Social Influence in Social Advertising: Evidence from Field Experiments
arXiv:1206.4327 · doi:10.1145/2229012.2229027
Abstract
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.
arXiv comment16 pages, 8 figures, ACM EC 2012
References in corpus (4)
Cited by in corpus (11)
- DeepInf: Social Influence Prediction with Deep Learning
- Robust Preference-Guided Denoising for Graph based Social Recommendation
- Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising
- Uncertainty in Online Experiments with Dependent Data: An Evaluation of Bootstrap Methods
- Bias and high-dimensional adjustment in observational studies of peer effects
- Shrinkage Estimators in Online Experiments
- Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests
- Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds
- Field evidence of social influence in the expression of political preferences: the case of secessionist flags in Barcelona
- Flow-based Influence Graph Visual Summarization
- Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach