Viewer Engagement in Movie Trailers and Box Office Revenue
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2015 48th Hawaii International Conference on System Sciences
Viewer Engagement in Movie Trailers and Box Office Revenue
Sehwan Oh JoongHo Ahn Hyunmi Baek
Seoul National University Seoul National University Hanyang University
sehwano@snu.ac.kr jahn@snu.ac.kr lotus1225@hanyang.ac.kr
Abstract As marketing strategies, movie marketers pay
As video consumption on social media becomes a attention to tease potential consumers with movie
major part of consumer activities, film marketers trailers via video-sharing social media. Recently, a
recognize that movie trailers on video-sharing social movie trailer which is designed to provide a taste of
media can play an important role in raising potential specific movie can be easily found and shared via
audience’s attention and interest. Focusing on
social media like YouTube.
consumer engagement in movie trailers and
Increasingly, as social media emerges as a major
subsequent activities of sharing trailers by consumers,
distribution and consumption platform for videos,
we examine the impact of movie trailers on box office
revenue. From December, 2013 to August, 2014, understanding consumer behavior on video-sharing
investigating view statistics of movie trailers on social media and meeting consumer needs is getting
YouTube and data of box office revenue from important to marketers and researchers. For instance,
Boxofficemojo.com, we find out that consumer catching the growing popularity of video
engagement in a movie trailer is positively related consumption via social media, Facebook and Twitter
with their activities of sharing the movie trailer, even attempt to incorporate video watching and
thereby influencing box office revenue of the movie. sharing features in their platforms.
Compared with a lot of research on text-based
social media channels such as blogs and
microblogging services, studies on consumer’s video
1. Introduction sharing and its impact on business have been very
limited. Also, in the context of electronic word-of-
Recently, with the growth of social media, mouth(eWOM), many studies on text-based social
diverse channels such as YouTube, Twitter and media have attempted to develop various metrics of
Facebook penetrate deep into our daily life and consumer response in the dimension of volume,
consumers come to spend huge amounts of time on valence and dispersion in eWOM. However, we have
social media. In the past, recording and consuming limited research on a useful metric of consumer
multimedia contents was difficult and expensive. engagement and interests in video contents.
However, video-sharing social media like YouTube In the context of video consumption, we attempt
makes it much easier and cheaper and gains to examine consumer engagement which is reflected
viewership in an unprecedented rate. Compared with as play time and viewer comments. Then, with a case
text-based social media such as blogs and of movie trailers, we examine whether consumer
microblogs, video-sharing social media emerged as a engagement in a movie trailer is positively related
new consumption and distribution channel for digital with consumer activities of sharing the movie trailer,
goods. thereby influencing box office revenue of the movie.
These days online video consumption on social
media becomes a major part of consumers’ activities.
comScore Inc. [1], a digital analytics company,
2. Literature Review
released that 189 million Americans enjoyed 49.1
billion videos as of October, 2013, while the volume 2.1 Film Marketing and Video-sharing Social
of video advertisement views amounted to be 24.5 Media
billion. According to Cisco [2], online video
consumers are expected to double by 2016, from 792 Among conventional film marketing materials
million users in 2011. such as posters, promotional websites, and trailers,
Among many industries, especially video-sharing film marketers come to pay attention to promotional
social media revolutionizes the entertainment advantages of movie trailers on social media. Firstly,
industry, for example, movie industry. Movie is a movie trailers make potential consumers taste the
representative form of experience goods which movie [3]. While movies are characterized as
cannot be evaluated in advance until consumption. experience goods, consumers hardly evaluate value
1530-1605/15 $31.00 © 2015 IEEE 1724
DOI 10.1109/HICSS.2015.207of movies until consumption. A trailer can give an engagement for a video can be measured by, for
opportunity to sense the movie in a shortened example, fraction of play time per video and number
version. Secondly, with the development of social of visits [10-13]. Measuring active customers,
media, movie trailers are easily shown and Ghuneim [14] classified various levels of customer
distributed at various kinds of channels. Now engagement such as bookmarking(low), commenting
consumers can watch movie trailers on video-sharing (medium), blogging(high), and networking(highest).
social media like YouTube and Vimeo and then share Other researchers also identified that customer
them via various channels such as YouTube, engagement can include various behaviors such as
Facebook, or Twitter. discussions, commenting, and information search
Typically, movies have limited life cycle for 4~6 [15-17].
weeks in theaters and most of box office revenue is As an outcome of customer engagement,
determined at the very early stage after movie release researchers suggested customer satisfaction, trust and
[4]. Film marketers find that social media can play an commitment [15-17]. Previous research has
important role in raising potential audience's attention acknowledged that satisfaction is both the result of
and interest in an early stage of film marketing. As a consumption and precursor of future behavior [18,
result, most of film studios run their own marketing 19]. After consuming products or services,
channels on video-sharing social media and upload consumers evaluate the outcome of their
official trailers for the purpose of promotion. consumption and feel satisfaction, if their evaluation
Considering short product life cycle of a movie, the is positive [20]. As a result of post-purchase
impact of social media in film marketing would be an evaluation, satisfaction is affective response to
important research topic for practitioners and experience from products or services [21].
researchers. Many researchers found out that consumer
In prior marketing and information systems satisfaction is positively associated with behavioral
studies, many researchers examined social media and intention such as re-purchase, positive word-of-
its impact on movie sales in the context of electronic mouth and recommendation [22-25]. For example, a
word-of-mouth(shortly, eWOM) [5-8]. However, satisfied consumer is often voluntary to recommend
previous studies focused on online reviews and didn’t those products and services to other consumers.
attempt to analyze the viewership of particular trailer Patwardhan, Yang and Patwardhan [19] defined
on video-sharing social media and related movie media satisfaction as “a positive general feeling of
sales. varying intensity evoked by users’ favorable post-
As movie trailers on video-sharing social media consumption evaluation of a medium, media genre,
emerge as a major promotional tool for studios and media program, media content or media-generated
distributors, it is important for academia and activity”.
marketers to investigate how movie trailers are With a case of television audience satisfaction, Lu
watched and shared via social media, thereby and Lo [18] proved that increased audience
influencing box office revenue. satisfaction leads to positive word-of-mouth and
repeat watching intention. Exploring video-
2.2 Consumer Engagement in Video and disseminating behaviors, other researchers argued
Sharing Behavior that viewers’ attitude to video content influences their
intention to forward it [26]. Examining entertainment
In general, there are two types of goods: search industry, Cronin Jr, Brady and Hult [24] argued that
goods and experience goods [9]. Search goods are satisfaction directly influences behavioral intentions.
products which consumers can have information
before consumption, whereas experience goods are 2.3 Social Influence and Purchase
products of which value can’t be evaluated without
consumption [9]. According to Nelson’s Viral marketing or buzz marketing has been
classification [9], video contents on social media defined as “the process of getting customers to pass
have characteristics of experience goods, because along a company’s marketing message to friends,
consumers can’t determine true value of video family, and colleagues” [27]. It was argued that viral
contents before consumption. marketing campaigns evoke interpersonal
To quantify user experience in video consumption, recommendation and thus drive sales of products and
researchers have been struggled to measure the services [28]. In general, consumers tend to be
Quality-of-Experience (QoE) with various metrics in influenced by other consumers’ consumption
terms of videos and viewers [10]. Especially, in terms experience.
of viewers, researchers argued that viewer
1725Rosen [29] contended that “[Purchasing] is a part measures of consumers’ engagement and interests. It
of social process”, because there involves a lot of is expected that average watching time of a video
information and influence exchanges around the content against total running time and the number of
consumer. Owing to characteristics of services such comments can reflect viewers’ overall engagement in
as intangibility, non-standardization and perceived video clips.
risk in consumption, Murray [30] argued that In the context of consumption of movie trailers,
personal or independent sources of information are we can expect that average time spent in watching a
more effective marketing channels for service trailer and number of viewers’ comments per trailer
customers. Comparing strong-tie and weak-tie would represent how much consumers are engaged
referral sources, Brown and Reingen [31] found out with the trailer. Considering satisfaction as an
that information from strong tie sources is perceived outcome of engagement, we expect that viewer
as more influential to consumers. engagement in trailers is positively related with the
In marketing research, social influence is number of sharing trailers.
recognized to influence buyers’ attitude and According to previous research, consumer’s
intention. Dividing social influence into two engagement and satisfaction leads to behavior of
categories as informational influence and normative intention, for example, recommendation. We expect
influence, Burnkrant and Cousineau [32] identified that engagement in consumption of a video content
the role of informational social influence and argued and resultant satisfaction is an antecedent of video-
that consumers consider others’ evaluation on sharing activities of a consumer. Thus, we set up the
products. For example, in movie consumption, hypotheses as follows:
consumers may have informational influence from
family or friends. Also, the media plays an important H1a: Average watching time of a movie trailer in
role in exerting informational influence to its total running time is positively related with the daily
audience. number of sharing the movie trailer.
With a case of online content sites, Subramani
and Rajagopalan [33] argued that functions like “send H1b: The daily number of comments is positively
this story to a friend” can make both informational related with the daily number of sharing the movie
and normative influences to receivers. They trailer.
contended that companies should make use of
influencers to fill potential consumer’s knowledge With the development of high-speed Internet,
gap [33]. movie trailers are easily found, consumed, and
distributed on video-sharing social media. Movie
3. Hypotheses Development trailers can give opportunities for consumers to sense
the movie in a shortened version, which leads to
consumers’ movie-going behavior. A lot of movie-
Acknowledging difficulty in measuring goers find movie information via movie trailers on
subconscious customer engagement, researchers have social media and recommend it to other potential
focused on objective and cost-effective measures [12, customers.
13]. Prior research paid attention to video watching To facilitate sharing information and experience
time as a measure of viewer’s engagement and by consumers, many social media platforms add
interests, making use of play time per video as sharing features like sending and recommending
viewer’s engagement metric [10-13]. Examining a webpage links to other people. For example,
peer-to-peer television system, researchers measured YouTube allows its users to share video clips via a
viewer’s interests with watching time per video [34]. lot of social media channels such as Facebook,
In general, viewer satisfaction with video content can Twitter, and Google Plus as Figure 1.
be reflected in play time [12].
When evaluating success of applications,
researchers proposed that the session time (user’s
stay with applications) can be a good measure of
user’s perceptions on system performance, though it
may not reflect user’s satisfaction directly [13]. Also,
it has been argued that customer’s commenting Figure 1. Sharing features on YouTube
behavior can be represented as engagement [14, 17].
In line with previous studies, this study puts a Suggesting framework for viral marketing in
focus on play time and viewer comments per video as terms of externalities and recommender roles,
Subramani and Rajagopalan [33] defined the context
1726of “Targeted Recommendation (TR)” where an In general, there are multiple versions of movie
influencer can make both informational and trailers per movie on YouTube. So, we selected one
normative influences within his/her network. They representative movie trailer per movie, which
argued that utility of TR depends on recommender’s displayed the largest number of hits right before
ability in understanding recipient’s interests and movie release. After identifying the representative
preferences, which may serve as more efficient movie trailers, we set the Web-crawler to
marketing tactic. Thus, we set up the second automatically retrieve data at a fixed time in a daily
hypothesis as follows. basis.
Along with view statistics of movie trailers on
H2: The daily number of sharing a movie trailer YouTube, we collected movie-related data from
is positively related with the daily box office revenue Boxofficemojo.com (www.boxofficemojo.com)
of the movie. which was referred by previous studies [5-8]. Chang
and Ki [37] suggested that there can be psychological
4. Research Methodology or economic approaches in studies on box office
performance. Suggesting a conceptual framework for
analyzing the success of a particular movie, they
4.1 Data Collection addressed four groups of determinants in measuring
movie success: (a) brand-related variables (sequel,
To investigate consumer’s behavior in director, and actor), (b) objective features (production
consumption of video contents through social media, budget, genre, and MPAA rating), (c) information
we focused on YouTube, the world-largest social sources (critics’ rating and audience rating), and (d)
media for video contents. Established in 2005, distribution-related variables (distributor’s market
YouTube became the third most popular website in power, and release periods).
the world with approximately 11.9 page views and Based on their research framework, this research
17.5 minutes per visitor in a daily basis [35]. develops the framework by adding new variables
As of 2013, YouTube reported that 100 hours of with trailer consumption on video-sharing social
video are uploaded every minute and over 6 billion media. Controlling for three movie-related
hours of video are consumed each month on its determinants such as brand-related variables,
platform [36]. According to comScore Inc., in terms objective features, and distribution-related variables,
of online video content property, Google sites this study attempts to highlight the impact of sharing
(primarily, YouTube) ranked top with 164.8 million trailers on box office revenue.
unique viewers, followed by Facebook (70.1 million) In addition to daily box office revenue, we
and AOL, Inc. (62.3 million) [1]. collected movie-specific control variables such as
To collect video view statistics, we built a Java- MPAA ratings, genre, distributor, production budget,
based Web-crawler and retrieved the data by using and release date from Boxofficemojo.com. Regarding
YouTube’s open application programming interface production budget, if we could not find related
(API) and scrapping HTML subpages of view information from Boxofficemojo.com, then we
statistics. Though we were able to use YouTube’s referred to other websites such as IMDb.com, the-
API for basic view statistics of a specific video clip, Numbers.com, and Wikipedia. Next, we merged
we had to resort to parsing YouTube’s subpages for movie sales data from Boxofficemojo.com and view
detailed statistics such as the number of views, the statistics of movie trailers on YouTube, which
number of sharing a video clip and average watching resulted in panel data across movies from the opening
time of a video as Figure 2. dates of movies.
During the period of data collection from
December, 2013 to August, 2014, we could collect
YouTube view statistics of movie trailers and movie-
related data for 40 movies as Appendix A. Though
there are dozens of movies which are newly released
in U.S. market every week, we could secure limited
samples which showed the series of box office
revenue in a daily basis.
Among many determinants of movie sales, we
Figure 2. View statistics of a video clip on made use of the number of screens, the amount of
YouTube production budget, the number of days after movie
release, weekend dummy, MPAA ratings and genre
1727as control variables. In summary, Table 1 presents means users watch movie trailers around 70% of the
the variables for this analysis. total running time in average.
As to movie-related data, daily box office
Table 1. Description of variables revenue(DailySales) is $1,125,345 in average. The
Variable Description average number of screens(Screen) is 1,433, while
Daily number of viewing a average production budget(Budget) is $41.8 million.
DailyViewi,t Average opening period of movies(DaysRelease) is
movie trailer i at day t
Daily number of comments per a 26.7 days. Table 3 presents the correlations of key
DailyCmti,t variables. To smooth the distribution of variables, we
movie trailer i at day t
Daily number of sharing a take the logarithm of the key variables.
DailySharingi,t
movie trailer i at day t
Average watching time of a 4.3 Analysis Model
AvgWatchingi,t movie trailer i at day t to the
total running time To verify hypotheses, this study sets up two-
Daily box office revenue of equation system with Equation 1 and 2. Equation 1
DailySalesi,t
movie i at day t (in US$) sets the daily number of sharing a movie trailer as
The daily number of screens of dependent variable, whereas Equation 2 sets the daily
Screeni,t
movie i at day t box office revenue of related movie as dependent
Production budget of movie i (in variable.
Budgeti
Mil. US$)
Weekend dummy (weekend=1, ln(DailySharingi,t)= 0 + 1*AvgWatchingi,t-1
Weekendi,t + 2*ln(DailyCmti,t-1)
weekdays=0) of movie i at day t
The number of days after movie + 3*ln(DailyViewi,t-1)
DaysReleasei,t + 4*ln(DailySalesi,t-1)
release of movie i at day t
MPAA ratingsi MPAA ratings of movie i + ui,t + i,t (1)
Genrei Genre of movie i
ln(DailySalesi,t )= 0+1*ln(DailySharingi,t-1 )
+ 2*ln(Screeni,t) + 3*Weekendi,t
4.2 Descriptive Data
+ 4*ln(Budgeti) + 5*DaysReleasei,t
+ 6*MPAAi + 7*Genrei
Table 2 provides descriptive statistics of view + 8*ln(DailySalesi,t-1 )
statistics and movie sales. Though we start with 40 + ui,t + i,t (2)
movies, the total number of observations amounts to
be 1533~1575, due to using unbalanced panel data Previous eWOM research acknowledged the
and incorporating lagged variables. interdependence between box office revenue and
eWOM, and developed simultaneous equation system
Table 2. Summary statistics of the daily data in model specification [7]. Likewise, considering
Variable N Mean Std. Dev. interrelationship between sharing a movie trailer and
DailyView 1533 16480.5 21838.6 box office revenue of the movie, this study employs
DailyCmt 1533 8.0 29.9 simultaneous equation model. In addition, to take
DailySharing 1533 16.2 28.5 advantage of panel data structure, we apply panel
AvgWatching 1575 0.7 0.1 simultaneous equation model to our analysis.
DailySales 1575 1125345 2551422 In Equation 1, ln(DailySharingi,t) denotes the
Screen 1575 1433.1 1275.7 daily number of sharing a movie trailer with movie i
Budget 1575 41.8 50.4 at day t in log-transformation. As key independent
DaysRelease 1575 26.7 21.2 variable, we incorporate average watching time
against total watching time per trailer at previous day
In terms of view statistics on YouTube, the daily and the daily number of comments at previous day,
number of views(DailyView) and the daily number of which are denoted as AvgWatchingi,t-1 and
comments(DailyCmt) per movie trailer is 16,480 and ln(DailyCmti,t-1). In this model, we set up the lagged
8 respectively, while the daily number of sharing a variables, because viewer’s engagement which is
movie trailer(DailySharing) is 16 in average. The measured by play time and number of comments may
ratio of average watching time of a movie trailer to lead to sharing activities by consumers in the
the total running time is 0.7(AvgWatching), which following period.
1728Table 3. Correlation matrix of key variables
Variable 1 2 3 4 5 6 7 8
ln(DailyView) 1
ln(DailyCmt) 0.63*** 1
ln(DailySharing) 0.79*** 0.70*** 1
AvgWatching 0.04 0.21*** 0.28*** 1
ln(DailySales) 0.24*** 0.35*** 0.37*** 0.13*** 1
ln(Screen) 0.26*** 0.32*** 0.32*** 0.13*** 0.89*** 1
ln(Budget) -0.17*** -0.06*** -0.15*** -0.14*** 0.33*** 0.33*** 1
DaysRelease -0.18*** -0.31*** -0.31*** -0.03 -0.48*** -0.36*** 0.12*** 1
***phypothesized, we can also find out that the daily revenue. Filling current research gap, this study
number of sharing a movie trailer is positively related contributes to highlight the role of consumer
with the daily box office revenue of the movie, which engagement in video content and its impact on actual
supports H2. sales.
Furthermore, as expected, the number of screens, In the context of eWOM, based on text-based
the amount of production budget, and weekend are social media, most of researchers have focused on
positively related with box office revenue, whereas three metrics of eWOM: volume (the number of
the number of days after movie release is negatively review postings), valance (the average star rating or
related. the positive (or negative) ratings), and dispersion
(spread of eWOM across social networks). However,
Table 5. Estimation results for Equation 2 we have had limited research on an appropriate
Equation 2 metric in measuring overall consumer response on
(DV: ln(DailySalesi,t)) video-sharing social media.
FE 2SLS This study contributes to examine the possibility
(1) (2) of measuring consumer engagement in video contents
0.29*** 0.61*** with average watching time and the number of
ln(DailySharingi,t-1) comments on video-sharing social media. This
(0.02) (0.05)
0.76*** 0.74*** research suggests that they may serve as good
ln(Screeni,t) indicators how much particular video clip makes
(0.02) (0.02)
0.85*** 0.85*** customers engaged. With the growth of social media,
Weekendi,t it is expected that more research is required about
(0.02) (0.03)
-0.03*** -0.02*** customer engagement in an online setting.
DaysReleasei,t In a practical perspective, this research shows that
(0.00) (0.00)
marketers should take advantage of sharing features
7.16*** 6.35***
Constant of social media. Subramani and Rajagopalan [33]
(0.14) (0.19)
confess that we have limited data on the effectiveness
R2 0.931 0.916 of sharing features which are provided by most of
N 1158 1029 current websites. Showing the positive relationship
between sharing a movie trailer and box office
Note: Standard errors in parentheses revenue of the movie, this research reveals that
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Appendix A. Movie list in the sample
No. Title Release Date No. Title Release Date
1 Believe 2013-12-25 21 13 Sins 2014-04-18
2 The Nut Job 2014-01-17 22 The Other Woman 2014-04-25
3 Ride Along 2014-01-17 23 The Quiet Ones 2014-04-25
4 Gimme Shelter 2014-01-24 24 Maleficent 2014-05-30
A Million Ways to Die in the
5 Vampire Academy 2014-02-07 25 2014-05-30
West
6 3 Days to Kill 2014-02-21 26 The Fault In Our Stars 2014-06-06
7 Pompeii 2014-02-21 27 Jersey Boys 2014-06-20
8 In Secret 2014-02-21 28 Think Like A Man Too 2014-06-20
9 Repentance 2014-02-28 29 Begin Again 2014-06-27
10 Bad Words 2014-03-14 30 Snowpiercer 2014-06-27
11 Cesar Chavez 2014-03-28 31 Deliver Us From Evil 2014-07-02
12 Noah 2014-03-28 32 Earth to Echo 2014-07-02
Dawn of the Planet of the
13 Sabotage 2014-03-28 33 2014-07-11
Apes
14 Frankie & Alice 2014-04-04 34 Planes: Fire and Rescue 2014-07-18
15 Island of Lemurs: Madagascar 2014-04-04 35 The Purge: Anarchy 2014-07-18
16 Oculus 2014-04-11 36 Sex Tape 2014-07-18
17 Cuban Fury 2014-04-11 37 And So It Goes 2014-07-25
18 A Haunted House 2 2014-04-18 38 Hercules 2014-07-25
19 Heaven Is For Real 2014-04-16 39 Lucy 2014-07-25
20 Transcendence 2014-04-18 40 Get On Up 2014-08-01
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