Improving Prelaunch Diffusion Forecasts: Using Synthetic Networks as Simulated Priors

Authored by William Rand, Michael Trusov, Yogesh V. Joshi

Date Published: 2013-12

DOI: 10.1509/jmr.11.0508

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

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.

Tags

Agent-based models Complex systems diffusion Bayesian inference consumer networks prelaunch forecasts