Social Influence in Social Advertising: Evidence from Field Experiments

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.

💡 Research Summary

The paper investigates how social cues embedded in social advertising influence consumer behavior, focusing on two mechanisms: (1) peer affiliations serve as proxies for unobserved consumer traits that are correlated across the network, and (2) the presence of social information triggers direct social influence (social proof, conformity). To isolate these effects, the authors conduct two massive field experiments on the Facebook platform.

In Experiment 1, the authors manipulate the number of peer cues displayed alongside a “word‑of‑mouth” ad. Users see 0, 1, 2, or 3 friends who have liked the advertised page, with the assignment randomized at the impression level. Over two million impressions are recorded, and outcomes are click‑through rate (CTR) and the formation of a new connection with the advertised entity (e.g., friend request, follow). Hierarchical logistic regressions reveal a monotonic increase in both metrics as the number of cues rises: each additional cue lifts CTR by roughly 12 percentage points and connection formation by about 8 points. Notably, the marginal effect accelerates after two cues, indicating a “signal accumulation” phenomenon consistent with social proof theory.

Experiment 2 tests a minimalist intervention: a traditional banner ad is augmented with a faint gray line stating, “Friend X likes this brand.” No other visual changes are made, allowing the authors to attribute any performance shift solely to the social cue. This treatment yields an average CTR lift of 5 pp and a 3 pp increase in connection formation, effects that hold across diverse ad categories (fashion, electronics, travel, etc.).

A key contribution is the measurement of tie strength. The authors compute a “communication intensity” metric based on the total number of messages exchanged between a user and each peer. The top 20 % of dyads are classified as strong ties, the remaining 80 % as weak ties. Strong‑tie cues generate substantially larger lifts: CTR improves by a factor of 1.8 and connection formation by roughly twofold compared with weak‑tie cues. This finding aligns with social‑psychological literature that emphasizes trust and identity alignment in strong relationships.

Methodologically, the experiments benefit from random assignment and massive sample sizes, which mitigate confounding variables and enable precise estimation of causal effects. The authors employ Bayesian hierarchical models to capture heterogeneity across users and ad types, and they conduct robustness checks (e.g., placebo tests, alternative tie‑strength definitions) that confirm the stability of the results.

The discussion translates these insights into practical recommendations. First, ad platforms should prioritize displaying cues from strong ties and consider showing multiple peer endorsements when possible, as this maximizes both perceived credibility and social influence. Second, even a subtle, low‑cost cue can meaningfully boost performance, suggesting that existing ad inventories can be retrofitted without major redesign. Third, the dual role of social data—as a targeting proxy and as an influence lever—offers a unified framework for marketers and social scientists alike.

Limitations are acknowledged: the experiments are confined to Facebook, and cultural or platform‑specific dynamics may affect generalizability. Future work could explore other networks (e.g., Twitter, TikTok), examine long‑term outcomes (purchase, brand loyalty), and test richer cue designs (e.g., visual avatars, multi‑modal endorsements).

Overall, the study provides rigorous field evidence that social cues—especially those originating from strong ties and presented in greater numbers—significantly enhance ad effectiveness, informing both advertising optimization and broader theories of social influence.