Improving Penetration Forecasts Using Social Interactions Data
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.