Network analysis: Understanding consumers' choice in the film industry and predicting pre-released weekly box-office revenue

Predicting weekly box-office demand is an important yet challenging question. For theater exhibitors, such information will enhance negotiation options with distributers, and assist in planning weekly movie portfolio mix. Existing literature focuses on forecasts of pre-released total gross revenue or on weekly predictions based on first-weeks observations. This work adds to the literature in modeling the entire demand structure forecasts by utilizing information on movie similarity network. Specifically, we draw upon the assumption that aggregated consumers' choice in the film industry is the main key in understanding movies' demand. Therefore, similar movies, in terms of audience appeal, should yield similar demand structure. In this work, we propose an automated technique that derives measurements of demand structure. We demonstrate that our technique enables to analyze different aspects of demand structure, namely, decay rate, time of first demand peak, per-screen gross value at peak time, existence of second demand wave, and time on screens. We deploy ideas from variable selection procedures, to investigate the prediction power of similarity network on demand dynamics. We show that not only our models perform significantly better than models that discard the similarity network but are also robust to new sets of box-office movies.