Names versus Faces A case study on movie

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Names versus Faces

A case study on movie-trailers

Frank van ‘t Ende

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Names versus Faces

A case study on movie-trailers

Frank van ‘t Ende

University of Groningen, Department of Marketing Master Thesis

10-8-2014

Peizerweg 69A, 9726 JE Groningen 06 249 529 00

fvtende@gmail.com

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Management Summary

The goal of this research is to investigate how consumers react to stars in movies and in their corresponding movie-trailer. Respondents were asked to first rate actors of a movie-cast and afterwards stating how likely it was for them to see that movie. After this, respondents were shown the movie-trailer and respondents were asked if they perhaps recognized actors by face from the trailer, and if so, they could give a rating to that actor. Again it was asked how likely it was for them to see the movie, but now with the extra movie-knowledge of the trailer.

The found data was used to analyze the star-power effect for a text-based info-sheet and for the additional star-power that could be gathered by showing a trailer where consumers recognized more faces.

The findings showed that star-power indeed does exist. However, it can be negative. The effect of liked actors in movies has an almost equal positive effect on movie success as disliked actors have a negative effect. Interestingly, it was also found that the more actors respondents knew the greater the odds for movie-success will get. This effect is bigger for name recognition, but also every additional recognized actor by face, after seeing the trailer, has a significant positive effect on the movie success.

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Preface

This research was performed to finish the final episode of the marketing course for me. It’s of course hard to select a good topic for such a final research. Trying to take a look into how I would like to see my future I made the decision to do a research which is linked to the entertainment industry. In this case the final decision was made on the movie industry.

The process of finishing this project was a bit of a struggle, yet the findings came out to be rather interesting and might even be translated to some other industries. The most interesting outcome is that consumers recognize actors, of which they did not know name, from a trailer and if they do recognize them their chances of seeing the movie is greater.

In conclusion, I would like to thank all the teachers who have learned me something that has contributed to this research. Special thanks go to Felix Eggers, who was the supervisor of this

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Introduction

The international market for movies is slowly declining (Domestic Movie Theatrical Market Summary 1995 to 2014). In 2002 the annual tickets sale were 1.58 billion, as for 2013 this number has dropped to 1.31 billion ticket sales, a 17 percent drop in just over ten years. However, the total revenues have slowly increased since then and stabilized in the last couple of years towards a revenue of 10.90 billion in 2013. The drop in ticket sales creates a challenge for studios and directors to get the consumer specifically to their productions. Can movie studios try to organize things in a way that the odds turn in their favor?

Movie-makers use different techniques to get the consumer to their movies. There is scientific evidence that movie budget, and advertising budget in particular (Moon et al, 2010), has a positive effect on the commercial success of movies (Ravid, 1999; Teti, 2013). Budget is, logically, partly allocated to actors, and actors can also help draw consumers to a movie and create a buzz. (Wallace et al, 1993; Karnouchia, 2011; Gazley et al, 2011) Choosing for an actor who has a big fan-base can raise revenues, but these actors also ask higher fees. To what extent are people influenced by the cast of a movie then?

Another possible way try and turn the odds in favor of a movie might be the trailer. But what makes a movie-trailer successful? There is hardly any scientific knowledge about trailers and their

effectiveness, yet the trailers of movies are used frequently by cinema’s and movie studios to reach out to potential visitors. At least they think the trailer has a positive effect on the commercial success then.

Does ‘star power’, meaning having a star that creates buzz in your, have an effect on the efficiency and success of a trailer?

Literature Review

As mentioned before, there is hardly any scientific research about the effects of movie trailers. However, there is some research that can relate to movie-trailers and their possible effects on the success of a movie.

Star Power

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Several articles are written that prove the effect of star power. (Ainslie, Drèze, and Zufryden 2005; Albert 1998; Basuroy, Chatterjee, and Ravid 2003; Elberse and Eliashberg 2003; Faulkner and Anderson 1987; Litman and Kohl 1989; Neelamegham and Chintagunta 1999; Prag and Casavant 1994; Sawhney and Eliashberg 1996; Sochay 1994; Wallace, Seigerman and Holbrook 1993)

However, other researchers also found that the star power effect is non-existent. (DeVany and Walls 1999; Litman 1983; Litman and Ahn 1998; Ravid 1999) Ravid, for example, stated the effect that is mentioned as star power by the first couple of researchers is not actually the effect of star power, but more the effect of movie budget. DeVany & Walls, state that the distribution of the possible outcome of the success of a movie is so uncertain and has so many possible factors that affect the success that there can not be good forecast of the success of a movie at all. DeVany & Walls (1999) stated that stars are thus not ‘bankable’, but will only increase the odds for success.

However, Elberse (2007) mentioned a few examples where actors helped boosting revenue to a great extent.

Scientifically, different methods have been used to indicate ‘stars’. Sawhney & Eliashberg (1996) use a marketvalue figure, other articles use a survey of movie-insiders, like executives in the movie industry, to find ‘stars’ (Elberse & Eliashberg, 2003; Ainslie, Dreze & Zufryden, 2005). Ravid (1999) uses top-grossing film participation and nominees of Acadamy Awards.

There are a few notable star power effects. For example, rental income is for one third explained by star power (Wallace, 1993), this same research also found that star power changes over time. It found that the surplus an actor adds to a movie revenue declines and grows over a period of time. Furthermore, this figure is different depending on the country where the movie is released, since Hennig-Thurau, Walsh & Bode (2004) found that star-power’s positive effects are bigger in the USA than in Germany.

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movie studios to measure star-power of actors before release and already construct a campaign emphasizing the star-actors to countervail possible negative effects if necessary.

The star power effect on first hand, looks a bit similar to brand loyalty, an attitude or feeling consumers have towards a product or brand. Brand loyalty is the phenomenon when a consumer picks a brand because the consumer is loyal to that brand. The product that was picked might not be the best suitable product for the consumer, but it is still bought based on loyalty. (Tucker, 1964)

The effects of brand loyalty however can not be translated into star power in movies and their trailers directly. A movie has a cast of many more actors and a certain crew that movie-goers might be loyal, or disloyal, to. Thus, if consumers choose a particular movie, loyalty is likely to have some effect. But this loyalty is a sum of loyalty and disloyalty towards the total cast and crew of a movie. Evidence for the statement that a total cast plus crew can have a synergy was found by Nelson & Glotfelty (2012). It was found that certain actors who are put together can gain even an extra surplus of revenue. (Cattani et al, 2013) Familiarity of cast and crew from former movies was also linked to a bigger success, even when ruling out sequels and prequels. Perhaps this success is moderated by the perfectionized ‘attachment’ a consumer has when more actors are put together.

For this study therefor the knowledge of brand loyalty is not used, since loyalty is typically based on one brand. Therefor in this study the effect of ‘star power’ will be used.

Expectations & Trailer-effect

Another effect that is known from other markets, can be linked to movies and their trailers. That is the effect of expectations.

Consumers build expectations based on what they have experienced before and what they know of a product, so former movies for example, and what they are promised to expect from the current product. (Stayman & Alden, 1992; Einhorn & Hogarth, 1981)

When it comes to new product information or getting to know more about a movie, something a trailer also offers to consumers, confirmation of what was expected has a positive influence on the likeliness to buy a product. (Sohn, Ci & Lee, 2007)

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expectations are proven to be negative. (Yoon & Kim, 2000) Even in the long run, since it can even affect loyalty. Olshavsky & Miller (1972), found this in a research on tape recorders.

Fishbein & Ajzen’s (1975) ‘Theory of reasoned action’ state that if consumers say they ‘intent’ to do, buy or act for example, something it is likely that they do so. This theory states that this intention is made up out of the ‘attitude’ towards the action and the social norms of a person.

Conceptual Model

The literature review leaves space to investigate how star-power drives the likelihood of watching a movie. And how is the likelihood changed when the trailer was shown, where viewers might have recognized actors by face leading to additional star power.

The conceptual model for the current research looks as follows:

Figure 1: Conceptual model

Hypotheses

The hypotheses are:

 H1: Star power has a positive effect on the probability to watch a movie

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Likeliness of seeing movie (Home & Cinema) (0-10

scale)

Research Design

Data Collection

Data was collected of 128 respondents to a survey. Every respondent, after filling in some socio-demographic information, were presented five similar sets of questions about five different movies (Appendix A). Since all questions were identical, just about different movies, the data is pooled for all five movies.

Survey setup

The survey was made in a way that it would lead to variables and constructs that would be sufficient to test the stated hypotheses. For every movie, each respondent, simple plain text info of the movies was first shown. Nothing more then just the movie-title, top 3 actors (according to IMDB) and the director were stated (Appendix A). After this info it was asked if respondents knew any of them, and if they did they were asked how good they were (flowchart above). Then it is how likely it is that a respondent would see the movie, either in cinema or at elsewhere. The next phase is where the trailer of that movie is shown. After this, respondents are asked the same questions, but this time they have seen their face and might have recognized the actor.

Do you know ‘actor name #1’?

Do you know ‘director name’?

How would you rate him/her (0-10)

Yes No

No How would you rate him/her

(0-10)

How would you rate him/her (0-10)

No

Trailer is shown. Afterwards, same questions are asked, but now not for names,

but for faces. If respondents said they

knew an actor by name, they wont be asked based on face-recognition again.

Yes

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Constructs

The variables that are used in the analyses are dummy-coded or counted variables. Since

respondents were asked to how they liked actors if they know them there are 3 different dummy-variables. Firstly, the base value for the dummy, so when either of the dummy-variables is zero, is when people don’t know and thus can’t like the cast of a movie. Then there are two dummies that tell whether respondents liked or disliked the cast of a movie. The cutoff-point for dividing these two from each other was when respondents liked the cast with a grade of 7 or higher. Every value below 7 was dummy-coded as a dislike of the case. This choice was based solely on the fact that choosing for this cut-off point was closest to a fifty-fifty dividing of these groups which might help the analyses itself since groups are equal in size. Combined with two variables where respondents had to answer how likely it is that they would see a movie, either in cinema or somewhere else, would be enough to answer H1.

To answer H2, a new dummy is added and the data is doubled. A dummy is used to show whether or not the trailer was shown. The data is doubled, since questions about actors were asked before and after the trailer was shown in the questionnaire. One half of the data gives information for how likely it was for respondents to see a movie with only knowing the cast. The other half gives information for the addition trailer knowledge. Furthermore, to find the moderating effect of ‘addition star power’, two variables are added. Firstly, there is the number of known actors before seeing the trailer and secondly there is a variable for all additional actors that are known by face after seeing the trailer.

Method of Analysis

Ordinary least squares regression models are used to analyse the relationships stated in the conceptual model.

The following model is estimated:

Meaning of abbreviations

PtW = Probability to watch

= Index for place to watch (at home or in cinema) KL = Knows & Likes (7+ average) cast

KD = Knows & Dislikes (7- average) cast

NK = Number of Known actors before trailer was shown (0-4) T = Trailer (0 = not seen trailer, 1 = seen trailer)

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Model analysis

Sample

The sample that was used to test the stated hypotheses has a size of 758. This are the 379

respondents times two, since every respondents is used twice in the analysis, once with data of ‘pre-trailer’ variables and once with ‘post-‘pre-trailer’ variables.

The age of the respondents ranged from 15 years old to 64 years old. But over 50 percent of the sample was between 18 and 30 years old.

Educational levels are more leveled out, about 40 percent of the sample has a University professional education. High School, University Bachelor and University Master level are all somewhere around 20 percent of the sample.

Males were overrepresented in the sample with over 87 percent of the sample. The figures underneath depict how many movies respondents watch.

How often do you go to the cinema? Percentage

Never 9,8 %

Less than Once a Month 62,8 %

Once a Month 14,0 %

2-3 Times a Month 9,5 %

Once a Week 4,0 %

How often do you watch movies? Percentage Less than Once a Month

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Regression output

Probability to watch at home

Standardized

Beta

Sig-value

VIF

Constant ,000

Know & Like ,222 ,000 2,838 Know & Dislike -,218 ,000 2,337 #Known Actors Pre-Trailer ,404 ,000 2,215 Trailer ,022 ,505 1,187 #Additional Known Actor Post-Trailer ,170 ,000 1,540

Probability to watch at cinema

Standardized

Beta

Sig-value

VIF

Constant ,000

Know & Like ,153 ,008 2,841 Know & Dislike -,191 ,000 2,340 #Known Actors Pre-Trailer ,308 ,000 2,215 Trailer -,056 ,126 1,187 #Additional Known Actor Post-Trailer ,180 ,000 1,540

Model Analysis

The analyzed model for watching the movie at home has an R-square of 0,361, meaning that around 36 percent of the dependent variable is explained by the 5 chosen independent variables. For the model that analyzes the probability to watch a movie in cinema has an R-square of 0,206.

For both models the found beta for the trailer was not significant, but apart from that the selected variables were highly significant and thus the found beta’s for the model within this sample are trustworthy.

Furthermore, the collinearity figures are safe and suggest that the selected variables don’t interfere much with each other.

Hypotheses

There were two hypotheses to test.

H1: Star power has a positive effect on the probability to watch a movie

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H2: The trailer has a moderating effect on the effect that star power has on the probability to watch a movie

The second hypothesis is supported. However, the effect of the trailer is not found to be significant. On the other hand, people who recognized actors from the trailer are slightly more likely to watch a movie, both in cinema and/or at home.

Conclusions

Based on the analysis it is safe to say that star power affects the success of a movie positively. Moreover, it is found that a trailer can be used to improve the odds for success of a movie.

First, the star power effect. It is analyzed that if actors are known by name, consumers can rate that actor and that rating will affect their likeability to see a movie. The negative effects of disliked actors is quite equal to the positive effects of liked actors. This is a new support to many of the finding of previous researches. Ravid (1999) however, stated that the effect is not from star-power, but from budget spend on actors. This statement of Ravid was not tested unfortunately. When mentioning the finding of Ravid, the effect of the number of known actors also comes to mind. Every actor that is known more has a positive effect on the likeability of seeing a movie, either if the actor was liked or disliked by the respondent. This does not support or reject the statement of Ravid, it does however show that well-known actors will have a positive effect overall, since more consumers will know the actor, which then makes it more likely for them to watch the movie. Well-known actors are likely paid more than less known actors, which can be a support for Ravid’s statement. The found effects do support the findings of DeVany & Walls (1999), who stated that actors are not really ‘bankable’, but having well-known actors in a movie does improve the odds for success.

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the main-actors of movies are recognizable on the trailer of the movie, because it can help the success of a movie.

Thirdly, Basuroy (2003) examined the counter-effects of star-power when reviews of the movie with that star were negative. It was found that effects of a negative reviews can slightly be countered by star-power. The analysis of this report did not have any review variable in it. However it is supported that knowing more actors, thus having a greater star-power, will have a positive effect on the possible success of a movie. Furthermore, Basuroy stated, negative reviews influence the movie-success more than positive reviews. Which would mean that the respondents of this report who said they disliked an actor should have a bigger negative effect than those who liked an actor have positive effect. This was only the case for the model where cinema-success was forecasted, for the at home-success the influence was equal.

The effect the trailer has on consumers is totally new for this research. However, it is found that the trailer can have a positive effect. The trailer effect itself was found to be insignificant, yet the variable for knowing more actors after seeing a movie-trailer was significant and positive. Suggesting that a trailer can be used to strengthen the chances of success of a movie.

Limitations

Firstly, the questionnaire contained five movie trailers that were picked based on ‘face validity’, only by the judgement of the author of this report. The five movies were of five different genres. It might be that findings would be very different if it was performed only with thrillers for example. It could very well be that the found effects differ per movie-genre, this was not measured.

Furthermore, the trailers will differ per movie. For example a comedy-trailer would have jokes in it, where an action movie would likely have explosions and chases in the trailer. All genres were taken together. It’s not strange to think that actors need different qualities for different genre’s as well. Perhaps respondents knew an actor, and thought that this actor was good until they saw that this actor was in a movie of a genre that did not at all fit to what they think this actor should act. This would be in line with the finding of Sohn, Ci & Lee (2007) who found that confirmation of what is expected has a positive influence on the likeliness to buy something.

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have been in the trailer to test the face recognition effect. There is no knowledge if consumers will recognize names and faces more if actors had a longer career or have made more movies recently. Moreover, some actors might be known from other things they do besides being an actor, like being a singer, which makes them more likely to be known. In case of face recognition, one might argue that some faces of persons are easier to remember which also might have influenced the outcome of this research.

Thirdly, the magnitude of the found effects are likely to differ per country based on previous research by Hennig-Thurau (2004), who found that star-power effects are less big in Germany when compared to the USA.

Fourthly, at a certain point announcing more actors, who probably have minor roles in the movie, might get annoying and this might have a negative effect on the likeability to watch for a consumer. This might even be considered as a kind of ‘spam’.

Last, it might be important to have a measure on how long the movie cast or trailer is remembered and if the likeliness to watch a movie, and thus the commercial success of a movie, drops after a couple of days. This is something Elberse (2007) measured by using an online market simulation and checking the effect in days after announcements of movies. It showed that effects will diminish over time. This effect is likely to be apparent for trailers as well, however it was not measured.

Managerial implications

The movie studio managers can take some of these findings to help the odds of success of a movie.

This research found that when consumers knew more names they were more likely to watch a movie. This was found both for names and for faces, the effect of knowing actors by name was bigger however. It might therefor be wise for movie-studios to announce as many names as possible. However, it was not found if there is some kind of optimum for the number of actors that should be announced or that should be in a movie. It might be that announcing more name will diminish the found effects. Movie-studios might try to focus on announcing different names to different

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Furthermore, the found effects got bigger when respondents would go and see a movie at home. This is something that might be hard to handly for movie-studios, since all possible info can usually be found when a movie is released on DVD or can be rented. If a movie gets older it is likely to be easier to find info about the movie. On the short-term movie studios can help steer people in the right direction by directing them to the information that would benefit the likeability to watch a movie. This is likely to get harder over time.

Future Research

There still are many things that are not known about movie-trailers and movies effectiveness when it comes to marketing. The principle star-effect has been proven again, this time also for movie-trailers and face recognition. However, when consumers are loyal to the faces of actors, what is their loyalty really? Are they perhaps loyal towards the role that they know this actor from, maybe from just one or two movies? Or do they know something personal about this actor that made consumers link this actor to their own image? For example, some actors who played certain roles for multiple years have a hard time to show that they are not the person of that long played role, maybe actors of long-aired tv-series struggle with this. If an actor for example played a dumb person in a comedy for many years, this actor would perhaps not be taken seriously by viewers if he would play an professor in another movie or series. So, then the actor is know, yet not playing the expected role which might be negative according to the theory of Yoon & Kim (2000)

Furthermore, it might be interesting to research whether or not the found effects differ per genre. Does this effect work just as strong for every single genre? And on top of that, what if an actor is in a movie that, genre-wise, would not fit this actor. Will this actor lose some loyalty perhaps? And are some genres perhaps more related to each other. Can an actor who played many action movies still play a drama? Or can an actor who is known for horror, play in a romantic movie according to the consumer?

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Another interesting follow-up study would be to check specifically for some actor and/or director groups that are considered to belong to each other. Some actors work together very often, will this effect be more positive then when they work together for the first time? An easy statement would be to think that when actor worked together before, a consumer could have a better idea of what to expect.

References

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 Albert, A., 1998, “Movie Stars and the Distribution of Financially Successful Films in the Motion Picture Industry”, Journal of Cultural Economics, Vol. 22 Issue 4,

 Basuroy, S., Chatterjee, S., Ravid, S.A., 2003,”How critical are critical reviews? The box office effects of film critics, star power and budgets”, Journal of Marketing, Vol. 67 Issue October

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 De Vany, A.; Walls, W. J. Cult. Econ., 1999, “Uncertainty and the movie industry: does star power reduce the terror of the box office?”, Vol. 23, p285-318

 Elberse, A., 2007, “The Power of Stars: Do Star Actors Drive the Success of Movies?” Journal of Marketing, Vol. 71 Issue 4, p102-120

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 Einhorn H.J., Hogarth R.M., 1981, “BEHAVIORAL DECISION THEORY: PROCESSES OF JUDGMENT AND CHOICE”, Annual Review of Psychology, Vol. 32 Issue 1, p53-88

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 Nelson, R., Glotfelty, R., 2012, “Movie stars and box office revenues: an empirical analysis”, Journal of Cultural Economics, Vol. 36 Issue 2, p141

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 http://www.the-numbers.com/market/

Appendix A

Movies that were in the questionnaire

1. Interstellar Cast:  Matthew McConaughey  Anne Hathaway  Michael Caine Director: Christopher Nolan

Names versus Faces A case study on movie

Names versus Faces

A case study on movie-trailers

Names versus Faces

A case study on movie-trailers

Management Summary

Preface

Table of Contents

Introduction

Literature Review

Star Power

Expectations & Trailer-effect

Conceptual Model

Research Design

Data Collection

Constructs

Method of Analysis

Model analysis

Sample

Regression output

Probability to watch at home

Standardized

Beta

Sig-value

VIF

Probability to watch at cinema

Standardized

Beta

Sig-value

VIF

Model Analysis

Hypotheses

Conclusions

Limitations

Managerial implications

Future Research

References

Appendix A

Master Thesis – Defense Meeting

Names vs Faces

Contents

› Literature Review

•

Star Power

•

Expectations

› Conceptual Model + Hypotheses

› Research Design

› Analysis

› Results + Limitations

Literature Review – Star Power

Support SP

Counter SP

•

Rental income for one third

explained by SP (Wallace, 1993)

•

SP differs per country

(Hennig-Thurau et al., 2004)

•

SP can counter negative reviews

(Basuray, 2003)

•

SP-effect is really just

budget-effect (Ravid, 1999)

•

Distribution of movie-success is to

uncertain to forecast (DeVany &

Walls, 1999)

•

SP not bankable, only increases

odds (DeVany & Walls, 1999)

Literature Review – Expectations

Theory of reasoned action (Fishbein & Ajzen, 1975)

•

If people say they intend to do something, they are

likely to do so.

Expectation based on:

•

Previous experiences of a product (Stayman &