My paper with Ron Shachar, When Kerry Met Sally: Politics and Perception in the Demand for Movies, has been accepted for publication at Management Science and is available for download via Articles in Advance. I've also filmed a short video in which I talk about the main results, and co-authored (with Imogen Moore) a brief summary article.

The paper presents a predictive model of local demand for movies with two unique features: First, arguing that consumers' political tendencies have unutilized predictive power for marketing models, we allow consumers' heterogeneity to depend on their voting tendencies. Basically, we take voting data and use it in much the same way we would demographic data. Second, instead of using the commonly used genre classifications (e.g. the ones used by IMDB) 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 movie-going behavior.

A box of popcorn
We find that not only are consumers' preferences related to their political tendencies—for example, counties that voted for congressional Republicans prefer movies starring young, white, female actors over those starring African-American, male actors—they also improve the predictive power of the model. Second, the perceived attributes we estimate provide new insights into consumers' preferences for movies. For example, one of these attributes is the movie‚Äôs degree of seriousness. Together, the two improvements we propose have a meaningful impact on forecasting error, decreasing it by 12.6 percent.

I'm really happy with this paper, as I think each of the two contributions can have a meaningful impact on practice. The success of the perceived attributes in predicting demand for movies supports the idea that the movie industry's narrow classification of movies into binary categories for drama, comedy, action, etc., may not be very meaningful to consumers. Instead, classifications describing cast demographics, or the degree of emotional intensity, may be closer to how consumers think about movies.

But perhaps the most exciting result is the success of political data in explaining consumer heterogeneity across different local markets. We found that political data outperformed a wide range of demographic data regarding race, income, family size, and myriad other variables. Indeed, when we estimated models with both political and demographic data, it was the political data that explained preferences. Political data are updated far more frequently than demographic data and almost as easy to obtain. My hope is that other marketing researchers will start to use these data to explain consumer heterogeneity for other goods.

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