Is a poster a strong signal of film quality? evaluating the predictive power of visual elements using deep learning

Abstract

A film is considered an experience good, as its quality is only revealed after consumption. This situation creates information asymmetry before consumption, prompting producers, who are aware of their film’s quality, to search for methods to signal this. Economic literature specifies that a signal to disclose a product’s quality must be strong, meaning only producers of good-quality films can effectively utilize such a signal. However, a poster represents the most economical signal, and all producers, regardless of film quality, have access to this option. To study whether a poster can signal film quality, we first apply a low-dimensional representation of poster images and cluster them to identify quality-related patterns. We then perform a supervised classification of films into economically successful and unsuccessful categories using a deep neural network. This is based on the hypothesis that higher quality films tend to sell more tickets and that all producers invest in the highest quality poster services. The results demonstrate that a film’s quality can indeed be predicted from its poster, reinforcing its effectiveness as a strong signal. Despite the proliferation of advanced visual media technologies, a simple yet innovative poster remains an effective and appealing tool for signaling film information. Notably, posters can classify a film’s economic success comparably to trailers but with significantly lower processing costs.