


It’s challenging work. Even data scientists using powerful statistical tools find it difficult to tease out influence from other factors. Getting it right can improve sales and profits or adherence to social programs. Getting it wrong can lead to marketing practices that simply target users who were already likely to buy. And some academics who’ve studied the role of influence in consumer behavior have begun to doubt whether it’s as powerful as Gladwell and others believe. For instance, in his book Everything Is Obvious, Duncan Watts argues that trends are just as likely to start with average Joes as with influencers like Kutcher. To resolve these debates we need data—in particular, experimental data.

My colleagues and I have conducted research experiments with social media networks in order to more accurately estimate influence. I’ve also worked with several companies, including Facebook, Yahoo, and the New York Times, to help them better understand how customers influence one another. Although insights from this work are still emerging, our initial results can help organizations learn what kinds of people are most influential and who is most susceptible to their influence.

Birds of a Feather…

The main reason it’s hard to understand influence is that people confuse correlation and causation. Countless studies have shown that human behaviors tend to cluster among friends and in time: For example, friends often take up activities (watching House of Cards, drinking bourbon cocktails) at about the same time. The question is whether this is the result of peer-to-peer influence or of something else.

One possible explanation is the sociological phenomenon called “homophily”—people’s propensity to associate with others who are like them (recall the saying “Birds of a feather flock together”). By definition, this means that our preferences, interests, and behaviors are highly correlated with our friends’. We buy the same products at the same time. We watch the same TV shows and visit the same websites—so we’re exposed to the same ads. We eat at the same restaurants, go to the same gyms, and follow similar commuting patterns. All these things mimic social influence but may have nothing to do with it. Researchers call them “confounding factors.”

To accurately estimate social influence, we need to identify confounding factors so that we can differentiate behavioral tendencies from behavioral changes. To determine how much you may have influenced my purchase decision, we need to know the effect of your behavior or recommendation over and above my prior probability of purchasing. In practice, that can be extremely hard—but with social media, we have a new laboratory for experimentation.

We studied the adoption of a mobile service product among 27 million users in a Yahoo instant messaging network and used statistical techniques to separate influence (specifically, how a friend’s use or recommendation of the product affected another person’s decision to use it) from homophily and other confounding factors. We found that traditional models overestimated the power of influence by a factor of seven and that half of perceived influence was really just homophily and other confounds. We also learned that overestimates of influence were particularly extreme early in the product’s life cycle. That’s because early adopters are more likely to be similar to one another than late adopters are. If you want to see this in action, go to an Apple store the next time a new iPhone is released and look at the people camped out in front.

These findings have dramatic implications for marketing strategy. Imagine you’re the chief marketing officer for a brand that’s planning a product launch. Your company’s data scientist presents evidence that predicts that when someone adopts your product, many of his friends will start using it too. If your data scientist demonstrates that 90% of the correlation has to do with influence, you’ll probably allocate a lot of the marketing budget to peer-to-peer and word-of-mouth strategies—for instance, by offering a “friends and family” promotion that allows users to share a discount code. But if she suggests that 90% of the correlation has to do with homophily, you’ll realize that a peer-to-peer campaign is unlikely to work and that you’d be better off segmenting the market into discrete demographics and targeting likely adopters (regardless of who their friends are) through traditional ads and promotions.

In a second experiment, we studied how influence affected the decision to download a commercial movie application among 1.4 million Facebook users. We divided application adopters into three randomized groups. When those in the first group downloaded the app, they had the ability to personally invite friends to use it. For the second group, an automated notification alerted friends that the member was using the app. For the third group, neither invites nor notifications went out. We found that 6% of those who received a personal invitation downloaded the app—three times more than among those who received an automated notification. We also found that adopters used the app 17% more over the long term when friends joined them in using it because of a personal invitation than when friends joined in because of an automated notification. Overall, these strategies not only created sustained increases in engagement but generated a significant increase in total adoption—all for a onetime fixed cost of about $600 to design and implement the viral features.

We also parsed the data for insights into what kinds of people were influential and what kinds were susceptible to influence. We found that men were more influential than women; that women influenced men more than they influenced other women; that people over 30, on the whole, were more influential and less susceptible than younger people; and that married people were less susceptible than single people. And we determined that influence and susceptibility traded off—people who were more influential were less likely to be influenced, and people who were more susceptible to influence tended to be less influential. Although these results wouldn’t necessarily hold true for other products, they illustrate a general method for measuring influence and susceptibility and show how research can give companies valuable insights into what types of consumers are likely to influence adoption of their products.

In a later study we explored how companies can incentivize users to recommend on their behalf. We experimented with three types of incentives to promote a web-based flower delivery service. We offered users in one group $10 to invite friends to use the service—a program we called a “selfish incentive.” We offered those in the second group the chance to give a friend a $10 discount for signing on—a “generous incentive” (the recommender received no reward). We offered the third group a “fair incentive,” in which the recommender received $5 and the friend received a $5 discount. The results surprised us: The generous and fair incentives generated more sends than the selfish incentive did. It turns out that people don’t like spamming their friends unless they can pass a benefit along. These results conform to the sociological notion of a “gift economy,” in which generosity confers status.

The insights I’ve described have applications far beyond marketing consumer products. For instance, in a pilot program in South Africa, we are conducting a variation of our flower delivery experiment in order to investigate what kinds of incentives will lead people to obtain HIV testing. With MTV, we are researching ways in which people’s use of social media might influence their friends’ political mobilization.The future of social influence marketing will depend on robust analytics to increase our understanding of what drives behavioral change. Mistaking any of the myriad confounding factors for influence can lead to costly mistakes—in marketing strategy and in public policy.
