Optimal Advertising Decision Interacting with Social Learning in the U.S. Theatrical Market

Social learning is thought to influence people’s movie-watching behavior, including whether and when to watch a film. Through social learning, potential movie-goers learn about the quality of a movie from people who have watched the movie. This can be a double-edged sword for motion picture distributors: when a movie is good, social learning can enhance the effectiveness of movie advertising, but when a movie is bad it can mitigate the effectiveness of advertising. This paper develops an equilibrium model of consumers’ movie-going choices and movie distributors’ advertising decisions. I 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) and an initial level of advertising. Consumers are assumed to have uncertainty about movie quality that is resolved over time through Bayesian updating that depends on the number of previous viewers and their Internet ratings. I estimate model parameters using data pertaining to 236 movies that were shown in theaters nationwide in the U.S. between January 1, 2002 and December 31, 2003.

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