The Power of Branding

ESE BACHELOR HONOURS CLASS

AN EMPIRICAL INVESTIGATION ON SPONSORSHIPS

Mirnesa Ibiševid, 408590 Hanna van Holten, 407411

Word count: 4301

May 29, 2016

Abstract This paper investigates the relationship between tennis players’ ranking points obtained in the ATP Rankings and the brands of their clothing sponsors. Various top 100 players are investigated, using data on players’ sponsors as well as their performance. This way the relation between certain sponsoring brands and players ranking points is estimated and tested using statistical methods. It has been found that larger brands do in fact sponsor players with higher ranking points, and that these sponsorship decisions vary between the brands. Furthermore, the effect of these obtained ranking points on the sponsor’s revenues is estimated in various models. Statistical evaluation suggests that this effect is close to zero. This investigation has led to the identification of four different sponsorship strategies used by the clothing sponsors of these players. Ibisevic van Holten Introduction

Sponsorships can be defined as “business relationships between a provider of funds, resources or services and an individual, event or organisation which offers in return some rights and association that may be used for commercial advantage” (Sleight, 1989). These sponsorships have experienced major growth in the marketing activities of companies worldwide. The total spending of all sponsorships in general in the world was 57.5 billion U.S. dollars in 2015, of which more than one third came from the United States. In 2016, this number has been estimated to grow with 4.7% to 60.2 billion dollars (Statista, 2016). This growth can be explained by several contributing factors, including the rising costs of media advertising, the fragmentation of traditional media, restrictive government policies on alcohol and tobacco advertising and the greater media coverage of sponsored events (Fahy & Jobber, 2012).

Sports sponsorship is one of the biggest and most popular sponsorship media. The FIFA World Cup of 2014 in Brazil collected more than 1 billion dollars from 20 different companies through various sponsorship deals (The Telegraph, 2014). Its popularity is due to the opportunity for companies to interact with their customers on an emotional level, increasing customer loyalty and thereby also increasing their market share and revenues. Sports can offer high visibility and reach a broad range of the community (Fahy & Jobber, 2012). Sport sponsorships are also popular because sports can solve a major problem that companies experience, namely that a high fraction of advertisement expenditures is wasted, but it remains difficult to identify these wasteful campaigns. By using sports, specific market segments can be targeted, because every sport attracts a specific type of customer. For example, if a company identifies richer people as potential clients, it can target them by sponsoring yacht races and golf tournaments as these activities are mainly attended by its target customer (The Economist, 2008).

Despite this large growth of sponsorships, little research on the impact and implications of these big sponsorship deals is done. Meenaghan (2001) investigates the effects of commercial sponsorships on consumers by analysing goodwill, image transfer and fan involvement. This framework provides a clearer understanding of sponsorships by considering the customers’ response to commercial activities, as these responses will differ from responses to other types of mass media, such as advertising.

Additionally, Speed and Thompson (2000) focus explicitly on sports sponsorship by researching the effects of consumers’ attitudes to a sports event on the sponsorship response. Findings include that 2

Ibisevic van Holten the sponsor-event fit, perceived sincerity of the sponsor, perceived ubiquity of the sponsor and attitude towards the sponsor are key element in producing a positive response from the sponsorship. These findings emphasise that managers should have a clear idea about the notions of their target audience.

Literature has hereby shown that companies should carefully examine which players to sponsor, as this will impact the effect of the sponsorship. In order for the sponsorship to give desirable results, firms ought to sponsor parties who create positive associations between the brand and the sponsor, which should also align with the firm’s own strategy. This could, for example, be based on the player’s performance. However, other studies have shown that sports fans do not make a distinction between brands that spend a lot on sponsoring famous sporting events and those that do not, indicating that sponsorships are merely a waste of money (Day, 2003). Therefore it is relevant to study the relationship between the sponsors and the clubs or players that they are sponsoring as it will provide insight into the strategy employed.

For the purpose of this investigation, the performance of individual tennis players will be examined in relation to their clothing sponsors. Tennis is an attractive sport for many sponsors because of its global nature and the demographics of its fans (Forbes, 2015). Tennis is the second most popular sport in Europe and South America; hence a large part of the population can be reached through promotion and sponsorships. Furthermore, the average income of tennis tournaments’ visitors is above average and a very large fraction holds at least a bachelor’s degree. This also explains the popularity of luxury brands to sponsor tennis players and tournaments. Therefore, this paper will investigate the following research question:

“What is the relationship between the performance of individual tennis players and their clothing sponsors?”

First, the theoretical framework will consider sponsors and their main objectives. The research question will then be split into two hypotheses. Furthermore, data will be extracted and tested using statistical methods, which will be explained in the data and methodology parts. These outcomes will be presented in the results section of the paper. Finally, the findings will be summarised and, based on the results, some conclusions will be drawn. Limitations of this research and suggestions for further research are also provided.

Ibisevic van Holten Theoretical Framework

Sponsorships are an important source of income for tennis players. In 2015, Roger Federer and Novak Djokovid were the highest paid tennis players of the world, with 67 million and 48.2 million U.S. dollars respectively. Although most players receive large amounts of money from winnings, endorsement deals are large contributions to players’ salaries. Between June 2014 and June 2015, the twelve best paid players earned a total of 71 million American dollars from prize money, whereas total earnings from endorsements and appearances added up to 216 million dollars (Forbes, 2015). This shows that it can be expensive to sponsor top players, due to the high interest in sponsorship deals with these players. On the other hand, lower performing players usually have no sponsors or only sponsors that have a relatively low market share. This would indicate that the sponsored player’s ranking points are partially explained by the sponsoring of large sports brands, in which large indicates that the company engages in many sponsorship deals and is highly present in the tennis market. This leads to the first hypothesis:

Hypothesis 1A: Larger brands are involved in sponsorships with higher ranked players.

Generally, companies may also expect certain performance obligations from the players. According to CNBC1, Novak Djokovid, the current world number one tennis player, signed a contract with Sergio Tacchini in which they agreed to pay him more should he perform better (Rovell, 2012). Therefore, it is to be expected that successful sports brands are able to sponsor better performing players with higher ranking points. However, other brands will focus on players that fit better within their budget. This leads to the second part of the first hypothesis:

Hypothesis 1B: The performance of tennis players sponsored by larger brands varies between the brands.

Due to relatively high costs, sponsorships are an expensive form of marketing. Companies therefore carefully examine which players they wish to sponsor; this decision must align with their core strategy and business mission. Companies must therefore consider what they want to achieve with their sponsorship. There are several objectives for sponsorship (Fahy & Jobber, 2012). The most prevalent reason is to gain publicity. Worldwide tennis tournaments provide the perfect opportunity

1 The CNBC is an American basic cable, internet and satellite news television channel. 4

Ibisevic van Holten for sponsors to reach a global media coverage, resulting in increased consumer awareness. Another objective is to create entertainment opportunities for customers through large sports events. The third important objective is to promote favourable associations for the brand and company. By creating a relationship between the sponsors and the sponsored event or player, values associated with the event are being transferred to the sponsor. During such an event, the brand’s logo and other symbols are spread through all objects and spaces that are being used, which creates an association in the customer’s mind between the sponsor and that certain activity. Improving community relations could be another objective to engage in sponsorships. The last objective is creating promotional opportunities by selling different products with the brand’s logo and name. This could also be an opportunity to attract new customers.

These objectives make the choice of the individual player important, as customers will create a certain association between the brand and these players. It has been proven that people assimilate certain attributes and behaviour from their admired celebrities into their own lifestyle (Fraser & Brown, 2002). Therefore it is expected that the sponsoring brands of top players will lead to higher sales, as more people have positive associations with the brand and thus are more willing to buy goods by this particular brand. This relationship will be investigated in the second hypothesis:

Hypothesis 2: The performance of a sponsored player has a positive effect on the sponsoring brand’s revenues.

Data

Data have been collected on the performance of tennis players and the sponsoring brands. This research focuses merely on male tennis players, because comprehensive data about the performance of female tennis players is not available on the website of the Women’s Tennis Association (WTA). However, this should not cause any major problems, as still many players can be included in the data. Also, male tennis is generally considered to be more popular among the audience, making the chosen data a good representation of the tennis industry.

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For both hypotheses, the performance of the tennis players is measured by the total ranking points that they have collected based on the ATP Rankings2. These rankings are calculated by adding all the points that the player has earned in the tournaments for the immediate past 52 weeks3. They serve as a good indicator of the performance of players as the number of ranking points that can be earned is based on the type of tournament that the player participates in (i.e. Grand Slams reward more ranking points than the ATP World Tour 500) and on the performance in each tournament (i.e. reaching the finals awards more points than reaching the quarter finals). The total ranking points of all players are found on the website of the ATP World Tour, which collects all statistics on professional male tennis players and tournaments, making it a reliable source.

For the first hypothesis, different players’ ranking points will be examined over multiple years. The data set covers the time span of 2005-2015, in order to make the rankings of the players more representative. Most current players started their careers in the early 2000’s, resulting in a relatively low ranking in that particular year. However, this ranking does not give a good indication of their average performance. Additionally, low initial rankings at the start of the players’ career will skew the data when making computations. The ranking points have been collected at the end of November for every year. This is right after the end of the ATP World Tour Finals, which is generally being considered as the end of the tennis season. In order to limit the scope of this research, data have been collected for 37 tennis players, based on whether enough information is available regarding their sponsorships. These players are currently ranked in the top 100, which allows for a fairer comparison between the different players. For the first hypothesis, the impact of the following brands will be investigated: Nike, Adidas, Lotto, Sergio Tacchini, Uniqlo, Lacoste, Asics and K-Swiss. These brands sponsor the most tennis players currently ranked in the top 100. Other brands have little or no players ranked in the top 100, so there would be too few observations available.

The total revenues needed for the second hypothesis have been collected for Nike, Adidas, Asics, Uniqlo and Yonex only, as not all brands have published their financial statements. These documents are available in their annual reports. Only the years 2006-2015 are included as some information before 2006 is not available.

2 The ATP Rankings is an objective method introduced by the Association of Tennis Professionals (ATP) for deciding on entry and seeding in all tournaments for singles and doubles. 3 These include the four big Grand Slams, the eight ATP World Tour Masters 1000 tournaments, the Barclays ATP World Tour Finals of the ranking period and the player’s best six results from the ATP World Tour 500, ATP World Tour 250, ATP Challenger Tour and Futures tournaments. 6

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Table 1 summarises the data about the ranking points for each individual brand separately. These numbers show that the average ranking points differ quite a lot among the brands, with Asics and K- Swiss having the lowest average ranking points throughout the years. Uniqlo has the highest average ranking points, but also the largest standard deviation. This is because Uniqlo is currently sponsoring only two players. They have been sponsoring Djokovid, the world’s number one ranked tennis player, since 2012. At this point, he had already achieved a high position in the ATP Rankings. However, they are also sponsoring Nishikori who has much lower ranking points at the beginning of the time period, as he has been professionally active for a relatively shorter time span.

Table 1. Descriptive statistics on ranking points. Brand Observations Players Mean Std. Dev. Min Max Nike 75 10 3188.16 3776.50 0 13350 Adidas 110 13 1351.46 1517.41 0 10590 Lotto 36 6 1692.31 1682.34 1 6755 Sergio Tacchini 19 4 1970.63 3138.22 113 13630 Uniqlo 13 2 5359.85 5796.82 85 16585 Lacoste 47 7 1002.32 769.27 0 3300 Asics 20 3 691.50 626.82 0 1920 K-Swiss 9 2 675.22 933.94 0 2560

The descriptive statistics of the brands’ revenues are presented in Table 2. Nike and Adidas have clearly the highest revenues of all brands. Furthermore, it can be noticed that both brands sponsor the most players in total, whereas Yonex, having the lowest revenues, is sponsoring only one player.

Table 2. Descriptive statistics on revenues (in billions of U.S. Dollars). Brand Observations Players Mean Std. Dev. Min Max Nike 10 10 21.680 5.094 14.954 30.601 Adidas 10 13 12.741 2.372 10.084 16.915 Uniqlo 9 2 6.351 1.509 3.655 7.904 Asics 10 3 2.507 0.587 1.449 3.23 Yonex 3 1 0.428 0.025 0.41 0.457

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Finally, some transformations have been made. In order to answer the first hypothesis the logarithm of the average and total ranking points has been taken as these variables are not normally distributed, but skewed to the right. The distribution of the log average ranking points is approximately normal, making t-tests and F-tests appropriate. This is shown in Appendix A. considering the second hypothesis, the growth rates of the revenues have also been generated, in order to eradicate absolute differences in revenues between the brands.

Methodology

Considering the first hypothesis, a linear regression of ranking points on brands is performed. The coefficients are estimated using Ordinary Least Squares (OLS) regression. The term ranking points in the regression is an average of the ranking points of all players (i) sponsored by a specific brand in a given year (t). As the players appear in multiple data points the regression is performed using volatility clustering. This way the standard errors are adjusted for the dependent observations. Dummy variables will be created for each separate brand. In this case, the reference category includes players that either had a sponsor that only sponsored very few tennis players or no sponsor at all. Other variables that are significantly correlated with the dependent variable are added to the regression as control variables. The first control variable is the number of years of experience of the tennis player, as it is expected that a player with more experience has a higher performance. Also, the dummy variable Europe is included which takes the value of 1 when the player is from a European country and 0 otherwise. A majority of the top ten players are from a European country, indicating that there could be a correlation between the countries and the total ranking points. This is translated into a linear regression of the following form:

Log (Ranking pointsi,t) = ß0 + ß1 * Nikei,t + ß2 * Adidasi,t + ß3 * Lottoi,t + ß4 * SergioTacchinii,t + ß5 *

Uniqloi,t + ß6 * Lacostei,t + ß7 * Asicsi,t + ß8 * K-Swissi,t + ß9 * Years Activei,t + EUi + i,t

Due to the variation in the number of players sponsored, and as some players’ ranking points inflate the average ranking points, the analysis was repeated using the sum of the ranking points of all the players as the dependent variable for a given brand (i) in a given year (t). In this regression, the number of players that each brand is sponsoring and the sum of the total years of experience of these players are added as control variables. Dummy variables for whether the brand is European or 8

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Japanese (value of zero indicated otherwise) are also added. These variables are added as there are many European players in the top ten in this sample. These players are likely to be sponsored by European brands; the same holds for Japanese players. The following regression is performed:

Log (Sum Ranking Pointsi,t) = ß0 + ß1 * Nikei,t + ß2 * Adidasi,t + ß3 * Lottoi,t + ß4 * SergioTacchinii,t + ß5 *

Uniqloi,t + ß6 * Lacostei,t + ß7 * Asicsi,t + ß8 * K-Swissi,t + ß9 * Players + ß10 * SumYearsExperience + ß11 *

EUi + ß12 * JPNi + i,t

In order to test if the effect on ranking points varies per brand, multiple F-tests are performed to show if there are significant differences between the coefficients of the brands. Another F-test will also be used to test for the joint significance whether at least one of the coefficients is not equal to the zero.

For the second hypothesis, again a linear OLS regression is performed with the percentage change of the revenues over the past year (t) as the dependent variable and the sum of the ranking points for a given brand (i) as the independent variable. The dummy variable for whether the sponsored brand is from Japan or a European country (making U.S. brands the reference category) is also added to the regression to see if there is a bias in the first model. The regression run is as follows:

% change in Revenuest,i = ß0 + ß1 * SumRankingPointsi,t + ß2 * EURi + ß3 *JPNi + i,t

Results

Three different models have been estimated for hypothesis 1a concerning the ranking points per player. The results are shown in Table 3. The first regression has been performed without any control variables. Most brands turned out to be significant at the 5% level; only the brands K-Swiss, Sergio Tacchini and Asics were not significant. The numbers of years active and the dummy variable for Europe have been added to the final model, both showing a significant effect on ranking points. The F-statistic of this final model was 64.30, indicating that the variables have a joint significant effect on the ranking points. This last model is therefore preferred to the other models, as both control variables are significant. Also, the R2 is higher in model three. Approximately 71% of the variations in the ranking points can be explained by the regressors.

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Table 3. Regression output using the average ranking points

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The second regression based on the sum of the brands’ ranking points showed slightly different results (Appendix B, Table 5), although the output of model three confirms results found earlier. The sum of the years active is significant and has a positive impact on the sum of the ranking points. This is also true for the number of players and both dummy variables for Europe and Japan. The coefficient for Uniqlo is not significant anymore, as they are sponsoring only two players.

In order to answer hypothesis 1b, the coefficients of the significant brands were tested for equality. The F-statistics of these tests are displayed in Table 6 and 7 of Appendix C. When testing the significant coefficients of the regression using average ranking points, Uniqlo is found to be significantly different from all other brands. This is most likely because Uniqlo’s average ranking points are inflated by the sponsoring of Djokovid. When using the regression with a summation of ranking points per brand, some brands were found to have a significantly different effect on ranking points. This indicates that the effect of the sponsorship on ranking points differs per brand. Based on these results there is enough evidence to accept the first hypothesis, both part A and part B.

Various models have been estimated for the second hypothesis, shown in Table 4. The first model only included the sum of the ranking points as independent variable, which had a significant effect. The dummy variable for whether the player comes from a European country or from Japan was added to the second model, as it is believed that both the total ranking points and the revenues of the brands are correlated with this variable. The dummy variable for Europe is significantly correlated with the revenues at the 5% level. The coefficient for the sum of the ranking points decreased, indicating that there was an upward bias in the first model. However, the coefficients of the independent variables did not change from the second to the third model, showing no potential bias. The third model is preferred over the other two, based on the R2, which increased from 6.6% to 20.6% between the first and third model. The coefficient of the sum of the total ranking points is significant in this model, indicating that the total ranking points the brands’ sponsored players obtained that year (negatively) affect the brands’ percentage change in revenues. However, the coefficient of the sum of the ranking points is close to zero. This shows that higher ranking points of a tennis player do not increase the revenues of the brand. These findings are also supported by the scatterplot, presented in Figure 2 of Appendix D, which shows a rather constant range of both percentage change in revenues and total ranking points. Therefore, the second hypothesis is rejected based on these results.

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Table 4. Regression output for the second hypothesis

Conclusion

This paper has investigated whether tennis players that are sponsored by certain brands have higher average ranking points than those sponsored by other brands and those that have no sponsors, and also whether this effect varies between the brands. In fact, the brands had a significant impact on the players’ ranking points and this effect also turned out to be different for the different brands investigated. It has been found that some brands, such as Uniqlo, have sponsored top players only and have therefore obtained high average ranking points, whereas other brands reach this result by sponsoring many different players. The same investigation has also been performed for the sum of the total rankings points per brand throughout the years, corrected for the number of players. In this case, the coefficient of Uniqlo was no longer significant, showing Uniqlo’s strategy was to sponsor simply two top players. The results suggest that different strategies can be distinguished used by the various brands for their sponsorships. Some brands may wish to sponsor only a few high performing players, such as Uniqlo who is only sponsoring Djokovid and Nishikori, whereas other brands are sponsoring many lower ranked players (Lacoste). Another (but costly) strategy is to sponsor many high performing players.

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The results suggest that Nike uses this strategy, as it is also the brand with the highest revenues and is thus able to afford this. Lastly, companies can also choose to sponsor only a few lower performing players, which is the case for Asics and K-Swiss in this investigation.

Furthermore, it has been examined whether the total number of ranking points of the players sponsored by a given brand has an effect on the percentage change in the brand’s revenues. This effect was found to be significant, but very close to zero. Further research will need to investigate the relationship between the success of the sponsor and the ranking points of their players.

Limitations and Further Research

There are several limitations that could be improved in further research. Due to the summation of ranking points per brand, the number of observations in the dataset is reduced which affects the explanatory power of the models. Collecting data on more players and brands will increase the size of the reference category in both hypotheses, but will also make the sample of the brands more representative as all the sponsored players are included. Furthermore, several control variables were added to the regressions to better model the dependent variable, but there could be other things correlated with the number of ranking points which are not included in this research, for example health condition and intensity of the trainings. However, these data are generally hard to measure and were therefore not taken into account. Another limitation concerns the second hypothesis, in which the effect of the players’ ranking points was examined by taking into consideration the percentage change for different initial revenues levels over the years. These findings are limited as revenues are also affected by many external factors such as the state of the economy. This can be accounted for by collecting data on the global market share in the tennis sector for each brand. Using purely the tennis sector will partly correct for differences amongst countries, and capture effects more closely related to the sponsoring of tennis players. Further research could also investigate what the returns on sponsorships are for the companies in the long run, as this research has only considered the change in revenues. Sponsoring better performing players is costly, so companies will have to face a certain trade-off between the performance of the player and the costs of the sponsorship. Finally, this paper has focused merely on male tennis players, but further research could investigate whether the same conclusions can be drawn for female tennis players. This would be particularly interesting, because women are typically even more sensitive to the fashion choices of role models and the latest developments regarding fashion. 13

Ibisevic van Holten Bibliography

Day, J. (2003, February 26). Sports fans ‘ignore sponsorship’. The Guardian. Retrieved from http://www.theguardian.com/media/2003/feb/26/marketingandpr

Fahy, J., & Jobber, D. (2012). Foundations of marketing. London, United Kingdom: McGraw-hill.

Forbes. (2015, August 31). Federer, Djokovic Lead The 2015 List Of Highest-Paid Tennis Players. Retrieved from http://www.forbes.com/sites/kurtbadenhausen/2015/08/31/federer- djokovic-lead-the-2015-list-of-highest-paid-tennis-players/#219bebf5553c

Fraser, B.P., & Brown, W.J. (2002). Media, Celebrities, and Social Influence: Identification With Elvis Presley. Mass Communication and Society, 5(2), 183-206.

Meenaghan, T. (2001). Understanding Sponsorship Effects. Psychology & Marketing, 18, 95-122.

Rovell, D. (2012). Sergio Tacchini, Djokovic Shockingly Part Ways. Retrieved from http://www.cnbc.com/id/47519672

Sleight, S. (1989). Sponsorship: What it is and How to Use it. Maidenhead, United Kingdom: McGraw Hill, 4.

Speed, R., & Thompson, P. (2000). Determinants of Sports Sponsorship Response. Journal of the Academy of Marketing Science, 28, 226-238.

Statista. (2016). Sponsorship Spending Worldwide by Region 2009-2016. Retrieved April 29, 2016, from http://www.statista.com/statistics/196898/global-sponsorship-spending-by-region- since-2009/

The Economist. (2008, July 31). Sponsorship form. Retrieved from http://www.economist.com/node/11825607

The Telegraph. (2015, May 13). Sports sponsorship is big business. Retrieved from http://www.telegraph.co.uk/investing/business-of-sport/sports-sponsorship/

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Appendix A

5.0e-04

4.0e-04

3.0e-04

Density

2.0e-04

1.0e-04

0 0 0 5000 10000 15000 0 2 4 6 8 10 RankingPoints Log_RankingPoints

Figure 1. Histogram of the ranking points with and without the logarithm

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Appendix B

Table 5. Regression output using the sum of the ranking points

Ibisevic van Holten Appendix C

Table 6. F-statistics of test for equality of the coefficients using average ranking points

Table 7. F-statistics of the test for equality of coefficients using the sum of ranking points per brand

Ibisevic van Holten Appendix D

Revenue_Diff .1

0 -.1

0 10000 20000 30000 Sum_RP

Figure 2. Scatterplot between the sum of the ranking points and the percentage change in revenues