Television Advertising and Online Word-of-Mouth: An Empirical Investigation of. Social TV Activity. Beth L. Fossen 1. David A. Schweidel 2.

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1 1 Television Advertising and Online Word-of-Mouth: An Empirical Investigation of Social TV Activity Beth L. Fossen 1 David A. Schweidel 2 April Doctoral Candidate in Marketing, Goizueta Business School, Emory University. Address: 1300 Clifton Road Northeast, Atlanta, Georgia 30322, bfosssen@emory.edu 2 Associate Professor of Marketing, Goizueta Business School, Emory University. Address: 1300 Clifton Road Northeast, Atlanta, Georgia 30322, dschweidel@emory.edu The authors thank Kantar Media and the Goizueta Business School research support for providing the funds to acquire and build the dataset in this research.

2 2 Television Advertising and Online Word-of-Mouth: An Empirical Investigation of Social TV Activity Abstract In this research, we investigate how television advertising drives online word-of-mouth (WOM). We first explore if television advertising (1) affects online WOM about the brand advertised and (2) associates with changes in online WOM about the program in which the advertisement airs. We further examine if the media context in which the advertisement appears the television program impacts the relationship between television advertising and online WOM. By investigating the integration of consumer social media participation with television programming, known as social TV, we aim to improve the field s understanding of the consumer experience with television, advertising, and social media. Using data containing television advertising instances and the volume of minute-by-minute social media mentions, our analyses reveal that television advertising impacts online WOM for both the brand advertised and the program in which the advertisement airs. We additionally find that the programs that receive the most online WOM aren t necessarily the best programs for advertisers in terms of online engagement. These results suggest the need for social TV activity to be viewed in terms of viewer engagement with both programs and advertisements. Moreover, the results indicate that the program in which the advertisement airs affects the extent of online WOM for both the brand and program following television advertising. Overall, this research sheds light on how marketers, television networks, and program creators can (1) increase online WOM for their respective brands and programs through media planning and advertisement design strategies and (2) incorporate online WOM into the media planning and buying process. Keywords: online word-of-mouth, advertising, television, social media, social TV

3 3 1. Introduction Does television advertising drive online word-of-mouth (WOM)? Is an advertisement s ability to generate online WOM determined by the brand advertised, or does the context in which the advertisement airs the television program also play a role? Furthermore, does television advertising have the ability to affect the volume of online conversations about the brand advertised and the program in which the advertisement airs? Research in the marketing literature has established that online WOM matters and can increase new customer acquisition (e.g., Trusov et al. 2009), television ratings (e.g., Godes and Mayzlin 2004), and sales (e.g., Chevalier and Mayzlin 2006; Moe and Trusov 2011; Kumar et al. 2013; Rishika et al. 2013). While the positive consequences of online WOM have been identified, research into the drivers of online WOM is still in its infancy, and the extant literature has yet to explore these questions. As the rise of multi-screen media consumption has outpaced the field s understanding of the activity (e.g., Hare 2012; Poggi 2012; Copeland 2013), the relationship between television advertising and online WOM is an increasingly important topic to investigate in order to assess the extent to which marketers can leverage such multi-screen behavior. In this research, we address these questions and investigate how television advertising contributes to online conversations, bridging the gap in extant literature on online WOM and television viewing. Specifically, we explore how primetime television advertising contributes to online WOM about (1) the brand featured in the advertisement and (2) the television program in which the advertisement airs. We further examine how specific brand, advertisement, and program characteristics impact online brand and program chatter and assess the potential value of social TV activity, defined as social media interaction with television programming (Hill et al. 2012), to marketers.

4 4 Nielsen (2014) estimates 84% of tablet and smartphone users engage in multi-screen behavior while watching television. Joint work from Twitter, FOX, and the Advertising Research Foundation finds that 85% of Twitter users active during primetime programming contribute to online conversations about television and 90% of users exposed to this chatter have taken TVrelated action such as switching channels to watch a program or searching online for additional program information (Midha 2014). Overall, the global media industry s interest in social TV is substantial as social-media related television businesses comprised a $151 billion industry in 2012; this number is estimated to grow to $256 billion in 2017 (Lomas 2012). Despite this rapid growth in social TV activity, advertisers and networks are facing challenges trying to grasp the value of this behavior (e.g., Hare 2012; Poggi 2012; Copeland 2013). With this study, we contribute to research on online WOM and television advertising by advancing understanding of social TV behavior and exploring the value of this activity for both advertisers and networks. We construct a data set that includes television advertising instances on network broadcasts, minute-by-minute social media data of Twitter conversations mentioning brands and programs, and data on brand, advertisement, and program characteristics. We supplement this data by coding the use of calls-to-action in each advertisement (e.g., does an ad feature a hashtag?) to assess their impact on online WOM. Our data include over 9,000 ad instances for 264 brands across 15 categories that aired on 84 primetime network programs during the fall 2013 television season 1. We jointly model the immediate change in online mentions for both the brand and the program following an advertisement s airing. To assess if the context in which a television ad airs influences its impact on online WOM, we incorporate a measure of brand-program fit based on advertisers choices of which programs to air their ads during (Schweidel et al. 2014), which 1 Additional details of our data are presented in Tables A1 and A2 in the Online Appendix.

5 5 we refer to as brand-program synergy. We use this model framework to evaluate the impact of brand and advertisement characteristics (e.g., calls-to-action) and program characteristics (e.g., genre, network) on online WOM. In doing so, our analysis sheds light on how advertisers and television networks can encourage online WOM for their respective brands and programs. We find evidence of increases in online mentions for both brands and programs following advertisements, illustrating that television advertising has the potential to increase online WOM about the advertised brand and the program in which the ad airs. This result reveals the potential benefit of social TV for advertisers and also challenges the industry perspective that television viewers are less likely to engage in online program chatter during commercials (Nielsen 2013). We also find that the increase in online WOM from television advertising depends on brandprogram synergy, with online WOM increasing more for both brands and programs when there is high synergy. This suggests that the context in which an advertisement airs influences online WOM and could have implications for advertiser-network negotiations as it provides an incentive for both parties to consider brand-program synergies in the media buying process. Interestingly, we also find that brands that advertise in programs that experience higher than expected online program chatter following television advertisements don t necessarily experience increases in online WOM for the brand. This suggests that the programs that receive the most online WOM aren t necessarily the best programs for advertisers seeking to generate online chatter. Our results also shed light on the factors that influence social TV activity as we discover that numerous brand, advertisement, and program characteristics impact online brand and program WOM following television advertising. Of note, we find that advertisements featuring digital calls-to-action can increase immediate online WOM for the advertised brand. This effect, however, only occurs if the advertisement is shown in the first ad slot in a

6 6 commercial break. These results have implications for advertisement design strategies and the importance that ad position should play in media buy negotiations for advertisers interested in online WOM. The remainder of this research is organized as follows. In the next section, we briefly review related literature on online WOM as well as research on television consumption and advertising. We then describe the data, present model-free evidence, and discuss the modeling approach for jointly assessing the immediate impact of television advertising on online WOM for brands and programs. We present our results and conclude with a discussion of implications of our research, as well as opportunities for future research, in the contexts of online WOM, social TV, and the media planning process. 2. Background Literature 2.1 Online WOM Why would marketers, television networks, and program creators want to encourage online WOM? Recent research on online WOM illustrates that online conversations matter. For example, consumer participation in social media has been shown to increase sales and create a positive return on investment (Kumar et al. 2013) and increase shopping frequency and profitability (Rishika et al. 2013). Additional studies have shown that other forms of online WOM, such as online reviews and ratings (Chevalier and Mayzlin 2006; Moe and Trusov 2011) and social earned media from online communities and blogs (Stephen and Galak 2012), can also impact sales. Online WOM has additionally been linked to increased online customer acquisition (Trusov et al. 2009). A recent Nielsen survey with participants from 56 countries supports these findings, discovering that 46% of survey respondents used social media to inform purchase

7 7 decisions (Nielsen 2012). Further research on online WOM also illustrates the value of online chatter to television networks and program creators. Godes and Mayzlin (2004) show that online WOM activity relates to television program ratings. A recent investigation by Gong et al. (2014) also finds that Tweets from a program s microblog account and influential re-tweets can increase program ratings. Industry reports from Kantar Media and Nielsen complement this research by illustrating that Twitter activity during television broadcasts correlates with higher program ratings (Subramanyam 2011; Kantar Media 2014). Online WOM also can serve as a proxy for brand engagement. Hoffman and Fodor (2010) argue that marketers can use customer social media interactions to measure engagement. Recent research has operationalized various forms of online WOM as measures of engagement, such as comments and replies on social networking websites like Facebook and Twitter (Kumar et al. 2013; Rishika et al. 2013). This suggests that online conversations can serve as an indicator that an advertisement (or program content) is engaging. While the above literature highlights the importance of online WOM for marketers, research on how the consumer generation of online WOM can be encouraged is still in its early stages. Work in this area by Berger and Schwartz (2011) finds that more WOM is generated for products that are publicly visible or cued by the surrounding environment. Research has also begun to explore the drivers of online WOM including how content affects individuals decisions to share news articles online (Berger and Milkman 2012). Additional investigations have examined the dynamics in consumer decisions to contribute to online content (e.g., Godes and Silva 2012; Moe and Schweidel 2012). This research, however, has not considered social TV behavior. Thus, we have limited insight into how marketers, television networks, and program creators can increase online WOM for their respective brands and programs, and our

8 8 understanding of the consumer experience with television, advertising, and social media remains incomplete. 2.2 Television Consumption and Advertising In addition to work on online WOM, our research also relates to investigations of television consumption and advertising. Television viewing is still the most prominent form of media consumption in the U.S. with Americans watching on average five hours of television a day (Katz 2013). Marketers spent over $64 billion on television advertising in the U.S. in 2012 with a 30 second advertisement on primetime network broadcasts ranging in costs from $24,000 to $545,000 with an average cost of around $121,000 (emarketer 2013; Katz 2013). Why might television advertising impact online WOM both for the brand advertised and for the program in which the advertisement airs? Recent research on cross-media effects has presented initial evidence that television advertising can influence online behavior as it has been found to impact branded keyword search (Joo et al. 2014), shopping behavior in terms of website traffic and online sales (Liaukonyte et al. 2015), and blogging activity for movies and wireless phone services (Onishi and Manchanda 2012). Additionally, Gopinath et al. (2014) explore the relationship between total advertising instances across all channels, online WOM, and brand performance. They find initial evidence that advertising in one month can impact online WOM in the next month. This research, however, does not consider the integration of consumer social media participation with television programming. Online WOM also may be influenced by television advertising in a more direct manner if an advertisement includes a call-to-action. For example, an advertisement featuring a hashtag, a keyword used to contribute to an online conversation about a topic, may increase online brand WOM by informing viewers that an online dialog exists. Alternatively, featuring an offline call-

9 9 to-action such as a toll-free number may have the reverse effect, decreasing online brand WOM by encouraging an offline action. This view is consistent with research on direct response advertising that has shown that toll-free numbers in television advertisements can significantly increase the number of incoming calls, an offline response (e.g., Tellis et al. 2000). In addition to online WOM for brands, calls-to-action in television advertisements also may impact online WOM for programs, as consumers that respond to an advertisement s call-to-action may be less likely to engage in online discussion about the program. We empirically examine these possibilities and assess what impact various calls-to-action have on online WOM for both brands and programs. Television advertising also may influence online WOM for both brands and programs because commercial breaks are natural pauses in program content, and viewers may be more likely to engage in online conversations during natural breaks (Dumenco 2013). However, Nielsen (2013) argues that television viewers are actually less likely to engage in online program chatter during commercials. We empirically explore these contrasting viewpoints and assess if television advertising is associated with increases in online WOM for both brands and programs. Why might the program in which an advertisement airs influence the ad s impact on online WOM? Literature on media context effects evaluating the effect of media engagement on advertising response has generally found that more engagement with a television program relates to improved consumer response to the advertising (e.g., Murry et al. 1992; Feltham and Arnold 1994; Tavassoli et al. 1995; Wang and Calder 2009). While this literature does not address online WOM and generally focuses on program engagement rather than advertisement engagement, it suggests that the media context should matter when assessing consumer response

10 10 to advertising. Recent investigations in the marketing literature have called for further research on how television content and television advertising interact (Wang and Calder 2009). We expand upon this work on television consumption and advertising by exploring social media interactions with television viewing to gain a broader understanding of the consumer experience with television, advertising, and social media. Additionally, we contribute to research on media context effects by extending this body of work into the online WOM context, exploring online engagement with advertisements, and evaluating the potential interaction between advertisement and television program WOM in social TV behavior. 3. Data Description 3.1 Television Advertising Data Data on primetime television advertising during the fall 2013 television season (Sept. 1 Dec. 31, 2013) were gathered from Kantar Media s Stradegy database. Data were collected for advertisements that aired in primetime (8pm-11pm) on broadcast networks (ABC, CBS, CW, FOX, and NBC) during the initial airing of recurring programs 2. We exclude advertisements that are joint promotions, ads that feature two or more brands, from our analysis 3. Our final data set of television advertisements consists of 9,103 ad instances for 264 brands across 15 categories that aired on 84 television programs. 3.2 Social Media Data We combine the television advertising data with minute-by-minute level data of brand and program mentions on Twitter. The data were accessed using Topsy Pro which, at the time that data were collected, was a certified Twitter partner with access to the Twitter Firehose of 2 Recurring programming does not include programs that only air once, such as the Miss America competition. 3 Joint promotions made up less than 1% of advertising instances in our data.

11 11 comprehensive, real-time Twitter posts dating back to We focus our analysis of social TV activity on Twitter posts because the majority of public social media chatter about television occurs on Twitter 5. We utilize the number of online mentions for brands and programs to assess online WOM. A search of program mentions on Twitter was conducted by capturing Tweets that contain a program s name or nickname (e.g., Parks and Recreation, Parks and Rec ), hashtags featuring a program s name or nickname (e.g., #parksandrecreation, #parksandrec, #parksandrecnbc), or the program s Twitter handle 6. We employed a similar strategy to search brand mentions on Twitter, capturing Tweets that mention the brand, a hashtag featuring the brand name, a hashtag included in the brand s advertisement, or the brand s Twitter handle. Parent brand names were used as the focus of the search over product brand names (e.g., Colgate versus Colgate Optic White ) for almost all brands. The exceptions include motion pictures (e.g., parent brand Warner Brothers; product brand Gravity), books (e.g., parent brand Little, Brown And Company; product brand Gone by James Patterson), tech products (e.g., parent brand Amazon; product brand Kindle), and brands that share a name with a common word (e.g., Coach, Halls, Nationwide). For these exceptions, product brand names were incorporated into the search of Twitter brand mentions. Tables A4 and A5 in the Online Appendix present search terms used to capture brand mentions for a subsample of the 264 brands in our analysis. 3.3 Data on Brand, Advertisement, and Program Characteristics We supplement the data set of television advertising instances and online WOM with additional brand characteristics. We control for the category of the advertised brand as certain categories 4 Topsy Pro was acquired by Apple in late 2013 and is no longer available to the public. 5 Bluefin Labs estimates 95% of public social media chatter about television occurs on Twitter (Schreiner 2013). 6 Note that this search strategy does not double count conversations that include more than one of these elements.

12 12 may vary in their ability to generate online WOM 7. We also account for if a brand does not have a Twitter profile as it may reflect a brand s indifference toward online WOM and may influence consumer decisions to discuss the brand online. We additionally include a number of advertisement characteristics in our analysis. Most notably, we code the use of calls-to-action in each advertisement, indicating if an ad contains a phone number, Facebook page link or icon, hashtag, and/or web address. We also control for ad length and ad position both in a commercial break (first, middle, or last non-promo ad) and in a program (ad break position and if an ad airs near a half-hour interval). This data were extracted from Stradegy. Lastly, we account for if an advertisement runs in a simultaneous commercial break with another ad instance by the same brand. We define simultaneous as an advertisement for the same brand airing within two minutes (either before or after) of the advertisement of interest. This time window was chosen in correspondence with our dependent variable, which will be discussed in the following section. Finally, we account for program characteristics as they may influence how an advertisement impacts online WOM. We control for network, program genre, Nielsen program rating, day of the week the program airs, and time the program airs. This data were gathered from the Stradegy database with the exception of program ratings which were collected from TV by the Numbers. We further control for season premiere and fall finale episodes as these episodes may generate more social chatter compared to other episodes. 3.4 Descriptive Statistics Tables A1 and A2 in the Online Appendix display advertising instances by category, all 84 programs explored in this analysis, and the most advertised brands by category and program genre. The most advertised categories are movies, beauty, and wireless providers which account 7 Table A1 in the Online Appendix shows the 15 categories utilized in our analysis as well as the number of advertisement instances and top advertisers in each category.

13 13 for nearly 40% of the ad instances in the data. The most advertised brands are Apple, Microsoft, AT&T, Nokia, Sprint, and Bank of America which account for 20% of the ad instances in our sample. Table 1 shows summary statistics for the brand, advertisement and program characteristics. Of note, the majority of the advertising instances in our sample (65%) are 30 seconds. Only 1% of ad instances are longer than 30 seconds in length. Additionally, 59% of the advertisement instances in our data feature a web address, 17% include a hashtag, 6% contain a phone number, and 5% include a Facebook page link or icon. We also see that nearly half of the ad instances in our data (47%) air on Drama/Adventure programs, as about half of the programs in our data are classified in this genre (45%). Lastly, the average Nielsen program rating for episodes in our sample is 1.85, and the majority of ads (62%) air on episodes with ratings between Figure A1 in the Online Appendix shows the frequency of advertisement instances by program rating, day of the week, and time block. [Insert Table 1 about Here] 3.5 Model-free Evidence Can Television Advertising Impact Online WOM about the Brand? Our model-free evidence suggests that television advertising can increase online WOM for the advertised brand, illustrating that social TV activity may benefit marketers. Figure 1 shows the average brand mentions per minute on Twitter for brands in several categories from thirty minutes prior to the advertisements airings to thirty minutes after the ads air 8. This figure consistently shows increases in online brand mentions immediately following the advertisements airings. This increase in per minute mentions spikes very quickly, around two minutes after the advertisements air. The lifts in online WOM from television advertising vary across categories with movies on average increasing their Twitter mentions by more than ten mentions per minute 8 In all the model-free evidence figures, the bold, dashed center line indicates when the advertisement airs.

14 14 following an advertisement while other categories see smaller lifts. Across all brands explored in our analysis, the largest average increases in online WOM for brands two minutes after an advertisement airs (compared to two minutes prior) occur for movie brands with Mandela: Long Walk to Freedom, Insidious 2, and Lee Daniels The Butler seeing increases of 329, 175, and 122 mentions per minute, respectively. [Insert Figure 1 about Here] We also use model-free evidence to explore how online WOM for brands may be impacted by key advertisement and program characteristics. Figure 2 provides evidence that using calls-to-action in advertisements may influence online WOM for brands and that the magnitude of these effects may be driven by ad position in a commercial break. Utilizing hashtags and web addresses in an advertisement that airs first or in the middle of a commercial break appears to positively impact online brand WOM compared to using these calls-to-action in an ad that airs in the last slot. Figure 2 also illustrates how program ratings may influence an advertisement s effect on online brand chatter, suggesting that ads airing in programs with higher ratings generally receive a greater lift in online WOM compared to ads airing in programs with lower ratings. This is most notable by the large post advertisement spikes in per minute Twitter mentions for brands airing in programs with ratings above [Insert Figures 2 about Here] Can Television Advertising Impact Online WOM about Programs? We find model-free evidence that television advertising can encourage increases in online WOM for programs. Figure 3 shows the average program mentions per minute on Twitter around the airings of advertisements and illustrates that, across all advertisements in our data, per minute program chatter spikes shortly after the ads air. The largest spikes are seen following advertisements that

15 15 air in the first position of a commercial break. In our data, the largest average increases in online program WOM two minutes after an ad airs (compared to two minutes prior), regardless of ad position in the commercial break, occur for ABC s Scandal, FOX s Glee, and CW s Vampire Diaries, which see average increases of 276, 120, and 111 in per minute mentions, respectively. These findings are consistent with the viewpoint that television viewers are more likely to engage in online WOM during commercial breaks, possibly because they are natural pauses in programming content (Dumenco 2013), and challenges the belief advocated by Nielsen (2013) that television viewers are not reserving online program chatter for commercial breaks. [Insert Figures 3 about Here] Does Context Matter? Figure 4 provides model-free evidence that the media context in which a television advertisement airs influences the ad s impact on online WOM and helps motivate the need to account for potential brand-program synergy in our formal model. For example, Maybelline on average sees increases in their online brand chatter following the airing of their television advertisements on ABC s Scandal and FOX s GLEE but on average experiences decreases in online brand WOM following their ads airing on ABC s 20/20. As another example, Microsoft sees immediate increases in online WOM after their advertisements air on FOX s GLEE but see immediate decreases in online brand mentions following their ads airing on ABC s 20/20 and ABC s Scandal. Figure 5 also presents evidence that the relationship between brand and program matters when assessing the effect of television advertising on online WOM. This figure shows the impact of advertisements by Sprint and the movie Gravity on online brand WOM across different programs that aired on Sept. 23, 2013, and illustrates that the impact varies based on the program in which the brand chooses to air the advertisement. [Insert Figures 4 and 5 about Here]

16 16 Overall, as suggested by Figures 1-5, we see evidence of substantial heterogeneity in both online brand and program WOM across various brand, advertisement, and program characteristics. However, as this model-free evidence does not account for multiple factors and does not broadly explore the synergistic association between online brand and program WOM following the airing of advertisements, a formal model is needed to explore the relationship between television advertising and online WOM. Toward this end, we next describe our modeling framework. 4. Model Development 4.1 Joint Model of Online WOM about Brands and Programs We jointly model the immediate change in online brand and program mentions following the airing of a particular television advertisement. We measure this immediate change using narrow two-minute windows before and after the advertisement airs. We specify the dependent variables of interest for our primary analysis as follows: (1) Y i1 log( PostBWOM i PreBWOM i 1), log( PostBWOM i Pr ebwom i 1), PostBWOM PostBWOM i i PreBWOM PreBWOM i i 0 0 where i indexes the ad instance in our data, PostBWOMi is the number of online Twitter mentions for the brand in ad instance i two minutes after the advertisement airs, and PreBWOMi is the number of online mentions for the brand two minutes before the advertisement airs. We specify Yi2 in the same manner with PostPWOMi and PrePWOMi, the number of online Twitter mentions for the program corresponding to ad instance i two minutes after and two minutes before, respectively, the advertisement airs. We use log specifications as there is large variance in these differences across brands. We attribute the differences between these pre- and postmeasures to the ad insertion. Narrow event windows have been employed in past research to

17 17 investigate the effects of television advertising on online behavior (e.g., Hill et al. 2012; Liaukonyte et al. 2015). Furthermore, our model-free evidence consistently illustrates that almost all of the immediate change in online WOM for both brands and programs occurs within two minutes of the advertisement s airing. Thus, we utilize the two-minute windows both before and after the advertisement airs to assess the impact of television advertising on online WOM. We consider a number of alternative specifications of the change in online mentions brought about by television advertising. Specifically, we evaluate alternative models that consider dependent variables that are not constructed as difference measures, account for WOM valence, and utilize different pre- and post-advertisement time windows. The substantive results for these alternative specifications do not differ from those of our main model. Detailed discussions for these supplementary analyses are provided in the Online Appendix. This narrow event window is also advantageous as it is key to the identification strategy and alleviates endogeneity concerns that advertisers or television networks could choose a certain time window to air an advertisement in order to impact online WOM. In advertisernetwork media buy negotiations, the specificity of timing when an ad will air is limited to the quarter-hour level, and this timing is not stipulated in the advertiser-network contracts, 80% of which are completed in the May up-front market several months prior to the start of the fall television season (Liaukonyte et al. 2015). Additionally, television networks commonly order advertisements across commercial breaks at random (Wilbur et al. 2013). Therefore, it is not plausible to time an advertisement to air during a specific four minute period to influence social media activity. We model the immediate change in brand b s online mentions (Yi1) and program p s online mentions (Yi2) from television advertisement instance i as follows:

18 18 (2) Yi Yi 1 2 Yˆ i ~ N ˆ Yi 1 2, 58 Yˆ (3) 1 brand b[ i],1 p[ i],1 1 i k BPSynergy b[ i], p[ i] ˆ Y 2 prog b[ i],2 i p[ i],2 2 k k where μbrand and μprog are respective intercepts. We account for brand-specific effects (αb.) and program-specific effects (γp.) in each equation for the 264 brands and 84 programs in our data and use them to explore (1) if a specific brand or program experiences changes in online WOM following an advertisement s airing and (2) potential cross-effects that is if a specific brand (program) influence online program (brand) WOM following an ad s airing. Furthermore, the brand- and program-specific effects αb. and γp. control for many of the potential unobservables related to advertisers, networks, or program content that may influence Yi1 and Yi2 (e.g., audience heterogeneity). Xi.is a vector of ad instance-specific brand, advertisement, and program characteristics described in the data section above. Additionally, Xi. includes measures to explore if ad break position in a program has a non-linear relationship with online WOM and if the effects of calls-to-action on online chatter are influenced by ad position in a commercial break, as this could impact the perceived time a viewer has to respond to a call-to-action (Danaher and Green 1997) 9. Finally, to account for potential correlation between the two dependent variables of interest, we allow for contemporaneous covariance in Τ. BPSynergybp is a latent measure of brand-program synergy that we construct to assess if the media context (i.e., the television program) in which a brand s advertisement airs interacts to impact online WOM. BPSynergybp can be thought of as a brand-program interaction as it assesses if the synergy or fit between brand b and program p influences online chatter for both b and p above and beyond the main effects associated with the brand (αb.) and the program (γp.). X ik 9 A summary of the variables in X i. is presented in Table A3 in the Online Appendix

19 19 We construct BPSynergybp using a proximity model (Bradlow and Schmittlein 2000; Schweidel et al. 2014) of the brands choices of in which programs to advertise. By jointly modeling the advertisers program choices with equations (1)-(3), our modeling framework not only allows for the construction of a measure of brand-program synergy, but it also recognizes that advertisers program selections may be non-random (Manchanda et al. 2004; Schweidel et al. 2014). We assume that the probability that brand b advertises in program p is a function of the latent distance between b and p in a Euclidean space (LatentDistbp). We let Zbp=1 if brand b advertises in program p during the 2013 fall television season and 0 otherwise, and let the probability that b advertises in p be specified as follows: (4) P( Z bp 1 1) 1 ( LatentDist bp ) b If λb > 0, it follows that brand b is less likely to advertise in program p as LatentDistbp increases, which we find to be the case. LatentDistbp is constructed as the Euclidean distance between brand b s location and program p s location in a latent space and is specified as follows: (5) LatentDist bp 2 2 ( Bb1 Pp1 ) ( Bb2 Pp 2) where Bb1 (Pp1) and Bb2 (Pp2) specify the location of brand b (program p) in the two-dimensional Euclidean space 10. We use the estimates of these locations to construct brand-program synergy measures BPSynergybp, which we assume to be inversely related to LatentDistbp: (6) BPSynergy bp 1 LatentDist bp 4.2 Estimation 10 To avoid shifts and rotations of the axes during model estimation, we assume the locations for three brands in the Euclidean space prior to estimation. We position brand 1 at the origin (B 11=B 12=0) to avert an axis shift, brand 2 on the positive x-axis (B 21>0, B 22=0) to prevent rotation over the y-axis, and brand 3 such that B 32>0 to avoid rotation over the x-axis. The remaining brands and all programs locations are estimated from the data.

20 20 The above equations are estimated jointly using a Bayesian hierarchical regression and Markov chain Monte Carlo techniques in WinBUGS ( We assume p 2 ~ 2 that b 1 ~ N(0, 1), b 2 ~ N(0, 2), p 1 ~ N(0, 1), and N(0, ), with diffuse inversegamma priors for the variances. We specify μbrand, μprog, η., and θk. with diffuse normal priors, and Τ with a diffuse inverse Wishart prior. Additionally, we assume that N(, ) b ~, B N( B, ), B N( B, ), P N( P, ), and P N( P, ), with diffuse normal b1 ~ 1 B1 b2 ~ 2 B2 p1 ~ 1 P1 p2 ~ 2 P2 priors for B. and P. and diffuse inverse-gamma priors for the variances. The above equations are estimated from three independent chain runs of 40,000 iterations with the first 20,000 iterations discarded as a burn-in. Our inferences are based on the remaining 20,000 draws from each chain. Model convergence is assessed through the time series plots of the posterior draws for each parameter, and these plots provide evidence consistent with model convergence. 5. Results 5.1 Model Comparison To assess the importance of accounting for both brand- and program-level effects as well as brand-program synergy in our model of television advertising s impact on online WOM, we compare our proposed model to several alternative models in Table 2. Deviance information criterion (DIC), a likelihood-based measure that penalizes more complex model specifications, and the mean absolute error (MAE) are used to compare our proposed model to the alternative specifications. Lower DIC and MAE indicate better model fit. We first consider a baseline model that includes only the brand, advertisement, and program characteristic variables in Xi. (Model 1). We then build upon this baseline model to assess how model fit is impacted when only brand effects (Model 2) or only program effects

21 21 (Model 3) are included. Additionally, we consider a model in which no cross effects (Model 4) are incorporated and models in which only brand cross effects (Model 5) or only program cross effects (Model 6) are included. Finally, we consider a model in which BPSynergybp is withheld from equation (3) (Model 7). Adding BPSynergybp to Model 7 gives us our proposed model (Model 8). The DIC and MAE estimates in Table 2 establish that accounting for brand- and program-specific effects in our model of television advertising s impact on online WOM improves model fit. Furthermore, we find that a meaningful relationship does exist between brand and program online WOM following an advertisement s airing, as incorporating brandprogram synergy into Model 8 improves overall fit. Additionally, excluding cross effects that is not accounting for brand-specific (program-specific) effects in the model of advertising s impact on online program (brand) WOM hurts model fit. These observations provide evidence that the media context in which advertisements air matters when investigating the relationship between television advertising and online WOM. As Model 8 is our best fitting model, we focus our discussion on the results from this model estimation. [Insert Table 2 about Here] 5.2 What Impacts Online WOM about Brands? We begin by discussing the results pertaining to how brand and advertisement characteristics affect online WOM about brands 11. As seen in Table 3, we find that online brand chatter varies across categories with movies experiencing larger increases in online WOM. Our results also indicate that ad length affects subsequent online brand chatter, with shorter ads seeing less online WOM. One potential explanation for why longer advertisements result in increases to online 11 Posterior mean estimates of the variance and heterogeneity parameters in equations (2)-(3) are presented in Table A6 in the Online Appendix.

22 22 WOM about brands may be because they stand out to consumers; in our data, only 1% of ad instances were longer than 30 seconds. Longer advertisements also may offer more opportunities for consumers to be exposed to the brand name, which could also explain the increase in online WOM that they generate. These potential explanations are consistent with Teixeira et al. (2012) who find that ad length increases attention concentration and decreases zapping behavior. [Insert Table 3 about Here] We see that calls-to-action can impact an advertisement s effect on online brand WOM. Table 3 shows that including a phone number in a television advertisement actually reduces subsequent online brand WOM. This call-to-action may decrease immediate online brand WOM by encouraging an offline action (e.g., Tellis et al. 2000). In contrast, including a hashtag or web address in an advertisement can successfully increase online WOM about the brand. But, this effect only occurs when the advertisement airs in the first ad slot of a commercial break. This suggests that digital calls-to-action can stimulate online brand engagement. The interaction with ad position in a commercial break may occur because consumers feel as if they have more time to respond to a digital call-to-action and engage in online WOM at the beginning of the commercial break without interrupting program viewing (Danaher and Green 1997). This finding highlights the importance of ad position in a commercial break for advertisers interested in increasing online WOM about their brands. Table 4 presents the results of the influence of program characteristics on online brand WOM following television advertising. We find a positive relationship between program ratings and an advertisement s impact on online brand chatter. As higher program ratings indicate a larger viewing audience that can be exposed to the advertised brand, this larger base may

23 23 contribute to more online WOM for the brand. We also see that advertisements airing on the CW network, known for its younger audience base, see more subsequent online brand WOM. [Insert Table 4 about Here] 5.3 What Impacts Online WOM about Programs? The results in Table 3 show that brand and advertisement characteristics can impact online program WOM following television advertising. We see evidence that online WOM about programs varies across the categories of the brands that advertise in the program, with programs experiencing higher (lower) levels of subsequent online program chatter following movie ads (cable providers and non-profit/psa ads). This result suggests that the selection of advertisers by television networks has an impact on online program WOM. We also find that ad position in a commercial break is associated with changes in online program mentions. More (less) online program WOM is seen following advertisements that air in the first (last) ad slot of a commercial break. Online program chatter following the first ad occurs early in the commercial break, whereas such chatter following the last ad of a break occurs during the program content. Thus, this finding is consistent with the argument that viewers are more likely to engage in online WOM during commercials, possibly because they are natural pauses in programming (Dumenco 2013). Given the desire to avoid interrupting program viewing, such actions are more likely to be taken earlier in a commercial break (Danaher and Green 1997). This finding further challenges the argument made by Nielsen (2013) that television viewers are actually less likely to engage in online program chatter during commercials as our results indicate that there is a substantial increase in online program WOM two minutes following the first advertisement s airing (compared to two minutes prior).

24 24 We also find a non-linear relationship between ad break position in a program and online program chatter following advertisements. We find that subsequent online WOM increases, but at a decreasing rate, during advertisements that air in later ad breaks in the program. This finding may reflect that online WOM increases as the program progresses but eventually tails off as viewers become engrossed by the program content and disengage from their second screen. Table 4 illustrates that program characteristics influence online program WOM following television advertising. We see variation in online program WOM following advertisements across program genres. Compared to Slice-of-Life/Reality programs, we see that Comedy, Drama/Adventure, News/Magazine, and Suspense/Mystery/Police programs all experience more online program chatter. These differences may reflect variations in characteristics of the program in each genre or differences in characteristics of the viewers. In addition, we find variations across broadcast networks with programs on FOX and NBC experiencing fewer online mentions following advertisements (relative to programs on ABC). Finally, ads airing in season premieres and fall finales are associated with more online program WOM than ads that air on episodes in the middle of the season. 5.4 Brand-Program Synergy From the model of advertisers program choices 12, we find, as expected, that > 0 based on the 95% higher posterior density (HPD) interval. This indicates that as the proximity between brand b and program p increases, the probability that b advertises in p increases. As shown in Table 4, we find that higher brand-program synergy is associated with increases in online WOM following television advertising for both brands and programs. This suggests increased online WOM for both the brand and program when they exhibit higher synergy. This illustrates that the media context in which a television advertisement airs influences the ad s impact on online 12 The full results from this model are shown in Table A7 in the Online Appendix.

25 25 WOM. Some examples of brand-program pairs with high synergy include Amazon-ABC s Grey s Anatomy, Frozen (the movie)-fox s X Factor, Citibank-CBS s Undercover Boss, and Bank of America-CBS s Amazing Race. Some examples of brand-program pairs with low synergy include Microsoft-CW s Carrie Diaries, Old Spice-FOX s X Factor, and Elizabeth Taylor-FOX s Simpsons. Future research can explore potential explanations for these high and low brand-program synergy pairs, which may be driven by the characteristics of the viewers (e.g., demographics) and/or characteristics of the program content (e.g., product placements). Using our estimates of αb. and γp., we can explore if brands that advertise in programs with more socially engaged audiences also experience more online brand WOM. While numerous studies on media context effects have found that more consumer engagement with a television program relates positively to advertising response (e.g., Murry et al. 1992; Feltham and Arnold 1994; Tavassoli et al. 1995; Wang and Calder 2009), other investigations have argued that having an audience engaged with a program may actually hinder advertising response (e.g., Danaher and Green 1997). Figure 6 shows the posterior mean estimates of the program-specific effects on online WOM about the program (γp,2) and about the brand (γp,1) following advertisements. We find that twenty-five programs in our sample (30%) have positive posterior mean estimates for both γp,1 and γp,2 (upper right quadrant), indicating that these programs experience more WOM for the program and for the brands that advertise in the program than one would expect based on other model variables. Some examples of such programs include ABC s Scandal, CBS s How I Met Your Mother, CW s Vampire Diaries, FOX s Glee, and NBC s Revolution. We also observe eleven programs (13% of our sample) that experience lower than expected online WOM for the program and higher than expected online WOM for brands (upper left quadrant). Some examples

26 26 of such programs include ABC s Revenge, CBS s 2 Broke Girls, and FOX s Family Guy. While the current dominant focus of the social TV industry is on program engagement (e.g., Dumenco 2012; Lomas 2012), this result suggests that online program chatter is only part of the story and that brands can experience increases in online WOM (all else being equal) even when they advertised in programs with low online engagement. [Insert Figure 6 about Here] We also find that twenty programs in our data (24%) have higher than expected online program chatter but that online WOM for brands is less than expected all else being equal (lower right quadrant). These programs may be less attractive for advertisers seeking to amplify their audience through social media. Audiences that engage online about these programs may be doing so at the expense of online brand engagement. This finding illustrates that programs that receive the most online WOM aren t necessarily the best programs for advertisers. Some examples of such programs include ABC s Back in the Game, ABC s Modern Family, CBS s NCIS, FOX s Bones, and NBC s Blacklist. Finally, twenty-eight programs in our data (33%) have negative posterior means for both γp,1 and γp,2, indicating that these programs experience lower than expected online WOM for both the program and the brands that advertise in the program given the other model controls. Overall, marketers interested in increasing online WOM following their television advertising, all else being equal, may want to focus their advertising buys on programs that fall into the top two quadrants of Figure 6 and avoid programs that fall in the bottom half of the figure. Additionally, the results suggests that how engaged a program s audience is online does not necessarily indicate how the audience will engage with the advertised brands online. Some programs that focus on being social shows online may miss the mark for advertisers while other

27 27 programs with low engagement may offer brands higher than expected online WOM. These results highlight the importance of considering advertisement engagement in assessments of social TV behavior as program engagement does not tell the whole story. [Insert Figure 7 about Here] In Figure 7, we investigate the relationship between program ratings and programspecific effects on online brand WOM (γp,1). Program ratings currently dictate the cost advertisers pay to air an advertisement during a program, with higher ratings equating to higher costs. Marketers looking to capitalize on increased online brand chatter following their advertisements without paying for higher ratings could look to advertise in programs that fall in the upper-left quadrant of Figure 7. Brands that advertise in these programs, which have below average ratings, experience higher than expected online WOM. Some examples of such programs include CBS s Hostages, CW s Supernatural, FOX s Glee, and FOX s X Factor. Lower ratings could be offset by higher online WOM, making these programs potential bargains that may appeal to advertisers given that advertising costs are mostly driven by ratings. Television networks and broadcasters could also leverage the findings in Figure 7 to negotiate higher advertising rates in programs that offer increased online engagement for the brands that advertise in these programs. 6. Conclusion While the positive effects of online WOM on marketing outcomes have been widely discussed in the literature, research on the drivers of online WOM is still in its infancy. In this investigation, we examine if television advertising can contribute to increased online WOM for the brand advertised and for the program in which the ad airs. Overall, our results suggest that television

28 28 advertising can impact online WOM for both the brand and program and that the media context in which an advertisement airs (i.e., the television program) does influence the relationship between television advertising and online WOM. Our findings illustrate that social TV activity can be beneficial to marketers as it can result in increases to online brand WOM following advertisements. Additionally, we find that programs that receive the most online WOM aren t necessarily the best programs for advertisers. These are meaningful results because, given the current focus of the social TV industry on online program engagement (e.g., Dumenco 2012; Lomas 2012), our findings suggest that advertisers may misjudge the benefits of social TV for brands. Our results point to the need for social TV activity to be viewed in terms of viewer engagement with both programs and advertisements. Additionally, we find that online program WOM increases substantially following the first advertisement in a commercial break. While increasing program engagement, this may hurt consumer attention to advertisements airing early in the commercial break. This is relevant to media buying strategies, as the first ad slot in a commercial break is considered to be the most coveted ad position by advertisers (Katz 2013; Wilbur et al. 2013). Consumers multi-screen behavior reveals a potential downside for advertisements airing in the first ad slot. However, we also do find evidence that advertisers can increase online WOM for their brands following advertisements airing in the first ad slot by incorporating digital calls-to-action, specifically a hashtag or web address, into the creative. Thus, these results have implications for not only advertisement design strategies but also for the importance that ad position should play in media buy negotiations for advertisers interested in online WOM. Our results concerning brand-program synergy further shed light on how online WOM may be considered in the media buying process. Specifically, we find that increases in online

29 29 WOM from television advertising depend on brand-program synergy, with online WOM increasing more for both brands and programs with high synergy. This finding provides an incentive for both brands and programs interested in online WOM to consider brand-program synergies in the media buying process. Networks may consider offering more program-specific recommendations for marketers interested in finding the right balance between engaged program audiences or program ratings and increases in online brand chatter. Our analysis is subject to limitations which may be fruitful avenues of future research. First, we do not observe if a television viewer actively sees an advertisement and subsequently engages in online WOM. Such individual- or device-level data could allow us to attribute changes in online WOM directly to television advertising. The narrow time window used in our analysis helps us avoid potential unobservable influences of this limitation. Second, we focus on the volume of online mentions. Analysis of the content of the online WOM could permit a more nuanced investigation of the relationship between television advertising and different types of online WOM (e.g., recommendation versus attribute-oriented WOM investigated by Gopinath et al. 2014). Finally, while our investigation of social TV activity has focused on television advertising s role as a driver of online WOM, future research may build upon this foundation to explore the relationship between television advertising, social TV, and other brand performance measures, such as sales or brand health.

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33 33 Table 1: Descriptive Statistics for Brand, Advertisement, and Program Characteristics Group Variable Description Frequency Mean (SD) Brand Characteristics Advertisement Characteristics Program Characteristics No Twitter % of brands that do not have a Twitter account Ad break position Ad break position in a given program 3.30 (2.21) Ad length Ad length in seconds (8.14) Ad position in a commercial break Calls-toaction Half-hour break Simultaneous ad % of ads that are less than 30 seconds % of ads that are more than 30 seconds 1.18 % of ads that are the first non-promo ad in a given commercial break % of ads that are the last non-promo ad in a given commercial break % of ads that contained a phone number 6.22 % of ads that contained a hashtag % of ads that contained a Facebook icon or URL to a Facebook page 5.00 % of ads that contained a web address % of ads that aired within 2 minutes of a half-hour break % of ads that aired within 2 minutes of another ad by the same brand Genre % of ads on Drama/Adventure programs Fall finale % of ads on News/Magazine programs 7.37 % of ads on Suspense/Mystery/Police programs 3.35 % of ads on Comedy programs % of ads on Slice-of-Life/Reality programs % of ads that aired during fall finale episodes Network % of ads on ABC Program length Program ratings Season premiere % of ads on CBS % of ads on CW % of ads on FOX % of ads on NBC Length of program in minutes (25.11) Nielsen program ratings - reflects % of population of TVs tuned to a particular program for demographic % of ads that aired during season premieres (0.97)

34 34 Table 2: Model Comparison Model Description What's Included DIC Brand MAE Program MAE Model 1 Baseline μbrand, μprog, Xi Model 2 Brand effects only μbrand, μprog, Xi., αb,1, αb, Model 3 Program effects only μbrand, μprog, Xi., γp,1, γp, Model 4 No cross effects μbrand, μprog, Xi., αb,1, γp, Model 5 Brand cross effects only μbrand, μprog, Xi., αb,1, αb,2, γp, Model 6 Model 7 Program cross effects only Main model without brand-program synergy μbrand, μprog, Xi., αb,1, γp,1, γp, μbrand, μprog, Xi., αb,1, αb,2, γp,1, γp, Model 8 Main model Model 7 + BPSynergy bp (9.93, 10.02) (144.9, 145.5) (9.82, 9.94) (144.8, 145.4) (9.92, 10.01) (144.1, 145.0) (9.82, 9.94) (144.1, 145.0) (9.82, 9.94) (144.0, 144.9) (9.79, 9.91) (144.1, 145.0) (9.79, 9.92) (144.0, 144.9) (9.74, 9.90) (143.9, 145.7) Note: MAE estimates are presented with 95% highest posterior density (HPD) intervals from 20,000 draws of three independent MCMC chains. The MAE estimates are in terms of the untransformed differences between PostBWOM i (PostPWOM i) and PreBWOM i (PrePWOM i).

35 35 Table 3: Impact of Brand and Advertisement Characteristics on Online WOM following Television Advertising Brand WOM Program WOM Brand WOM Program WOM Variable Posterior Mean Posterior Mean Variable Posterior Mean Posterior Mean μ brand/μ prog (-0.65, 0.26) ** (-3.76, -1.55) Length - Less than 30 Seconds ** (-0.35, -0.15) 0.11 (-0.09, 0.31) Ad break position * (-0.09, 0.01) 0.39 ** (0.27, 0.51) Length - More than 30 Seconds 0.41 ** (0.10, 0.72) 0.10 (-0.57, 0.78) Ad break position (-0.00, 0.01) ** (-0.04, -0.02) No Twitter (-0.18, 0.13) (-0.28, 0.27) Ad near half-hour (-0.13, 0.06) (-0.39, 0.05) Simultaneous ad 0.04 (-0.09, 0.16) 0.04 (-0.24, 0.31) First ad (-0.28, 0.03) 3.16 ** (2.83, 3.50) Category Last ad 0.03 (-0.58, 0.65) * (-2.54, 0.05) Apparel 0.19 (-0.21, 0.59) 0.36 (-0.40, 1.11) Calls-to-Action Beauty (-0.32, 0.25) 0.39 (-0.15, 0.93) Phone ** (-0.43, -0.04) 0.10 (-0.30, 0.50) Cable providers (-0.60, 0.37) ** (-2.18, -0.45) Hashtag (-0.16, 0.12) (-0.40, 0.15) Computer accessories (-0.71, 0.09) 0.16 (-0.60, 0.93) Facebook 0.05 (-0.15, 0.26) 0.10 (-0.32, 0.51) Computers, notebooks, or * (-0.60, 0.03) 0.35 (-0.23, 0.93) tablets Web (-0.13, 0.07) (-0.25, 0.15) Dental care (-0.45, 0.29) 0.55 (-0.13, 1.25) Phone X First ad (-0.37, 0.32) (-1.01, 0.52) Financial (-0.53, 0.09) 0.16 (-0.41, 0.74) Hashtag X First ad 0.36 ** (0.14, 0.58) (-0.54, 0.43) Non-profit/PSAs (-0.43, 0.30) ** (-1.63, -0.19) Facebook X First ad (-0.44, 0.34) (-1.52, 0.21) Hair care 0.01 (-0.35, 0.37) 0.24 (-0.46, 0.94) Web X First ad 0.17 * (-0.01, 0.35) 0.02 (-0.37, 0.42) Insurance 0.15 (-0.18, 0.48) 0.08 (-0.56, 0.71) Phone X Last ad 1.05 (-0.48, 2.56) (-3.67, 2.42) Medicine/vitamins 0.03 (-0.26, 0.32) (-0.73, 0.41) Hashtag X Last ad 0.10 (-0.73, 0.95) 0.11 (-1.65, 1.88) Movies 1.49 ** (1.21, 1.77) 0.71 ** (0.17, 1.26) Facebook X Last ad (-2.49, 1.18) 2.14 (-1.36, 5.64) Phones * (-0.64, 0.03) 0.35 (-0.27, 0.95) Web X Last ad (-0.74, 0.61) 0.69 (-0.73, 2.12) Wireless providers (-0.41, 0.30) 0.46 (-0.15, 1.07) Note: Table 3 presents posterior mean estimates along with the 95% HPD intervals from the 20,000 draws of the three independent MCMC chains. We denote posterior mean estimates for which the 95% HPD interval excludes zero with a double asterisk (**) and estimates for which the 90% HPD interval excludes zero with a single asterisk (*). The baseline for the category variable is other category.

36 36 Table 4: Impact of Program Characteristics and Brand-Program Synergy on Online WOM following Television Advertising Brand WOM Program WOM Variable Posterior Mean Posterior Mean Day program airs (Baseline: Monday) Tuesday 0.06 (-0.10, 0.21) 0.64 ** (0.25, 1.04) Wednesday 0.07 (-0.11, 0.26) (-0.64, 0.31) Thursday 0.12 (-0.06, 0.29) 0.31 (-0.13, 0.78) Friday 0.08 (-0.11, 0.28) 0.19 (-0.29, 0.69) Saturday 0.38 (-0.25, 0.99) 0.23 (-1.38, 1.90) Sunday 0.16 (-0.05, 0.37) 0.58 ** (0.03, 1.14) Fall finale * (-0.21, 0.02) 0.30 ** (0.05, 0.54) Genre (Baseline: Slice-of-Life/Reality) Drama/Adventure 0.08 (-0.11, 0.27) 1.14 ** (0.60, 1.66) News/Magazine (-0.48, 0.27) 1.25 ** (0.19, 2.27) Suspense/Mystery/Police 0.22 (-0.14, 0.58) 1.05 ** (0.10, 1.99) Comedy (-0.42, 0.05) 0.64 ** (0.02, 1.26) Network (Baseline: ABC) CBS (-0.28, 0.05) 0.29 (-0.16, 0.75) CW 0.22 * (-0.01, 0.45) 0.53 * (-0.08, 1.15) FOX 0.14 (-0.04, 0.33) ** (-1.47, -0.45) NBC (-0.23, 0.14) ** (-1.06, -0.03) Program length (-0.00, 0.00) (-0.01, 0.00) Program rating 0.23 ** (0.16, 0.31) * (-0.33, 0.02) Season premiere 0.09 (-0.03, 0.21) 1.02 ** (0.76, 1.29) Time program airs (Baseline: 8:00pm) 8:30pm 0.07 (-0.06, 0.20) ** (-0.79, -0.19) 9:00pm 0.10 (-0.04, 0.24) (-0.57, 0.08) 9:30pm 0.08 (-0.08, 0.24) ** (-0.89, -0.11) 10:00pm 0.02 (-0.16, 0.20) (-0.77, 0.12) 10:30pm 0.13 (-0.07, 0.34) 0.01 (-0.49, 0.50) Brand-program synergy (BPSynergy bp) 0.08 ** (0.02, 0.14) 0.15 ** (0.05, 0.27) Note: Table 4 presents posterior mean estimates along with the 95% HPD intervals from the 20,000 draws of the three independent MCMC chains. We denote posterior mean estimates for which the 95% HPD interval excludes zero with a double asterisk (**) and estimates for which the 90% HPD interval excludes zero with a single asterisk (*).

37 Figure 1: Change in Online Brand WOM following Television Advertisements across Brands in Different Categories 37

38 38 Figure 2: Change in Online Brand WOM following Television Advertisements across Ad and Program Characteristics Figure 3: Change in Online Program WOM following Television Advertisements

39 39 Figure 4: Change in Online Brand Mentions from Television Advertisements Airing on ABC s 20/20, FOX s Glee, and ABC s Scandal Figure 5: Change in Online Brand WOM following Television Advertisements for Sprint and Gravity across Programs September 23, 2013 Primetime Online Twitter Conversations about Sprint September 23, 2013 Primetime Online Twitter Conversations about Gravity

40 Figure 6: Relationship between Program-specific Effects on Online Program WOM (γp,2) and Online Brand WOM (γp,1) following Television Advertising 40

41 41 Figure 7: Relationship between Average Program Ratings and Program-specific Effects on Online Brand WOM (γp,1) following Television Advertising Note: The quadrant lines in Figure 7 are drawn at zero for γ p,1 and the overall mean program rating in our sample (1.85).

42 42 Online Appendix This Online Appendix presents summary statistics and empirical results from the main model as well as empirical results from several alternative model specifications that were excluded from the main text for conciseness. A.1 Data Overview Tables A1 and A2 present a detailed overview of the 15 categories and the 84 programs explored in our analysis. These tables show the frequency of advertising instances by category and the most advertised brands by category and program genre. Additionally, Figure A1 shows the distribution of ad instances by day of the week, time block, and program rating. Finally, Table A3 presents a summary of the fifty-eight brand, advertisement, and program characteristics discussed in the Data Description section which are included in Xi. in equation (3). A.2 Searching Brand Mentions on Twitter Table A4 presents the search terms used to capture brand mentions on Twitter for a subsample of the brands used in our analysis. Recall that we capture Twitter conversations that mention the brand, a hashtag featuring the brand name, a hashtag included in the brand s advertisement, or the brand s Twitter handle. Parent brand names were used as the focus of the search over product brand names (e.g., Colgate versus Colgate Optic White ) for almost all brands with the exceptions being motion pictures, books, and tech products. For these exceptions, product brand names were incorporated into the search of Twitter conversations for brand mentions. Additionally, in some cases a brand shares a name with a common word, such as Halls or Nationwide. For such brands, we also make use of product brand names to focus the search of Twitter conversation on brand chatter. Examples can be seen in Table A5.

43 43 A.3 Additional Results from Equations (1)-(6) Table A6 shows the posterior mean estimates of the variance and heterogeneity parameters from equations (1)-(3). Table A7 shows the results from the model of advertisers choices of programs shown in equations (4)-(6). A.4 Alternative Model Specifications We estimate several alternative specifications of the joint model of the change in online WOM for brands and programs following television advertising. First, we estimate our modeling framework using dependent variables that are not constructed as difference measures. Next, we evaluate our model when negative online brand and program mentions are excluded from the data. Finally, we estimate sixteen alternative specifications utilizing different pre- and postadvertisement time windows. Non-Difference Dependent Variables. We estimate our modeling framework in which the dependent variables are not constructed as difference measures but, rather, are specified as follows: (A1) PostBWOM PostPWOM i i Yˆ i ~ N ˆ Yi 1 2, (A2) Yˆ i ˆ Yi b[ i b[ i p[ i p[ i 58 1 brand ],1 ],1 i 1 k1 BPSynergy b[ i], p[ i] 2 prog ],2 ],2 i 2 k1 k 2 1 PreBWOM 2 PrePWOM X ik where PostBWOMi (PostPWOMi) is the number of online Twitter mentions for brand b (program p) two minutes after b s advertisement airs in p and PreBWOMi (PrePWOMi) is the number of online mentions for brand b (program p) two minutes before b s advertisement airs in p. We jointly estimate equations (A1) and (A2) along with the model of the advertisers program

44 44 choices specified in equations (4)-(6) using the same estimation procedures detailed in Model Development. The substantive results from this alternative specification are consistent with those from our main model estimation. We highlight a few key similarities and discuss two notable differences below. To begin, the posterior mean estimates for φ. are positive (95% highest posterior density (HPD) intervals exclude zero). Additionally, as found in our main analysis, brand-program synergy (BPSynergybp) positively impacts online WOM for both brands and programs. This result indicates that if brand b and program p have high synergy, both will experience increases in online WOM (all else being equal) following b s advertisement airing in p. This further illustrates the robustness of the result that the media context in which a television advertisement airs influences the ad s impact on online WOM. There are two key differences to note between the estimation results for the alternative specification detailed in equations (A1) and (A2) and our main model estimation. First, the 90% HPD interval for the interaction of featuring a web address in an advertisement and the first ad position does not exclude zero in this non-difference dependent variable specification. Thus, in this alternative specification, we find that only one digital call-to-action, hashtags, increases online brand WOM after airing in the first ad slot of a commercial break. Second, given the explanatory power of PrePWOMi, many of the control variables in Xi. no longer have significant impacts on online program chatter in the program WOM model. Excluding Negative WOM. We also estimate an alternative model in which we consider the valence of the online conversations. In our primary analysis, we do not distinguish between positive, neutral, and negative WOM since we are interested in the volume of conversations and understanding how television advertising drives online WOM overall. However, we recognize

45 45 that some marketers and television networks may be interested in how television advertising drives positive and neutral online WOM about brands and programs. To explore this relationship, we leverage the valence coding of the online brand and program mentions provided by Topsy Pro. Topsy uses a proprietary software to code Tweets as positive, neutral, or negative by analyzing the weighted sentiment of words and phrases using an automated process which they validate through manual checks of Tweet content. For this alternative model, we exclude from the data brand and program Twitter mentions that Topsy codes as negative and use the same modeling framework and estimation procedure discussed in Model Development. The substantive results between this alternative specification and our main model specification are the same. In general, the estimation results are nearly identical between these two specifications. For example, only four (3.4%) of the posterior mean estimates for the 118 coefficients in η. and θk. change sign between these two specifications, and the 90% HPD intervals for all four of these estimates include zero in both specifications. The only notable difference between these two specifications is that only the 90% HPD interval for η1 excludes zero in this alternative specification, while in the main model specification, the 95% HPD interval for η1 excludes zero. Different Time Windows. We estimate a series of alternative models with different preand post-advertisement time windows to test the robustness of our results as time from the advertisements airings increases. The modeling framework and estimation is the same as the main model; only the length of PostBWOMi, PreBWOMi, PostPWOMi, and PrePWOMi is changed. We explore the following time lengths for these four variables for a total of sixteen alternative specifications: 3-15 minutes, 30 minutes, 45 minutes, and 60 minutes. Recall that at the most granular level, advertisers can aim to air their advertisements in specific quarter-hour

46 46 periods (Liaukonyte et al. 2015). Thus, for the alternative models in which PostBWOMi, PreBWOMi, PostPWOMi, and PrePWOMi are eight minutes or longer (such that the total time window would exceed fifteen minutes), it would be feasible for advertisers or television networks to choose certain time windows to air advertisements in order to impact online WOM. While feasible, we argue that this is very unlikely. To elaborate, 80% of the advertiser-network negotiations are completed in the May up-front market several months prior to the start of the fall television season, and timing of the ad is not stipulated in advertiser-network contracts (Liaukonyte et al. 2015). Furthermore, television networks commonly assign advertisements to commercial breaks at random (Wilbur et al. 2013). Thus, even as we explore the impact of television advertising on online WOM as the time since the advertisement s airing increases, we do not think it is probable that the results from these alternative models suffer from the endogeneity concern that advertisers or television networks could choose a certain time window to air an advertisement in order to impact online WOM. The results across these sixteen alternative specifications are very consistent, and we see significant evidence that are substantive findings concerning the relationship between television advertising and online WOM hold as time since the advertisements airings increases. In fourteen of the sixteen alternative models (88%), we find that brand-program synergy (BPSynergybp) impacts online WOM following television advertising. These effects are most persistent on online chatter about programs, as BPSynergybp continues to have a positive influence (95% HPD interval excludes zero) on online program WOM even sixty minutes after the advertisement airs (compared to sixty minutes before). Additionally, in fourteen of the sixteen alternative specifications (88%), we also see evidence that using digital calls-to-action in an advertisement that airs in the first ad slot influences online brand WOM. The substantial impact of featuring

47 47 hashtags in the first ad of a commercial break continues to have a positive impact on online brand chatter even sixty minutes after the advertisement airs (compared to sixty minutes before). We additionally see other effects of utilizing calls-to-action in television advertising on online WOM emerge as time since the advertisements airings increases. For example, as time since the advertisements airings increases, we find that featuring a phone number in a television advertisement no longer negatively impacts online brand WOM and that featuring a Facebook page link or icon in an advertisement that airs first in a commercial break negatively impacts online WOM about the brand on Twitter (95% HPD intervals for the posterior mean estimate exclude zero). Additionally, we find that television advertisements featuring web addresses that air in the last ad slot in a commercial break negatively impact online program WOM (95% HPD interval for the posterior mean estimate excludes zero) as the time since the advertisement s airing increases.

48 48 References Liaukonyte, J., T. Teixeira, and K. C. Wilbur (2015), Television Advertising and Online Shopping, Marketing Science, forthcoming. Wilbur, K. C., L. Xu, and D. Kempe (2013), Correcting Audience Externalities in Television Advertising, Marketing Science, 32(6),

49 49 Category Table A1: Advertising Instances and Most Advertised Brands by Category Description of Category Advertisement Instances (%) Apparel Apparel, jewelry, and shoes 183 (2.01%) Beauty Cable providers Computer accessories Computers, notebooks, and tablets Dental care Financial Hair care Insurance Medicine/vitamins Beauty care, makeup, fragrances, and toiletries Cable networks, satellite providers, and internet webcasts Computer accessories, components, and software 1220 (13.40%) 135 (1.48%) Most Advertised Brands by Category Cotton Incorporated, Fruit of the Loom, Forevermark, Hanes, Skechers Maybelline, L'Oréal, Neutrogena, Clinique/Cover Girl (tie) WIGS (Web Channel), DirecTV, Big Ten Network, Showtime, Fox Deportes 171 (1.88%) Microsoft, Intel, Google, Apple/Canon (tie) Notebook, laptop, and tablet computers 950 (10.44%) Microsoft, Amazon, Google, Apple, Lenovo Toothpaste, toothbrushes, whiteners, mouthwashes, and dental supplies Non-insurance financial products, credit cards, banking, retirement services, investments, and loan/credit products Shampoos, hair color, hair styling, and hair accessories Automobile, medical, life, and homeowners insurance products Digestive aids, cold/cough/pain/sleep remedies, and vitamins/minerals 274 (3.01%) Crest, Colgate, Sensodyne, Listerine, Act 937 (10.29%) Bank of America, Citibank, Capital One, Chase, American Express 216 (2.37%) Garnier/Tresemme (tie), L'Oréal, Clairol, Pantene 508 (5.58%) Movies Motion pictures 1227 (13.48%) Non-profit/PSAs Public service announcements and nonprofit/u.s. Government announcements Farmers Insurance, Geico, United Healthcare, Progressive, State Farm 934 (10.26%) DayQuil/NyQuil, Tylenol, Advil, Zicam, Zyrtec 243 (2.67%) Gravity, The Hobbit: The Desolation of Smaug, The Counselor, Frozen, Captain Phillips/Last Vegas (tie) CBS Cares, FosterMore, Autism Speaks/Common Sense Media/It Can Wait (tie) Phones Phones 892 (9.80%) Apple, Nokia, Motorola, Samsung, LG Wireless providers Wireless/internet telecom providers 976 (10.72%) AT&T, Sprint, Verizon, T-Mobile, Virgin Mobile Other Products not included in the above categories 237 (2.60%) Pampers, USPS, Hallmark/NFL (tie), Dr. Scholls/Luvs (tie)

50 50 Table A2: List of Programs by Program Genre Program Genre Programs (Network) Most Advertised Brands by Genre Drama/Adventure News/Magazine Suspense/Mystery/Police Comedy Slice-of-Life/Reality Almost Human (FOX), Arrow (CW), Beauty and the Beast (CW), Betrayal (ABC), Blacklist (NBC), Blue Bloods (CBS), Bones (FOX), Carrie Diaries (CW), Castle (ABC), Chicago Fire (NBC), Criminal Minds (CBS), Dracula (NBC), Elementary (CBS), Glee (FOX), Good Wife (CBS), Grey's Anatomy (ABC), Grimm (NBC), Hart of Dixie (CW), Hawaii Five-0 (CBS), Hostages (CBS), Lucky 7 (ABC), Marvel's Agents of S.H.I.E.L.D. (ABC), Nashville (ABC), NCIS (CBS), NCIS: Los Angeles (CBS), Once Upon a Time (ABC), Once Upon a Time in Wonderland (ABC), Originals (CW), Parenthood (NBC), Person of Interest (CBS), Reign (CW), Revenge (ABC), Revolution (NBC), Scandal (ABC), Sleepy Hollow (FOX), Supernatural (CW), Tomorrow People (CW), Vampire Diaries (CW) 20/20 (ABC), 48 Hours (CBS), Dateline (NBC) CSI (CBS), Ironside (NBC), Law & Order: SVU (NBC), Mentalist (CBS) 2 Broke Girls (CBS), American Dad! (FOX), Back in the Game (ABC), Big Bang Theory (CBS), Bob's Burgers (FOX), Brooklyn Nine-Nine (FOX), Crazy Ones (CBS), Dads (FOX), Family Guy (FOX), Goldbergs (ABC), How I Met Your Mother (CBS), Last Man Standing (ABC), Michael J. Fox Show (NBC), Middle (ABC), Millers (CBS), Mindy Project (FOX), Modern Family (ABC), Mom (CBS), Neighbors (ABC), New Girl (FOX), Parks and Recreation (NBC), Raising Hope (FOX), Sean Saves the World (NBC), Simpsons (FOX), Super Fun Night (ABC), Trophy Wife (ABC), Two and A Half Men (CBS), We Are Men (CBS), Welcome to the Family (NBC) Amazing Race (CBS), America's Next Top Model (CW), Biggest Loser (NBC), Dancing with the Stars (ABC), MasterChef Junior (FOX), Shark Tank (ABC), Survivor (CBS), Undercover Boss (CBS), Voice (NBC), X Factor (FOX) Apple, Microsoft, AT&T, Nokia, Maybelline FosterMore, Citibank, Sprint, AT&T/Zicam (tie) Apple, Citibank, Google, AT&T, Chase/Microsoft/Nokia/Sprint (tie) Microsoft, Nokia, Apple, Verizon, Bank of America Bank of America/Sprint (tie), Microsoft, Nokia, Apple

51 51 Table A3: Brand, Advertisement, and Program Characteristics in Xik Group Parameter Variable Description Brand Characteristics Advertisement Characteristics Program Characteristics Brand category X i1-x i14 Dummy variables for the categories listed in Table A1 (BASELINE: other category) No Twitter X i15 Dummy variable for if a brand does not have a Twitter account Calls-to-action (CTAs) Ad length Ad position in commercial break Ad break position in program X i16-x i19 X i20-x i21 X i22-x i23 X i24-x i25 Dummy variables for if ad contains a hashtag, Facebook icon or URL for a Facebook page, phone number, and/or web address Dummy variables if ad is less than 30 seconds or if ad is more than 30 seconds (BASELINE: 30 second ad) Dummy variables if ad appears in first slot in a given commercial break or if ad appears in last slot of a given commercial break (BASELINE: ad in a middle slot of the commercial break) Ad break position in a given program and quadratic of ad break position in a given program Half-hour break X i26 Dummy variable for if ad airs within two minutes of a half-hour interval Simultaneous ad CTA and ad position in break interactions X i27 X i28-x i35 Program rating X i36 Nielsen program rating Network Genre X i37-x i40 X i41-x i44 Dummy variable for if a brand's ad runs in a simultaneous commercial break with another ad instance by the same brand Interactions between the four call-to-action dummy variables and the two ad position in commercial break dummy variables Dummy variables for if the program airs on CBS, CW, FOX, or NBC (BASELINE: ABC) Dummy variables for if the program is a Drama/Adventure, News/Magazine, Suspense/Mystery/Police, or Comedy (BASELINE: Slice-of-Life/Reality program) Special Episodes X i45-x i46 Dummy variables for season premiere and fall finales episodes Program length X i47 Length of program in minutes Day of the week X i48-x i53 Dummy variables for day of the week the ad airs (BASELINE: Monday) Time X i54-x i58 Dummy variables for time the ad airs created in half-hour increments from 8:00pm- 11:00pm (Baseline: 8:00pm)

52 52 PARENT BRAND PRODUCT BRAND Amazon Apple Aveeno Table A4: Examples of Terms Used to SearchTwitter Conversations about Brands Amazon Kindle Fire HDX: Tablet Computers Apple ipad Air: Tablet Computer Aveeno Daily Moisturizing Body Lotion SEARCH TERMS Brand Name(s) Brand Hashtag(s) Hashtag used in Ad Brand Twitter Handle "Kindle" "ipad" "Aveeno" "#Kindle" OR "#KindleFire" OR "#KindleFireHDX" OR "#AmazonKindle" "#ipad" OR "#appleipad" OR "#ipadair" "#aveeno" DirecTV DirecTV "DirecTV" "#DirecTV" Hulu Hulu Plus "Hulu" "#hulu" Lions Gate Pictures The Hunger Games: Catching Fire "Hunger Games" OR "Catching Fire" "#TheHungerGames" OR "#HungerGames" "#CatchingFire" Old Spice Old Spice Toiletries "Old Spice" "#OldSpice" Revlon Revlon Lash Potion "Revlon" "#revlon" "#CastASpell" Special K T-Mobile Special K Protein Meal Bar & Protein Shake T-Mobile Consumer Wireless Service "Special K" "#SpecialK" "@SpecialKUS" "T-Mobile" OR "TMobile" OR "T Mobile" Tylenol Tylenol "Tylenol" "#Tylenol" "#TMobile" "#Hate2Wait" "@TMobile"

53 53 Table A5: Examples of Terms Used to Search Twitter Conversations about Brands that Share a Name with a Common Word PARENT BRAND PRODUCT BRAND Coach Halls Luvs Nationwide Outlook Warner Bros Pictures Coach Poppy Blossom: Women's Fragrance Halls Mentho-Lyptus: Cough Remedy Tablets Luvs Disposable Diapers Nationwide Auto Insurance Outlook.com Prisoners SEARCH TERMS Brand Name(s) Brand Hashtag(s) Hashtag used in Ad Brand Twitter Handle ("Coach" AND "Poppy Blossom") ("Halls" AND "cough") ("Luvs" AND "diapers") ("Nationwide" AND "insurance") "Outlook.com" OR ("Outlook" AND " ") ("Prisoners" AND "movie") "#CoachPoppyBlossom" OR ("#Coach" AND "Poppy Blossom") ("Halls" AND "#cough") OR ("#Halls" AND "cough") "#Nationwide" "#Outlook" OR "#Outlook.com" "#theprisonersmovie" "#luvs" "#prisonersmovie" ("@Coach" AND "Poppy Blossom") "@Luvs" "@Nationwide" "@Outlook"

54 54 Table A6: Results of Television Advertising s Effect on Online WOM Brand WOM Program WOM Variable Parameter Posterior Mean Variable Parameter Posterior Mean Variance for Y i1 Τ (2.32, 2.46) Variance for Y i2 Τ (11.49, 12.19) Covariance Τ (-0.113, 0.108) Covariance Τ (-0.113, 0.108) Heterogeneity for α b1 τα (0.04, 0.11) Heterogeneity for α b2 τγ (0.03, 0.17) Heterogeneity for γ p1 τα (0.02, 0.07) Heterogeneity for γ p2 τγ (0.22, 0.59) Note: Table A6 presents posterior mean estimates along with the 95% highest posterior density (HPD) intervals from the 20,000 draws of the three independent MCMC chains. Table A7: Results of from Model of Advertisers Choices of Program(s) Variable Brand location on dimension 1 B b1 Brand location on dimension 2 B b2 Program location on dimension 1 P p1 Program location on dimension 2 P p2 Slope Mean Parameter Posterior Mean Heterogeneity Parameter Posterior Mean τb1 (1.58, 3.24) (1.57, 2.86) τb2 (0.22, 1.24) (0.28, 1.02) τp1 (1.85, 3.58) (0.46, 1.05) τp2 (-1.14, -0.08) (0.23, 0.55) τλ (3.29, 4.22) (3.18, 5.79) Mean (SD) Summary Statistics for LatentDist bp 2.20 (0.77) Summary Statistics for BPSynergy bp 0.54 (0.30) Note: Table A7 presents posterior mean estimates along with the 95% HPD intervals from the 20,000 draws of the three independent MCMC chains.

55 55 Figure A1: Distribution of Advertisement Instances by Day of the Week, Time Block, and Program Rating Note: The times labeled in Distribution of Ad Instances by Time Block are in Eastern Standard Time (EST).