Ordinal classification/regression for analyzing the influence of superstars on spectators in cinema marketing

Montañés, Elena; Vázquez, Ana Suárez; Quevedo, José Ramón

doi:10.1016/j.eswa.2014.07.011

Cited by 12 publications

(7 citation statements)

References 32 publications

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“…Then substituting (A.9) into (A.2)-(A.4), we also obtain (4)- (6). Finally, substituting (4)-(6) into (A.1), and get (7).…”

Section: Concluding Remarks and Managerial Implicationsmentioning

confidence: 94%

“…(2) Spectator Heterogeneity. Anecdotal evidence suggests that the presence of superstars is not always a guarantee of success and, hence, a deeper study is required to analyze the potential audience of a movie [7]. Therefore, this paper considers the film audience to be heterogeneous.…”

Section: Star Selection and Spectator Heterogeneitymentioning

confidence: 99%

“…There are some cases where promising movies show poor box-office performance, and inconspicuous movies show unexpectedly good results [3]. "Make no mistake" shows that investing in films is a risky business [4][5][6][7][8]. If investment decisions fail, the costs of these mistakes for consumers are only the ticket price and an opportunity cost, but the costs for investors or producers are highly expensive [9].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Then substituting (A.9) into (A.2)-(A.4), we also obtain (4)- (6). Finally, substituting (4)-(6) into (A.1), and get (7).…”

Section: Concluding Remarks and Managerial Implicationsmentioning

confidence: 94%

Section: Star Selection and Spectator Heterogeneitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…However, little attention has been given to the problem of predicting the success of telemarketing calls for selling bank longterm deposits (Javaheri, Sepehri, & Teimourpour, 2014;Lau, Chow, & Liu, 2004;Moro, Cortez, & Rita, 2014;Moro, Laureano, & Cortez, 2012). Indeed, marketing data mining and analytics are essential to companies to choose the right strategic decision-making policies for selling the right product to the right customer to better generate profits (You et al, 2015), to acquire new customers and increase customer loyalty (Duman, Ekinci, & Tanrıverdi, 2012), and to analyse attitudes and emotions of the customer, and the potential factors that influence them (Montañés, Suárez-Vázquez, & Quevedo, 2014). Therefore, the development of an effective system to predict the outcome of a telemarketing call is a very important issue to choose the right selling strategy and to increase customer loyalty.…”

Section: Introductionmentioning

confidence: 99%

A two‐step system for direct bank telemarketing outcome classification

Lahmiri

2017

Intell. Sys. Acc. Fin. Mgmt.

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Summary A two‐step system is presented to improve prediction of telemarketing outcomes and to help the marketing management team effectively manage customer relationships in the banking industry. In the first step, several neural networks are trained with different categories of information to make initial predictions. In the second step, all initial predictions are combined by a single neural network to make a final prediction. Particle swarm optimization is employed to optimize the initial weights of each neural network in the ensemble system. Empirical results indicate that the two‐step system presented performs better than all its individual components. In addition, the two‐step system outperforms a baseline one where all categories of marketing information are used to train a single neural network. As a neural networks ensemble model, the proposed two‐step system is robust to noisy and nonlinear data, easy to interpret, suitable for large and heterogeneous marketing databases, fast and easy to implement.

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“…Prediction of the ticket sales is not only an important means of ensuring the return on film investment and controlling film release risks, but also provides helpful information for investment decision [3]. Due to the large number of factors that influence ticket sales and the difficulty in quantifying these factors, it is really hard to accurately predict the profitability of films during their lifetime [4][5]. Ascertaining the factors in ticket sales and the interrelations among them is significant for reducing market risks and improving the operation and management of the film industry.…”

Section: Introductionmentioning

confidence: 99%

Research of Box-Office Prediction based on Rough Set and Support Vector Machine

Liu¹,

Zhao²

2016

IJHIT

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In this paper, a novel prediction method for box-office is proposed based on the rough set data processing function and support vector machine (SVM) classification mechanism. The front-end processor, optimizes the input variables by attribute reduction, in order to improve the performance of classifier. Then, in view of the lack of guidance of scientific theory problem of domestic movie box office prediction, the classifier for the box-office prediction, the influence factors of the box office revenue as the input variables, the boxoffice income categories as output variables, data preprocessing and training test. Results show that the classifier can effectively solve the box office prediction problem, the results of the multilayer perceptron is better than that of Ramesh S. and Dursun D. using the prediction method, and the prediction error is less than 10%, to meet the requirements of the film market, show the powerful classification ability.

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Ordinal classification/regression for analyzing the influence of superstars on spectators in cinema marketing

Cited by 12 publications

References 32 publications

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

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

A two‐step system for direct bank telemarketing outcome classification

Research of Box-Office Prediction based on Rough Set and Support Vector Machine

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