Learning, and Profiting, from Online Friendships

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The hope is that if Marlow and his team manage to track the paths of influence among its communities, the company might be able to offer more effective and lucrative advertisements and promotions.

An early step is to separate each user's friends into clusters. Marlow pulls out a chart illustrating the social network of one of his colleagues, Alex Smith. It shows different groups of dots and their connecting links. One big and busy group represents fellow workers at Facebook. Others are high school friends, family, in-laws, frat brothers. Understanding these types of relationships could provide valuable context.

Marlow's team recently carried out a study to determine how close we are to our friends online. They looked at how often people clicked on their friends' news or photos, how often they communicated, and if the communications traveled in both directions. Studying this data, they determined that an average Facebook user with 500 friends actively follows the news on only 40 of them, communicates with 20, and keeps in close touch with about 10. Those with smaller networks follow even fewer. What can this teach advertisers? People don't pay much attention to most of their online friends. By focusing campaigns on people who interact with each other, they'll likely get better results.

It's an inexact science, to be sure. But that's not stopping a host of startups from hitching friendship analysis to advertising and media campaigns. A New York company, 33Across, has partnerships with social networks, instant chat providers, and makers of online applications known as widgets. Each of these partners tags users with bits of tracking code known as cookies. These let 33Across stitch together friendship profiles of tens of millions of people, says CEO Eric Wheeler. The people remain nameless numbers, but the company knows which ones are connected to which, how strong the connection is, and how many others are in their circles. Working with packaged goods companies, 33Across has focused on thousands of people who have bought a product online, sprinkling ads for the same item along the online pathways of millions of their friends.

FRIENDSHIP DATA

In an industry where the majority of ads go unclicked, even a small boost can make a big difference. One San Francisco advertising company, Rapleaf, carried out a friend-based campaign for a credit-card company that wanted to sell bank products to existing customers. Tailoring offers based on friends' responses helped lift the average click rate from 0.9% to 2.7%. Although 97.3% of the people surfed past the ads, the click rate still tripled.

Rapleaf, which has harvested data from blogs, online forums, and social networks, says it follows the network behavior of 480 million people. It furnishes friendship data to help customers fine-tune their promotions. Its studies indicate borrowers are a better bet if their friends have higher credit ratings. This might mean a home buyer with a middling credit risk score of 550 should be treated as closer to 600 if most of his or her friends are in that range, says Rapleaf CEO Auren Hoffman.

Such intelligence could prove useful for a financial company. While no one would automatically green-light borrowers based on their friends, the friendship data could lead them to assign a human to see if the mathematical model is missing something. "They pay more than $100 in marketing to [attract] customers," Hoffman says. "If they reject you, they lose it."

Friendship data promise insights into not only the marketplace but also the corporation. Researchers can trace the hidden networks, identifying both the people who transmit valuable information and those who appear to block it—and how workers bypass them. By studying these patterns, managers can promote effective networkers and try to bring less communicative colleagues—outliers—into the flow.

To build up communication within the company, IBM Research scours its networks for employees with similar interests and expertise—and suggests them as friends. One key laboratory for IBM is its internal social network called Beehive, in which nearly 60,000 employees discuss patents, critique software code, and even post photos of pets.

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