《当克里遇见莎莉:电影需求中的政治和观念》——在线附录

Jason M. T. Roos, R. Shachar
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引用次数: 26

摘要

电影制作方和放映方在做出各种决定时,都需要了解当地观影者的偏好。这类决策的两个例子是放映商对电影银幕的分配,以及制片商在全国不同地区对广告的分配。本研究提出了一个具有两个独特特征的本地电影需求预测模型。首先,我们认为消费者的政治倾向对营销模式具有未被利用的预测能力,我们允许消费者的异质性取决于他们的投票倾向。其次,我们不是依靠常用的类型分类来描述电影,而是估计潜在的电影属性。这些属性并不是由业内专业人士先验地决定的,而是反映了消费者的感知,正如他们的观影行为所揭示的那样。美国中西部25个县5年来的票房数据为这一模型提供了支持。首先,消费者的偏好与他们的政治倾向有关。例如,我们发现投票给国会共和党人的县更喜欢由年轻的白人女性演员主演的电影,而不是由非裔美国男性演员主演的电影。其次,感知属性为了解消费者偏好提供了新的视角。例如,这些属性之一是电影的严肃程度。最后,也是最重要的一点,本文提出的两项改进对预测误差产生了有意义的影响,将预测误差降低了12.6%。这篇论文被市场部的Pradeep Chintagunta接受。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
When Kerry Met Sally: Politics and Perceptions in the Demand for Movies - Online Appendix
Movie producers and exhibitors make various decisions requiring an understanding of moviegoer's preferences at the local level. Two examples of such decisions are exhibitors' allocation of screens to movies and producers' allocation of advertising across different regions of the country. This study presents a predictive model of local demand for movies with two unique features. First, arguing that consumers' political tendencies have an unutilized predictive power for marketing models, we allow consumers' heterogeneity to depend on their voting tendencies. Second, instead of relying on the commonly used genre classifications to characterize movies, we estimate latent movie attributes. These attributes are not determined a priori by industry professionals but rather reflect consumers' perceptions, as revealed by their moviegoing behavior. Box-office data over five years from 25 counties in the U.S. Midwest provide support for this model. First, consumers' preferences are related to their political tendencies. For example, we find that counties that voted for congressional Republicans prefer movies starring young, Caucasian, female actors over those starring African American, male actors. Second, perceived attributes provide new insights into consumers' preferences. For example, one of these attributes is the movie's degree of seriousness. Finally, and most importantly, the two improvements proposed here have a meaningful impact on forecasting error, decreasing it by 12.6%. This paper was accepted by Pradeep Chintagunta, marketing.
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