Social influence in social advertising: evidence from field experiments

By Eytan Bakshy, Dean Eckles, Rong Yan, Itamar Rosenn. 2012

291 citations recorded by the source corpus.

Social advertising uses information about consumers' peers, including peer affiliations with a brand, product, organization, etc., to target ads and contextualize their display. This approach can increase ad efficacy for two main reasons: peers' affiliations reflect unobserved consumer characteristics, which are correlated along the social network; and the inclusion of social cues (i.e., peers' association with a brand) alongside ads affect responses via social influence processes. For these reasons, responses may be increased when multiple social signals are presented with ads, and when ads are affiliated with peers who are strong, rather than weak, ties. We conduct two very large field experiments that identify the effect of social cues on consumer responses to ads, measured in terms of ad clicks and the formation of connections with the advertised entity. In the first experiment, we randomize the number of social cues present in word-of-mouth advertising, and measure how responses increase as a function of the number of cues. The second experiment examines the effect of augmenting traditional ad units with a minimal social cue (i.e., displaying a peer's affiliation below an ad in light grey text). On average, this cue causes significant increases in ad performance. Using a measurement of tie strength based on the total amount of communication between subjects and their peers, we show that these influence effects are greatest for strong ties. Our work has implications for ad optimization, user interface design, and central questions in social science research.

Subfields: Communication, Sociology and Political Science

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Linking biases impact the study of, e.g., social networks structure and evolution, social inﬂuence, and information diﬀusion phenomena (Wilson et al., 2009; Cha et al., 2010; Bakshy et al., 2012).”
Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment (2014, background): “and Bakshy et al. (2012a), who examined the moderating role of”
Taming the Unpredictability of Cultural Markets with Social Influence (2017, background): “Personalized social cues may have a greater impact than anonymized (aggregated) signals in the decisions of news reading [15] and advertising [4].”
Optimizing Ad Allocation in Social Advertising (2015, background): “Recent Article submitted to http://arxiv.org ; manuscript no. ﬁeld studies (Bakshy et al. 2012)(Tucker 2012) ﬁnd that social advertising is more eﬀective than conventional demographically targeted or untargeted ads.”
Identity and Opinion: A Randomized Experiment (2014, unknown): “The procedure is fast, parallelizable, and was previously used in Bakshy et al. (2012a) to eﬃciently analyze an experiment with a similar dependency structure between observations.”
Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds (2016, result): “Similar results have been shown in experiments on online social networks: a user is more likely to share a link on Facebook if it appears in her social feed [9] and to click on an ad if it is endorsed by her friends [6].”
Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks (2014, methodology): “Further, in addition to peer inﬂuence and homophily, other unobserved external correlations (Bakshy et al. 2012b), say a targeted marketing promotion to friends or ofﬂine interactions, can also generate similar observational data.”
Estimating peer effects in networks with peer encouragement designs (2016, background): “(18) randomize the number of personalized social cues in advertisements.”
Do Online Advertisements Increase Political Candidates’ Name Recognition or Favorability? Evidence from Randomized Field Experiments (2014, background): “…or affect ‘offline’ behaviors; most existing research focuses on immediate online purchases, click behavior, installation of software applications, and online charitable giving (Goldfarb and Tucker 2011; Bakshy et al. 2012; Lewis et al. 2012; Ryan 2012; Aral and Walker 2010; Lacetera et al. 2012).”
DeepInf: Social Influence Prediction with Deep Learning (2018, background): “With the global penetration of online and mobile social platforms, people have witnessed the impact of social influence in every field, such as presidential elections [7], advertising [3, 24], and innovation adoption [42].”