A new ad created by Nike might lead the way in marketers using their data in entirely an entirely new manner. The ad is based on a data visualization of 12 months worth of Nike+ run data from New York City, Digital Buzz blog writes. It is serving as a digital installation in-store ad as well as a reward for the Nike Free Run+2 City Pack Series, it says. "The reward allowed customers to sync their runs to create a custom data painting, that was provided as a high resolution print for them to take home."
Nike laser etched the runner’s name, the distance he or she ran and run path onto a custom fabricated shoe box, which contained a pair of the ‘City Pack’ shoes from their city of origin. YesYesNo NYC help create the campaign.
Cool Ad Equals Higher Stock Price
Nike, of course, is no stranger to unique and innovative - not to mention star-studded - digital advertising. In particular it has been pushing social media - again, not surprisingly. What may be surprising, though, is at least one of the drivers behind these campaigns. Besides raising awareness and sales, there is a statistically significant correlation between social media popularity of key brands and their respective share prices, according to a study conducted by a researcher at Pace University, in association with Famecount.com.
Furthermore, this correlation was also found when a ten, and a thirty day lag was introduced into the study, suggesting that social media popularity may be a lead indicator of stock price performance.
The pilot study, undertaken by Arthur O’Connor, a doctoral student at Pace University, focused on three of the most popular brands on social networks, Starbucks, Coca Cola and Nike. The study looked at the daily movements in popularity for each company’s major social network accounts at three of the most popular services: Facebook, Twitter and YouTube, using Facebook Like, Twitter Follower and YouTube View data. This data was tracked against daily stock price movements for each of these companies, relative to an index of consumer stocks. ANOVA and linear regression analysis was conducted on the data set and statistical significance was found for all three regressions.
"By using social network popularity data on three major consumer brands, we were able to reliably predict their respective daily stock prices over a 10 month period – during which the stocks of the companies experienced radically different returns, with Starbucks climbing 29%, Nike appreciating by 14%, and yet Coke declining by nearly 6% – even when the social media data was lagged by as much as 30 days," O'Connor said.
