We propose an approach for using individual-level data on social interactions (e.g., number of recommendations received by consumers, number of recommendations given by adopters, number of social ties) to improve the aggregate penetration forecasts made by extant diffusion models. We capture social interactions through an individual-level hazard rate in such a way that the resulting aggregate penetration process is available in closed form and nests extant diffusion models. The parameters of the model may be estimated by combining early aggregate penetration data with social interactions data collected from a sample of consumers in as few as one time period. We illustrate our approach by applying it to the mixed influence model (Bass model) and the more recent asymmetric influence model. A field study conducted in collaboration with a consumer packaged goods company and a marketing research company confirms that incorporating social interactions data using the proposed approach has the potential to result in improved aggregate penetration forecasts in managerially relevant settings.
Toubia, Olivier, Jacob Goldenberg, and Rosanna Garcia. "Improving Penetration Forecasts Using Social Interactions Data." Management Science 60, no. 12 (2014): 3049-3066.
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