Improving Pre-Launch Diffusion Forecasts: Using Synthetic Networks as Simulated Priors
Journal of Marketing Research (2013)
78 Pages Posted: 20 Nov 2013
Date Written: August 13, 2013
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.
Keywords: agent-based models, complex systems, Bayesian inference, consumer networks, diffusion, pre-launch foecasts
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