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Conventional wisdom among marketers seeking to reach consumers through social networks is that personal recommendations from friends are the most effective viral messages for promoting “product contagion.”
But research published recently in the journal Management Science indicates that’s not always the case.
Researchers Sinan Aral and Dylan Walker designed an experiment involving a Facebook application that tested two kinds of product contagion messages against each other.
The first was personalized and active: users actively selected a subset of their social network to receive personal referrals from them. The second was an automated broadcast: when the user engaged the product, his or her actions were broadcast to all of his or her contacts.
Previous research had indicated that personalized and active messages tend to go to a person’s closer and more strongly related contacts, and that we tend to trust information from close and trusted sources more, and therefore respond more often to them.
Automated broadcasts, on the other hand, build awareness among friends of new activities or products a user is adopting or engaging with, and can encourage, in a more passive way, those friends to eventually adopt the product themselves.
Surprisingly, the research found that passive-broadcast viral features produced a 246% increase in peer influence and social contagion, whereas adding active-personalized viral features resulted in only an additional 98% increase. Why? More people received the automated messages. The researchers wrote:
Although active-personalized viral messages are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often, generating more total peer adoption in the network.
Aral and Walker’s full article, “Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks,” can be found in Management Science. Aral will be giving a presentation about his research on social network analysis at