Improving Prelaunch Diffusion Forecasts: Using Synthetic Networks as Simulated Priors

Abstract

Although the role of social networks and consumer interactions in new product diffusion is widely acknowledged, such networks and interactions are often unobservable to researchers. What may be observable, instead, are aggregate diffusion patterns for past products adopted within a particular social network. The authors propose an approach for identifying systematic conditions that are stable across diffusions and thus are “transferrable” to new product introductions within a given network. Using Facebook applications data, the authors show that incorporation of such systematic conditions improves prelaunch forecasts. This research bridges the gap between the disciplines of Bayesian statistics and agent-based modeling by demonstrating how researchers can use stochastic relationships simulated within complex systems as meaningful inputs for Bayesian inference models.