An inside look into the complexity of box-office revenue prediction in China

Xiao, Jia; Li, Xin; Chen, Shanzhi; Zhao, Xuhui; Xu, Meng

doi:10.1177/1550147716684842

Cited by 14 publications

(11 citation statements)

References 41 publications

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“…Court, Gillen, McKenzie, and Plott (2018) employ two information aggregation mechanisms to forecast the opening weekend box office revenues of movies. Complexity in the box office prediction for the Chinese movie market is examined in Xiao, Li, Chen, Zhao, and Xu (2017). At a more general level, Piergiovanni, Carree, and Santarelli (2012) consider how the factors of regional economic growth affect creative industries using many parameters.…”

Section: Related Literaturementioning

confidence: 99%

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Vecchio

Kharlamov

Parry

et al. 2020

Journal of the Operational Research Society

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Improving productivity in the entertainment industry is a very challenging task as it heavily depends on generating attractive content for the consumers. The consumer-centric design (putting the consumers at the centre of the content development and production) focuses on ways in which businesses can design customized services and products which accurately reflect consumer preferences. We propose a new framework which allows to use data science to optimize content-generation in entertainment and test this framework for the motion picture industry. We use the natural language processing methodology combined with econometric analysis to explore whether and to what extent emotions shape consumer preferences for media and entertainment content, which, in turn, affect revenue streams. By analyzing 6,174 movie scripts, we generate the emotional trajectory of each motion picture. We then combine the obtained mappings into clusters which represent groupings of consumer emotional journeys. These clusters are then plugged into an econometric model to predict overall success parameters of the movies including box office revenues, viewer satisfaction levels (captured by IMDb ratings), awards, as well as the number of viewers' and critics' reviews. We find that emotional arcs in movies can be partitioned into 6 basic shapes. The highest box offices are associated with the Man in a Hole shape which is characterized by an emotional fall followed by an emotional rise. This U-shaped emotional arc results in financially successful movies irrespective of genre and production budget. Implications of this analysis for generating on-demand content and improving productivity in entertainment industries are discussed.

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Section: Related Literaturementioning

confidence: 99%

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Vecchio

Kharlamov

Parry

et al. 2020

Journal of the Operational Research Society

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show abstract

“…As they had only 755 movies, 10-fold cross-validation was used while performing the experiments. Xiao et al (2017) used Linear Regression to predict revenue generated by movies in China. In their research, authors mainly focused on the Genre and Cast Popularity of the movie.…”

Section: Previous Workmentioning

confidence: 99%

“…But when one class away accuracy (80.43%) is considered then our model performs better. Xiao et al (2017) designed their Linear Regression model primarily based on the popularity of cast which includes the director, producer, actor/ actress, supporting actor/actress and the genre of the film. Figure 19 shows that their model achieves a bingo accuracy of 42.91% and the one class away accuracy is 81.56%.…”

Section: Comparing With Other Architecturesmentioning

confidence: 99%

A Machine Learning Approach to Predict Movie Revenue Based on Pre-Released Movie Metadata

Mahmud¹,

Shuchi²,

Tawsif³

et al. 2020

Journal of Computer Science

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With the growth of the movie industry, it is becoming increasingly important for the stakeholders to get an idea about the probable profit made by the movie in the box office. In fact, among movies produced between 2000 and 2010 in the United States, only 36% had box office revenues higher than their production budgets, which further highlights the importance of making the right investment decisions. To address this issue, different machine learning algorithms like Logistic Regression, Support Vector Machine (SVM) and Multi Layer Perceptron (MLP) are used in this study to predict the box office return of a movie based on the data available before the release of the movie. The models use 35 movie parameters from 3200 movies as inputs to predict the profit made by a movie and classify the success of a movie from "flop" to "blockbuster" based on the generated revenue. An analysis of different machine learning architectures is also presented in this research. Finally, a system is proposed that produces comparable results with existing researches in this field and it can predict the profit generated by a movie with a "one class away" accuracy of 85.31% without using any sales information.

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“…Nowadays, there are multiple channels, both online and offline, for advertising delivery. Typical examples are Weibo (microblog), WeChat, variety show, and outdoor billboard [11]. Frequent new product introductions and rapid turnover lead movie studios to advertise heavily.…”

Section: Introductionmentioning

confidence: 99%

Goodwill and System Dynamics Modeling for Film Investment Decision by Interactive Efforts

Jian

Liu

2018

Discrete Dynamics in Nature and Society

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Academic research pertaining to the marketing of film industry has identified advertising, film-making, and star power as the important factors influencing a movie’s market performance. Prior research, however, has not investigated the joint influences of these factors. The current study has extended previous research by analyzing the investment decision of studios or investors. In order to analyze the optimal film investment decision in advertising, film-making, and stars power, this paper develops a goodwill model and system dynamic (SD) model, which allow us to disentangle the effects of advertising, film-making, and star power on film market performance. The results show that the film producer should increasingly lay emphasis on investing in advertising to absorb moviegoers’ attention. Then the film producer should focus on investing in film-making when film quality has a great impact on the movie's reputation and audience's viewing decision. Furthermore, the film producer should pay more attention to the higher cost-performance stars who have more reasonable remuneration, better acting skills, and bigger box-office guarantee. Moreover, the numerical analysis reveals that rational audience contribute more than fans to a movie's box-office and bankable stars contribute more than high-profile stars to a movie's returns. Through SD simulation analysis, the film series yields higher profits than new theme movies although the cost of investment is the same.

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An inside look into the complexity of box-office revenue prediction in China

Cited by 14 publications

References 41 publications

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

A Machine Learning Approach to Predict Movie Revenue Based on Pre-Released Movie Metadata

Goodwill and System Dynamics Modeling for Film Investment Decision by Interactive Efforts

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