Social advertising

Social Advertising: How Advertising that Explicitly Promotes Social Influence Can Backfire Catherine Tucker∗ June 3, 2016 Abstract In social advertising, ads are targeted based on underlying social networks and highlight when a friend has ‘liked’ a product or organization This paper explores the effectiveness of social advertising using data from field tests of different ads on Facebook by a nonprofit We find evidence that social advertising is somewhat effective, but that social advertising is less effective if the advertiser explicitly states they are trying to promote social influence in the text of their ad Indeed, automated endorsements appear to backfire in general unless the advertiser refrains completely from promoting social influence in their ad content We exploit variation in the appearance of endorsements due to differences in privacy settings, and find that the effectiveness of social advertising is due to the ability of targeting based on social networks to uncover similarly responsive consumers, especially for consumers in non-traditional target markets Our results suggests that advertisers must avoid being overt in their attempts to use automated social endorsements in their advertising ∗ Catherine Tucker is the Sloan Distinguished Professor of Marketing at MIT Sloan School of Management, Cambridge, MA and Research Associate at the NBER Thank you to Google for financial support and to an anonymous nonprofit for their cooperation Thank you also to Jon Baker, Ann Kronrod, Preston Mcafee, and seminar participants at the George Mason University Roundtable on the Law and Economics of Internet Search, Carnegie Mellon, the University of Rochester, UCLA and Wharton for valuable comments All errors are my own Electronic copy available at: https://ssrn.com/abstract=1975897 Introduction Recent advances on the internet have allowed consumers to interact across digital social networks This is taking place at unprecedented levels: Facebook was the most visited website in the US in 2010, accounting for 20% of all time spent on the internet, a higher proportion than Google or Yahoo! (ComScore, 2011).1 However, it is striking that traditional paid marketing communications have been at the periphery of this explosion of social data despite the documented power of social influence on purchasing behavior (Algesheimer et al., 2005) To address this lack, Facebook2 and LinkedIn3 have introduced a new form of advertising called ‘social advertising.’ A Social Ad is an online ad that ‘incorporates user interactions that the consumer has agreed to display and be shared The resulting ad displays these interactions along with the user’s persona (picture and/or name) within the ad content’ (IAB, 2009) This represents a radical technological development for advertisers, because it means that potentially they can co-opt the power of an individual’s relationships online to target advertising and engage their audience This paper asks whether such automated ad units designed to promote social influence are effective, and what messaging advertisers should use around them We explore these questions using data from a field experiment conducted on Facebook by a nonprofit This field experiment compared the performance of social ads with conventionally targeted and untargeted ads, and examined how the performance varied with different message combinations The social ads were targeted to the friends of ‘fans’ of the nonprofit on Facebook The This has increased in 2016 to 50 minutes each day See http://www.nytimes.com/2016/05/06/ business/facebook-bends-the-rules-of-audience-engagement-to-its-advantage.html Many other social media platforms have embraced such ads See http://www.iab.com/wp-content/ uploads/2015/09/Social-Advertising-Best-Practices-0509.pdf for examples Linkedin appears to have started this practice in 2011 See - http://www.businessinsider.com/fyilinkedin-is-using-your-photo-and-your-actions-in-social-advertising-2011-7 Electronic copy available at: https://ssrn.com/abstract=1975897 ads featured that fan’s name and the fact that they had become a fan of this nonprofit.4 We find that on average these social ads were effective and that this technique is particularly useful for improving the performance of untargeted campaigns Through randomized field tests, we investigate the effectiveness of advertisers deliberately promoting social influence in their advertising copy through including a statement that encourages the viewer to, for example, ‘be like their friend.’ We find that consumers reject attempts by advertisers to explicitly harness or refer to a friend’s actions in their ad copy This result contrasts with previous empirical research that finds consistent benefits to firms from highlighting previous consumer actions to positively influence the consumers’ response (Algesheimer et al., 2010; Tucker and Zhang, 2011) This rejection is reasonably uniform across different wording, though slightly less severe for ads that make a less explicit reference to friendship We present evidence that this happens both when we consider actual subscriptions and when we control for variation in impressions We then use non-experimental variation due to differences in friends’ privacy settings to explore whether the inclusion of an endorsement or the use of an individual’s online social network explain our findings Comparing the performance of these ads that contained the name of the fan and were targeted towards the fan’s friends with those that were simply targeted to that fan’s friends suggests that their effectiveness stems from the ability of social targeting to uncover similarly responsive consumers Indeed, the fact that including endorsements appear to backfire on average in our data can be explained by a negative reaction of Facebook users when that automated endorsement is coupled with an advertiser explicitly trying to promote social influence We then present additional evidence to rule out two potential alternative explanations for our findings First, we rule out that the overt mention of social influence simply made While Facebook has now abandoned the ‘fans’ terminology, they still offer this ad unit in a slightly different form - see https://www.facebook.com/notes/facebook-and-privacy/an-update-to-facebookads/643198592396693/ Electronic copy available at: https://ssrn.com/abstract=1975897 people aware they were seeing an ad rather than something organic to the site We this by comparing an ad that states it is an ad with an ad that does not, and finding no difference Second, we investigate whether it was simply that messages referring to friendship were bad ad-copy, by examining how the ads perform for a group of Facebook users who have shown a visible propensity for social influence We identify such users by whether or not they have a stated attachment to a ‘Fashion Brand’ on their Facebook profile These users, in contrast to our earlier results, react more positively to the advertiser explicitly co-opting social influence than to a message that did not This suggests that it was not simply that the message was badly communicated, but instead that our results reflect distaste for explicit references to social influence accompanied by automated endorsements among most, though not all, of consumers we targeted This paper contributes to three main literatures The first literature studies how social networks affect adoption of new products and services and how such social networks can be used to target consumers.5 Provost et al (2009) show how to use browsing data to match groups of users who are socially similar Similarly, Oestreicher-Singer and Sundararajan (2012) show the importance of such connections in recommendations systems Aral and Walker (2012) discuss how to use social networks to target potential users, and Aral and Walker (2011) discuss viral marketing strategies on such networks Hill et al (2006) show that ‘Network neighbors’ - those consumers linked to a prior customer - adopt a service at a rate 35 times greater than baseline groups selected by the best practices of a firm’s marketing team Our paper builds on this result and shows that in particular in untargeted populations, social networks can be used to predict response to advertising as well as adoption We extend Hill et al (2006), by emphasizing that though targeting social networks with advertising to Zubcsek and Sarvary (2011) present a theoretical model that examines the effects of advertising on a social network, but assume that a firm cannot directly use the social network for marketing purposes Instead, firms have to rely on consumers to organically pass their advertising message within the social networks Electronic copy available at: https://ssrn.com/abstract=1975897 promote adoption may seem attractive, firms themselves should be wary of explicitly trying to push social influence in the wording of their marketing communications Bakshy et al (2012) is unusual in this literature, since it focuses on advertising and evaluates the effectiveness of increasing the number of social cues explicitly mentioning friends present in a Social Ad unit on Facebook They randomly vary whether a Social Ad displays endorsements from either none, one, two or three friends and find that click rates increase for one or two friends, but only marginally increase for three friends This is an important finding, given work by Centola (2010) that shows that adoption is more likely when participants receive social reinforcement from multiple neighbors in their social network Bakshy et al (2012) also find around a 5% increase in clicks from naming an individual relative to the total number of people who like a product Our paper provides two contributions that build on this work First, unlike Bakshy et al (2012) who study ad units that did not allow variation of messaging, we show that there can be a negative effect of ad units displaying social endorsements if they are accompanied by an ad campaign that explicitly refers to and tries to embroider upon them, unless the ad is targeted at people who have already embraced commercialization of their social networks Second, using non-experimental variation, we highlight that if an advertiser does not account for the underlying like-mindedness of those who are socially connected, they are likely to vastly overestimate the effectiveness of social endorsements appearing in ads - this is something that Bakshy et al (2012) control for but not measure The second literature this paper contributes to is a small literature that highlights the difficulty for advertisers of explicitly harnessing social influence in marketing communications Lambrecht et al (2014) shows that it is difficult to engage the kind of people who propagate trends on Twitter Tucker (2015) shows that advertising content that is naturally viral is also often less persuasive Bakshy et al (2011) uses Twitter user data to model the effects of potential influencers in spreading a message, and find that the size of an influ5 Electronic copy available at: https://ssrn.com/abstract=1975897 encer’s network does not provide clear guidance on whom to compensate The finding of this paper, that attempts to explicitly coopt social influence in advertising wording can backfire, provides further and concrete evidence that advertisers need to be careful in how they shape marketing communications designed to take advantage of online social networks Third, this paper contributes to a literature that evaluates the effectiveness of personalization in advertising content Some of this research highlights positive effects of personalization For example, Sahni et al (2016) show that even adding a name to email headers can increase effectiveness However, Lambrecht and Tucker (2013) suggests that too much specificity, unless it is timed right, can backfire, and Tucker (2014) suggests that similarly without appropriate privacy controls personalization in messaging can backfire By contrast, this paper studies a new and potentially sensitive form of personalized content, which is the automated inclusion of endorsements from an online social network In line with the general themes of this literature, our research suggests that such messaging can be effective but that the advertiser has to be careful to not directly refer to such endorsements in their messaging Managerially, our results have important implications Social advertising is effective However, when advertisers attempt to reinforce this social influence in ad copy, consumers are less likely to respond positively to the ad This is, to our knowledge, the first piece of empirical support for emerging managerial theories that emphasize the need for firms to not appear too obviously commercial when exploiting social media (Gossieaux and Moran, 2010) Electronic copy available at: https://ssrn.com/abstract=1975897 Field Experiment The field experiment was run by a small nonprofit that provides educational scholarships for girls to attend high school in East Africa Without the intervention of this nonprofit, and other nonprofits like them, many girls not attend secondary school because their families often prioritize the education of sons Though the nonprofit’s main mission is funding these educational scholarships, the nonprofit has a secondary mission which is to inform young people in the US about the state of education for African girls It was in aid of this secondary mission that the nonprofit set up a Facebook page This page serves as a repository of interviews with girls where they describe the challenges they have faced In general, their fans on Facebook not have direct experience with the African education system, ruling out some of the prosocial behavior studied in Small and Simonsohn (2008) To launch the field experiment, the nonprofit set up 18 simultaneous campaigns on Facebook.6 Table summarizes the 18 conditions that were run Since legal restrictions shaped the design of this field test in a manner which prevented a full factorial design, this section lays out in detail the different variations of ads tested, and how they were decided upon One focus of the experiment was that 15 of the ad campaigns employed the Facebook ad option which meant that they were targeted only to users who were friends of existing fans of the nonprofit Assuming their friend had not opted out of having their name displayed on Facebook, such ads also displayed a ‘social endorsement’ where the name of the friend who had liked the nonprofit was shown at the bottom of the ad as shown in Figure The Facebook ad platform does not allow advertisers to separately test the effects of a social This kind of horserace between campaigns was described by Facebook as being the best way for an advertiser to set up ‘A/B’ testing or randomized testing between different ad campaigns on their platform Such an approach is described in ‘A/B Testing your Facebook Ads: Getting better results through experimentation’ (Facebook, 2010) Since this paper was written, the general structure and set up of campaigns at Facebook has changed in a manner which makes experimentation easier - see https://adespresso.com/academy/blog/facebook-ads-split-testing-101/ for an example Electronic copy available at: https://ssrn.com/abstract=1975897 endorsement and social targeting.7 Instead, these are combined in a single Social Ad unit that the advertiser can compare against not using a Social Ad unit Later in this paper, we use non-experimental variation to tease apart the difference, but this is obviously less clean than being able to directly randomize the two Therefore, the experimental variation we study in the paper is the difference between the Social Ad unit and the non-Social Ad unit Figure displays as an example of a Social Ad unit an anonymized sample ad for Campaign The blacked-out top of the ad contained the nonprofit’s name The grayed-out bottom of the ad contained a supporter’s name, who had ‘liked’ the nonprofit and was a Facebook friend of the person who was being advertised to It is only with developments in technology and the development of automated algorithms that such individualized display of the friend’s name when pertinent is possible At the time the experiment was run, these ads appeared on the right hand of users’ Facebook pages At the time, Facebook did not share data with its advertisers on whether these ads were seen on a ticker or on other pages that a user may be browsing As can be seen in Figure 1, each different ad was accompanied by the same picture of an appealing secondary-school student who had benefited from their program Based on the work of Small and Verrochi (2009), this girl had a unsmiling expression The nonprofit also explored different ad-text conditions The different ad texts were broadly designed to cover the kinds of normative and informational social influence described by Deutsch and Gerard (1955); Burnkrant and Cousineau (1975).8 These differences in ad text are intended to represent a traditional taxonomy of social One advantage enjoyed by the researchers in Bakshy et al (2012) is that because they worked inside Facebook, they were able to perform randomizations that are not available to advertisers which allowed them to test different versions of the Social Ad unit In this paper, we focus instead on measuring randomized variation which is accessible to outside advertisers, such as message content and demographic targeting, and understanding its interaction with the Social Ad unit Other forms of social influence studied in the literature involve network externalities where there is a performance benefit to multiple people adopting (Tucker, 2008), but it is unlikely that there is a performance benefit here Electronic copy available at: https://ssrn.com/abstract=1975897 influence rather than exploring more subtle categories developed by state-of-the-art social influence research Ultimately, any message that refers to any form of social influence in the uncontrolled context of an ad unit can potentially evoke a multitude of other forms of social influence, and consequently cannot distinguish cleanly between different types of social influence We not claim that the variation in message conditions captures the frontier of our understanding of social influence The recent literature on social influence emphasizes that the underlying mechanism is nuanced and more complex than traditional taxonomies might suggest For example, Cialdini and Goldstein (2004) suggest that social influence may be driven at the subconscious rather than the conscious level However, such advances are even harder to capture in such a constrained setting where there is no individual data, data on the user’s state of mind, or an ability to manipulate the external environment in which a user sees the ad Similarly, as outlined by Kallgren et al (2000), newer work emphasizes that social influence is mediated by focus, and given the lack of attention most users give to ads, again this is difficult to manipulate in such an uncontrolled setting However, the variation in messages does allow us to study whether explicit advertising messages that attempt to use different types of wording to evoke social influence are effective in general, and the extent to which advertisers should try and follow various implications of these more traditional taxonomies in their advertising wording As shown in Table 1, the Social Ad units displayed all five variants of potential messages For each of the non-Social Ad campaigns, we ran the baseline variant of the ad text, which simply says ‘Help girls in East Africa change their lives through education.’ The nonprofit could not run the other four message conditions that refer to others’ actions, because federal regulations require ads to be truthful and they did not want to mislead potential supporters There was also a demographic layer to the experiment The ad campaigns were targeted to three different groups The first group was a broad untargeted campaign for all Facebook users aged 18 and older in the US who were not already affiliated with the nonprofit The Electronic copy available at: https://ssrn.com/abstract=1975897 second group were people who had already expressed interest in other charities These people were identified using Facebook’s ‘broad category targeting’ of ‘Charity + Causes.’ The third group were people who had already expressed an interest in ‘Education + Teaching.’ These latter two categories were chosen by the nonprofit as they wanted to have a benchmark to compare the performance of social advertising that was more nuanced and likely to be employed than a simple untargeted campaign Previously, the nonprofit had tried such reasonably broad targeting with little success and was hopeful that social advertising would improve the ads’ performance (Tucker, 2014) In all cases, the nonprofit explicitly excluded current fans from seeing its ads Table A3 in the appendix describes the demographics of the roughly 1,500 fans at the beginning of the campaign Though the initial fans were reasonably spread out across different age cohorts, they were more female than the average population, which makes sense given the nature of the nonprofit At the end of the experiment, the fans were slightly more likely to be male than before The way that Facebook deploys its ‘A/B’ does not exclusively allocate users to a condition This means that a Facebook user could see a socially enabled ad one day and a non-socially enabled ad the next day The randomization in the ordering of which ads are displayed means that the average effect of exposure to the different ad-types should hold However, the lack of individual-level data provided by Facebook to external researchers means that we cannot study whether there are explicit complementarities or substitution effects between the two different types of advertising One issue, of course, with an uncontrolled setting such as Facebook, is what would have happened in the absence of advertising Data from the nonprofit that covers the months prior to the campaign suggest that it attracted roughly five new subscribers to its Facebook Newsfeed each month, or on average one new supporter a week Analysis of aggregate data suggested that this pattern continued during the five weeks that the experiment ran There 10 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 1: Summary of 18 campaigns Campaign Social Endorsement Social Targeting Demo Targeting? Social Endorsement Limited to Friends of Fans Untargeted Social Endorsement Social Endorsement Social Endorsement Social Endorsement Social Endorsement Social Endorsement Social Endorsement Social Endorsement 10 Social Endorsement 11 Social Endorsement 12 Social Endorsement 13 Social Endorsement 14 Social Endorsement 15 Social Endorsement 16 No Endorsement 17 No Endorsement 18 No Endorsement All campaigns are limited to people Ad Content Help girls in East Africa change their lives through education Limited to Friends of Fans Untargeted Be like your friend Help girls in East Africa change their lives through education Limited to Friends of Fans Untargeted Don’t be left out Help girls in East Africa change their lives through education Limited to Friends of Fans Untargeted Your friend knows this is a good cause Help girls in East Africa change their lives through education Limited to Friends of Fans Untargeted Learn from your friend Help girls in East Africa change their lives through education Limited to Friends of Fans Charity Supporters Help girls in East Africa change their lives through education Limited to Friends of Fans Charity Supporters Be like your friend Help girls in East Africa change their lives through education Limited to Friends of Fans Charity Supporters Don’t be left out Help girls in East Africa change their lives through education Limited to Friends of Fans Charity Supporters Your friend knows this is a good cause Help girls in East Africa change their lives through education Limited to Friends of Fans Charity Supporters Learn from your friend Help girls in East Africa change their lives through education Limited to Friends of Fans Education Supporters Help girls in East Africa change their lives through education Limited to Friends of Fans Education Supporters Be like your friend Help girls in East Africa change their lives through education Limited to Friends of Fans Education Supporters Don’t be left out Help girls in East Africa change their lives through education Limited to Friends of Fans Education Supporters Your friend knows this is a good cause Help girls in East Africa change their lives through education Limited to Friends of Fans Education Supporters Learn from your friend Help girls in East Africa change their lives through education Not Limited to Friends of Fans Charity Supporters Help girls in East Africa change their lives through education Not Limited to Friends of Fans Education Supporters Help girls in East Africa change their lives through education Not Limited to Friends of Fans Untargeted Help girls in East Africa change their lives through education who use Facebook in US over the age of 18 who are not already fans of the nonprofit 34 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 2: Overall campaign performance summary Aggregate totals across over all campaigns over the course of the experiment Total Total Impressions 8703877 Total Reach 3884379 Total Clicks 3185 Total Unique Clicks 3176 Total Subscriptions 1700 35 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 3: Summary statistics at daily level for each campaign Mean Std Dev Min Max Average Impressions 13815.7 13898.6 98037 Average Clicks 5.06 5.17 37 Connections 2.70 3.52 24 Unique Clicks 5.04 5.14 36 Daily Click Rate 0.11 0.10 1.27 Impression Click Rate 0.045 0.047 0.50 Cost Per Click (USD) 0.98 0.40 0.31 3.90 Cost Per 1000 views (USD) 0.52 1.37 24.5 Ad-Reach 6165.7 6185.0 60981 Frequency 2.32 0.82 9.70 18 ad variants at the daily level for weeks (630 observations) 36 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 4: Summary statistics by campaign at the daily level Campaign 10 11 12 13 14 16 17 18 Average Daily Impressions Average Daily Clicks 10846 15091 11988 15249 21491 13739 21003 20684 11157 12288 10198 10626 5690 12726 13016 15939 5.2 6.1 5.1 4.6 10.6 4.0 8.9 4.6 2.3 3.6 6.3 5.1 2.4 3.5 3.7 3.8 37 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 5: Aggregate logit: Social advertising is less effective if an advertiser is too explicit about their intention to promote social influence (1) Unique Clicks (2) Unique Clicks (3) Unique Clicks (4) Unique Clicks 0.622∗∗∗ (0.0563) 0.663∗∗∗ (0.0664) 0.815∗∗∗ (0.0730) -0.231∗∗∗ (0.0327) 0.811∗∗∗ (0.0742) SocialTargetingAndEndorsement SocialTargetingAndEndorsement × Explicit SocialTargetingAndEndorsement × Don’t be left out SocialTargetingAndEndorsement × Be like your friend SocialTargetingAndEndorsement × Learn from your friend SocialTargetingAndEndorsement × Your friend knows Untargeted SocialTargetingAndEndorsement × Untargeted Date Controls Observations Log-Likelihood Yes 3884379 -25557.2 -0.496∗∗∗ (0.0996) 0.227∗ (0.104) Yes 3884379 -25537.9 -0.495∗∗∗ (0.0994) 0.234∗ (0.105) Yes 3884379 -25523.6 -0.113∗ (0.0451) -0.157∗∗∗ (0.0415) -0.350∗∗∗ (0.0573) -0.326∗∗∗ (0.0564) -0.498∗∗∗ (0.0993) 0.239∗ (0.106) Yes 3884379 -25515.7 Aggregate Logit Estimates: Dependent variable is whether someone who was exposed to an ad clicked Robust standard errors * p < 0.05, ** p < 0.01, *** p < 0.001 38 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 6: Aggregate Logit: Social targeting rather than endorsement is key driver of ad effectiveness SocialTargeting SocialTargeting × Endorsement (1) Clicks (2) Clicks (3) Clicks (4) Clicks 0.777∗∗∗ (0.0677) -0.193∗∗∗ (0.0555) 0.713∗∗∗ (0.121) -0.0647 (0.0860) 0.770∗∗∗ (0.111) 0.0591 (0.0592) -0.0513 (0.0948) -0.226∗ (0.0920) 0.769∗∗∗ (0.112) 0.0561 (0.0594) SocialTargeting × Explicit SocialTargeting × Endorsement × Explicit SocialTargeting × Don’t be left out SocialTargeting × Endorsement × Don’t be left out SocialTargeting × Be like your friend SocialTargeting × Endorsement × Be like your friend SocialTargeting × Learn from your friend SocialTargeting × Endorsement × Learn from your friend SocialTargeting × Your friend knows SocialTargeting × Endorsement × Your friend knows Untargeted SocialTargeting × Untargeted SocialTargeting × Endorsement × Untargeted Date Controls Observations Log-Likelihood Yes 3884379 -25614.6 -0.491∗∗∗ (0.0906) 0.489∗∗∗ (0.126) -0.318∗ (0.138) Yes 3884379 -25591.0 -0.488∗∗∗ (0.0983) 0.478∗∗∗ (0.135) -0.299∗∗ (0.110) Yes 3884379 -25572.9 0.0588 (0.122) -0.221∗ (0.104) -0.0699 (0.105) -0.107 (0.114) -0.206 (0.117) -0.179 (0.155) -0.0194 (0.154) -0.394∗∗ (0.149) -0.492∗∗∗ (0.0982) 0.481∗∗∗ (0.134) -0.296∗∗ (0.109) Yes 3884379 -25563.6 Aggregate Logit Estimates: Dependent variable is whether someone who was exposed to an ad clicked Clicks are used rather than unique clicks as Facebook does not report social breakdown of unique clicks Robust standard errors * p < 0.05, ** p < 0.01, *** p < 0.001 39 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 7: The results are similar using subscriptions as outcome variable SocialTargetingAndEndorsement SocialTargetingAndEndorsement × Untargeted (1) Connections (2) Connections (3) Connections 0.367∗∗∗ (0.0735) 0.798∗∗∗ (0.192) 0.604∗∗∗ (0.0813) 0.810∗∗∗ (0.192) -0.374∗∗∗ (0.0595) 0.600∗∗∗ (0.0815) 0.818∗∗∗ (0.192) SocialTargetingAndEndorsement × Explicit SocialTargetingAndEndorsement × Don’t be left out SocialTargetingAndEndorsement × Be like your friend SocialTargetingAndEndorsement × Learn from your friend SocialTargetingAndEndorsement × Your friend knows -1.376∗∗∗ (0.183) Yes 3884379 -14668.0 Untargeted Date Controls Observations Log-Likelihood -1.374∗∗∗ (0.183) Yes 3884379 -14648.8 -0.187∗ (0.0825) -0.366∗∗∗ (0.0879) -0.527∗∗∗ (0.0928) -0.448∗∗∗ (0.0934) -1.376∗∗∗ (0.183) Yes 3884379 -14642.9 Aggregate Logit Estimates: Dependent variable is whether someone who was exposed to an ad subscribed to the Newsfeed of the nonprofit Robust standard errors * p < 0.05, ** p < 0.01, *** p < 0.001 40 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 8: The results are similar using multiple impressions rather than reach SocialTargetingAndEndorsement SocialTargetingAndEndorsement × Untargeted (1) Clicks (2) Clicks (3) Clicks 0.225∗∗∗ (0.0599) 0.379∗∗ (0.119) 0.448∗∗∗ (0.0654) 0.392∗∗∗ (0.119) -0.340∗∗∗ (0.0426) 0.443∗∗∗ (0.0656) 0.401∗∗∗ (0.119) SocialTargetingAndEndorsement × Explicit SocialTargetingAndEndorsement × Don’t be left out SocialTargetingAndEndorsement × Be like your friend SocialTargetingAndEndorsement × Learn from your friend SocialTargetingAndEndorsement × Your friend knows -0.302∗∗ (0.112) Yes 8703877 -28275.3 Untargeted Date Controls Observations Log-Likelihood -0.300∗∗ (0.112) Yes 8703877 -28244.6 -0.176∗∗ (0.0590) -0.255∗∗∗ (0.0599) -0.487∗∗∗ (0.0641) -0.468∗∗∗ (0.0659) -0.303∗∗ (0.112) Yes 8703877 -28231.2 Aggregate Logit Estimates: Dependent variable is whether someone who was exposed to an impression of an ad clicked in Columns (4)-(6) Robust standard errors * p < 0.05, ** p < 0.01, *** p < 0.001 41 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 9: Results are mirrored in cost effectiveness SocialTargetingAndEndorsement SocialTargetingAndEndorsement × Untargeted (1) Cost Per Click (USD) -0.0258∗∗∗ (0.000216) -0.222∗∗∗ (0.000549) SocialTargetingAndEndorsement × Explicit (2) Cost Per Click (USD) -0.0833∗∗ (0.0277) -0.226∗ (0.0890) 0.0838∗∗ (0.0267) SocialTargetingAndEndorsement × Don’t be left out SocialTargetingAndEndorsement × Be like your friend SocialTargetingAndEndorsement × Learn from your friend SocialTargetingAndEndorsement × Your friend knows Untargeted Date Controls Observations Log-Likelihood -0.0199∗∗∗ (0.000553) Yes 8535930 1408767.3 -0.0202 (0.0860) Yes 8535930 1516379.3 (3) Cost Per Click (USD) -0.0781∗∗ (0.0270) -0.234∗ (0.0888) 0.0182 (0.0173) 0.0463∗ (0.0229) 0.0805∗∗∗ (0.0104) 0.198∗∗ (0.0578) -0.0179 (0.0862) Yes 8535930 1746508.7 Ordinary Least Squared estimates: Dependent variable is Cost Per Click Robust standard errors * p < 0.05, ** p < 0.01, *** p < 0.001 42 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 10: The results are not driven by lack of awareness of advertising SocialTargetingAndEndorsement (1) Unique Clicks (2) Unique Clicks 0.618∗∗ (0.216) 0.568∗∗ (0.218) 0.425 (0.390) Yes 209007 -976.7 SocialTargetingAndEndorsement × Please Read this Ad Date Controls Observations Log-Likelihood Yes 209007 -977.3 Aggregate Logit Estimates: Dependent variable is whether someone who was exposed to an ad clicked Robust standard errors * p < 0.05, ** p < 0.01, *** p < 0.001 In Column (1) Explicit simply refers to the inclusion of the message ‘Please read this ad’ rather than an explicit measure of social influence 43 Electronic copy available at: https://ssrn.com/abstract=1975897 Table 11: The results are not driven by universally unappealing ad copy SocialTargetingAndEndorsement (1) Unique Clicks (2) Unique Clicks (3) Unique Clicks 0.768∗∗∗ (0.0616) 0.669∗∗∗ (0.0572) 0.120 (0.0734) 0.660∗∗∗ (0.199) SocialTargetingAndEndorsement × Explicit SocialTargetingAndEndorsement × Don’t be left out SocialTargetingAndEndorsement × Be like your friend SocialTargetingAndEndorsement × Learn from your friend SocialTargetingAndEndorsement × Your friend knows Date Controls Observations Log-Likelihood Yes 499531 -2589.9 Yes 499531 -2589.7 0.210 (0.305) -0.0275 (0.321) 0.0294 (0.293) 0.276 (0.284) Yes 499531 -2588.5 Aggregate Logit Estimates: Dependent variable is whether someone who was exposed to an ad clicked Robust standard errors * p < 0.05, ** p < 0.01, *** p < 0.001 In Column (1) Explicit simply refers to the inclusion of the message ‘Please read this ad’ rather than an explicit measure of social influence 44 Electronic copy available at: https://ssrn.com/abstract=1975897 Figure A1: Control interface for switching off endorsement A-1 Electronic copy available at: https://ssrn.com/abstract=1975897 Table A1: Checking whether different levels of awareness about ‘Donations’ or ‘African education’ can explain our results (1) Unique Clicks (2) Unique Clicks (3) Unique Clicks 0.850∗∗ (0.272) -0.00310 (0.00343) 0.526∗ (0.262) 0.622∗∗∗ (0.0563) SocialTargetingAndEndorsement SocialTargetingAndEndorsement SocialTargetingAndEndorsement × African Ed Searches SocialTargetingAndEndorsement × Donation Searches Date Controls Observations Log-Likelihood Yes 3884379 -25557.2 Yes 3884379 -25556.4 0.00157 (0.00414) Yes 3884379 -25557.1 Aggregate Logit Estimates: Dependent variable is whether someone who was exposed to an ad clicked Robust standard errors * p < 0.05, ** p < 0.01, *** p < 0.001 A-2 Electronic copy available at: https://ssrn.com/abstract=1975897 Table A2: Using controls for the moderating effects of timing and impressions to explore the effect of the Facebook algorithm SocialTargetingAndEndorsement In Paper (1) Unique Clicks Date Time Trend (2) Unique Clicks First Day Interaction (3) Unique Clicks 0.699∗∗∗ (0.0466) 0.595∗∗∗ (0.0704) 0.0494 (0.0268) 0.735∗∗∗ (0.191) 0.0510 (0.0270) -0.0384 (0.0502) SocialTargetingAndEndorsement × Impressions (0000) SocialTargetingAndEndorsement × Week Time Trend (4) Unique Clicks SocialTargetingAndEndorsement × First Day -0.0698∗∗ (0.0251) Average Impressions (0000) Time Trend -0.0728∗∗ (0.0253) 0.0489 (0.0469) First Day Date Controls Observations Log-Likelihood Yes 3884379 -25623.4 Yes 3884379 -25616.7 Yes 3884379 -25616.0 Aggregate Logit Estimates: Dependent variable is whether someone who was exposed to an ad clicked Robust standard errors * p < 0.05, ** p < 0.01, *** p < 0.001 A-3 Electronic copy available at: https://ssrn.com/abstract=1975897 0.823∗∗∗ (0.189) -0.0372 (0.0498) 0.402 (0.404) 0.0378 (0.0467) -0.459 (0.281) Yes 3884379 -25621.6 Table A3: Demographics of the nonprofit’s fans before and after the field experiment Age 18-24 25-34 35-44 45-54 55+ Total Before Male 5 3 22 Experiment After Female Male 13 14 17 13 10 67 27 Experiment Female 14 14 16 13 10 67 The way that Facebook reports data means that we have access to the demographics only of the fans of the nonprofit, and cannot separate out in the final two columns the distinction between the 1700 users who became fans due to the experiment, the who joined for reasons other than the experiment, and the original 1500 fans All figures are reported as percentages The ‘Total’ row does not add up to 100% because fans who are below 18 years of age are omitted A-4 Electronic copy available at: https://ssrn.com/abstract=1975897 ... Social Endorsement Limited to Friends of Fans Untargeted Social Endorsement Social Endorsement Social Endorsement Social Endorsement Social Endorsement Social Endorsement Social Endorsement Social. .. Social Endorsement Social Endorsement 10 Social Endorsement 11 Social Endorsement 12 Social Endorsement 13 Social Endorsement 14 Social Endorsement 15 Social Endorsement 16 No Endorsement 17 No... results suggest that social advertising works particularly well for untargeted populations, which may mean that social advertising is a particularly useful technique when advertising to consumers