Comparing Ads, F2C Impressions, and C2C Messages on Brand Building - Prof. Huertas García, Ejercicios de Administración de Empresas

¡Descarga Comparing Ads, F2C Impressions, and C2C Messages on Brand Building - Prof. Huertas García y más Ejercicios en PDF de Administración de Empresas solo en Docsity! Lisette de Vries, Sonja Gensler, & Peter S.H. Leeflang Effects of Traditional Advertising and Social Messages on Brand-Building Metrics and Customer Acquisition This study examines the relative effectiveness of traditional advertising, impressions generated through firm-to- consumer (F2C)messages on Facebook, and the volume and valence of consumer-to-consumer (C2C)messages on Twitter and web forums for brand-building and customer acquisition efforts. The authors apply vector autoregressive modeling to a unique data set from a European telecom firm. This modeling approach allows them to consider the interrelations among traditional advertising, F2C impressions, and volume and valence of C2C social messages. The results show that traditional advertising ismost effective for both brand building and customer acquisition. Impressions generated through F2C social messages complement traditional advertising efforts. Thus, thoroughly orchestrating traditional advertising and a firm’s social media activities may improve a firm’s performance with respect to building the brand and encouraging customer acquisition. Moreover, firms can stimulate the volume and valence of C2C messages through traditional advertising that in turn influences brand building and acquisition. These findings can help managers leverage the different types of messages more adequately. Keywords: traditional advertising, social media, brand building, customer acquisition, vector autoregressive modeling Online Supplement: http://dx.doi.org/10.1509/jm.15.0178 Every year, U.S. firms invest approximately $130 billionin traditional advertising (e.g., television, radio, print,and outdoor) to build their brands and increase sales (eMarketer 2014). Yet empirical evidence has suggested that firms are gradually shifting their traditional advertising in- vestments to, for example, social media to pursue similar objectives (eMarketer 2016; Hudson et al. 2016; Statista 2016). Many firms have established a social media presence by operating pages on social networking sites such as Face- book. Firms post messages on these pages to interact with consumers by exploiting the network structure and to ulti- mately build the brand and stimulate sales (DeVries, Gensler, and Leeflang 2012). We call these posts firm-to-consumer (F2C) social messages. To leverage these messages, managers need to know how effective F2C social messages are for building the brand and influencing consumer behavior. Previous research has shown that F2C social messages have a positive effect on existing customers’ expenditures (e.g., Goh, Heng, and Lin 2013; Kumar et al. 2016). However, we lack knowledge about the effectiveness of firms’ social media activities in comparison to their traditional advertising investments. Moreover, we know little about potential complementary effects of F2C social messages and traditional advertising (Kumar et al. 2016). Such knowledge is, however, critical for managers to leverage and orchestrate traditional advertising and F2C social messages effectively (Chen and Xie 2008; Edelman 2010). Furthermore, previous studies have focused on the impact of F2C social messages on existing customers’ behavior but have not in- vestigated the potential impact on new customer acquisition. In addition to a firm’s own efforts to build the brand and affect consumer behavior, it is well known that messages initiated by consumers influence other consumers (e.g., Babić Rosario et al. 2016; Hennig-Thurau, Wiertz, and Feldhaus 2015; You, Vadakkepatt, and Joshi 2015; Zhu and Zhang 2010). Such messages can be product reviews as well as messages posted on forums, microblogs (e.g., Twitter), brand communities, and other social media sites. We call messages that are initiated by consumers and targeted to other con- sumers consumer-to-consumer (C2C) social messages. Man- agers need a clear understanding of the effects of C2C social messages on the brand and consumer behavior relative to the impact of their own efforts. Moreover, managers need to knowwhether their own communication activities affect C2C social messages because this would allow them to exert some influence on what consumers say about the brand. Previous studies that compare traditional advertising and C2C social messages have indicated that C2C social messages can be more effective for increasing sales and customer acquisi- tion (e.g., Trusov, Bucklin, and Pauwels 2009). Moreover, these studies have suggested that traditional advertising and Lisette de Vries, Store Support BV, Amsterdam (e-mail: lisettede_vries@ hotmail.com). Sonja Gensler (corresponding author), Marketing Center Münster, University of Münster (e-mail: s.gensler@uni-muenster.de). Peter S.H. Leeflang is Frank M. Bass Distinguished Professor of Marketing, University of Groningen, and Honorary Professor, Aston Business School, Aston University (e-mail: p.s.h.leeflang@rug.nl). The authors are listed in alphabetical order. The authors thank Nielsen and an anonymous telecom firm for providing data, and the JM review team for constructive feedback. Special thanks go to Thorsten Wiesel for his valuable feedback and sug- gestions. Praveen Kopalle served as area editor for this article. © 2017, American Marketing Association Journal of Marketing ISSN: 0022-2429 (print) Vol. 81 (September 2017), 1–15 1547-7185 (electronic) DOI: 10.1509/jm.15.01781 consumer messages are complements (Fossen and Schweidel 2017; Gopinath, Thomas, and Krishnamurthi 2014). Yet few studies have considered C2C and F2C social messages jointly, and the findings of these studies with respect to the effec- tiveness of these messages are mixed (Goh, Heng, and Lin 2013; Kumar et al. 2013). To date, no empirical research has considered traditional advertising, F2C social messages, and C2C social messages simultaneously to compare the effec- tiveness of the different types of messages. Thus, we also have little knowledge about the interrelations among the different messages, though there is no doubt that such interrelations are likely to exist (Hewett et al. 2016). The aim of this study is to close this gap in the literature by examining the relative effectiveness of traditional advertising, F2C social messages, and C2C social messages for both brand building and customer acquisition over time, and to study the interrelations among the different messages. We focus on customer acquisition because it is a critical performance mea- sure that has just recently received more attention (Katsikeas et al. 2016). By considering customer acquisition (i.e., number of new customers), we are able to study the behavioral outcomes of traditional advertising, F2C social messages, and C2C social messages. By accounting for brand-building metrics (i.e., consumers’ brand awareness, consideration, and preference), we can examine both indirect and direct effects of the dif- ferent messages on customer acquisition (Bruce, Peters, and Naik 2012). We collected a unique data set from a European telecom firm (which maintains contractual relationships with con- sumers) and Nielsen that contained weekly data on traditional advertising, F2C social messages, and C2C social messages over 119 weeks. We also have weekly information about brand-building metrics and customer acquisition. The tradi- tional advertising measure comprises the firm’s joint expen- ditures on television, radio, print, and outdoor advertising. The number of impressions of firm-initiated messages on Facebook based on likes, comments, and shares of the firm’s original messages represent F2C social messages. We there- fore use the term F2C impressions when describing and dis- cussing the results of the empirical study. The impressions provide information about the spreading of a firm’s message. We consider Facebook because it is the firm’s main social media platform to communicate with consumers. Consumer-to- consumer social messages include the number (C2C volume) and valence (C2C valence) of messages initiated by consumers about the firm on Twitter and the most popular forums in the countrywhere the focalfirmoperates.Wedonot consider online reviews because the content of the reviews is mostly about phones and less about the specific services offered by the focal firm. By taking C2C social messages on Twitter and forums into account, we cover the majority of C2C social messages about the focal firm. To elicit the effectiveness of traditional advertising, F2C impressions, and C2C social messages (C2C volume and C2C valence), we use vector autoregressive (VAR) modeling. This methodology allows us to determine the relative effectiveness of traditional advertising, F2C impressions, and C2C social messages by computing their elasticities for the brand-building metrics and customer acquisition on the basis of impulse response function (IRF) analyses (e.g., Dinner, Van Heerde, andNeslin 2014). In addition, theVARmodel approach enables us to examine the interrelations among traditional advertising, F2C impressions, andC2C socialmessages (Hewett et al. 2016). With our work, we contribute to the extant literature in several ways. First, we consider traditional advertising, im- pressions generated through F2C social messages, and C2C social messages simultaneously and compare their effec- tiveness. Second, we elaborate on the complementary effects of and interrelations among traditional advertising, F2C impressions, and C2C social messages. Third, we take both brand-building and behavioral metrics into account to assess the effectiveness of the different messages over time. Using brand-building and behavioral metrics allows us to address current calls to consider multiple performance metrics at different levels to derive more insightful managerial impli- cations (Katsikeas et al. 2016). Accordingly, our study is more comprehensive than previous studies and allows for richer insights that help managers to orchestrate the different messages effectively. The results show that the different messages are effective in building a brand and enhancing customer acquisition. With respect to building a brand, traditional advertising is most effective in creating awareness and consideration. However, C2C valence is most effective in spurring preference. Tra- ditional advertising is again most effective in enhancing customer acquisition, followed by F2C impressions and C2C volume. The results suggest that the firm’s social media activities complement its traditional advertising efforts. In addition, traditional advertising enhances the volume and valence of C2C social messages, which in turn spur con- sumers’ preference and acquisition. Given the effectiveness of traditional advertising, managers should carefully trade off its effectiveness and costs (i.e., efficiency) when making marketing investment decisions. In the next section, we elaborate on previous research related to our study and highlight the need for an empirical study that addresses the gap in research. Then, we describe our data and introduce the modeling approach. Subsequently, we present and elaborate on the empirical findings. Finally, we conclude with a discussion of the study’s implications, lim- itations, and research opportunities. Previous Research on the Effectiveness of Traditional Advertising, F2C Social Messages, and C2C Social Messages The effectiveness of traditional advertising, F2C, and C2C social messages can be assessed by examining their impact on brand-building and behavioral outcomes. Brand aware- ness, consideration, and preference are three commonly used metrics to evaluate the effects on brand building (e.g., Draganska, Hartmann, and Stanglein 2014; Srinivasan, Vanhuele, and Pauwels 2010). Recent studies have dem- onstrated the brand-building and sales capabilities of a single type of message—traditional advertising (e.g., Sethuraman, Tellis, and Briesch 2011; Srinivasan, Vanhuele, and Pauwels 2 / Journal of Marketing, September 2017 Control variables. Several other factors could also affect the brand-building metrics and customer acquisition. Namely, we consider promotions,media and buzz events, holidays, and competition. First, promotions are important stimuli to attract new customers and might also affect brand-building metrics (Pauwels, Hanssens, and Siddarth 2002). We gathered all the individual descriptions of price promotions for the focal firm and its four main competitors. The price promotions apply to annual or two-year subscription plans (e.g., 50% discount for 24 months). All telecom providers in the market use similar promotions. To control for the effect of price promotions, we consider a variable that reflects the promotion intensity of the focal firm—that is, the number of price promotions of the focal firm in a specific week divided by the total number (focal firm + competitors) of price promotions in that week (Table 2). The value of this variable ranges between 0 and 1 and equals 1 if the focal firm is the only firm in a given week that offers a price promotion. Second, we control for media and buzz events to consider extraordinary short-term interventions. To control for media events, we searched national news archives for important news related to the telecom sector, specific telecom providers, or new telecom-related technology. These news items might describe service failures (e.g., a fire caused service disruptions), new subscription terms being introduced by telecom providers, introduction of new mobile phone models, or major quality improvements of the network. News could probably also cover major price shifts of one or more telecom provider. However, during our observation period no such interventions occurred. Moreover, we identified social media buzz events by inspecting F2C impressions and C2C volume. Buzz events are described by a large deviation from themean value (i.e., mean +3 SD) and could be either positive or negative. We identified three buzz events, which were related to announcements of new mobile service offers of the focal brand that created large amounts of short-term online buzz. Third, we consider national holidays. Public (e.g., Easter, Christmas) and school holidays could affect the number of acquisitions. The school holiday during the summer actually covers almost the complete months of July and August. In TABLE 2 Description of Variables Variable Name Description Measurement Unit Source Endogenous Variables Traditional advertising Telecom firm’s traditional grossmedia expenditures on TV, radio, print, and out-of-home advertising Gross media expenditures (V) Nielsen F2C impressions Number of impressions of the focal firm’s messages on Facebook based on likes, comments, and shares of those messages Impressions Facebook Insights C2C volume Total number of C2C social messages (positive, neutral, and negative) on forums and Twitter Volume Online tool of the telecom firm C2C valence Sentiment in the marketplace [(positive C2C messages – negative C2C messages)/(all C2C messages)] Share Online tool of the telecom firm Unaided awareness Respondents list all telecom providers they know Percentage of respondents External party via telecom firm (survey) Consideration Respondents list the telecom providers they would consider if they had to choose one Percentage of respondents External party via telecom firm (survey) Preference Respondentsname the telecomprovider theywould prefer if they had to choose a new telecom provider Percentage of respondents External party via telecom firm (survey) Acquisition Number of newly acquired customers Volume Telecom firm’s database Control Variables Holidays Public and school holidays Dummy Own research Media events Important news items related to the telecom sector, specific telecom providers, or new technology Dummy News archives online Buzz events Important interventions that created online buzz Dummy Social media Promotions Number of promotions by focal firm divided by the number of promotions by focal firm + four most important competing firms Percentage Nielsen Traditional advertising competition Traditional media expenditures on television, radio, print, and outdoor by the four most important competitors Gross media expenditures (V) Nielsen C2C social messages competition Number of C2C messages on forums and Twitter about the four most important competitors Volume Online tool of the telecom firm Traditional Advertising and Social Messages on Brand Building / 5 these months, many consumers are traveling. National holi- days might also be related to investments in traditional advertising and consumers’ social media activities. Finally, we consider competitors’ advertising activities and the volume of C2C social messages related to compet- itors, both of which lead tomore clutter andmight decrease the likelihood that consumers notice traditional advertising or C2C social messages by or about the focal firm. We cannot control for competitive F2C social messages/impressions, because this information was not available. Because the main competitors have a much smaller Facebook presence, we believe this is not problematic (Pauwels 2004; Srinivasan, Vanhuele, and Pauwels 2010). Descriptive Statistics Table 3 illustrates the substantial variation in traditional advertising, F2C impressions, and C2C volume and valence for the focal brand over time. The gross media expendi- tures for traditional advertising are, on average, 407,347 EUR. The average number of weekly F2C impressions is 121,153. According to a manager of the focal firm, the firm posted, on average, one F2C socialmessage per day during our observation period. Thus, the weekly number of impressions is generated by about seven firm posts. However, the F2C messages differ with respect to their virality. The F2C social messages reach approximately 46,055 unique consumers every week who are not “fans” of the firm’s social media page (not reported in Table 3). The average number of active users of the page is 17,340 per week, with amaximumof active users of 126,566 in a specific week (not reported in Table 3). The average number of C2C social messages is 1,778. The average valence of C2C social messages equals -.50, which indicates that the sentiment in the market is generally negative. This observation is not surprising given that we study a commodity. To keep the absolute acquisition numbers anonymous, we constructed an index. Table 3 shows that customer ac- quisition also varies over time. Moreover, we observe large variations in the brand-building metrics. For example, brand awareness equals 53% on average but ranges between 37% and 68%. This rather large range might seem sur- prising; however, the considered brand is relatively smaller than its four main competitors. The variation actually suggests that brand-building metrics might be affected by traditional advertising, F2C impressions, and C2C social messages. In the Web Appendix, we provide time series graphs and highlight some interesting potential relations between the different messages and the brand-building metrics. For ex- ample, these graphs suggest a positive relation between tra- ditional advertising, awareness, and consideration. Moreover, the time series graphs suggest a positive relation between F2C impressions and consideration. In addition, peaks in prefer- ence seem to follow peaks in C2C volume, which might indicate that C2C socialmessages positively affect preference. This model-free evidence suggests that the different messages might be related to variations in the brand-building metrics. Yet part of the variation in brand-building metrics might also be due to measurement error (e.g., Naik and Tsai 2000). Because we are interested in the relative effectiveness of traditional advertising, F2C impressions, and C2C social messages, a bias induced by measurement error might not be that critical. However, to test for potential biases due to measurement error, we conduct a robustness check. Table 4 reports the bivariate correlations among the variables whereby we eliminated any trend in the variables before computing the correlations. In general, many cor- relations are significant, which seems promising for further analyses. Insignificant correlations might be a result of the multivariate nature of the relations. Thus, we might find significant relations when we consider the multivariate nature of the relations appropriately. Methodology We are interested in the effects of traditional advertising, F2C impressions, and C2C social messages on both brand building TABLE 3 Descriptive Statistics of Relevant Variables M SD Min Max Endogenous Variables Traditional advertising (EUR) 407,346.90 329,632.80 21,430.00 1,246,570.00 F2C impressions 121,152.80 305,556.50 1,570.00 2,262,655.00 C2C volume 1,778.28 718.67 568.00 3,430.00 C2C valence -.50 .22 -.99 .21 Unaided awarenessa (share) .53 .07 .37 .68 Consideration (share) .30 .05 .18 .42 Preference (share) .15 .03 .08 .22 Acquisitionb (index) 100.00 38.42 40.74 218.89 Control Variables (Exogenous) Promotions (share) .30 .23 .00 1.00 Traditional advertising competition (EUR) 4,884,590.00 1,336,693.00 1,632,151.00 7,956,551.00 C2C volume competition 29,872.75 14,657.07 15,015.00 153,314.00 aWe deleted one outlier whose value was three times the standard deviation below the mean. bFor confidentiality reasons, we provide an index for customer acquisition. Notes: This table reports weekly averages. 6 / Journal of Marketing, September 2017 and customer acquisition over time, aswell as the interrelations among them. Thus, we need to employ a method that allows for considering these complex (inter)relations. We use a VAR model with exogenous variables (VARX). We focus on the cumulative effects (i.e., short- and long-term effects) of the different messages over time and compute elasticities with impulse response functions. This way, we can compare the relative effectiveness of traditional advertising, F2C impres- sions, and C2C social messages. We first test whether traditional advertising, F2C impres- sions, C2C social messages (volume and valence), brand- building metrics, and acquisition are actually endogenous. To this end, we conduct Granger causality tests. We use one to four lags when conducting the Granger causality test and report the lowest p-values of this test in Table 5 (Trusov, Bucklin, and Pauwels 2009). The results in Table 5 show that 41 out of 56 effects are significant at the 10% level. Thus, most variables Granger-cause each other. We model a full dynamic system to adequately capture endogeneity and account for interrelations and feedback effects. Feedback effects include effects among brand-building metrics; effects of brand-building metrics and customer acquisition; and effects of brand-building metrics and customer acquisition on traditional advertising, F2C impres- sions, and C2C social messages. Moreover, there are no the- oretical reasons to impose restrictions on the parameters, which might cause biases in the later impulse response analyses (Enders 2004, p. 292). Next, we test for stationarity of the time series. Because we consider a constant term and a deterministic time trend to capture the impact of omitted, evolving variables, we use the Phillips–Perron (PP) test to assess stationarity (Pauwels 2004). The widely used augmented Dickey–Fuller test has low power in this case (e.g., Enders 2004). All metric variables are stationary because the PP test is significant for all variables (Table 6). We specify the full dynamic system of the VARXmodel in Equation 1. The vector of endogenous variables—traditional advertising (TA), F2C impressions (F2C), volume of C2C social messages (C2C_vol), valence of C2C social messages (C2C_val), awareness (A), consideration (Con), preference (Pref), and customer acquisition (Acq)—is explained by its own past values, and it accounts for the dynamic relations among those variables. We include constant terms (a) and a deterministic time trend (dt) for all endogenous variables (e.g., Pauwels 2004). We control for media and buzz events (X1(2) equals 1 if an event occurs and 0 otherwise), holidays TABLE 4 Correlations Among Variables (Detrended) ln(TA)t ln(C2C_vol)t ln(C2C_val)t ln(F2C)t ln(A)t ln(Con)t ln(Pref)t ln(Acq)t ln(TA)t–1 .568*** .149 .309*** -.199** .206** .216** .040 .299*** ln(C2C_vol)t–1 .166* .868*** .180* -.031 .069 -.106 -.149 -.267*** ln(C2C_val)t–1 .278*** .238*** .319*** -.169* .046 -.017 .124 .316*** ln(F2C)t–1 .062 -.074 -.075 .282*** -.126 .022 -.061 .001 ln(A)t .066 .017 .029 .045 1.000 .212** .115 .025 ln(Con)t .143 -.153* .012 -.118 .212** 1.000 .422*** .225** ln(Pref)t .004 -.174* .136 -.050 .115 .422*** 1.000 .281*** ln(Acq)t .202** -.288*** .265*** -.009 .025 .225** .281*** 1.000 Promotionst .062 -.188** .025 -.103 .149 .261*** .197** .402*** ln(TAcomp)t .129 .041 .149 .078 .086 -.018 .053 .132 ln(C2Ccomp)t .110 .665*** .297*** .003 .007 -.128 -.033 -.124 *p < .10. **p < .05. ***p < .01. TABLE 5 Results of the Granger Causality Tests Dependent Variables Dependent Variable Granger-Caused By … Traditional Advertising C2C Volume C2C Valence F2C Impressions Awareness Consideration Preference Acquisition Traditional advertising — .056 .081 .002 .001 .016 .060 .008 F2C impressions .001 .083 n.s. — .078 .071 .057 n.s. C2C volume .027 — .025 .000 .086 .094 .041 n.s. C2C valence n.s. .100 — n.s. n.s. n.s. .063 n.s. Awareness n.s. .078 .077 .031 — .037 .055 n.s. Consideration .016 n.s. .074 n.s. .087 — n.s. .080 Preference .041 .002 n.s. .046 .005 .094 — .085 Acquisition .003 .052 .030 n.s. .018 .026 .000 — Notes: n.s. = not significant (p > .10). Minimum p-values across four lags. Traditional Advertising and Social Messages on Brand Building / 7 T A B L E 7 C u m u la ti ve E ff ec ts (E la st ic it ie s) o fT ra d it io n al A d ve rt is in g ,F 2C Im p re ss io n s, an d V o lu m e an d V al en ce o fC 2C S o ci al M es sa g es o n B ra n d B u ild in g an d C u st o m er A cq u is it io n an d In te rr el at io n s R es p o n se s o f … Im p u ls es in … T ra d it io n al A d ve rt is in g F 2C Im p re ss io n s C 2C V o lu m e C 2C V al en ce E la st ic it y W ea r- In W ea r- O u t E la st ic it y W ea r- In W ea r- O u t E la st ic it y W ea r- In W ea r- O u t E la st ic it y W ea r- In W ea r- O u t A w ar en es s .0 24 2 4 — — — — — — — — — C on si de ra tio n .0 22 2 2 .0 07 3 3 — — — — — — P re fe re nc e — — — — — — — — — .0 42 2 2 A cq ui si tio n .2 02 2 9 .1 03 1 6 .0 56 3 5 — — — T ra di tio na l ad ve rt is in g .2 23 2 4 — — — — — — F 2C im pr es si on s -. 34 5 2 4 — — — -. 26 5 2 3 C 2C vo lu m e .0 37 1 1 — — — — — — C 2C va le nc e .0 96 3 5 — — — — — — N ot es :D as he s in di ca te in si gn ifi ca nt ef fe ct s; em pt y ce lls in di ca te ow n ef fe ct s, w hi ch w er e no te xa m in ed .W ea r- in in di ca te s th e w ee k in w hi ch th e ef fe ct fi rs to cc ur s; w ea r- ou ti nd ic at es th e w ee k in w hi ch th e ef fe ct di es ou t. 10 / Journal of Marketing, September 2017 across the different channels (i.e., social media and traditional advertising) on the basis of its understanding of the market. Another marketing manager exemplified in another personal conversation that she believes that social media is very effective for the target group and might influence effectiveness of traditional advertising positively. Therefore, she initiates marketing campaigns on socialmedia followed by investments in traditional advertising. The parameters in the VARX model also reflect this firm behavior. We find a positive parameter for F2C impressions in week t - 1 on traditional advertising investment in week t (V = .100, t = 2.310), whereas we find a negative parameter for traditional advertising in week t - 1 on F2C impressions in week t (V = -.314, t = -1.874). Con- sequently, we find a positive elasticity for F2C impressions on traditional advertising and a negative elasticity for traditional advertising on F2C impressions when conducting the IRF analyses (Table 7). Moreover, we find that a 1% increase in traditional ad- vertising positively affects C2C volume with .037%, con- firming previous research showing that a firm’s advertising messages spur online messages among consumers (e.g., Fossen and Schweidel 2017; Hewett et al. 2016; Onishi and Manchanda 2012). Thus, the firm’s advertising stimulates consumers to talk about the firm to others. In addition, con- sumers who do talk tend to react favorably to traditional advertising; a 1% increase in traditional advertising increases the valence of C2C social messages by .096%. In addition, we find a negative elasticity from valence of C2C social mes- sages to F2C impressions (-.265). There might be multiple explanations for this effect (e.g., no spillover effect between platforms, the firm does not react to favorable C2C social messages in their F2C social messages). Unfortunately, we cannot use our data to explore the specific reason. Feedback Effects and Control Variables We find evidence for some feedback effects and discuss the most noteworthy ones. Improvements in acquisition lead to more F2C impressions (a 1% increase in acquisition leads to .239%more impressions), which could be caused by increases in the number of consumers who like the brand and become active users of the page—at least temporarily. Moreover, awareness positively affects volume and valence of C2C social messages; a 1% increase in awareness leads to a .028% increase in C2C volume and a .129% increase in C2C valence. This result suggests that traditional advertising also indirectly affects the volume and valence of C2C social messages through awareness. We next discuss some of the notable findings from the exogenous parameters (Web Appendix). The deterministic trend is significant and negative for traditional advertising (a = -.014), indicating that traditional advertising invest- ments slightly diminish over time. The deterministic trend for C2C volume is instead significant and positive (a = .006), indicating a slight positive trend over time. The media events dummy affects volume of C2C social messages as well as acquisition significantly and positively (q = .072 and q = .096, respectively). The effects could be caused by the fact that this variable captures, for example, new phone introductions, which might lead consumers to talk about these introduc- tions and to an increase in newly acquired customers. The buzz events stimulate awareness (q = .075). However, buzz events are negatively related to C2C valence and preference (q = -.592 and q = -.324, respectively). These results indicate that consumers discussed the focal firm’s new mobile service offerings that created the buzz critically. Comparison with Alternative Models To test whether our proposed model is appropriate and robust, we also estimated a restricted model, which is based on the idea that there exists a certain ordering among the brand- building metrics such that there is a path from awareness to consideration to preference (e.g., Vakratsas andAmbler 1999; see Web Appendix).4 We estimated this model by using a seemingly unrelated regression model because this is most appropriate when the right-hand side variables of the equa- tions are not identical (Enders 2004). The results and the explanatory power of the seemingly unrelated regression model are comparable to the VARX model (Web Appendix). However, the unrestricted VARX model fits conceptually better to the suggested relationships, is generalizable, and thus seems more appropriate. It allows for adequately capturing the complex (inter)relations between the different mes- sages, the brand-building metrics, and customer acquis- ition over time. To show the robustness of our results and to examine whether the brand-building metrics might be prone to mea- surement error, we also estimate a VARX model without the brand-building metrics, all else being equal. Measurement error could possibly lead to inconsistency or upward biases in the parameter estimates (Bruce, Peters, and Naik 2012; Naik and Tsai 2000). Model fit of this model is slightly lower than that of the original model (ΔAIC = 2.26; ΔSC = .78). We computed the elasticities for the effects of traditional adver- tising, F2C impressions, and C2C social messages on ac- quisition. A 1% increase in traditional advertising leads to a .219% increase in acquisition (full model: .202%), a 1% increase in F2C impressions leads to a .126% increase in acquisition (full model: .103%), and a 1% increase in C2C volume leads to a .102% increase in acquisition (full model: .056%). Again, the valence of C2C messages does not affect acquisition. We observe that the effect of volume of C2C social messages on acquisition is higher in this reduced model, but the substantive findings do not change. Thus, the substantive results are robust against different model specification and do not seem to be affected by potential measurement errors in the brand-building metrics. As a final robustness check, we estimated a VARXmodel with F2C reach instead of F2C impressions. This model fits the data equally well (AIC = 4.22, SC = 7.35). We again find that traditional advertising is most effective in stimulating acquisitions, followed by F2C reach and C2C volume (.205, 4We thank one of our anonymous reviewers for this suggestion. We also tested another VARXmodel where we exogenously control for months by adding 11 monthly dummies to the VARX model instead of the HOLIDAY dummy. However, because many addi- tional parameters need to be estimated, model fit does not improve. Traditional Advertising and Social Messages on Brand Building / 11 .088, and .072, respectively). To conclude, the alternative model specifications show robustness of our results. Discussion Summary of Findings and Theoretical Contributions Our study contributes to the literature on the effectiveness of traditional advertising, F2C impressions, and C2C social messages by demonstrating their impact on brand building and customer acquisition. By considering attitudinal and behavioral outcome measures, we respond to a recent call to consider multiple outcome measures in empirical studies (Katsikeas et al. 2016). Whereas previous empirical studies have questioned the relative effectiveness of traditional ad- vertising (e.g., Trusov, Bucklin, and Pauwels 2009; Villanueva, Yoo, and Hanssens 2008), we find support that traditional advertising is still effective today. More specifically, the results indicate that traditional advertising is the most effective way to influence consumers’ awareness, consideration, and customer acquisition. Firm- to-consumer social messages and the impressions generated through these messages are also effective in stimulating consideration and acquisitions beyond traditional advertis- ing. This finding is in line with previous studies (Kumar et al. 2016). Consumer-to-consumer social messages, instead, are effective in creating preference and acquisitions. That is, valence of C2C social messages stimulates preference while volume of C2C social messages stimulates acquisitions. However, C2C social messages are least effective in stim- ulating customer acquisitions. Our results differ in that regard from previous findings suggesting that C2C social messages are more effective in generating sales and acquisition than traditional advertising (Stephen and Galak 2012; Trusov, Bucklin, and Pauwels 2009; Villanueva, Yoo, and Hanssens 2008). Yet it is important to note that the previous studies considered communities and word-of-mouth (WOM) referrals, which are different from the types of C2C social messages we consider in this study. As such, the specific type of C2C social messages might influence the effectiveness of these messages. Moreover, we find that interrelations among traditional advertising, F2C impressions, and C2C social messages exist. Our results thus support previously discussed complementary relations (e.g., Bruce, Foutz, and Kolsarici 2012; Fossen and Schweidel 2017). Our study provides additional evidence that traditional advertising spurs volume of C2C social messages (Fossen and Schweidel 2017). Furthermore, traditional adver- tising generatesmore favorableC2C socialmessages.Wedo not find a relation from F2C impressions on C2C social messages, as some previous studies did (Kumar et al. 2013). A potential reason for the insignificant relation might be that Kumar et al. (2013) specifically design a social media campaign tomaximize C2C social messages through F2C social messages. Finally, we find evidence for feedback effects. These findings illustrate the complexity of the relations among the firm’s “echoverse” and outcome variables and highlight the need for methodological approaches that can capture those relations to effectively orchestrate a firm’s efforts to build a brand and improve cus- tomer acquisition (Hewett et al. 2016).We illustrate that VARX models can capture the complex (inter)relations and allow for assessing the relative effectiveness of traditional advertising, F2C impressions, and C2C social messages. Managerial Implications This study offers four important managerial implications. First, traditional advertising is still an effective medium to build a brand and to enhance customer acquisition. If managers con- sider shifting marketing investments from traditional adver- tising to other types ofmessages, they should take not only costs but also effectiveness into account. Our results further suggest that F2C social messages can complement traditional adver- tising efforts if they spread through the social network (Fulgoni 2015). Overall, traditional advertising and the firm’s social media page are powerful means for brand building and customer acquisition. Thoroughly orchestrating traditional advertising and F2C social messages might improve a firm’s performance. Second, investments in traditional advertising prompt more and more favorable C2C social messages. The positive impact of traditional advertising on the volume and valence of C2C social messages allows managers to exert greater influence on the echoverse and, finally, on critical performance metrics (Hewett et al. 2016). Third, the positive feedback effect of customer acquisition on F2C impressions suggests that newly acquired customers engage with the brand through social media and leverage the firm’s marketing efforts. Fourth, for managers it is useful to track the effects of traditional advertising, F2C impressions, and C2C social messages on both brand-building and behavioral metrics. Monitoring brand-building and behavioral metrics leads to insights that help managers to orchestrate and leverage dif- ferent types of messages more adequately. Limitations and Further Research The study also has some limitations that offer fruitful areas for further research. Becausewe did not observe the costs of current levels of monetary investments in the different messages, we cannot offer specific advice about how to allocate marketing budgets efficiently. Further research should try to derive specific implications on budget allocation in a complex world where traditional advertising, F2C social messages, and C2C social messages are interrelated. The data set did not comprise information about, for example, paid social media; online reviews; or display, search engine, andmobile advertising, because these types ofmessages are rarely used by the focal firm or not relevant. Therefore, not considering these different types of messages did not affect our substantial results. However, future studies might extend the set of messages under investigation to enhance our knowledge about the relative effectiveness of the messages and their interrelations in specific settings. Moreover, we did not observe traditional WOM, which might have resulted in an omitted variable bias. Thus, future studies should collect information on traditional WOM. Further research might also consider competitive actions more extensively.5 In our study, the main competitors of the 5We thank one of our anonymous reviewers for this suggestion. 12 / Journal of Marketing, September 2017