“I love you,” Katy Perry recently tweeted to her 110 million followers. “Grateful for great friends,” Justin Bieber tweeted to 90 million. “Nothing like New York, Baby,” Lada Gaga tweeted to 79 million fans.
But these were more than just tweets. According to recent research from William T. Dillard Professor of Marketing Asim Ansari, there is a direct relationship between an artist’s social media commentary and increased song plays from the artist’s social network site over the short and long term. Having a popular song is about more than just the song, Ansari found — it is also about actively engaging with fans.
In the study “Building a Social Network for Success,” published in June’s Journal of Marketing Research, Ansari and his coauthors found that artists benefit more from making commentary than from requesting friends, evidenced by changes to the size and cohesiveness of fan networks as a result of artist activity. These changes drive long-term demand with effects far exceeding the direct short-term relationship, which has implications from a broader marketing perspective for how firms should build audiences on social media.
“In addition to focusing on the number of friends or followers, marketers should also use networking activities to shape a cohesive network in which members are richly connected,” Ansari wrote with co-authors Mark Heitmann and Lucas Bremer of the University of Hamburg and Florian Stahl of the University of Mannheim.
How musicians build fan bases is a ripe area for research. Columbia Business School’s Miklos Sarvary teaches a case study on Lady Gaga’s use of social media for his course, Media Platforms and Content. The case was developed by Harvard Business School, which has also created a study on the rise of Beyoncé and her use of Facebook and Instagram.
“You cannot avoid social media to create a community,” notes Sarvary, the Carson Family Professor of Business.
To better understand how important social media is for musicians, Ansari and his coauthors gathered 11 months of data from a European social networking website dedicated to the music community. On the site, artists can publish blogs, photos, songs, or videos on their profiles—not unlike how other US social media sites operate.
From a sample of 441 music artists, the researchers collected information on all song uploads, song plays, artist comments, and friendship connections within each artist’s so-called ego network. On average, fans streamed about 61 songs a month from an artist’s profile, while artists posted 3.37 comments per month and sent 2.73 friend requests per month.
In analyzing the data, the researchers discovered that every comment an artist wrote resulted in 9.23 additional song plays in the short term and 25.40 incremental plays in the long term, on average. Similarly, one friend request resulted in one additional song play in the short term and 2.40 additional song plays in the long term, on average.
In effect, an artist’s number of comments on social media was far more important than increasing their number of friends, when song plays from the artist’s community website on the social network was concerned. Moreover, the researchers found that friend requests could lower the cohesiveness of an ego network with a resultantly negative impact on the spread of information within an artist’s fan base.
For businesses outside the music industry, the research suggests that marketing managers should focus less on their firm’s number of followers and more on engaging with their existing audience and building a loyal and like-minded social network. Over the long term, this difference in focus can have a significant impact on network communication.
“The drivers of online success are not simply limited to the obvious effect of ego network size,” according to Ansari and his co-authors. “Rather, a strong and cohesive fan community… positively influences an artist’s success beyond the effect of network size.”
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About the researcher
Professor Ansari's research addresses customer relationship management, e-commerce personalization and targeting, social network modeling, and Bayesian models of consumer actions. He is...Read more.