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<\/a><\/p>\nThis week, Decision Science News is doing a special cross-posting with Messy Matters<\/a>. The post below is by Sharad Goel<\/a> and describes work that he and your Decision Science News editor Dan Goldstein are jointly undertaking at Yahoo!<\/p>\n Do you know what the #$*! your social media strategy is<\/a>? Perhaps it\u2019s “to facilitate audience conversations and drive engagement with social currency”? Or maybe, “to amplify word of mouth by motivating influencers”? Well, given all the lies and damned lies being told about social, fellow yahoo Dan Goldstein<\/a> and I decided to enter the fray with statistics<\/a>. We measured the extent to which your friends\u2019 behavior predicts your own, and found that in several consumer domains the effect is substantial, complementing traditional demographic and behavioral predictors.<\/strong><\/p>\n That friends are similar along a variety of dimensions is a long-observed empirical regularity\u2014a pattern sociologists call homophily<\/a>. As McPherson et al. write in their canonical review<\/a> on the subject, \u201chomophily limits people\u2019s social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience.\u201d Turning this statement around, where there is homophily, one can in principle predict an individual\u2019s behavior based on the attributes and actions of his or her associates<\/strong>.<\/p>\n To assess the quality of such network-based predictions, we merged a large social network (based on email and IM exchanges) with offline sales data at an upscale, national department store chain. Thus, for each of over one million users, we had their past purchase amounts in dollars, and had the same information for each of their network contacts. Think about this for a minute: we not only know how much these individuals themselves spent at an offline<\/em> retailer, but also how much their social contacts spent, a testament to how profoundly the Internet is changing the way we study human behavior. (Despite bolstering social science research, these newfound tools raise serious privacy issues. We left the matching to a third party that specializes in doing this securely, so neither we nor the department store had access to the other\u2019s complete customer database.)<\/p>\n The plot below summarizes our findings. First, as indicated by the top line, consumers whose friends spent a lot, also spent a lot themselves, consistent with the hypothesis that homophily extends to consumer behavior. When friends (alters) on average spent $400 during the six-month observation period, the consumer herself (ego) spent nearly $600, more than twice the typical consumer (indicated by the dotted line). As our aim is prediction, however, the relevant question is not just whether friends are similar in their purchasing behavior, but rather how much information is conveyed by social ties relative to other attributes. One might conjecture that ties simply indicate demographic (i.e., age and sex) similarity, that those who spend a lot are more likely to be middle-aged women\u2014the primary market segment for this department store\u2014and that friends of middle-aged women tend also to be middle-aged women. To test this hypothesis, we first paired each individual with a randomly chosen consumer of identical age and sex. The bottom line shows that this demographically matched group is, perhaps surprisingly, pretty ordinary. In other words, looking only at age and sex, you can\u2019t identify consumers whose friends spend a lot (and who we know spend a lot themselves).<\/p>\n <\/a><\/p>\n Though it\u2019s standard marketing practice to target consumers based on their demographics, it\u2019s an admittedly noisy profiling technique. So, to put social through the wringer, we next took the \u201csocially select\u201d group\u2014consumers whose friends spent a lot\u2014and matched them to random consumers with identical age, sex, and past purchase amounts. Each social candidate, that is, was matched to a consumer not only of the same age and sex, but one who spent approximately the same amount as the social candidate during the previous six months. Even relative to this formidable baseline, social cues still provide considerable information. As the middle line indicates, knowing a consumer\u2019s age, sex and past purchases, but not that their friends are shopaholics, one would still underestimate their future sales.[1]<\/sup><\/a><\/p>\n We repeated this analysis for two other domains\u2014examining signups for Yahoo! Fantasy Football<\/a>, and clicks on ten online banner ads for movies, apparel, government programs, and beyond\u2014again finding that the predictive power of social persists even after adjusting for age, sex, and past behavior<\/strong>. Lest you run off to rejigger your social strategy, we should mention a couple of caveats. First, we have shown that consumers with big-spending friends tend to spend a lot\u2014more, in fact, than demographics and past purchases alone would suggest. But since most people, even premium customers, don\u2019t have shopaholic friends, social cues do not substantially boost average<\/em> predictive performance. Second, though social signals help predict how much<\/em> consumers spend, they don\u2019t always help identify which<\/em> consumers will spend the most. Those who recently spent fifty grand on sartorial elegance are likely to be habitual top spenders, regardless of what you know about their friends.<\/p>\n Assessing the value of social, as with most things, is a messy affair. On the one hand, network ties convey information not captured by the usual egocentric metrics, a conclusion that at the very least we find scientifically interesting. On the other hand, it\u2019s not immediately obvious how to use that knowledge to take over the world. Well, rest assured that an army of social strategy gurus are waiting in the wings with a game-changing, technology-disrupting way to, you know, “leverage the social graph to deliver personalized experiences” or something.<\/p>\n N.B.<\/em>Thanks to Randall Lewis and David Reiley<\/a> for acquiring the sales data, Jake Hofman<\/a> for assembling the email data, and Duncan Watts<\/a> and Dan Reeves<\/a> for comments. For related work in the telecom domain, check out the paper, “Network-Based Marketing: Identifying Likely Adopters via Consumer Networks<\/a>,” by Shawndra Hill<\/a>, Foster Provost<\/a>, and Chris Volinsky<\/a>.<\/p>\n Illustration by <\/em>Kelly Savage<\/a><\/em><\/p>\n<\/a><\/p>\nFootnotes<\/h3>\n