Improving Pre-Launch Diffusion Forecasts : Using Synthetic Networks as Simulated Priors

While the role of social networks and consumer interactions in new product diffusion is widely acknowledged, such networks and interactions are often unobservable to the firm/researcher. What may be observable, instead, are aggregate diffusion patterns for past products adopted within a particular social network. We propose an approach for identifying systematic conditions that are stable across diffusions, and hence are "transferrable" to new product introductions within a given network. Using Facebook apps data, we show that incorporation of such systematic conditions improves pre-launch forecasts. Our research bridges the gap between the disciplines of Bayesian statistics and agent-based modeling by demonstrating how stochastic relationships simulated within complex systems can be used as meaningful inputs for Bayesian inference models