Leveraging pre-launch advertising data to optimize movie marketing strategies : an econometric approach

Abstract

This research examines how pre-launch advertising affects movie demand and studios allocate pre-launch advertising budgets. Using weekly data on box-office revenues, advertising expenditures, and movie characteristics (studio, budget, genre, star value, critics’ ratings, etc.) for 1,276 U.S. movies (2019-2012), we employ a logit diffusion model to show that pre-launch advertising significantly impacts demand, with an average elasticity of 0.35 across major studios. On the supply side, structural econometric models reveal studios maximize total expected profit across their yearly slate. Four policy experiments explore managerial implications of the advertising model.