MODELLING OF SEQUEL PRODUCTION IN THE AMERICAN MOVIE INDUSTRY
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CONTENTS 6
INTRODUCTION 8
CHAPTER 1. LITERATURE REVIEW 10
1.1. Industry b ackground 10
1.2. Factors affecting box-office success 12
1.3. Motivation for sequel production 18
1.4. Summary of Chapter 1 21
CHAPTER 2. MODEL DESIGN 22
2.1. General description of the model 22
2.2. Identifying the box office success factors 23
2.2.1. Sample description 23
2.2.2. Methodology and variables 27
2.3. Statistical analysis of sequel determinants 32
2.3.1. User rating and user reviews distribution 32
2.3.2. Modelling of user rating and user reviews 34
2.4. Simulating net profit for sequel releases 35
2.4.1. Cost calculation 35
2.4.2. NPV calculation 36
2.5. Summary of the Chapter 2 38
CHAPTER 3. MODEL FINDINGS 39
3.1. Sample description 39
3.2. Model findings 40
3.3. Discussion 43
3.4. Summary of Chapter 3 46
CONCLUSION 48
REFERENCES 49
APPENDICES
INTRODUCTION 8
CHAPTER 1. LITERATURE REVIEW 10
1.1. Industry b ackground 10
1.2. Factors affecting box-office success 12
1.3. Motivation for sequel production 18
1.4. Summary of Chapter 1 21
CHAPTER 2. MODEL DESIGN 22
2.1. General description of the model 22
2.2. Identifying the box office success factors 23
2.2.1. Sample description 23
2.2.2. Methodology and variables 27
2.3. Statistical analysis of sequel determinants 32
2.3.1. User rating and user reviews distribution 32
2.3.2. Modelling of user rating and user reviews 34
2.4. Simulating net profit for sequel releases 35
2.4.1. Cost calculation 35
2.4.2. NPV calculation 36
2.5. Summary of the Chapter 2 38
CHAPTER 3. MODEL FINDINGS 39
3.1. Sample description 39
3.2. Model findings 40
3.3. Discussion 43
3.4. Summary of Chapter 3 46
CONCLUSION 48
REFERENCES 49
APPENDICES
The global movies and entertainment market has seen fluctuating growth rates over the period of 2011-2015 growing with a compound growth rate of 2.5%. Within the industry box office segment accounted for half of the market in 2015 with a total value of 39 bln USD. Geographically the United States keeps the leadership position in the market contributing with 33% of overall spending. However, developing markets see staggering growth in total spending on movies and entertainment as the general income level increases. Specifically China’s movie market grew by 34% annually over the observed period with box office segment to be the most lucrative of all.
As the competition within the movie industry tends to strengthen, creating a successful and profitable film is a very challenging task. Firstly, films’ production is expensive and their success is highly unpredictable. The median movie made in United States loses money. As average movie budget and marketing costs continue to escalate beyond 200 mln USD with intensifying competition on the entertainment market, studios are actively seeking for success formulas of revenue generating films. However, since every movie is unique in its contents, identifying success features for a movie is complicated. The industry itself becomes difficult to analyze with the films’ qualities being not easily described or measured as in cases of other consumption goods.
Beginning from the moment of initiating a movie, studios make financial decisions which story to choose among many other competing proposals, how much money to invest in production and then decide when exactly to sell rights. In the given context sequels should be viewed as an increasingly important strategy for new product introduction by film-making studios as they are based on movies that are already familiar to the public and can guarantee to film-makers some level of public interest without new investments. When the successful formula of plot, actors and timing is found in the first movie, studios are willing to try it again in a sequel film (Ravid, 1994).
In general, studies of the movie market have been concentrating on the factors that drive box office sales as well as understanding the performance difference between sequels and non-sequels, while very few studies to be focused on integrating the observed interrelations between factors and provide film studios with a workable tool of decision making about whether to continue with shooting sequels or not.
The research goal of this paper is to create and test a model that would be able to forecast the financial performance of shooting sequels and then test it on real life cases.
In order to achieve the outlined goal, we define the following objectives:
• To identify the theoretical background on movie industry and sequel production;
• To study existing literature on the factors that drive box office revenues and differences in performance between sequels and non-sequels;
• To propose an empirical methodology behind the simulation model;
• To build and describe a sample for the analysis;
• To run the model on a sample of movies;
• To interpret results and compare the model findings with the real life cases.
The research approach is practical and uses such methods as quantitative analysis using econometric tools built in the Stata software and simulation modelling in Excel.
The main sources of information that were used for the purposes of research were academic articles devoted to: theoretical studies of the movie industry, motivation to shot sequels, determinants of box office success of both sequels and non-sequels, on marketing strategies for sequels and their specificities. To gather data for modelling the film performance, we use special databases about the movie industry as IMDB.com, Metacritic.com and the AMPAS database for the data about Academy Awards.
In order to achieve the defined goal of the research, we structure the thesis as follows: introduction, three chapters that cover all the goals of the study and conclusion. The introduction constitutes goals and objectives of the research as well as the motivation and background of the study. The first chapter covers first two research objectives and is mainly focused on analyzing the findings of other scholars in the chosen problem field.
The second chapter covers mainly the third and the forth research objectives since it is devoted to description the suggested architecture of the simulation model as well as the steps that were taken to build it and the sample of movies used for this purpose. In the third chapter we analyze the practical results of running the model on a sample of new movies, analyze the performance of the model and interpret the results.
Finally, the conclusion summarizes the research in relation to the goals set. The research paper was also complemented with summaries at the end of each chapter to ease the process of reading. The appendices include sample descriptions as well as the VBA code used in the simulation model.
As the competition within the movie industry tends to strengthen, creating a successful and profitable film is a very challenging task. Firstly, films’ production is expensive and their success is highly unpredictable. The median movie made in United States loses money. As average movie budget and marketing costs continue to escalate beyond 200 mln USD with intensifying competition on the entertainment market, studios are actively seeking for success formulas of revenue generating films. However, since every movie is unique in its contents, identifying success features for a movie is complicated. The industry itself becomes difficult to analyze with the films’ qualities being not easily described or measured as in cases of other consumption goods.
Beginning from the moment of initiating a movie, studios make financial decisions which story to choose among many other competing proposals, how much money to invest in production and then decide when exactly to sell rights. In the given context sequels should be viewed as an increasingly important strategy for new product introduction by film-making studios as they are based on movies that are already familiar to the public and can guarantee to film-makers some level of public interest without new investments. When the successful formula of plot, actors and timing is found in the first movie, studios are willing to try it again in a sequel film (Ravid, 1994).
In general, studies of the movie market have been concentrating on the factors that drive box office sales as well as understanding the performance difference between sequels and non-sequels, while very few studies to be focused on integrating the observed interrelations between factors and provide film studios with a workable tool of decision making about whether to continue with shooting sequels or not.
The research goal of this paper is to create and test a model that would be able to forecast the financial performance of shooting sequels and then test it on real life cases.
In order to achieve the outlined goal, we define the following objectives:
• To identify the theoretical background on movie industry and sequel production;
• To study existing literature on the factors that drive box office revenues and differences in performance between sequels and non-sequels;
• To propose an empirical methodology behind the simulation model;
• To build and describe a sample for the analysis;
• To run the model on a sample of movies;
• To interpret results and compare the model findings with the real life cases.
The research approach is practical and uses such methods as quantitative analysis using econometric tools built in the Stata software and simulation modelling in Excel.
The main sources of information that were used for the purposes of research were academic articles devoted to: theoretical studies of the movie industry, motivation to shot sequels, determinants of box office success of both sequels and non-sequels, on marketing strategies for sequels and their specificities. To gather data for modelling the film performance, we use special databases about the movie industry as IMDB.com, Metacritic.com and the AMPAS database for the data about Academy Awards.
In order to achieve the defined goal of the research, we structure the thesis as follows: introduction, three chapters that cover all the goals of the study and conclusion. The introduction constitutes goals and objectives of the research as well as the motivation and background of the study. The first chapter covers first two research objectives and is mainly focused on analyzing the findings of other scholars in the chosen problem field.
The second chapter covers mainly the third and the forth research objectives since it is devoted to description the suggested architecture of the simulation model as well as the steps that were taken to build it and the sample of movies used for this purpose. In the third chapter we analyze the practical results of running the model on a sample of new movies, analyze the performance of the model and interpret the results.
Finally, the conclusion summarizes the research in relation to the goals set. The research paper was also complemented with summaries at the end of each chapter to ease the process of reading. The appendices include sample descriptions as well as the VBA code used in the simulation model.
The main goal of this research was to create and test a decision making algorithm for movie studios to allow them make a financial forecast whether it will be profitable to shoot sequels or not. Using a multiple regression, we have identified a number of variables affecting box office success of a movie. Explaining 56% of box office results in the first weekend it provided us with 5 significant determinants of first week revenue that were used in a model.
The developed model, in turn, incorporates two techniques to supplement each other, i.e. statistical and regression analyses, and finally produces an algorithm for practitioners (distributors of motion pictures). The algorithm is intended to enable movie studios to make managerial decisions about whether to purchase rights and shoot the sequel if the parent movie was already released.
Big sample size employed in this study allowed to identify key determinants of box office performance and then use it as a basis for predicting future ticket sales in the first week after the movie release. Since two variables, i.e. user rating and user reviews, were found to be randomly distributed, we used the Monte Carlo simulation for forecasting revenues. We analyzed 138 sequel lines to obtain the interval of variations of these factors between the sequels and the preceding movies.
The model allowed to predict the minimum first weekend box office needed to make profitable sequels and trequels as well as the probability of a positive net present value of a sequel project. The variance between the revenue forecast and the revenue obtained turned out to vary within 20 per cent with two movies having considerable difference in performance. It could be explained with the fact of new director shooting the sequel in contrast to the parent movie (Alice in Wonderland case) and new plot line introduced in the sequel movies (The Fantastic Four case).
In order to conduct a thorough analysis, we used 73 references; and the contribution of this study is creation and testing of a decision making algorithm for the movie studios. However, there is clearly a scope for future research. The work can be expanded via suggesting strategies to enhance box office performance of sequels after release, further investigation of the relatedness of sequels in one movie line, an analysis of marketing tools to increase hype around a movie and thus bringing more user reviews.
The developed model, in turn, incorporates two techniques to supplement each other, i.e. statistical and regression analyses, and finally produces an algorithm for practitioners (distributors of motion pictures). The algorithm is intended to enable movie studios to make managerial decisions about whether to purchase rights and shoot the sequel if the parent movie was already released.
Big sample size employed in this study allowed to identify key determinants of box office performance and then use it as a basis for predicting future ticket sales in the first week after the movie release. Since two variables, i.e. user rating and user reviews, were found to be randomly distributed, we used the Monte Carlo simulation for forecasting revenues. We analyzed 138 sequel lines to obtain the interval of variations of these factors between the sequels and the preceding movies.
The model allowed to predict the minimum first weekend box office needed to make profitable sequels and trequels as well as the probability of a positive net present value of a sequel project. The variance between the revenue forecast and the revenue obtained turned out to vary within 20 per cent with two movies having considerable difference in performance. It could be explained with the fact of new director shooting the sequel in contrast to the parent movie (Alice in Wonderland case) and new plot line introduced in the sequel movies (The Fantastic Four case).
In order to conduct a thorough analysis, we used 73 references; and the contribution of this study is creation and testing of a decision making algorithm for the movie studios. However, there is clearly a scope for future research. The work can be expanded via suggesting strategies to enhance box office performance of sequels after release, further investigation of the relatedness of sequels in one movie line, an analysis of marketing tools to increase hype around a movie and thus bringing more user reviews.