One question that every single company and service provider in the world asks is; what is the most efficient way to market our product? Companies, for example, E*TRADE that provide a service or companies which provide material goods like Coca-Cola or Oreo are constantly evolving the way they market their product to consumers. Magazines, newspapers, social media outlets, billboards and television are just a few of the tools companies use to attract the attention of the consumer. Television commercials are one of the most successful ways in which companies advertise their products. Commercials shown during various televised events have more or less of an impact on how many potential investors are exposed to the company’s product depending on the event the advertisements are shown during.
The 2013 Super Bowl held on February 3rd between the Baltimore Ravens and the San Francisco 49ers in New Orleans attracted the attention of over one hundred and eight million Americans. The Super Bowl has consistently been the most watched televised event in United States history and continues to increase in popularity every year. For any company with a product to offer, the Super Bowl is the ultimate marketing opportunity. What other way can a company ensure that a maximum number of potential investors are being exposed to their product at the exact same time?
Multiple studies have been conducted to test the relationship between commercials shown during the Super Bowl and the respective company’s stock price. What effect do commercials really have on consumers? What aspect of a commercial makes a product more or less attractive to the audience? Even if someone is affected by a certain advertisement, whether it elevates their mood or allows them to make some sort of personal connection, does that necessarily mean that they will be more prone to invest in that product?
Mentioned earlier, the Super Bowl is one of the most watched televised events in history. In one previous study done by Frank Fehle, Sergey Tsyplakov and Vladimir Zdorovtsov, they concluded that due to its vast media attention, the Super Bowl is one of the most successful outlets for companies to expose their product to the public. So many people tune in to watch the Super Bowl each year, more specifically, Super Bowl broadcasts reach more than fifty percent of United States households in just a few hours. These researchers developed a null hypothesis that states commercials have no effect on market stock prices for various companies while the alternative hypothesis states that stock price and mood are directly correlated to easily recognizable advertisements. This hypothesis is called The Price Pressure Hypothesis. These researchers found that the number of commercials displayed by a company along with repeated exposure to a commercial has a direct effect on how recognizable the advertisement is to the audience, therefore; resulting in larger stock market returns. Laura Frieder and Avanidhar Subrahmanyam also support this notion in their research on the relationship between product perceptions and consumers market decisions. In the same study, Frieder and Subrahmanyam found that consumers are more attracted to easily recognizable brands. Researchers have studied various aspects of commercials to understand what it is that really influences the consumer. Details like how the name of the product advertised corresponds to the company name and whether the company name is presented visually or audibly are just two of the many aspects of commercials examined.
In the study by Fehle, Tsyplakov and Zdorovtsov, two additional hypotheses were developed. One states that advertisements for larger companies have less of an impact on the consumer than smaller companies. This could possibly be the result of people overlooking commercials for companies they are familiar with and paying more attention to commercials they have never seen before. The second states that investors with less experience are more inclined to be effected by advertisements than others that may be more financially literate. This hypothesis is supported by Brad Barber and Terrance Odean in their study of the effects the news and media have on investors buying behavior.
Fehle, Tsyplakov and Zdorovtsov concluded in their study that less sophisticated and less experienced investors are influenced more by the advertisements they are subject to and that there is a direct relationship between how easily identifiable an advertisement is and the company’s stock price.
Various factors influence the effect a commercial has on the audience as is evident from extensive testing and hypothesizing. In order to sufficiently examine the relationship between commercials aired during the Super Bowl and the influence they have on companies stock price, other factors need to be analyzed. I examined how the overall stock price of various companies that aired commercials during the 2013 Super Bowl held on February 3rd, changed from February 1st to February 5th. The change in stock price could have been influenced by factors such as the number of appearances each company’s commercial made, the total length of air time each commercial was allocated, the average ACE Score of each companies commercials, the total views each commercial received after the fact, and whether or not a celebrity was endorsing the respective product. Although these are only a sample of the factors that can impact the relationship between a commercial and the respective company’s stock price, each of these five independent variables has shown a distinctive effect. As mentioned earlier and in other studies, the number of times a commercial appears plays a role in the influence a commercial has on the audience. Whether or not the product in the commercial had a celebrity endorsement can also have a significant impact. People tend to be drawn to familiar faces, especially if that familiar face is likeable. Companies use celebrities in their marketing campaigns because of the strong influence they can have on the consumer. When people see a celebrity using a product, it creates a desire to be like that celebrity, hence it creates the desire to use the same product. Other factors like total airtime of a company’s commercials and total views after the fact play into the notion mentioned earlier that the more someone is exposed to an advertisement or product, the more recognizable and likeable it becomes.
For this study, I examined twenty-four different products, which were advertised during the 2013 Super Bowl. I recorded data on the number of times the product was advertised, the average ACE Score, whether or not the product had a celebrity endorsement, the total length of the commercials measured in seconds and the total views after the fact measured in thousands. Most the data came from websites like www.acemetrix.com and www.nfl.com. I chose the products based on relevance of data and whether or not I could find data on all of my variables.
In the data, as you can see above, the maximum number of appearances was three, made by Budweiser. The mean for appearances was 1.25. The maximum average ACE Score was 621 held by SodaStream while Calvin Klein held the minimum score of 362. The mean score was about 547. This data shows that consumers were much more intrigued and felt the SodaStream advertisements were more relevant, watchable and likeable than other commercials, for example Calvin Klein’s. The only companies that had a celebrity endorse their product were Best Buy, Tide, Pepsi Next, Toyota, Samsung, and MiO. Budweiser, Bud Light and Samsung had the longest total commercial airtime with 123, 122 and 121 seconds respectively. The mean commercial airtime was about 54 seconds. Budweiser also had the most total views on www.nfl.com after the Super Bowl was over with over 720 thousand views while SodaStream had the least with a little over 115 thousand. The mean views after the fact was about 280 thousand. Samsung had the highest positive stock price chance between February 1st and February 5th with an increase of 25 points. Audi saw the biggest drop in stock market price with a decrease in 8 points. The mean change in stock price was a drop of about 0.136 points.
Various tests were conducted to test the overall significance of my right hand side variables. An F-Test done on all my variables returned an adjusted R-Squared of .28, which shows that the independent variables only explain about 30% of the variation in the dependent variable (Refer to appendix B1). The coefficients for the variables appearance and average ACE Score appear to be negative, which do not support my hypothesis that all of the variables have a positive influence on the dependent variable; change in stock price.
I found that the quadratic functional form explains my data better than any other functional form (Refer to appendix C1). In choosing which functional form to utilize, I looked at the relevance of the R-Squared and the P-Value. With an R-Squared of about .6, more than half of the variation in the dependent variable was explained by the independent variables. The Ordinary Least Squares (OLS) Regression for the quadratic functional form also yielded the lowest P-Value out of all the OLS Regressions of about .06 (Refer to appendix C1). In the T-Tests for each of the explanatory variables in the quadratic OLS Regression, I found that I was able to reject each Null Hypothesis except for the TOTLENG independent variable. I found a critical T value of about 1.7 (Refer to appendix C2-C5).
I found that the variable with the biggest influence was in fact whether or not the product had a celebrity endorsement. With a positive coefficient of about 3, the quadratic OLS Regression shows that that independent variable has the biggest influence on the dependent variable. The same OLS Regression also shows that the total length of the commercials had the smallest influence on the dependent variable. In my correlation matrix (Refer to appendix F1-F3), I witnessed high multicollinearity between TOTVIEW and APP. High multicollinearity shows that these two variables have a relatively strong relationship compared to the other variables. This can cause one to incorrectly interpret the coefficients, therefore, leading you to assume that these variables have a different affect on the dependent variable than they actually have (refer to appendix F1-F3).
One irrelevant variable I believe could have been omitted with out any variation in the results is the total views each commercial had after the Super Bowl. Referring to the quadratic OLS Regression (Refer to appendix C1) that independent variable had a very low coefficient of about 1.77e-06, which is extremely insignificant. One variable that could have had an effect on the stock price change but was not included in the original theory was the quarter during the game that the commercial aired. It is possible that commercials that aired later on in the game could have had more of a significant influence on the audience than commercials in the beginning of the game and therefore would have affected the results obtained on the dependent variable. In order to test for heteroskedasticity, I performed a White Test on my data. I found that the data matrix was close to singularity so I ran heteroskedasticity corrected regression (Refer to appendix G2) and compared the results to the chi-square test (Refer to appendix G3).
It is quite clear that there are a multitude of factors that play a role in the effectiveness a commercial has on the audience. Since commercials are extremely subjective and the effect they have on the consumer largely depends on the consumers themselves, it is difficult to pinpoint which variables and which factors have the largest influence. It is clear though that the more a person is exposed to a commercial and the more a person can recognize the product being advertised, the larger the effect on the consumer. It is not quite clear whether other variables such as the number of views a commercial received after the Super Bowl or the length of airtime a company had influence the stock price of the respective company positively or negatively. There is no step-by-step guide on how to market to a consumer, especially when everyone has different preferences. One person could be drawn to a specific product for personal reasons that could not possibly be calculated in an OLS regression. Everyone is drawn to different advertisements for different reasons. For companies that want to expose their product to a large number of people in a short amount of time, advertising during the Super Bowl is definitely the perfect opportunity but to determine the effect of those advertisements on the consumer and why they were affected the way they were is simply impossible.
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