Virtual Bass Model and the left-hand data-truncation bias in diffusion of innovation studies

The prevalence of left truncation in historical sales data and a method for dealing with the bias by the Virtual Bass Model are demonstrated and a database of parameter estimates that may be used in “guessing by analogy” are developed.

Abstract

One of the most important applications of the Bass model is "guessing by analogy" — the parameter estimates of the Bass model for analogous products can be used to predict the diffusion pattern of a new product. However, estimates based on left-hand truncated data will be biased unless care is taken to adjust for the bias. We demonstrate the prevalence of left truncation in historical sales data and present a method for dealing with the bias by the Virtual Bass Model. We use the model to develop a database of parameter estimates that may be used in "guessing by analogy."