Sales Forecasting of New Entertainment Media Products

Managers predict the sales of new entertainment products prior to their release using comparables, such as similar books from the same author or movies with the same actors. In this study, the authors analyze whether diffusion models for media products provide helpful support in the management task of predicting prelaunch sales of the first distribution channel for three different product categories. They compare the performance of predictions based on (a) simple success factor regressions (OLS) and (b) diffusion models against real management predictions. Based on samples covering the German music, film, and the literary market, we show that model-based forecasts outperform the forecasts of management teams for the majority of the products. In contrast, management is superior in forecasting top sellers. This is due to unobserved factors arising from more management attention attached towards super stars. The authors do not find substantial prediction differences between simple success factor regressions and more complex diffusion models. Thus, managers interested in total sales estimates can easily rely on OLS based success factor predictions. Advertising and product differentiation factors with respect to quality (e.g., star power, critics, or country of origin) are across all 3 industries highly relevant for sales predictions, whereas others variables (e.g., price, distribution power, season, or competition) differ across industries.