The Impact of Cultural Events on Consumers' Awareness on Cultural Supply

The Impact of Cultural Events on Consumers’ Awareness on Cultural Supply

Juan D. Montoro-Pons1 Manuel Cuadrado-Garc´ıa2

March 2018. Preliminary version

Abstract Cultural products compete for public awareness in a saturated market characterized by an unpredictable demand, oversupply and a short product life cycle. Altogether, this means that only a small fraction of all releases generate the necessary consumer awareness to achieve a significant commercial success. How (and what) information about contents is spread among consumers is key to understand market out- comes. This paper aims at identifying whether participating in an established music festival increases the public awareness of artists and bands. To this end we measure public awareness of an artist using and index of Internet searches, and quantitatively evaluate the effect of performing at the festival on this search index. Preliminary findings support the hypothesis of a significant but temporary surge in web searches.

Ponencia inclu´ıdaen el VIII Workshop en Econom´ıay Gesti´onde la Cultura (Universidad de Sevilla). 14 y 15 de Marzo de 2018

1Corresponding author. Departamento de Econom´ıaAplicada, Universitat de València, Spain. Email: [email protected] 2Departamento de Comercialización e Investigación de Mercados, Universitat de València,Spain

1 1 Introduction

The cultural industries rely on a steady and diversified flow of contents that caters for heterogeneous,unpredictable demand in a market characterized by oversupply, a short product life cycle and with only a small fraction of all releases achieving significant commercial success. Consequently, strategies in cultural markets aim, in the short run, at raising potential consumers’ awareness of the supply in the expectation of monetizing this increased awareness in the middle run. In the music industry, promotion and live performances stand out as the main tools performers use to make consumers aware of the choice set, inducing product discovery, knowledge spread and spillover effects on related cultural products (akin to those discussed in Hendricks and Sorensen 2009). Even though the role of live performances as a tool to raise sales of prerecorded music has become increasingly marginal (see Montoro-Pons and Cuadrado-Garc´ıa2011, on the weakening of the complementarity relation between live and recorded music), these could be linked to alternative direct and indirect income sources, such as streaming or the enlargement of [future] audiences. Indeed, with the increasing use of social media, consumer-based content becomes more and more relevant and amplified when the right con- ditions are met. One example are cultural events, whose attendees dissemi- nate information that, by a snow-ball effect, have an impact beyond actual audiences. In the case of live music, festivals are the most noticeable instance of such cultural events. Music festivals can be seen as market institutions that, through their greater flexibility and less constrained programming (see Frey 1994), aim at specific audiences by combining a (usually) diversified but coherent lineup in an attempt to prescribe and help shaping the taste of the public. The apparent contradiction in the previous statement stems from the hierarchical structure of the lineup which usually combines more successful (even superstars) artists with middle-class and lesser-known ones.

2 In this respect, Paleo and Wijnberg (2006) describe music festivals as selectors that, by fulfilling a signaling and classification function, help consumers reduce uncertainty. The relevance of this taste-formation role is linked to the brand equity and reputation of festivals, an asset that is built up over time through repeated interactions with the public and that is key to understanding the magnitude of increased public awareness performers at these festivals attract.1 Indeed, the average performer playing at a festival benefits from its brand value and from being associated with a larger roster of bands, which can reach out a more diverse audience. As Leenders (2010) shows, it is brand equity and not the program itself what explains atten- dance at music festivals. In this process, attendees move their focus from the lineup to the festival itself, which actually transforms them in gatekeepers. The aim of this paper is to estimate the impact, in terms of public awareness, of headlining an established music festival by analyzing the time series of the web search index for each performer. The research hypothesis is that there is a positive, albeit transient, impact on searches after performing at the festival. To this end the difference between observed and predicted searches is computed, where the latter are the counterfactual searches had the festival not taken place. In other words, we estimate the indirect value that a performance in the festival adds to a band as the difference between actual searches and what would have been observed should the band/performer not participated in the festival. Average relative change in awareness is computed for different time spans after the festival has taken place, and results show a support for the formulated hypotheses. The paper is structured as follows. Next the research hypotheses are introduced. Then the dataset is presented and described along with the identification of specific traits of the sample and the performers that form it. The methodology comes next followed by the results on the estimated

1As it has been already mentioned, this raised awareness, a process that extends beyond actual audiences through the impact in the media and social networks, is most of the times expected to be greater than what isolated acts would achieve performing individually.

3 impact for headliners of the festival. The paper closes with some concluding remarks.

2 Research hypotheses

To some extent, an individual’s cultural demand can be seen as path depen- dent, in that it is primarily accumulated knowledge and previous experiences that determine, by restricting the choice set, what is consumed. In this regard, discovery processes, such as those led by observational learning (see for instance Hendricks et al. 2012), allow consumers to become aware of the variety of the market supply. How this learning takes place is a complex issue, as cultural consumers are exposed to a large amount of information on the availability of content, however we consider that certain cultural events, acting as selectors in the market, trigger this process. The main argument of this paper is that festivals, by supplying a heterogeneous although coherent lineup of performances, add value (on average) to individual performances that ultimately induce a discovery process. To formalize this argument, we estimate the increase in public awareness on specific performers in the lineup of an established music festival after it has taken place. We do so by analyzing the evolution of a web search index as we assume it reflects the interest of the potential demand on the performer. The effect is mediated by the brand equity of the cultural event, such that the greater the brand equity, the more the expected impact ceteris paribus. In this way, the visibility of the music festival, i.e. the credibility and awareness among cultural consumers, and the positive associations it fosters, which can be either functional or intangible benefits that consumers identify, determine how strong the effect will be. In other words, performers temporarily capture part of the value of the brand equity an established cultural event has built up. Therefore, our primary research hypothesis is:

4 H1 A music festival increases the awareness of consumers on the participating artists/bands measured by an increase in web searches.

However, and given the constant ﬂow of new cultural products in the market, this eﬀect is considered to be temporary.

H1b The increased awareness declines after the festival has taken place.

We further hypothesize the expected effect is asymmetric depending on the relative awareness of the performer to that of the festival. In this sense, we expect the benefits of the participation in the festival to be biased towards the middle class and lesser-known acts, and superstars reaping less benefits for different reasons. The rationale under this assumption is as follows. On the one hand, larger (superstar) bands need not be associated to a diversified lineup to command public awareness. Quite on the contrary, their brand equity makes them an asset to a lineup expanding the awareness of the festival. On the other hand, the lineup effect, i.e. being associated with a varied lineup and with more successful acts, could increase the exposure of for middle class and lesser known acts. In other words, we expect the effect to be smaller (on average) for upper middle class bands and superstars. Therefore, we formulate the following hypothesis:

H2 The impact as per H1 is decreasing in the success/awareness of the band.

3 The dataset

To test the foregoing hypothesis we collected data on an index of Internet searches of the headliners in the Primavera Sound, a well established music festival that takes place annually in Barcelona (Spain). We focused on the 2016 edition, that attracted an audience of over 200,000 people attending 349 performances spread over four days (1-5 June). The metric used to

5 measure public awareness is the Google trends weekly search index for participating performers.2 From H1−H2 we hypothesize that there is a positive (and asymmetric), albeit transient, impact on searches for headliners at the festival. Weekly data on Internet searches was obtained for each performer headlining the Primavera Sound festival. Specifically, the dataset covers Google searches in Spain for 19 performers over a period of 261 weeks, from Febru- ary 2012 to February 2017. Two observations apply. First, the Primavera Sound festival took place in week 223 of the dataset. Second, headliners are defined as performers that appear with a larger and bolder typeface in the first block at the top of the general lineup poster for the festival.3 Two performers (John Carpenter and Beirut) were excluded due to the ambiguity of the search term. Table 1 shows the main features of the sample. The first column shows the name of the headliners of the festival. Next, columns two and three, show the average value of the search index over the whole period (full sample: February 2012 to February 2017) and a reduced one (restricted sample: starting the first week of 2016). The Google trends data is an unbiased sample of Google search data translated into a normalized 0-100 index: search results are proportionate to the time and location (Spain) of the query. The higher the average the more the number of searches over the period.4 Note that comparing the index for the full and restricted samples one may ob-

2The metrics used in this paper has been already analyzed in different papers dealing with information on Internet queries on specific terms. Examples include the short-term prediction of economic indicators (e.g Choi and Varian 2012; Vosen and Schmidt 2012); the prediction of cinema admissions (Hand and Judge 2012); the assessment of the impact of social networking in the film industry (Westland 2012); the identification of the traits of virtual currency users (Yelowitz and Wilson 2015); and to proxy public awareness of legislation (Danaher et al. 2014). 3The complete lineup included, from top to bottom of the poster, three blocks of performers, each one in a smaller and lighter typeface. 4Note that, as the index is normalized, all series have a minimum of 0 and maximum of 100.

6 Table 1: Descriptive summary of the sample.

Band Index Index∗ Ratio Ratio∗ 1st album Genre

Action Bronson 23.28 24.64 .005337 0 2011 Rap Animal Collective 19.89 20.59 .01572 .005675 2000 Experimental/Rock Beach House 15.67 7.475 .2923 .2029 2006 Rock Brian Wilson∗∗ 18.41 23.93 .08377 .06815 1988 Rock Deerhunter 16.38 11.84 .00167 .0006557 2004 Rock Dinousar jr. 22.02 21.26 .01953 .0166 1985 Rock Drive Like Jehu 7.218 19.79 0 0 1991 Rock Explosions in the Sky 19.12 18.36 .004857 .0006279 2000 Rock LCD Soundsystem 7.782 9.984 .009798 .0187 2002 Electronic/Rock Last Shadow Puppets 12.01 26.72 .03921 .1417 2008 Rock Moderat 13.34 24.16 .1334 .3325 2003 Electronic PJ Harvey 15.16 23.11 .2601 .4183 1991 Rock Pusha T 21.64 18.16 .01825 .0002075 2011 Rap Radiohead 17.66 22.62 1.44 1.781 1992 Rock Richard Hawley 12.36 8 0 0 2001 Rock Sigur Ros 15.52 9.23 .4319 .2746 1997 Rock Suede 14.82 14.48 .2147 .1604 1992 Rock Tame Impala 24.62 32.08 .3916 .4416 2008 Rock Vince Staples 19.41 33.2 0 0 2014 Rap

∗ Values calculated for a reduced sample starting in the ﬁrst week of year 2016. ∗∗ First release as a member of the Beach Boys in 1962.

7 tain an approximation of the awareness trend: if the mean index is larger in the restricted sample, then there has been a recent increase in searches and hence in public awareness, and the other way around. The Last Shadow Puppets are examples of the former, while Beach House of the latter. Next, columns four and five, are obtained from the pairwise compari- son of the search indices for each performer and the specific search term “Primavera Sound” as music festival. Again, the ratio is shown for the full and restricted sample. Note this ratio is positive without an upper bound, such that at any point it will be over 1 if the performer generates a greater number of searches than the festival does (the larger the ratio the more the searches on the performer) and below 1 otherwise. This, we expect, mea- sures the relative public awareness (hence a measure of the relative brand equity) of a band with respect to that of the festival. Based on the values of the ratio performers can be classified as: (1) superstar acts (at least in relation to the festival), if score higher than one (the performer commands more searches on average than the festival for the period under consideration); (2) middle class acts for values between 0 and 1 (the awareness of the festival is, on average, greater than that of the performer throughout the period under consideration); and (3) small acts, that score 0 in this item. Note that 0 does not imply 0 searches for the performer. It implies that the relative number of searches, when compared with those of the festival, are negligible. Figure 1 provides the distribution of the ratios for each performer in the sample. It also shows the reference line at 1 such that we can discriminate in terms of relative awareness for the different bands. Note that this graph allows us to group performers into categories. In this regard, three facts stand out. First, there are there are three acts for which this ratio is zero, meaning the amount of searches they command is negligible compare to that of the festival. These are what we could label as lesser known performers. Second, the distribution of the ratio for most headliners is to the left of

8 Drive Like Jehu Richard Hawley Vince Staples Deerhunter Explosions in the Sky Action Bronson LCD Soundsystem Animal Collective Pusha T Dinosaur jr. LS Puppets Brian Wilson Moderat Suede Beach House PJ Harvey Tame Impala Sigur Ros Radiohead

0 2 4 6 search indices ratio = index for act/index for primavera sound

Figure 1: Boxplot of the ratio of search indices across performers.

1 (even though some outliers could show to the right of this threshold), implying that the amount of searches of these individual acts is less than that of the festiva. This group could be labeled as the middle class. This is the larger group and at some point we will split it into those whose distribution (once outliers are removed) clearly lies below 1, middle class, and those in which 1 is included in the distribution, labeled as upper middle class. Third, only one case, Radiohead, the distribution shows that the group has, most of the times, generated more searches than the festival itself. This could be a case of a superstar. To conclude with table 1, note that it also includes information about the year of the first release, spanning from 1988 to 2014, which shows a mix of established and newer acts, and genre of the performer as classified by Pitchfork.com. In this respect we must acknowledge that the classification is rather uninformative as most acts are included in the Rock category. On a different note, figure 2 shows some of the patterns that were identi- fied for the evolution of searches over time. Each plot includes two reference lines at the time of the official announcement of the lineup (fourth week Jan-

9 uary 2016), and at the week the festival took place (first week June 2016). In both cases, the plots suggest shifts in the search activity that could be associated to the interventions, i.e. announcement and festival itself. How- ever, it is true that some time profiles are easier to identify than others, and that search activity might be affected by events other than the festival, e.g. a new release or being nominated or winning an award, that one should filter out. This is undertaken in next section.

4 Methodology

Once the distinctive features of web searches for the selected sample have been introduced, we introduce now a framework for estimating the impact of an event (i.e. festival) on the behavior of the time series. This framework is based on the estimation of the diﬀerence between observed and counterfactual (predicted) searches, had the festival not taken place. Following Brodersen et al. (2015), we use a structural time series ap- proach with contemporaneous covariates. Let yit be the value of the search index for performer i at time t. We assume the dynamics of y being driven by a set of observed covariates xit and latent state variable(s) zit. Using the state-space notation, we formulate the model

yit = βxi,t + Hzi,t + t (1)

zt+1 = Bzt + ηt (2)

2 2 with ∼ N(0, σ ) and ηt ∼ N(0, τ ). The goal of expressions (1)–(2) is to produce a forecast for the time series yit. This forecast will exploit the spatial information of the search index, such that we use it as a counterfactual against actual searches. Prediction of current values of the search index are performed using contemporaneous covariates, xi,t, in a static regression model, which aim at capturing market innovations to improve the forecast, and non-observed structural components of the time series. In this respect two modeling choices are to be made: ﬁrst what variables enter the linear

10 iue2 iesre ftewbsac ne o eetdperformers. selected for index search web the of series Time 2: Figure

100 100

75 75

50 50 Search index: PJ Harvey Search index:

Search index: Beach House Search index: 25 25

0 0 jun. 12 dic. 12 jun. 13 dic. 13 jun. 14 dic. 14 jun. 15 dic. 15 jun. 16 jun. 12 dic. 12 jun. 13 dic. 13 jun. 14 dic. 14 jun. 15 dic. 15 jun. 16 11

60 100

75 40

20 Search index: Moderat Search index:

Search index: Tame Impala Tame Search index: 25

0 jun. 12 dic. 12 jun. 13 dic. 13 jun. 14 dic. 14 jun. 15 dic. 15 jun. 16 jun. 12 dic. 12 jun. 13 dic. 13 jun. 14 dic. 14 jun. 15 dic. 15 jun. 16 regression; next what time series components are included in the unobserved part of the model.

As for the former, ideally we expect xi,t to capture all events that affect searches for a performer (e.g. release of a new record) other than the festival. In short, we aim at finding a set of observed variables that incorporates all the information about a performer bar the participation in the event. To this end we exploit geographical and scope information on searches. We do so by selecting the search index in a geography that, very likely, will be unaffected by the music festival taking place. We choose web and youtube search index in Australia for the acts in the sample. The choice of geography was based on the fact that, for obvious reasons, most performers did not tour Australia in 2016 during the forecasting period and that a moderate to large correlation between searches in Spain and Australia was found for performers in the sample.5 As for the time series component, different specifications were formulated and a local level was chosen based on the predictive power against a random walk with a drift.6 The empirical model is then:

yt = µt + β1x1,t + β2x2,t + t (3)

µt+1 = µt + ηt (4) with x1,t and x2,t the indexes for web and Youtube searches in Australia re- spectively, and µt the level. We measure the predictive power of expressions

5We use www.setlist.fm as the primary source of performers and tour information. Close countries such as UK and France were discarded based on the touring criteria, as most performers in the sample were also on tour on these countries which would introduce innovations other than those we aim at capturing. On a previous version of this paper Italy was chosen as fewer acts were on tour there. However Australia, for geographical reasons, is a better choice as logistics (mainly cost barriers) would prevent bands touring in Europe to perform there. 6Other combinations of components that were tested: local linear trend and generalized trend models with or without seasonal components. None was favored by the data.

12 (3)-(4) through the Harvey goodness of ﬁt statistic:

n 1 − X ν2/ X n(∆y − ∆¯y)2 i=1 i=2 where νt are one-step ahead predictive errors. Model estimation is carried out using 10,000 Markov chain Monte Carlo samples. Once the model is estimated, the sample is split into pre-treatment period —before the festival takes place, weeks 1-222 in our sample— and post- treatment period —weeks 223 on. Using the estimation results, predictions are generated for the post-treatment period and are used as counterfactual against actual searches, allowing us to compute the estimated impact on public awareness of the festival as the difference between actual and predicted searches. We do so for three time horizons —one, three and five weeks ahead— noting that the effect will eventually fade out as estimates becomes less accurate as the forecasting period increases. Finally, the relative increase in awareness and its standard error is computed, and its statistical significance is determined for each performer in the sample.

5 Results

Table 2 shows the relative estimated impact for the sample of performers one, three and five weeks after the festival took place. We also indicate, at the bottom of the table, if performers in the sample played in the control geography (Australia) after the festival was held; in this case we include the date of the first live performance. Note that this might lead to an underestimation of the effect when this performance takes place within the prediction period as searches might build up in the control geography leading to an overestimation of searches under the counterfactual. This was the case for three performers. The estimated effect is the difference between the actual and predicted search index, a positive effect being consistent with H1. This is the case of

13 Table 2: Estimated relative effect (standard error below) on the Internet search index and significance at different time points. Intervention takes place at week 223 (starting on 29/May).

Week 224 Week 226 Week 228 5/June date 3/July

Artist Rel.effect p Rel.effect p Rel.effect p

Action Bronson 1.008 0.024 0.437 0.136 0.289 0.175 (0.496) (0.400) (0.346) Animal Collective 1.523 0.000 0.431 0.139 0.107 0.363 (0.387) (0.400) (0.423) Beach House1 0.537 0.268 0.297 0.389 0.189 0.433 (0.869) (1.041) (1.107) Brian Wilson 1.948 0.000 0.843 0.004 0.451 0.057 (0.341) (0.296) (0.277) Deerhunter 2.787 0.001 1.303 0.066 0.710 0.209 (0.844) (0.860) (0.905) Dinosaur jr. 0.963 0.025 0.908 0.012 0.797 0.013 (0.489) (0.399) (0.360) Drive Like Jehu 3.301 0.000 1.876 0.000 1.239 0.000 (0.407) (0.354) (0.326) Explosions in the Sky 1.878 0.000 0.864 0.000 0.392 0.022 (0.294) (0.217) (0.191) Last Shadow Puppets 0.565 0.000 0.134 0.167 -0.083 0.257 (0.157) (0.139) (0.126) LCD Soundsystem 6.766 0.000 3.397 0.000 2.583 0.000 (0.393) (0.309) (0.275) Moderat 1.516 0.000 0.724 0.002 0.506 0.016 (0.255) (0.234) (0.240) PJ Harvey 2.074 0.000 1.050 0.000 0.538 0.008 (0.205) (0.209) (0.218) Pusha T 1.074 0.037 0.801 0.114 0.690 0.187 (0.591) (0.685) (0.779) Radiohead 0.437 0.001 0.187 0.104 0.009 0.472 (0.132) (0.148) (0.157) Richard Hawley 0.873 0.168 0.532 0.270 0.247 0.394 (0.910) (0.902) (0.978) Sigur Ros2 2.278 0.010 1.415 0.075 1.192 0.143 (0.991) (1.010) (1.112) Suede 3.582 0.000 1.679 0.005 1.082 0.040 (0.696) (0.626) (0.634) Tame Impala 1.292 0.000 0.648 0.021 0.713 0.025 (0.305) (0.322) (0.366) Vince Staples3 1.267 0.006 0.903 0.042 0.666 0.100 (0.507) (0.517) (0.525) 1On tour in Australia: 4/Jun/2016 2On tour in Australia: 24/Jul/2016 3On tour in Australia: 25/Nov/2016

14 most bands (specific violations are singled out next). In this respect, two findings emerge. First, all but two performers experience a positive and significant increase in web searches one week after the festival. Second, this number declines over time as the relative effect fades and/or the standard error of the estimate increases: as we move forward and further away from the intervention, the impact becomes smaller and fuzzier. This transitory effect is not unexpected, and consistent with H1b, due to the short life cycle in the music market and to consumers becoming exposed to new information. The decline, however, is not homogeneous: for roughly 32% of the sample a positive impact was still (at least marginally) significant 10 weeks after the festival had taken place. A zero or non-significant effect implies the festival does not generate a surge in awareness. Two cases would fit here: (1) lesser known performers who achieve only marginal exposure through the festival, hence a negligible impact; (2) upper middle class or superstar performers whose awareness is largely unaffected by the festival and who only achieve, but for different reasons, a marginal surge in searches. The two non-significant effects in the first week after the festival could fit in one of these categories (Beach House as upper middle class and Richard Hawley as a small act). For illustrative purposes, Figure 3 plots the graphical analysis for four performers. It displays observed and predicted search index values, with a dashed vertical line showing the intervention point. Figure 4 shows the cumulative impact on searches. Here we find different patterns of the effect: the first case (top-left) shows a non-significant effect (wide confidence inter- val including zero); next (top right) we see a case in which after an initial jump in awareness a steady decline follows; the other cases shows sustained or increasing point effect that become nonsignificant after the lower confidence level of the estimate cross the zero line. Finally, with respect to H2, table 3 shows the mean median and standard deviation of the estimated effect across two groups of performers. Though

15 not conclusive, it shows some evidence supporting the inverse relationship between the estimated eﬀect and overall public awareness of the band.

Table 3: Mean, median and standard deviation of estimated eﬀects by performer type.

Week 224 Week 226 Week 228 Lesser known and middle class 1.892 0.832 0.510 1.516 0.843 0.392 1.638 0.936 0.708

Upper middle class 1.611 0.799 0.389 1.683 0.849 0.269 1.199 0.648 0.421

Total 1.803 0.822 0.472 1.516 0.843 0.392 1.519 0.855 0.634

6 Conclusions

This paper equates a music festival to a planned intervention in the market that spreads knowledge among consumers. We test the hypothesis on whether this intervention translates into awareness and its magnitude by using data on Internet searches on headliners at the Primavera Sound. The one-period ahead estimates of the impact on searches is positive and sig- niﬁcant for roughly 90% of the performers, though the initial impact fades as one moves ahead from the intervention point. These results were robust when the same experiment was inverted to infer the impact of a non-existent market intervention in the control geography: these turned out to be non- signiﬁcant.

16 Empirical evidence is thus consistent with the research hypothesis and provides a framework for analyzing related issues, such as the effect on specific audiovisual searches (i.e. Youtube), the determinants of the magnitude and the time profile of the effect, the relevance of announcement effects or spillover effects on searches on related cultural products such as albums. To sum up, there is evidence that performers at Primavera Sound obtain an increased public awareness stemming from the festival’s reputation and brand equity. How participants monetize this increased awareness is beyond the scope of this research, however it shows that bands and performers have a window of opportunity to turn this raised awareness into revenue.

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18 iue3 bevd(oi)adpeitd(ahd erhidxfrfour for index search (dashed) predicted and (solid) performers. Observed 3: Figure

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20 Search index: Moderat Search index: 25 Search index: Tame Impala Tame Search index:

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Figure 4: Cumulative impact on Google search index.