IntroductionThe International Olympic Committee (IOC) adopted Rule 40 in 1991 to "preserve the unique nature of the Olympic games by preventing over-commercialization" and protect sponsors who spend millions of dollars for exclusive marketing rights. Preventing Olympians from hawking their own sponsors became increasingly problematic in the run-up to the 2012 London Olympic Games due to Facebook and Twitter’s emergence. Rule 40 was updated in 2015 so that unofficial sponsors could feature their athletes in campaigns. However, using “Olympic-related terms”, official Twitter hashtags, logos, or referencing Olympic location was prohibited. Athletes could be disqualified and stripped of medals if found in violation of Rule 40.

Attorneys argue that hashjacking, using a hashtag for something different than its intended purpose, is not trademark infringement because products or services are not being sold. Sally Bergesen, whose apparel company Oiselle sponsored 15 Olympic hopefuls, feels Rule 40 is too restrictive and effectively bars lesser-known athletes from earning money from their Olympic appearances. Zaileen Janmohamed, SVP of Client Services for GMR Marketing, thinks the wider playing field could devalue Olympic sponsorships.

Starting in late March and throughout the 2016 Rio Olympic Games, Origami Logic, a marketing analytics company and global leader in marketing performance measurement, tracked Olympics-related social activity of 40 brands, 38 worldwide and Team USA sponsors, and two non-sponsors (Adidas and Under Armour), highlighting the results on a campaign microsite developed by their demand generation agency, Spear Marketing Group. Origami Logic drove traffic to the microsite and campaign awareness through ads on Google AdWords and LinkedIn. Microsite visitors could click through to learn more about successful campaigns, engage with social media, and sign up to receive a “Brand Olympics” newsletter. Results were also promoted on Twitter (@brandolympics) and using the hashtag: #BrandOlympics2016.

Materials and methodsPicodash, an advanced Instagram search engine and social media management tool that helps Brands, Publishers, Researchers and Journalists search and curate Instagram content by location, hashtags and places, was used to obtain Nike, Adidas and Under Armour posts between March 27, 2016, through August 21, 2016, coinciding with the date non-sponsors were required to start advertising and ending on the date of the 2016 Rio Olympic Closing Ceremonies. Previously known as Gramfeed, the transition to Picodash complies with the new Instagram API Platform changes as of June 1, 2016, which no longer permit 3rd-party apps to display public Instagram content to just anyone accessing the app. Picodash provides its services via a paid subscription model, which includes a three-day trial period that does not charge Instagram users if cancelled within aforementioned time frame, and charges an $8 monthly subscription fee.

Based on communication with Picodash, although it was not publicly visible at the time, exporting data to a spreadsheet was possible. Once posts were loaded from Nike, Adidas and Under Armour accounts in Google Chrome, open console, (right click - inspect - console tab) and type exportMedia() to save as a csv file. While both regression and classification models were explored, which proved to be a rather frustrating experience, counsel from instructor prompted a change in direction since the data was nowhere near 1,000 instances. As a result, two-sample t-tests were done in statistical software Minitab 17.

AcknowledgmentsSpecial thanks to Vincent Malic for his invaluable counsel during the course of this project and Picodash for having a platform to work around Instagram API challenges.

Results ConclusionsWhile Nike had more followers, like and comments

than Adidas and Under Armour, they did not dominate

both likes/followers and comments/followers. Under

Armour, which has far less followers than Adidas, had

a much higher likes/followers score than both Adidas

and Nike, which were nearly identical. When

comparing comments/followers, although Nike had the

highest score of the three brands, substantially higher

than Adidas, there was very little separation between

Nike and Under Armour. Not only does the data

suggest that Nike did not have a huge advantage as

an Olympic sponsor, based on likes/followers and

comments/followers, Under Armour potentially had the

most engaging Instagram content.

Roderick Headrthead@indiana.edu

Literature citedAlba, D. (2016, August 12). Athletes Battle the Olympic Brass for the Right to Make Money. Retrieved

from Wired: wired.com/2016/08/olympians-take-back-social-media-rule40Chavez, C. (2016, July 25). What is Rule 40? The IOC’s rule on non-Olympic sponsors, explained. Retrieved

from Sports Illustrated: http://www.si.com/olympics/2016/07/27/rule-40-explained-2016-olympic-sponsorship-blackout-controversy

Chemi, E. W. (2016, July 28). Olympic committee on the prowl — for misuse of hashtags. Retrieved from CNBC: http://www.cnbc.com/2016/07/28/olympic-committee-on-the-prowl--for-misuse-of-hashtags.html

Origami Logic. (2016). Brand Olympics 2016 Report. Mountain View.Origami Logic Scores Gold with “Brand Olympics” Campaign. (2016, August 16). Retrieved from thepoint:

http://spearmarketing.com/blog/origami-logic-scores-gold-with-brand-olympics-campaign/Uyoe, I. (2016, June 2). IOC Rule 40: Olympic Sponsorship Achilles. Retrieved from LinkedIn:

linkedin.com/pulse/ioc-rule-40-olympic-sponsorships-achilles-heel-idy-uyoe

Future DirectionsDue to conclusiveness of the data, no additional work is needed to solidify the results.

N Mean StDev SE MeanNike Likes 32 384979 175936 31101Adidas Likes 32 72384 15007 2653

Difference = μ (Nike Likes) - μ (Adidas Likes)Estimate for difference: 31259495% CI for difference: (248932, 376256)T-Test of difference = 0 (vs ≠): T-Value = 10.01 P-Value = 0.000 DF = 31

N Mean StDev SE Mean

Nike Likes 32 384979 175936 31101Under Armour Likes 32 23320 7591 1342

Difference = μ (Nike Likes) - μ (Under Armour Likes)Estimate for difference: 36165995% CI for difference: (298168, 425150)T-Test of difference = 0 (vs ≠): T-Value = 11.62 P-Value = 0.000 DF = 31

N Mean StDev SE MeanNike Comments 32 2485 1538 272Adidas Comments 32 165 187 33

Difference = μ (Nike Comments) - μ (Adidas Comments)Estimate for difference: 231995% CI for difference: (1761, 2878)T-Test of difference = 0 (vs ≠): T-Value = 8.47 P-Value = 0.000 DF = 31

N Mean StDev SE MeanNike Comments 32 2485 1538 272Under Armour Comments 32 91 110 19

Difference = μ (Nike Comments) - μ (Under Armour Comments)Estimate for difference: 239395% CI for difference: (1837, 2949)T-Test of difference = 0 (vs ≠): T-Value = 8.78 P-Value = 0.000 DF = 31

T-tests are called such because the test results are based on t-values, an example of what

statisticians call test statistics. The higher the t-value, the lower the p-value, increasing the

likelihood of statistical significance between the two groups. Coupled with a zero p-value, there is

a zero percent chance there is no significant difference between Nike and Adidas and Nike and Under Armour with both likes and comments. DF

refers to degrees of freedom, which is one less than the sample size.