supply for businesses; resources readily available, mobile factors, storage
space, capacity of production, and complexion of the production cycle. Of these determinants, I
will mostly be focusing on the production cycle, mobile factors, and capacity of production.
I will be using this theory to look at various markets and products of the top companies
who advertise during the World Series, NBA Finals, and Super Bowl. This will give me more
accurate representations of my main hypothesis which is geared toward each companies’ profit
margins. I am breaking down two markets in which the first is categorized as the snack industry
and the second is about cars. Each of these two markets is different because of the overall impact
of the price elasticity of supply. In the snack industry, the supply curve will appear more elastic
(or flatter) graphically while the car industry will have a more inelastic (or steeper) supply curve.
For the snack industry, one reason it develops a more elastic supply curve is because the
process to produce items such as chips and soda are not as complex as other industries. It is
much easier for Frito Lay to produce a bag of Doritos than it is for General Motors to make a
vehicle. Frito Lay would more than likely have a better factory to keep workers efficiently
mobile throughout the production process in case of relocation. Finally, since producing a bag of
chips is not a challenging task, Frito Lay would not need to run at full capacity to cover all shifts.
Overall, consumers will not be influenced by the small increase in price which will cause the
16
quantity of snacks to increase allowing more revenue. Figure 2.3 shows the relationship of price
elasticity in the snack industry when the demand increases due to advertising.
Figure 2.3
III. Price Elasticity of Demand: Figure 2.4
For the car industry, the production process is more complicated which subsequently
leads to an inelastic supply curve. The car industry is completely opposite of the snack industry.
For the companies to sell their cars, they need to run near full capacity which creates friction for
the mobile factors set in place. This highlights a challenge for the car industry because workers
17
cannot produce at an equal or higher capacity than the snack industry. Although the change in
the price of a car is more noticeable, most people need cars to operate daily, which causes the
quantity to also increase allowing for increased earnings. To conclude, a firm strives to achieve a
higher price elasticity of demand because it makes them appear more competitive in the market.
Being more competitive helps businesses earn higher revenue and profits. Figure 2.4 shows the
same relationship for the car industry when demand increases after advertising in these events. In
advertising, there is this model known as AIDA (Attention, Interest, Desire, Arousal) that tries to
affect certain demand factors and cause them to shift upward. This is an idea of advertising that
affects the psychological aspects of consumers and helps understand which products they
purchase. If this idea was more quantifiable than in its current model, than it could potentially be
used to conclude the advertising intensity of each company involved in this project.
Figure 2.4
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IV. How Firms Compete
An important question that businesses must ask themselves on a consistent basis is, “How
can we create a unique product that allows us to capture most consumers”? There are many
strategies a company can use to market their product so that it achieves a greater appearance. A
common strategy used by many businesses attempting to market a more unique and useful
product is advertising. One of the most important outcomes from advertising is that it helps
companies differentiate between products in the market.
A. Bertrand vs. Cournot Model
The third ideology that supplements this project is how firms compete in the same market
by using the price or the quantity. In the Bertrand model, firms compete solely based on the
prices of their products, and, whoever sets the lower price usually gets the sale. In the Cournot
model, two firms compete over the quantity of the product. These two models are essential in
formulating conclusions about firms that produce similar products, such as companies in the
automobile industry. The benefits of this model are to include both maximum sales volume (a
larger share of the market) and higher prices (higher profitability) that way companies can seek a
profit maximizing quantity. The quantity that one produces directly affects the profits of the
other firms because the market price depends on total output. Additionally, in choosing its
strategy to maximize its profit, each firm considers its beliefs about the output its rival will sell.
19
This type of Cournot competition can be best described when looking at processes that
occur in the manufacturing industry, specifically among automobile companies. Two successful
companies that compete for better business and profits are Chevrolet and Ford. These companies
create homogenous products and will often use advertising to differentiate their products and
attract consumers. More specifically, these companies tend to advertise during highly watched
events such as the three sporting events being researched in this project. Companies will
advertise to differentiate their products in various types of ways. One company could be geared
toward attracting a higher customer base by explaining incentives that cover a vehicles
luxuriousness. On the other hand, its competing company could try and persuade consumers by
advertising that their product has the best physical attributes of any vehicle of its kind. Both are
interesting and effect ways that companies advertise to differentiate their products. The company
that can do this more efficiently will increase their sales based on quantity sold and ultimately
larger profits.
B. Case Study: Ford vs. Chevrolet
As explained earlier, Ford and Chevrolet are companies that offer good examples of how
the Cournot model is structured and how they differentiate their products. Two of the most
highly sold vehicles by both Ford and Chevrolet come from their truck series. Ford’s model is
the F-150 and Chevrolet’s is the Silverado 1500. These trucks are similar in specs such as
horsepower, torque, type of engine, and gas efficiency. However, the specs of these vehicles are
just the beginning stages used in determining how they will be marketed to consumers. Over
years of viewing advertisements from each company, it is rather easy to identify that their specs
may be similar, but these trucks are designed to arouse completely different desires; this is
20
because the two companies have different strategies persuading consumers to purchase one of
the two trucks.
For many years, Ford has been marketing their F-150 series as a means of the vehicles
durability as a work truck. Ford relies heavily on the information such as the regular specs of
horsepower, torque, max load capacity, etc. This gives off the impression that it is one of the best
trucks for jobs that require hard labor such as construction. To further explain this, the
spokesperson for the Ford truck series is Mike Rowe who also hosts the show Dirty Jobs. In
Dirty Jobs, Rowe completes nasty jobs such as cleaning the pipes at a sewage plant, etc.
Occupations like these are more common among the rural community, so it is accurate to say
that Ford markets their trucks this type of demographic.
On the other hand, Chevrolet advertises their trucks in a slightly different manner.
Chevrolet could have noticed years ago that this was Ford’s marketing strategy and understood
that they needed to convince customers to purchase the Silverado. Chevrolet’s advertising
campaigns gear more toward triggering a consumer’s desire for luxury. With this being said, the
Silverado 1500 is typically set apart from other trucks by stipulating its rich features such as
leather seats or a navigation system. Although both trucks have almost identical physical
attributes, Chevrolet decided to implement a two for one look on their truck; the Silverado can
serve as a work truck, but when it is not, it can also be viewed as a vehicle of high prestige. The
spokesperson for the Silverado is Rob Benedict, a popular actor who has appeared in shows such
as Burn Notice and Buffy the Vampire Slayer. As a Hollywood actor, Benedict is favorable
among the urban community which makes his fit with Chevrolet perfect for their scheme. All the
information used in generating this case study was gathered from two commercials from the year
2012 on each model of trucks.
21
Chapter 3: Empirical Analysis
I. The Data Set
In order to comprehend which event earns companies a larger return on their commercial
investments, an analysis needs to be conducted. An empirical analysis will be used to indicate if
the specific elements researched for this project have an impact on the thirty-one companies.
This framework has been used throughout the literature previously presented that analyzed
characteristics from the overall economic impact of advertising to choosing prices of products.
The goal of this chapter is to be able to analyze the data and either retain or refute the original
hypothesis, that companies could potentially earn higher margins of profit, and thus, greater
returns on investment advertising during the World Series and NBA Finals compared to the
Super Bowl. It is widely known that advertising has a positive impact on companies’, and the
regression for this project will look to indicate how large of an impact they have.
A. Sources of Data
Before analyzing any impactful results, it is equally important to talk about the data and
variables that were used in the regression. The numerical data was retrieved from Mergent
archives, Mergent online, USA Today Ad Meter, Statista, and Kanter Media for the years 2009
through 2013. One of the key components to this research was investigating the three quarters in
22
which the different sporting events were played. The companies’ annual business reports were
located in databases called Mergent Archives and Mergent Online which broke down the
quarterly costs and revenues along with the amounts that each company earned yearly. In the
data set, costs for each company and the event they advertised in were recorded in millions.
Mergent Online also offered a special service that showed a visual of the recent trends of each
companies’ stock up to ten years from the stock market. This feature helped with collecting each
companies’ financials. The USA Today Ad Meter showed which companies advertised during
the Super bowl and their average ranking out of a score of ten. There are a group of judges
chosen to personally average the commercials based on their knowledge of the advertising
industry.
The average ranking of advertisements could prove to be beneficial for companies’ when
looking at how well they need to produce their commercials. It is believed and previously
reviewed that highly ranked advertisements lead to that specific event earning companies’
greater returns on their investment. Although USA Today Ad Meter ranks the commercials aired
during the Super bowl, they do not offer this service for the other two events being studied.
Social media, specifically Facebook, played a large role in investigating the ranking of
commercials for the World Series and NBA Finals. Facebook has a feature that allows users to
create polls and surveys based on any criteria they desire. For example, MLB Network generates
polls on Facebook for the commercials that were aired during the World Series by embedding a
video of the businesses advertisement. They typically allow the poll to remain open for around
an hour and base their results by the amount of people who “liked” the commercial. A similar
experiment is conducted for the NBA Finals, however, instead of finalizing the results based on
“likes”, a tally is recorded of how many “yes” versus “no” responses given. A “yes” response
23
indicates that a person enjoyed the advertisement while a “no” response would give the opposite
opinion.
Statista is another database known for producing quality sources of information on many
topics. Statista was used to collect how much each company spent on the production stage and in
purchasing time slots. In all three events, advertisements are priced by a standard thirty-secondtime slot. Any time slots larger than that, for example, 60 seconds, have all their costs doubled to
adjust for these situations. The video archives of Fox studios and Turner Network Television
(TNT) were used to observe and record the commercials that were aired in the two smaller
events. The information recorded from the video archives include the company that advertised,
the number of commercials shown by a specific company from each year, and the amount of
time the commercial aired from start to finish. These are the some of the main criteria by which
the regression analysis for affecting each sporting events return on commercial investment was
established. Due to the size and nature of the Super bowl game, most of the data had previously
been collected and observed by databases such as Kanter Media and USA Today. Certain
companies were not listed under the NYSE because they were foreign, but they had their annual
reports and other important information listed on their respective corporate websites. Among
these types of companies were Hyundai, Toyota, and Samsung.
B. Discussion of Data
For this project, the dependent variables are the first to be discussed and include: return
on commercial investment (ROCI) from the Super bowl, return on commercial investment from
the World Series, and return on commercial investment from the NBA Finals. Each of these
𝑁𝑒𝑡 𝑃𝑟𝑜𝑓𝑖𝑡 𝑜𝑓 𝐶𝑜𝑚𝑚.
dependent variables were calculated using the formula, 𝑅𝑂𝐶𝐼 = 𝐶𝑜𝑠𝑡 𝑜𝑓 𝐶𝑜𝑚𝑚.
𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡
𝑥 100.
24
Net profit was not given on any company’s financial statements and also had to be calculated by
taking the revenues earned within the quarter that the given event was played and subtracting the
total advertising costs, mainly consisting of production costs and time slots. In order to calculate
the production costs for the advertisements, an average of the marketing expenditures from each
company had to be calculated. This was done by cross referencing the yearly compiled costs of
marketing/advertising found in Statista with the quarterly advertising expenditure data from
Mergent online. These calculated average production costs for advertisements were used in
determining the cost of commercial investment. The other pieces of information used in
calculating the cost of commercial investments is the time that the advertisement aired and the
quantity of the advertisements shown. To calculate the second half of the equation, the average
commercial production costs were added to the costs of airing a set number of commercials. The
return on commercial investment was generated for each company for the five-year period of
2009 to 2013. The three sports events return on commercial investments were recorded in the
data set as a percentage to show how impactful certain companies can advertise their products.
These three regression results of return on commercial investment for each event will lead to
several conclusions. The first answering the question of which event produces a larger return on
their investment and the second being the companies that are better off advertising in any of the
events studied.
The independent variables for the project include cost of the super bowl advertisements,
cost of the world series advertisements, cost of the NBA Finals advertisements, number of super
bowl advertisements, number of world series advertisements, number of NBA finals
advertisements, average commercial ratings for each event, and two sets of dummy variables.
The total cost of commercials is different for each event so it is imperative to distinguish
25
between them. For instance, the cost of world series advertisements is a characteristic that is
directly related to the dependent variable, ROCI of the world series. Without these calculations,
there would be no basis of interpretation to understand which event earns company’s a greater
return for their money spent. Since these two should be directly related to one another, as
advertising costs increase or decrease, there should be a positive coefficient. This would be
logical since studies in the past have indicated that certain sized companies’ profits from
advertising have been greater with larger costs. Essentially, this means if a large company is
putting in the time, effort, and money to produce a commercial, they should see a mutual return
on their money.
The number of commercials is an interesting variable because although relatively simple
to observe, it could offer a meaningful insight into what data effects profit and ultimately, a
companies’ return on investment. The theory behind the number of commercials for this project
is that there would be a limit between this variable and the effects it has on profits. This means
there should be some sort of cap on the number of commercials aired during one of these events
and the return on investment a company receives from it. For companies that have more than
three advertisements, there could be a negative coefficient which gives off the effect of
diminishing marginal return. After the three commercials, that company would not earn any
additional profits, and possibly even lose money on their commercial investment. However,
another scenario could be present since Stata, the program being used to run the OLS
regressions, will take into effect all the companies that do not fall under this category and weigh
them differently. In this case, the coefficient will more than likely be positive which would mean
that these companies could earn larger profits if they produce more than three commercials in an
event.
26
For the variables, return on commercial investment, whichever two not being interpreted
as the dependent variable, they will serve as explanatory variables to decide which event yields
the better returns. When these serve as independent variables, there could be mixed signs of
positive and negative coefficients. When looking at the return on commercial investment for the
super bowl, there could be a negative coefficient with the NBA Finals since it falls in the quarter
right after the super bowl, and a positive ROCI for the world series since there is a lapse between
quarters three and four. The same conclusion should be evident when looking at the other two
ROCI scenarios. The commercial ranking system is another unique way of trying to understand
what affects the return on investments. Super bowl advertisements were ranked out of a score of
ten, and the other two events were based on people’s responses to a survey or poll. These
variables should have obvious results when pertaining to one of three regressions. When looking
at the ROCI of the World Series for instance, there should be a positive coefficient because
higher ratings of commercials aired during this event should lead to a greater return. For the
other two, they could be negative because they do not pertain to that event in the regression. The
first set of dummy variables included in this project are food/beverage and automobile. These are
two of the most prolific industries that advertise during sporting events and can prove why they
are so successful in marketing in their products. Originally, there was a second set of dummy
variables created for the years, however, a time trend was used instead because of the results it
yielded. Table 3.1 at the end of the project is a summary of the variables: the means, standard
deviations, and min/max values. This table shows the general overview of the data set for this
project.
27
II. The Regression Models & Interpretation
A. Super Bowl Regression: Table 3.2
The first regression model listed below investigates the return on commercial investment
of the super bowl. The ROCI of the world series and of the NBA finals are included in this
regression because it will be used to observe how those two events relate to the super bowl. It
could also be used to predict how companies advertise during these events in the future. The
following two regressions will be similar; however, they will be looking at the ROCI’s of the
NBA Finals and the World Series.
𝑹𝑶𝑪𝑰𝑺𝑩 = 𝜷𝟎 + 𝜷𝟏 𝒚𝒆𝒂𝒓 + 𝜷𝟐 𝑪𝒐𝒔𝒕𝑺𝑩𝒂𝒅𝒔 + 𝜷𝟑 𝑪𝒐𝒔𝒕𝑾𝑺𝒂𝒅𝒔 + 𝜷𝟒 𝑪𝒐𝒔𝒕𝑵𝑩𝑨𝒂𝒅𝒔
+ 𝜷𝟓 #𝑺𝑩𝒂𝒅𝒔 + 𝜷𝟔 #𝑾𝑺𝒂𝒅𝒔 + 𝜷𝟕 #𝑵𝑩𝑨𝒂𝒅𝒔 + 𝜷𝟖 𝑹𝑶𝑪𝑰𝑾𝑺 + 𝜷𝟗 𝑹𝑶𝑪𝑰𝑵𝑩𝑨
+ 𝜷𝟏𝟎 𝑨𝒗𝒈𝑹𝒂𝒕𝒊𝒏𝒈𝑺𝑩 + 𝜷𝟏𝟏 𝑨𝒗𝒈𝑹𝒂𝒕𝒊𝒏𝒈𝑾𝑺 + 𝜷𝟏𝟐 𝑨𝒗𝒈𝑹𝒂𝒕𝒊𝒏𝒈𝑵𝑩𝑨
+ 𝜷𝟏𝟑 𝑭𝒐𝒐𝒅𝑩𝒆𝒗 + 𝜷𝟏𝟒 𝑨𝒖𝒕𝒐𝒎𝒐𝒃𝒊𝒍𝒆 + 𝜺
Throughout all three regressions, the same variables were used to indicate the effects they
had on the return on commercial investment and ultimately to which event yielded the larger
returns. There are also some other interesting pieces of information that go along with the results
that I was not expecting. When discussing the results from an OLS regression, there are three
stages of statistical significance, p < .10 (10%)*, p < .05 (5%)**, and p < .01 (1%)***.
Generally, the lower the p-value, the more reliable that source of information is in developing a
conclusion about the hypothesis.
28
The first variable, year, is statistically insignificant which was a surprising result because
as another year passes, the total costs, revenues, and profits mostly increase, however, at a slow
rate. The year variable being insignificant to the ROCI of the super bowl could indicate that
typically prices do not increase enough to affect the return on investment of an idea as small as a
companies’ advertisement. The cost of super bowl advertisements proved to be 1% statistically
significant with a positive coefficient. Since my costs were recorded in millions, the
interpretation of this would be an additional one million dollars spent on advertising leads to .068
% increase in the ROCI of the super bowl. This makes sense because the average costs for the
super bowls were around eleven and a half million dollars, so a one-million-dollar increase in ad
spending would not affect the ROCI greatly. The cost of world series and NBA finals
advertisements were statistically insignificant mainly because they are played after the super
bowl in the following quarters and should not have a large impact.
The number of advertisements for all three events were statistically insignificant as well.
This could indicate that since the super bowl is such a big event, and most businesses involved
are returning customers, many companies earn a smaller ROCI because their product is already
well established.
The average rankings for all three events were categorized in two distinct ways. For the
Super bowl, the units used were based on a zero to ten scale; a ranking closer to zero indicates
that the commercial was unpopular and at the opposite end, a ranking closer to ten indicates a
higher liking. For the NBA Finals and World Series, the ranking of advertisements was based on
results from the Facebook polls that were generated by calculating how many people “liked” the
video. The Super Bowl and NBA Finals were found to be statistically significant at the 1% level.
29
Since these two events are only four months apart, this could indicate that the effect the super
bowl commercials had on consumers was not as strong as previous months. This also could
indicate that the effect of the NBA finals commercials is taking over and generating a strong pull
from consumers.
Each of the two dummy variables were statistically significant; food and beverage at the
10% level and the automobile industry at the 1% level. Each of these variables tells an
interesting story. If a company is listed under the category of food and beverage, they have the
potential to increase their ROCI from the super bowl by 0.62%. This may seem like a small
percentage, but in the grand scheme of this project, it proves that medium sized companies such
as Gatorade, make out better than smaller or larger companies. As for the automobile industry,
typically the biggest companies, they seem to lose on their investment by around 0.89%.
B. World Series Regression: Table 3.3
The second regression model is listed below and investigates the return on commercial
investment of the World Series. The same independent variables were used as with the first
model.
𝑹𝑶𝑪𝑰𝑾𝑺 = 𝜷𝟎 + 𝜷𝟏 𝒚𝒆𝒂𝒓 + 𝜷𝟐 𝑪𝒐𝒔𝒕𝑺𝑩𝒂𝒅𝒔 + 𝜷𝟑 𝑪𝒐𝒔𝒕𝑾𝑺𝒂𝒅𝒔 + 𝜷𝟒 𝑪𝒐𝒔𝒕𝑵𝑩𝑨𝒂𝒅𝒔
+ 𝜷𝟓 #𝑺𝑩𝒂𝒅𝒔 + 𝜷𝟔 #𝑾𝑺𝒂𝒅𝒔 + 𝜷𝟕 #𝑵𝑩𝑨𝒂𝒅𝒔 + 𝜷𝟖 𝑹𝑶𝑪𝑰𝑺𝑩 + 𝜷𝟗 𝑹𝑶𝑪𝑰𝑵𝑩𝑨
+ 𝜷𝟏𝟎 𝑨𝒗𝒈𝑹𝒂𝒕𝒊𝒏𝒈𝑺𝑩 + 𝜷𝟏𝟏 𝑨𝒗𝒈𝑹𝒂𝒕𝒊𝒏𝒈𝑾𝑺 + 𝜷𝟏𝟐 𝑨𝒗𝒈𝑹𝒂𝒕𝒊𝒏𝒈𝑵𝑩𝑨
+ 𝜷𝟏𝟑 𝑭𝒐𝒐𝒅𝑩𝒆𝒗 + 𝜷𝟏𝟒 𝑨𝒖𝒕𝒐𝒎𝒐𝒃𝒊𝒍𝒆 + 𝜺
Again, the year variable was statistically insignificant for the world series and this could
be due to the same reason since the costs, revenues, and profits only gradually increase, it does
generate a large enough effect with the ROCI. The cost of super bowl advertisements was
calculated to be significant at the 5% level and this could indicate that since the super bowl is
30
such a large event compared to the world series, it relatively overpowers the effect of its
advertisements. The interpretation of this would be an additional one million dollars spent on
super bowl advertising leads to .013 % decrease in the ROCI of the world series. This makes
sense because if a specific company is allocating more of their advertising budget on the super
bowl, they will generate less returns in the world series unless they increase their budget. The
cost of world series commercials was statistically significant at the 1% level since it is directly
correlated with the ROCI for the world series; An additional million dollars spent on advertising
in the world series could earn a company up to a 0.070% increase in their ROCI for that event.
The costs for NBA Finals commercials, number of super bowl ads, and number of NBA
finals ads are all statistically insignificant. Since the NBA finals is equal in size of the world
series, both the number and cost of advertisements should not be any greater than that of the
world series, therefore, does not affect the ROCI. Both the ROCI’s of the super bowl and NBA
finals are statistically significant at the 5% and 1% respective levels. Since the world series falls
under the last quarter in the fiscal year, this could indicate that commercials during the NBA
finals could be affecting consumer’s choices. As for the super bowl, since it is the largest of the
three sporting events, people will be less likely to forget about commercials that aired during it
causing the world series to be less effective. The last few insignificant variables are the average
ratings of super bowl and NBA Finals commercials, and the food/beverage industry. This is
logical because the ratings of the other two events should have no impacting effects on the ROCI
of the world series since this events would have already passed. However, the average ratings of
world series commercials, based on Facebook “likes” is statistically significant at the 5% level
and the dummy variable for the automobile industry is also significant at the 1% level. For every
additional “like” received via the poll on Facebook, it causes the ROCI for the world series to
31
increase by 0.001%. This is not a significant increase by any means, however, it still proves that
ratings of commercials influence greater returns. Many of the commercials during the world
series relate to the automobile industry. It makes sense that an automobile company would
increase the ROCI of the super bowl by 0.22%.
C. NBA Finals Regression: Table 3.4
The third regression model is listed below and investigates the return on commercial
investment of the NBA Finals. The same independent variables were used as with the first and
second models.
𝑹𝑶𝑪𝑰𝑵𝑩𝑨 = 𝜷𝟎 + 𝜷𝟏 𝒚𝒆𝒂𝒓 + 𝜷𝟐 𝑪𝒐𝒔𝒕𝑺𝑩𝒂𝒅𝒔 + 𝜷𝟑 𝑪𝒐𝒔𝒕𝑾𝑺𝒂𝒅𝒔 + 𝜷𝟒 𝑪𝒐𝒔𝒕𝑵𝑩𝑨𝒂𝒅𝒔
+ 𝜷𝟓 #𝑺𝑩𝒂𝒅𝒔 + 𝜷𝟔 #𝑾𝑺𝒂𝒅𝒔 + 𝜷𝟕 #𝑵𝑩𝑨𝒂𝒅𝒔 + 𝜷𝟖 𝑹𝑶𝑪𝑰𝑾𝑺 + 𝜷𝟗 𝑹𝑶𝑪𝑰𝑺𝑩
+ 𝜷𝟏𝟎 𝑨𝒗𝒈𝑹𝒂𝒕𝒊𝒏𝒈𝑺𝑩 + 𝜷𝟏𝟏 𝑨𝒗𝒈𝑹𝒂𝒕𝒊𝒏𝒈𝑾𝑺 + 𝜷𝟏𝟐 𝑨𝒗𝒈𝑹𝒂𝒕𝒊𝒏𝒈𝑵𝑩𝑨
+ 𝜷𝟏𝟑 𝑭𝒐𝒐𝒅𝑩𝒆𝒗 + 𝜷𝟏𝟒 𝑨𝒖𝒕𝒐𝒎𝒐𝒃𝒊𝒍𝒆 + 𝜺
For the third time, the year variable was also statistically insignificant for the NBA finals
and this could be due to the same reason as the world series. The size of those two events could
have a role in determining why the years are not significant. The cost of super bowl and world
series advertisements were deemed not statistically significant. This could mean that since the
NBA Finals is such a significantly smaller event than that of the super bowl and is played in the
following quarter, the costs, revenues, and profits from the other two events will not have a
significant impact. The costs of NBA finals advertisements are directly related to the ROCI of
the event so since it is statistically significant at the 1% level is logical. The interpretation of this
would be an additional one million dollars spent on NBA finals advertising will lead to a 0.041%
increase in the ROCI of the NBA finals.
32
A strange finding in this regression was the fact that the number of commercials in the
NBA finals was insignificant to the ROCI while the number of commercials in the world series
generated a significant result at the 1% level. Although these results are strange, it is believed
that these were outliers in the data set and should not be interpreted with much thought. The
respective ROCI’s for the super bowl and world series are both statistically significant at the 5%
and 1% intervals. This indicates that advertising during these other two sporting events can prove
to be beneficial for a companies’ overall return on commercial investments for the entire year.
The average rankings for advertisements was calculated to be statistically significant at the 1%
level and an additional “yes” response to their survey increases the ROCI by 0.0049%. In this
last regression, the dummy variables were statistically insignificant which could indicate that the
NBA Finals usually have an even number of companies from both industries.
III. Potential Biases and Errors
A. Breusch-Pagan Test for Heteroskedasticity
To adjust for any potential biases or error, there are several tests to run that can alleviate
the mistakes from the regressions. The first error to test for is heteroscedasticity, where the
variance of your data set relies on more than one x variable. To check for heteroscedasticity, the
Breusch-Pagan test needs to be run. This is run by calculating the residual squared values as our
dependent variable to check for this problem. F-stat needs to be greater than F-critical for us to
reject our null hypothesis and conclude that there is heteroscedasticity. However, upon
calculations for these three regressions, I have found that my data set was homoscedastic and the
following will have the process of the calculations. For the first regression, F-stat was calculated
to be 1.01 and the F-critical was 1.67 per the F-distribution tables. I can conclude than that this
33
first model does not suffer from heteroscedasticity since F-stat is less than F-crit. The second
model has a calculated F-stat value of 1.27 and the same F-critical value of 1.67 so this model
does not suffer from heteroscedasticity either. The third and final model has an F-stat value of
1.33 and an F-critical value of 1.67. From this, it can be concluded that this model does not
suffer from heteroscedasticity. Since all models were homoscedastic, there was no need to
calculate any robust regressions. This also means that the statistical significance of my variables
can be interpreted without any caution.
B. Correlation Matrix for Multicollinearity
The second bias to test for is multi collinearity. This is when the independent variables
are highly correlated with each other and this makes it difficult to determine their statistical
significance. There are two ways to test for multi collinearity. The first is by running a
correlation matrix for all the independent variables and if any of them exceed the cut off value of
0.8, then those variables are similarly related to one another. In order to adjust for multi
collinearity, the related variables can either be dropped, which violates the zero-conditional mean
and causes omitted variable bias, or secondly, do nothing and acknowledge that the statistical
significance must be interpreted with caution.
The second test to run is called the variance inflation factor (VIF) which measures how
much variance of the estimated regression coefficients are inflated compared to when nonlinearly related. I ran both tests for multi collinearity, but only decided to incorporate the
correlation matrix into this project. The only variables that exceed the cut off value of 0.8 are in
the third regression for ROCI of the NBA Finals. The two variables are the average ranking of
NBA advertisements and number of NBA advertisements. However, since these variables were
34
not an issue in the other two regressions, there is no need to drop them leading to the conclusion
that I should interpret the statistical significance of the average ranking of NBA commercials
with slight caution. Finally, there are many several factors that can affect the ROCI for
companies within one of these sporting events. With this, the data may experience a low level of
omitted variable bias because it is difficult to determine exactly all the factors that affect the
dependent variable directly.
35
Chapter 4: Closing Remarks & Conclusion
I. Brief Overview of Study
The purpose of this paper was to determine if advertising during the Super Bowl, World
Series, or NBA Finals earns companies’ a larger return on commercial investment. This paper
suggests that there is a strong statistical significance between a companies’ ROCI and which
event they choose to participate in. The data gathered and observed showed that characteristics
such as the cost of advertisements, number of advertisements, and the ranking of commercials all
play a large role in determining the magnitude of ROCI. The data even formulated several other
interesting conclusions that I did not expect.
A. Findings from the Project
First, specific companies located in the automobile and food/beverage industry seem to
advertise in both the Super Bowl and the other two events. However, when it comes to the two
smaller events, companies tended to choose between the World Series or NBA Finals rather than
advertise in both. The World Series seemed to have more automobile companies advertising
during their games while the NBA Finals took on the food and beverage companies. A reasoning
behind this is that car companies offer a new model vehicle to the most valuable player of the
World Series every year. As for the NBA Finals, it is a much more demanding series, so for
36
companies such as PowerAde, it makes more sense for them to allocate their spending in this
event. Another conclusion that can be taken away from this research is that the size of a certain
company matters when looking at their return on commercial investment. When originally
beginning this project, there was an expectation that the largest companies would receive the
greatest ROCI. The reasoning behind this is that the more money a company allocates their
budget to advertising in one of these events, the better the final payoff. However, the results how
that medium sized companies tend to earn the best ROCI. Companies that fit under this category
would include Taco Bell, Gatorade, and Esurance. Large companies must earn a large return on
their commercial investments up until a certain point, and then the costs seem to outweigh the
benefit. This is useful information to the advertising industry and respective companies because
instead of producing and purchasing advertisements in all or two events, they could shift and
focus all their time/money into one.
B. Why the Super Bowl?
This project was a study to investigate whether smaller championship events such as the
World Series and NBA Finals could outlast the Super Bowl when discussing advertising.
Unfortunately, all the data points in the opposite direction with the Super Bowl retaining the title
of “best advertising event”. The largest piece of evidence that indicates that the Super Bowl is
the “best advertising event” is the magnitude of the companies’ return on commercial
investments. An important aspect to consider with this project is the size of the Super Bowl and
the effect that has on each companies’ return on investment. Out of these three events, the Super
Bowl is the largest per several characteristics. The Super Bowl is a one-time event and is not
divided up like the World Series and NBA Finals which have a seven-game series. This creates a
37
perception among consumers(viewers) that the Super Bowl is a much more interesting event to
watch. Also, the premise behind each commercial in the Super Bowl is more likely to persuade a
consumer to keep purchasing a favored product or switch to a completely different brand. This is
the main idea of advertising and the Super Bowl appears to capture this philosophy the best out
of the three studied in this project.
II. Possible Ideas for Future Research
Although previous literature does not focus on the World Series and NBA Finals, I still
believe that this paper’s hypothesis could hold true in the future. Every year there is an
increasing number of databases that hold a plethora of information. It is possible that someone
else could research a similar hypothesis in more depth and find different conclusions. A few
models that would be worth researching further for the future are called the AIDA model and
formula for advertising intensity. These overlapping ideas were researched into intermediate
stages, however, showed no correlation in affecting the return on investments for the companies
in each event. In order for these models to have any significant impact together, there needs to be
a way to numerically evaluate the AIDA model. AIDA is an acronym that stands for gain
Attention, hold Interest, arouse Desire, and take Action. These characteristics relate to the
psychology of a consumer and how they go about selecting and eventually purchasing certain
products. If someone could quantify these characteristics, it could help in furthering the topic on
how the Super Bowl can so easily persuade people into purchasing select items. Perhaps, to these
companies, advertising is not about receiving the greatest return on their investment, but rather
about continuously molding their brand image to consumers.
38
References
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Bridgestone annual report to shareholders, 'perfection in progress'. (). JCN Newswires
Cornwell, B. T. (2005). Global sport sponsorship. Oxford [u.a.]: Bloomsbury Publishing.
Farris, P. W., & Albion, M. S. (1980). The impact of advertising on the price of consumer
products. Journal of Marketing, 44(3), 17. Retrieved
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Gene M. Grossman, & Carl Shapiro. (1984). Informative advertising with differentiated
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How the super bowl ads ranked with viewers. (2011). Retrieved
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Super bowl ad scores continue to decline compared to 2008 and 2009 ads; -- top five ads of 2008
and 2009 receive higher ratings than 2010 super bowl ads. (2010, Feb 9,). PR
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Visible measures announces 2010 super bowl ads study results, doritos most-viewed brand in
social video. (2010, Feb 17,). PR Newswire Retrieved
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41
Appendix
A. Summary Statistics
Variable
Years (2009-2013)
Cost of Super Bowl Advertisements
Cost of World Series Advertisements
Cost of NBA Finals Advertisements
Number of Super Bowl Advertisements
Number of World Series Advertisements
Number of NBA Finals Advertisements
Super Bowl Return on Commercial
Investment
World Series Return on Commercial
Investment
NBA Finals Return on Commercial
Investment
Super Bowl Commercial Avg. Ratings
World Series Commercial Avg. Ratings
NBA Finals Commercial Avg. Ratings
Food and Beverage Companies
Automobile Companies
Mean
2011
1161.41
55.15
0.992453
0.89434
0.486793
54.95
Std.
Deviation
1.42
11.90
5.19
5.12
1.42
1.35
1.15
41.71
Min.
Max.
2009
0
0
0
0
0
1.16
2013
43.9
19
18.9
7
5
4
1.89
-2.70
6.30
0.132076
0.775
0
2.68
0.233962
0.726
0
3
0.237736
0.007547
0.026415
0.818868
0.818868
3.26
116.6
98.69
0.419
0.419
0
0
0
0
0
8.45
425
386
1
1
42
B. Regression Results: The following tables are listed in order (Table 3.2, Table 3.3, & Table 3.4)
Number of Observations = 155; F(14, 140) = 29.93; Adjusted R-squared = 0.7021
Coef.
Std. Error
T-Stat.
Return on Commercial Investment Super Bowl
-0.0003606
0.0625494
-0.01
Years (2009-2013)
0.0677766
0.0144067
4.70
Cost of Super Bowl Advertisements
-0.0537613
0.0407982
-1.32
Cost of World Series Advertisements
0.0197642
0.0382732
0.52
Cost of NBA Finals Advertisements
-0.1259097
0.1211217
-1.04
Number of Super Bowl Advertisements
-0.037905
0.1264735
-0.30
Number of World Series Advertisements
-0.1430801
0.1569445
-0.91
Number of NBA Finals Advertisements
World Series Return on Commercial
0.5662832
0.2316028
2.45
Investment
NBA Finals Return on Commercial
0.489763
0.2357794
2.08
Investment
0.3375473
0.0487472
6.92
Super Bowl Commercial Avg. Ratings
0.0002948
0.001427
0.21
World Series Commercial Avg. Ratings
-0.0058556
0.0023399
-2.50
NBA Finals Commercial Avg. Ratings
0.6176703
0.3708548
1.67
Food and Beverage Companies
-0.8942749
0.2347804
-3.81
Automobile Companies
0.9449399
125.7827
0.01
Constant
Number of Observations = 155; F(14, 140) = 38.74; Adjusted R-squared = 0.7743
Return on Commercial Investment World Series
Years (2009-2013)
Cost of Super Bowl Advertisements
Cost of World Series Advertisements
Cost of NBA Finals Advertisements
Number of Super Bowl Advertisements
Number of World Series Advertisements
Number of NBA Finals Advertisements
Super Bowl Return on Commercial Investment
NBA Finals Return on Commercial Investment
Super Bowl Commercial Avg. Ratings
Coef.
-0.0001916
-0.01349
0.0692395
-0.0191504
0.0087522
0.155207
-0.0094827
0.0723199
0.3442542
-0.0320985
Std. Error
0.022353
0.0054219
0.0134523
0.0135945
0.0434451
0.043267
0.056247
0.0295779
0.0804483
0.0200013
T-Stat.
-0.01
-2.49
5.15
-1.41
0.20
3.59
-0.17
2.45
4.28
-1.60
P>[t]
0.995
0.000
0.190
0.606
0.300
0.765
0.364
0.016
0.040
0.000
0.837
0.013
0.098
0.000
0.994
P>[t]
0.993
0.014
0.000
0.161
0.841
0.000
0.866
0.016
0.000
0.111
43
World Series Commercial Avg. Ratings
NBA Finals Commercial Avg. Ratings
Food and Beverage Companies
Automobile Companies
Constant
0.0009729
-0.0003138
0.1293772
0.2262112
0.4839432
0.0005034
0.0008543
0.1333896
0.0860442
44.95031
1.93
-0.37
0.97
2.63
0.01
0.055
0.714
0.334
0.010
0.991
T-Stat.
-0.01
-1.14
0.52
3.08
-0.51
-2.97
-0.90
2.08
4.28
-1.02
-1.49
5.95
0.99
0.31
0.01
P>[t]
0.993
0.256
0.601
0.002
0.609
0.003
0.372
0.040
0.000
0.308
0.140
0.000
0.325
0.758
0.991
Number of Observations = 155; F(14, 140) = 33.79; Adjusted R-squared = 0.7488
Return on Commercial Investment NBA Finals
Years (2009-2013)
Cost of Super Bowl Advertisements
Cost of World Series Advertisements
Cost of NBA Finals Advertisements
Number of Super Bowl Advertisements
Number of World Series Advertisements
Number of NBA Finals Advertisements
Super Bowl Return on Commercial Investment
World Series Return on Commercial Investment
Super Bowl Commercial Avg. Ratings
World Series Commercial Avg. Ratings
NBA Finals Commercial Avg. Ratings
Food and Beverage Companies
Automobile Companies
Constant
Coef.
-0.0001947
-0.0062098
0.0075842
0.0403348
-0.0219614
-0.1288326
-0.0496059
0.0610468
0.3359949
-0.0203207
-0.0007425
0.00449
0.1302061
0.0268491
0.4755342
Std. Error
0.0220832
0.0054484
0.0144787
0.0130887
0.0428868
0.0433186
0.0554154
0.0293889
0.0785182
0.0198667
0.0004999
0.0007543
0.1317631
0.0870492
44.40782
44
Acknowledgements
I would like to first thank my wonderful and loving parents, Heather and Jeff Boyer.
Without them, I would not be in the position I am today attending an institution as prestigious as
Allegheny College to further my academic and athletic careers. I would also like to thank my
other immediate family members, especially both sets of grandparents. Whenever I have needed
someone to talk with, their doors were always open and I cannot thank them enough for that. To
all my teammates on the baseball team, I thank you for pushing me to strive for better results
every single day on and off the field. To all the professors I have had an encounter with here at
Allegheny, you have helped guide my ways of understanding and reasoning more than you can
imagine. For this, I am eternally grateful. Lastly, I would like to give a special thank you to both
Dr. Hoa Nguyen and Dr. Janine Sickafuse for helping facilitate the process of the senior
composition.
45