Social Learning and Optimal Advertising in the Motion Picture Industry

Social learning is thought to be a key determinant of the demand for movies. This can be a double-edged sword for motion picture distributors, because when a movie is good, social learning can enhance the effectiveness of movie advertising, but when a movie is bad, it can mitigate this effectiveness. This paper develops an equilibrium model of consumers' movie-going choices and movie distributors' advertising decisions. First, we develop a structural model for studios' optimal advertising strategies, taking into account the expected social learning process, and a model for consumers' movie demand, given an initial indicator of movie quality (critic ratings) as well as an initial level of advertising. Consumers are assumed to be initially uncertain about movie quality. This, however, is resolved over time through Bayesian updating. That process depends upon (1) the number of previous viewers and (2) their ratings reported over the Internet. We then estimate the model parameters using data pertaining to 236 movies that were shown in theaters in the U.S. between January 1, 2002 and December 31, 2003. The empirical results show that social learning has a positive multiplier effect on movie advertising, with the multiplier effect being strongest for good movies. The simulation of the effects of social learning relative to a world without such learning shows that for good movies, producers spend substantially more on advertising when there is learning involved than they would if there were no learning. For bad movies, social learning makes much less difference to the level of advertising expenditures. Thus, the studio's advertising spending is sensitive to both consumer uncertainty about movie quality and the speed with which potential movie-goers learn about movie quality.